Migosi Thesis Final June 2012 (1)

FACTORS INFLUENCING RESEARCH PRODUCTIVITY AMONG ACADEMIC

STAFF IN SELECTED UNIVERSITIES IN KENYA.

By

Joash A. Migosi

A Thesis Submitted to the Faculty of Education in Partial Fulfillment

for the Requirements for Degree of Doctor of Philosophy

Department of Postgraduate Studies in Education

Faculty of Education

The Catholic University of Eastern Africa

September, 2009

DECLARATION

I, Joash Abere Migosi, the undersigned, declare that this

thesis in its form and nature, organization and content is a fruit of

my personal effort. To the best of my knowledge, it has never been

submitted for academic credit in any other University.

________________________ Date: 20 th

Sept, 2009

Joash Abere Migosi

ED/PhD/037/06/07

This thesis has been submitted with our approval as University

supervisors;

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_________________________ Date

_________________________

Prof. P. Ogula

Professor of Education,

Catholic University of East Africa

_________________________ Date

_________________________

Rev. Dr. M. Kawasonga

Senior Lecturer, Faculty of Education,

Catholic University of East Africa

Dedication

I dedicate this work to my parents, Mzee Zedekiah Migosi

Isoe and Mama Josephine Moraa, who, in spite of their limitations,

found it wise to take me to school.

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Abstract

The current academic climate in higher education in Kenya

threatens the Kenyan universities' ability to sustain the conditions

that support research productivity, teaching and service to

community. Increased demands on government and private

funding, a deteriorating physical infrastructure, increased pressure

on undergraduate programs, university expansion strategies and

general economic climate in the country have raised concerns

about the continued capacity of universities to maintain teaching,

research productivity and service to the community. This situation

dictates deliberate evaluation to be made in the scientific research

arena at all times. This study sought to examine the following;

factors that influence research productivity, the attitude of

academic staff to research and publishing among academic staff

and possible ways to enhance research productivity. Survey

research method and document analysis were employed in this

study. The questionnaire was used to collect information from 277

(70.2% male and 29.8% female) university academic staff and 17

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heads of departments drawn from 11 public and private

universities. SPSS (Version 15) was used to do descriptive

statistics, cross tabulations. Hypothesis testing was carried out

through ANOVA. Factor analysis was used for data reduction,

identification and description of the major factors influencing

research productivity as noted by respondents. Document analysis

dwelt on the analysis of the sampled research policies of the

selected universities. The results obtained from this study indicate

that the research productivity index for the universities in Kenya is

low. Most interesting conclusion indicated that Research content

knowledge and self motivation were the key factors that influenced

on individual researcher’s productivity. Others were resources for

research, equipments and availability of technology. There was

found to be significant relationship between age group, highest

degree obtained, individual university and highest degree obtained

and attitude towards research and publishing. The main

recommendation made by this study is for the development and

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application of national and institutional research policies to guide

and manage research in this country.

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Acknowledgements

This work will not be complete without mentioning the

special contributions made by numerous persons towards its

successful completion. First and foremost I must acknowledge the

generous gesture extended to me by my employer, the Ministry of

Higher Education Science and Technology for having awarded me

a full government scholarship to undertake this study. I am indeed

grateful to the Permanent Secretary Prof. Chrispus Kiamba for this

generous gesture. Were it not for this contribution, I am not sure

whether this work could have been completed successfully.

I also appreciate the guidance and supervision extended to

me by my two supervisors, Prof. P. Ogula and Rev. Dr. M.

Kawasonga. They have, in various ways helped shape this work to

its present stage. I also appreciate other members of staff from the

faculty of education at the Catholic University of Eastern Africa.

They helped in guiding this study from the onset; these include;

Drs. Akala, Ammanuel, Githui, Kanga and Prof. Onsongo. I

acknowledge the assistance I received particularly in research

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methodology from Mr. Makinda, Mr. Kiarie, and Prof O’Connor

Department of Psychology University of British Columbia –

Okanagan who assisted me with materials and interpretations on

Principal Component Analysis. My classmates also supported me

especially Liz, Kangethe, Ogoti, Jared and others. Mr. P. Nyaswa

willingly agreed to go through the final draft of this work and gave

constructive criticisms. Am grateful to Rev. Prof. Majawa who

introduced me to the truths of cosmotheandrian education for

transformative living and meaningful destiny during the earlier part

of this course. Indeed this has had a great impact in my life. Mr.

and Mrs. Elkana Osinde, my neighbors, gave me all support.

I will not forget Mr. Moturi Wisley who has done most of

the typing of this work and doubled up as a research assistant in

this work. I am indeed indebted to hundreds of academic staff

from 11 universities across the country who generously accepted to

give part of their time to give responses to the questionnaires,

HODs, Vice chancellors of the selected universities and all my

research assistants. To them all I say “thank you”.

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My immediate and current Director, Dr. Mokabi and Dr. E.

Mwangi respectively gave me easy time and encouraged me to

finish up. Many thanks to my workmates at the Directorate of

Research Management and Development, particularly my

officemate Dr. W. Omoto.

My family provided invaluable support for the three years I

was involved in this study. Regular travels to and from Eldoret,

notwithstanding the condition of the road then, was indeed a

challenge to all of us. I particularly appreciate the continuous urge

from my wife Zipporah, daughters Joy, Lewin; sons Allan and

Emmanuel to finish up. I sincerely thank them for their supporting

voices which cheered me on.

Last but not least, I appreciate the many graces from the

everlasting one. For, were it not for Him, all could have been in

vain.

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Table of Contents

CHAPTER ONE............................................................................25

INTRODUCTION.........................................................................25

1.1 Background to the Problem.........................................25

1.1.2 Background of University Education in Kenya...........5

1.1.3 Challenges Facing University Education in Kenya......8

1.2 The Statement of the Problem.....................................15

1.3 Research Questions.......................................................17

1.4 Hypotheses.....................................................................18

1.5 Significance of Study....................................................19

1.6 Scope and delimitation of the study............................20

1.7 Theoretical Framework................................................21

1.7.1 Expectancy Theory of Motivation - Victor Vroom.....21

1.7.2 Rationale for Motivation Theories..............................23

1.8 Conceptual Framework................................................29

1.9 Operational Definition of Terms..................................32

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1.10 Organization of the study.............................................33

CHAPTER TWO...........................................................................34

LITERATURE REVIEW..............................................................34

2. Introduction...................................................................34

2.1 Research Productivity and Theories...........................35

2.2 Research Productivity and Publications.....................41

2.3 Theories of Motivation.................................................50

2.4 Determinants of Research Productivity......................60

2.4.1 Research Productivity Measurement..........................62

2.4.2 Webometrics..................................................................65

2.4.3 Research Productivity and Type of Institution..........67

2.5 Factors that Influence Research Productivity............75

2.5.1 Factors associated with Personal Career Development

.........................................................................................92

2.6 Research Productivity and Academic Disciplines......94

2.7 Research Productivity and Technology

Transfer/Patents..........................................................106

2.8 Research Productivity and Teaching Effectiveness..109

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2.9 Summary of Literature Review.................................110

CHAPTER THREE.....................................................................112

RESEARCH DESIGN AND METHODOLOGY.......................112

3 Introduction.................................................................112

3.1 Research Design..........................................................112

3.2 Target Population........................................................112

3.3 Sample and Sampling Procedures.............................113

3.4 Description of Research Instruments........................113

3.4.1 Questionnaires.............................................................113

3.4.2 Questionnaire for Lecturers.......................................114

3.4.3 Questionnaire for Heads of Departments.................115

3.5 Piloting/Pre-Testing.....................................................117

3.6 Validity and Reliability of Instruments.....................117

3.6.1 Validity.........................................................................117

3.6.2 Reliability.....................................................................117

3.7 Description of Data Collection Procedures...............119

3.8 Description of Data Analysis Procedures..................120

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CHAPTER FOUR........................................................................122

PRESENTATION AND DISCUSSION OF THE FINDINGS...122

4.1 Introduction.................................................................122

4.2 Demographic characteristics of participants............122

4.2.1 Academic staff’s demographic information.............122

4.2.2 Head of departments’ demographic information.....128

4.3 Nature of Research Productivity among public and

private universities in Kenya between 2004-2008....131

4.3.1 Average publication output by university between

2004 - 2008...................................................................131

4.3.2 Test of Hypotheses.......................................................147

4.4 Lecturers’ views on individual and Institutional

factors influencing research productivity.................156

4.4.1 Lecturers views on individual factors influencing

research productivity..................................................156

4.4.2 Lecturers views on Institutional factors influencing

research productivity..................................................158

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4.4.3 Heads of Departments’ views on Individual factors

influencing research productivity..............................160

4.4.4 Heads of Departments’ views on Institutional factors

influencing research productivity..............................161

4.4.5 Principal Component Analysis..................................162

4.4.5.1Selecting the Variables for PCA.................................163

4.4.5.2Further Interpretation of the PCA............................168

4.5 Attitudes of academic staff on Research and

Publications in their institutions................................172

4.5.1 Lecturers attitude scores on research and

publications..................................................................172

4.5.2 University Academic Staffs’ Mean Attitude Score

towards Research and Publishing.............................175

4.5.2.1Hypotheses testing.......................................................176

4.6 Lecturers perceived factors hindering research

productivity.................................................................189

4.6.1 Enhancement of research productivity.....................196

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4.6.1.1Academic staff views on possible solutions to problems

hindering research productivity................................197

4.6.1.2Heads of Departments’ views on possible solutions to

problems hindering research productivity...............203

4.7 Document Analysis......................................................207

4.7.1 Summary of Document Analysis...............................217

CHAPTER FIVE.........................................................................218

SUMMARY, CONCLUSIONS AND

RECOMMENDATIONS............................................218

5.1 Summary......................................................................218

5.2 Conclusions..................................................................221

5.3 Recommendations.......................................................223

5.4 Suggestions for further studies..................................225

REFERENCES............................................................................227

APPENDIX I...............................................................................255

APPENDIX II..............................................................................262

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APPENDIX III.............................................................................269

APPENDIX IV.............................................................................270

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List of Tables

Table1.1: Summary of related studies on research productivity

employing the expectancy model..............................24

Table 2.1: Illustration of Lotka’s Law.........................................38

Table 4.1: Frequency Distribution of academic staff by Gender

......................................................................................................122

Table 4.2: Distribution of academic staff by type of university 122

Table 4.3: Distribution of academic staff by Age group...........123

Table 4.4: Distribution of academic staff by Position Held in

Their respective Universities....................................125

Table 4.5: Distribution of academic staff by years since last

highest degree was obtained...............................127

Table 4.6: Distribution of Heads of departments by gender......128

Table 4.7: Distribution of Heads of departments by type of

university......................................................................................128

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Table 4.8: Distribution of Heads of Departments by Age Group

......................................................................................................129

Table 4.9: Distribution of heads of department by highest degree

obtained........................................................................................130

Table 4.10: Distribution of heads of departments by years since

last highest degree was obtained...............................130

Table 4.11: Average publication output By University 2004-2008

......................................................................................................132

Table 4.12: Frequency distribution of those who Published by rank

......................................................................................................136

Table 4.13 Frequency distribution of those who did not publish by

rank..............................................................................................137

Table 4.14: Average publication output by gender 2004-2008. . .139

Table 4.15: Average publication output By Rank 2004-2008.....140

Table 4.16: Average publication output by Age group 2004-2008

......................................................................................................142

Table 4.17: Average publication output By University Type 2004-

2008..............................................................................................144

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Table 4.18: Average publication output by highest degree obtained

2004-2008....................................................................................145

Table 4.19: One way ANOVA test of difference in the mean score

of academic staff‘s type of university and research

productivity...............................................................147


of academic staff‘s age group and research

productivity...............................................................148


of academic staff‘s gender and research productivity.

...................................................................................149


of academic staff‘s rank and research productivity.. 151


of academic staff‘s highest degree obtained and

research productivity.................................................152


of academic staff ‘s years since last highest degree

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was obtained and research productivity....................153


of academic staff‘s university and research productivity

...................................................................................155

Table 4.26: Individual Factors influencing research productivity

......................................................................................................156

Table 4.27: Institutional Factors Influencing Research Productivity

......................................................................................................158

Table 4.28: Individual factors Influencing research productivity as

perceived by Heads of Departments..................160

Table 4.29: Heads of Departments attitudes on institutional factors

affecting academic staff research productivity.........161

Table 4.30: Rotated Component Matrix (a).................................165

Table 4.31: Correlation Matrix and Rotation...............................167

Table 4.32: KMO and Bartlett's Test...........................................168

Table 4.33: Frequency distribution of lecturers’ attitude scores on

Research and Publications in their institutions.........177

Table 4.34: Academic Staff gender mean attitude score.............179

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Table 4.35: ANOVA test of difference in the gender mean attitude

scores towards research and publishing....................180

Table 4.36: Academic Staffs’ types of university mean attitude

score.............................................................................................181

Table 4.37: ANOVA test of difference in academic staffs’ type of

university mean attitude scores towards research and

publishing..................................................................182

Table 4.38: Academic Staffs’ age group mean attitude score.....183

Table 4.39: ANOVA test of difference in academic staffs age

group mean attitude scores towards research and

publishing..................................................................184

Table 4.40: Academic Staffs’ rank mean attitude score..............185

Table 4.41: ANOVA test of difference in academic staffs rank

mean attitude scores towards research and publishing

...................................................................................186

Table 4.42: Academic Staffs’ highest degree mean attitude score

......................................................................................................188

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Table 4.43: ANOVA test of difference in academic staffs highest

degree mean attitude scores towards research and

publishing..................................................................188

Table 4.44: Academic Staffs’ years since highest degree was

obtained mean attitude score.....................................189

Table 4.45: ANOVA test of difference in academic staffs’ years

since highest degree was obtained mean attitude scores

towards research and publishing...............................190

Table 4.46: Academic Staffs’ University mean attitude score... .191

Table 4.47: ANOVA test of difference in academic staffs

university mean attitude scores towards research and

publishing..................................................................192

Table 4.48: Academic staff views on problems hindering research

productivity..................................................................................195

Table 4.49: Heads of Department views on problems hindering

research productivity..........................................200

Table 4.50: Academic staff views on possible solutions to

problems hindering research productivity.................203

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Table 4.51: Heads of Department views on possible solutions for

problems hindering research productivity...............209

Table 4.52: Research Career Development Framework..............221

List of Figures

Figure 1.1: Factors Responsible for Faculty Research Productivity

........................................................................................................30

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........................................................................................................49

Figure 4.1: Distribution of academic staff by Highest Degree

Obtained.......................................................................................127

Figure 4.2: Distribution of heads of departments’ by Position held

in university.................................................................................131

Figure 4.3: Scree plot for principal component analysis.............171

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List of Abbreviations/Acronyms

AAU Association of African Universities

AAUP American Association of University Professors

ANOVA Analysis of Variance

ARIPO Africa Regional Intellectual Property Organization

ASPA American Society for Public Administration

AUB American University of Beirut

CFA Confirmatory Factor Analysis

CLS Clinical Laboratory Science

CUEA Catholic University of Eastern Africa

FRP Faculty Research Productivity

HODs Heads of Department

IADR International Association for Dental Research

IPR Intellectual Property Rights

JIF Journal Impact Factor

KEPSA Kenya Private Sector Alliance

KIPI Kenya Industrial Property Institute

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KMO Kaiser-Mayer-Olkin

NCST National Council for Science and Technology

PAA Population Association of America

PAR Public Administration Review

PCA Principal Component Analysis

PhD Doctor of Philosophy

POM Production and Operations Management

PPPs Private Public Partnerships

PSDS Private Sector Development Strategy

R & D Research & Development

SEM Structural Equation Modeling

SPSS Statistical Package for Social Sciences

UASU University Academic Staff Union

UNESCO United Nations Education Scientific and Cultural

Organization

UON University of Nairobi

WIPO World Intellectual Property Organization

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CHAPTER ONE

INTRODUCTION

1.1 Background to the Problem

Governments expect universities to become more efficient and effective in

teaching, research and community service. However, there appears to be many

obstructions to research productivity that in turn cause low levels of research outcomes

(Lertputtarak, 2008). In Kenya, for example, the ranking of local Universities has

nosedived. This has been due to the recent innovation of module II in higher education,

massification of higher education and aggressive expansion strategies employed by

various universities. This has resulted in possibilities of imbalance between available

time for teaching and research roles of the academic staff in Universities.

Brewer, Douglas, Facer, and O'Toole (1999) describes the fears that Frederick C.

Mosher (a public administration scholar) had in the mid 1950s. Mosher had complained

that scholars of public administration were not doing enough to advance knowledge in

the field. He maintained that too little research had been performed, the stimulus for

research effort was inadequate, and research output was not meeting the needs of society.

According to Mosher, the problem was more than academic (pun intended). He felt that

research was the first step in improved practice, and that the real tragedy of poor research

performance was the human suffering that could be alleviated if public administrators

were provided with better knowledge.

Mosher argued that the close relationship between scholarship and practice was a

strength of public administration, and he encouraged scholars and practitioners to work

together to solve the "research problem". Furthermore, he recognized that the American

Society for Public Administration (ASPA) was one of the few professional societies in

any field with close connections to scholarship and practice, and he urged ASPA to assert

a leadership role and make the advancement of knowledge one of its primary objectives.

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As a result, the Public Administration Review (PAR) began providing a forum for

the debate over research, and this forum has been sustained by lively commentary for

more than forty (40) years. Unfortunately, the research problem has not been resolved.

Knowledge production by public administration scholars continues to be criticized as

insufficient to meet the field's needs (Brewer et al, 1999). Clearly this is a strong support

for research development in areas that are perceived not to be research oriented.

Due to the world-wide economic and social imperatives, universities in all

countries are engaged in a significant reconceptualisation of their public roles. Geiger

(1986) notes that the higher education sector in the twenty-first century is very different

from that of the late nineteenth and twentieth century’s. Universities now perform

important roles as the guardians of public knowledge. They are an important part of the

modern capitalist engine and are recognized as generators of public scientific and

technological knowledge. Clearly this shows the critical role universities are playing in

national development through research and efforts must be made not to deviate from this

noble responsibility.

African Governments are committed to the development of university education

on the premise that higher education is a most sensitive and productive area of

investment. It is politically and socially sensitive in that governments need both highly-

trained people and top-quality research to formulate policies, plan programmes, and

implement projects that are essential to national development (AAU, 1987). This is a

reaffirmation of the urgent need to develop university education in Africa so as to cater

for the divergent needs of society.

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The roles and responsibilities of college and university faculty members are

closely tied to the central functions of higher education. One primary formal description

of these functions was contained in the 1915 "Declaration of Principles" formulated by a

representative committee of faculty members including members of the American

Association of University Professors (AAUP). According to the Declaration, the

functions of colleges and universities are to promote inquiry and advance the sum of

human knowledge, to provide general instruction to the students, and to develop experts

for various branches of the public service (Joughin, 1969). These roles can be summed up

as teaching, research and community service.

The teaching, research, and community service roles of faculty members overlap

conceptually and practically. For example, instruction in a particular discipline or skill

yields a community service in the form of educated or appropriately trained persons, and

outreach to a farmer or small business owner may lead to an applied research project

undertaken by the faculty member. Some attempts have been made to validate the various

forms of faculty work and unify them conceptually. Perhaps the most famous recent

model has been the American educator and government official Ernest Boyer's (1990)

stipulation of discovery, application, integration, and teaching as separate but related

forms of scholarship. Among other outcomes, these models address concerns regarding

the implicit hierarchy that grants the most prestige to research and the least to community

service. (Education Encyclopedia, 2008)

In almost all African countries, public universities receive financial assistance

mainly from the state (Psacharopoulos, 1982). The result is that the level of higher

education activities in a country has for long depended on the soundness of national

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economic performance. From the 1980s, most African countries experienced financial

constraints due to poor economic performance and rapid population growth, added to the

need to provide other basic services like primary education, food, health and shelter.

University education, therefore, has faced severe competition from other sectors for

limited government funds (Psacharopoulos, 1985; World Bank, 1988). In recent years

governments and international donors have challenged universities in Africa to justify

their existence and their claim on the massive public funds allocated to them. (Fuller,

1992; Psacharopoulos; 1985; World Bank, 1988)

The Government of South Africa (2003) for example, through the Department of

Education came up with a document entitled “Policy for Measurement of Research

Output of Public Higher Education Institutions” The development of this policy was

driven by the imperatives for transformation of the higher education system as part of the

strategic objective envisioned by the National Plan for Higher Education. This policy

aims “to sustain research strengths and promote research and other knowledge outputs

required to meet national development needs.” (National Plan for Higher Education,

2003; 70) The purpose of this policy was to encourage research productivity by

rewarding quality research output at public higher education institutions. However, the

policy was not intended to measure all output, but to enhance productivity by recognising

the major types of research output produced by higher education institutions and further

use appropriate proxies to determine the quality of such output. It is important to note that

governments are realising the crucial role being played by research. Kenya can clearly

borrow a leaf from the Government of South Africa by determining research productivity

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of its academic researchers and establish elaborate reward scheme for its researchers both

at the university and at the research institutes.

1.1.2 Background of University Education in Kenya

The idea of an institution for higher learning in Kenya goes back to 1947 when

the Kenya Government drew up a plan for the establishment of a technical and

commercial institute in Nairobi. By 1949, this plan had grown into an East African

concept aimed at providing higher technical education for the region. In September 1951,

a Royal Charter was issued to the Royal Technical College of East Africa and the

foundation stone of the college was laid in April 1952 (UON, 2009)

Soon after the arrival of students at the college, the pattern of higher education in

East Africa came under scrutiny. Through the recommendation of a working party

formed in 1958, chaired by the Vice-Chancellor of the University of London, Sir John

Lockwood, the Royal Technical College of East Africa was transformed. On 25th June

1961, the College became the second University College in East Africa, under the name

"Royal College Nairobi." (Nafukho, 1999; UON, 2009)

The Royal College Nairobi was renamed "University College, Nairobi" on 20th

May 1964. On the attainment of "University College" status, the institution prepared

students for bachelor's degrees awarded by the University of London, while also

continuing to offer college diploma programmes. The University College Nairobi,

provided educational opportunities in this capacity until 1966 when it began preparing

students exclusively for degrees of the University of East Africa, with the exception of

the Department of Domestic Science (Abagi, 1999).

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With effect from July 1970, the University of East Africa was dissolved and the

three East African countries set up their national Universities. This development saw the

birth of the University of Nairobi set up by an Act of Parliament. Others were Makerere

and Dar Es Salaam Universities for the break-up of the University of East Africa was

partly due to ideological differences between the member states Uganda and Tanzania

respectively. Kenyatta College, a teacher-training institution situated on the outskirts of

Nairobi, became a constituent college of the University of Nairobi in 1972 and was

elevated into a full -fledged university in 1985 (Abagi,1999; Nafukho,1999; UON, 2009).

As years went by, the number of Kenyans seeking university education exceeded

the capacity of the University of Nairobi. This led to the establishment of Moi University

in 1984 as the second university in Kenya following the recommendations of the Mackay

Report of the Presidential Working Party on the Second University in Kenya

(Government of Kenya, 1981) which collected views from many people and found an

overwhelming support by Kenyans for the establishment of a second and technologically

oriented university in the country. From then, university education in Kenya has

expanded with a rise in student enrolments, expansion of universities, diversity of

programmes and setting up of new universities and campuses. A previous agricultural

college also gave way to Egerton University in 1988 (Nafukho, 1999).

The most salient feature of university education expansion in Kenya has been

rapid growth in the number of institutions and enrolments. The number of public

universities increased from one in 1970 to seven in 2009 while the university colleges

have grown to thirteen (13). Like its public counterpart, the private university sector in

Kenya has also grown tremendously. The private and accredited universities stand at 21

6

though most of them concentrate in theological studies. The numbers of university

students in Public and accredited universities stood at 118,300 (GOK, 2008).The

University of Nairobi intends to hit the 50,000 mark in terms of enrolment by September

2009 (UON, 2009). These figures indicate an increase in enrolment from 91,541 students

in 2004/2005 academic year to about 170,000 students in 2009 (CHE, 2009). With the

Kenyan economy experiencing negative growth for most of the 1980s and 1990s, the

Government of Kenya found itself no longer able to sustain its previous levels of

financial support to the public universities. This situation arose at the same time as the

growth in student numbers that resulted from both the pressure exerted from the

expanded lower levels of education as well as the fact that possession of higher education

qualifications was becoming more highly regarded as a ticket to formal sector

employment (Mwiria, 2007).

Chege (2006) notes that the watershed of higher education in Africa was

experienced at the establishment of colleges that were affiliates of the University of

London for example, Makerere was giving external degrees of the University of London.

Other institutions that were appendages of the University of London included University

of Ibadan in Nigeria, University College of Ghana at Legon and Fourah Bay College at

Freetown, Sierra Leone. The implication of this was that higher education institutions in

Africa should maintain the same parity in instruction, teaching and culture as the

University of London.

With the exception of universities established in Cairo (970 in Egypt), Fourah

Bay (1827 in Sierra Leone), Liberia (1862), and Omdurman (1912 in Sudan), most

African universities were founded around independence in the mid-1960s and 70s, this is

7

where the universities in East Africa fall. The new universities became a constituent part

of the new institutions that were created to satisfy the aspirations of the new nations.

Identified among the tasks of the new universities were the training of future leaders of

thought in the professions, commerce and industry. Additionally, they were to train a

highly educated cadre of persons who would give leadership by passing on their

education through formal and informal means. Simultaneous with the training of high-

level human resources, was the need to prepare a number of social and economic research

studies to serve as guidelines to the political leaders who were spearheading the

reconstruction of institutions inherited from both the traditional and colonial eras.

1.1.3 Challenges Facing University Education in Kenya

Kenya as a member of the international community is also having a good share of

her challenges in the higher education sector. The rapid expansion of university education

in Kenya has led to a number of challenges. According to UNESCO World Conference

on Higher Education (1998), low funding from the exchequer, increased enrolment,

limited access compared to the population level, increased enrolment without

commensurate improvement in available facilities, gender inequality, and a low research

capacity, are some of the problems facing universities in the region. These problems have

led to fears that quality of education is in a downward trend in most of these universities.

Nyaigotti-Chacha (2004; 6) made the following observation about

research in Kenya;

“Research is one of the core pillars of any university

system; Publication of research findings in reputable journals

8

is one of the ways in which these findings are widely

disseminated to stakeholders. Studies show that research and

publishing by faculty has sharply dropped over the last few

years. Due to heavy teaching responsibilities, brought about

by the rising student numbers, plus the need to moonlight so

as to make some extra money to supplement the meager pay

– faculties are not keen on undertaking meaningful research

and publishing their work”.

This observation, by one of the scholars in Kenya, is an indication that all is not

well in the research system at the University level in Kenya. There is need therefore to

find the extent to which this has gone.

Teferra, Altbachb (2004; 12) made the following observation on African scholarship, “African higher education, at the beginning of the new millennium,

faces unprecedented challenges. Not only is the demand for access

unstoppable, especially in the context of Africa's traditionally low

postsecondary attendance levels but higher education is recognized as a

key force for modernization and development. Africa's academic

institutions face obstacles in providing the education, research, and service

needed if the continent is to advance”.

This is indeed a clear observation that research function is also facing a crisis in

Kenya and Africa at large and possible solutions should be sought to bring research back

to its former status.

9

Atwoli (2008) observed that expanding university education and taking higher

education closer to the people in need of it is an important prerequisite for any country

aspiring for industrial development. However, he notes that this should be done with the

same degree of preparation. He notes that it is time our universities sat back and

rethought this whole expansion strategy. There is need therefore to strike a balance

between provision of higher education and quality issues.

Muchie (2008) noted that research universities are critical levers, along with

government and industry needed to shape a knowledge economy in any part of the world.

The key question for Africa is how universities can be aligned to support economic

development, the eradication of poverty and sustainable use of natural resources. Here

research and knowledge become critical to making poverty history and preparing

countries to cope with disasters. Africa needs a strong pan-continental community of

researchers to discover resourceful timely ways to deal with poverty’s many causes. This

requires the development of strong research universities, institutions with a strong

emphasis on graduate research, as opposed to undergraduate teaching, and where

graduates are taught by lecturers who themselves are expanding the frontiers of

knowledge. There is no doubt that the developing countries need research more than the

developed world.

Kelchtermans and Veugelers (2005) wondered on what makes someone a top

researcher, why a substantial part of academics hardly ever publish anything, what factors

explain differences in research productivity and the nature of the research system. He

questioned why some top performers managed to sustain their high productivity level

while others peak in scientific output only sporadically or never. This calls for

10

considerable effort in understanding insights and dynamics in the factors that drive

differences in research performance. This study’s concentration on the factors that

determine research productivity may illuminate some of the questions posed above.

Some scholars (Gibbons 1998; Kennedy, 1997; Trow, 1996) argue that

Universities are being challenged by other knowledge producers. Universities are no

longer the sole producers of knowledge. Knowledge is now being produced by a variety

of organizations like non governmental organizations, research organizations, business

firms, and government laboratories and even individual researchers. Whichever way one

looks at this, there is a case that has been brought forward and puts the universities on the

defensive. These concerns clearly demonstrate that Universities are at the crossroads and

must do more to remain relevant in the present society. In Kenya just like other parts of

the world, various research institutes, firms and even individuals are taking part in the

generation of knowledge, so universities are being challenged. The universities, however,

will not respond appropriately without data in the area of research management.

Information has to be availed on the research output and this is what this study has tried

to do.

Kenya has been doing well in terms of research and publishing. Ngome (2003)

observes that in the 1970s and early 1980s, the volume of research carried out at the

University of Nairobi, the oldest and largest public university in the country was one of

the highest in Africa. One of the key factors that stunted the growth of research in the

Kenyan university system was lack of adequate research funds. The large portion of

support (although inadequate) for postgraduate and staff training and research work came

11

from donors and international organizations. Lack of adequate qualified researchers

constituted the second major constraint to research expansion.

The Government of Kenya recognizes that research and development plays a crucial role

in wealth creation and enhancement of human development in the socio economic

development of the country. The importance placed upon research by the Government of

Kenya is stated in Sessional Paper Number 1 of 2005 p.85:

“Research and development (R&D) is a means of creating wealth and

enhancing human development and is a critical component of higher

education and training. It also plays a vital role in industrial

transformation, economic growth and poverty reduction. However, quality

research requires sufficient funding, availability of highly trained research

staff, adequate and appropriate facilities and equipment. For Kenya to

meet her needs in R&D, there is need to give R&D priority in national

development.”

The strategies recorded in the above quoted Sessional Paper seek to strengthen research

and development through: Increased investment in Research & Development (R & D),

creation of a strong linkage between national goals, aspirations, linkages and research and

wide dissemination of research findings for operational activities.

Despite this, the government acknowledges that researchers are faced with

various challenges which must be overcome (GoK, 2005). One major highlight in

Kenya’s National Strategy for University Education reform process is the emphasis on

12

the creation of a culture of innovation through acquisition, creation and application of

knowledge. In the strategy report, the strategic goal for quality and relevance of

University is stated as: To improve quality and relevance of learning through research for

socio-economic transformation of society (Kenya). (GoK, 2007).

Kenya’s current Mid Term Plan (MTP) of Vision 2030 P.102 states thus;

The rapid increase in enrolments at all levels of education without

commensurate increase in infrastructure and personnel has led to

overstretched facilities, overcrowding in learning institutions and high

student staff ratios. All these challenges have had a negative effect on the

quality of education. In addition, the different curriculum has not kept

pace with the demands of globalization. For instance, rapid expansion in

the demand for University education has strained the existing facilities and

adversely affected the teaching and learning, research productivity and the

intellectual climate of universities as a whole.

These challenges need resolution and elimination in order for universities to

perform to the expected standards. Currently, this is a critical issue facing higher

education institutions in Kenya, and the purpose of this research is to focus on the factors

that have an influence on the research productivity of academic lecturers in universities in

Kenya. Ouma (2008) brings in a new concept of marketisation. He argues that

universities in Kenya started enrolling full fee-paying students at a time when they were

strained in terms of institutional capacity. There were no enough physical facilities, and

most of those available were suffering decay following many years of neglect. They did

not have enough teaching staff, a problem, which the marketisation agenda has made

13

worse. These views suggest that rapid university expansion in one way or the other affect

the core mandates of the university in Kenya.

Insights in the factors that drive differences in research performance and its

dynamics have important policy implications. Policy makers in Kenya have started

assessing research performance (NCST, 2009; GOK 2009). The use of publications as

instruments for evaluation of individual scientists within research institutes as well as for

funding decisions for universities as a whole is becoming more widespread and gaining

acceptance. Furthermore, the allocation of research funding is increasingly being driven

by criteria of scientific excellence, resulting in a concentration of more funds in fewer

hands. Yet, there are few academic studies done in Kenya on what drives research

productivity.

Publications are the major output of scientific research (Rennie, Yank, &

Emanuel 1997). They are the most commonly used vehicles through which new scientific

discoveries are conveyed to the rest of the world (Nelkin, 1998). They are also the

principal currency for academic recognition and promotion for researchers in most

westernized countries (Horton, 1998). Traditionally, the United States (US) has been

leading the world in publication output (Stossel & Stossel 1990). However, with

increased globalization of over the past two decades, other nations are gaining ground

(Stossel, 1990; Nahrwold, Pereira, Dupuis, 1995). Despite this trend towards

internationalization of research, there remain large variations in publication output among

nations (Tompkins, Ko & Donovan 2001). The exact reason(s) for this variation are

largely unknown. This study has employ books and journal publications as a surrogate for

research productivity. It has centred on those factors that are influencing research output

14

among Kenyan scholars. This study has shown that there are a number of factors that

influence the variation in research output among scholars in Kenya.

Kelchtermans et al (2005) notes that many empirical researches have recently

emerged in the west that attempts to pin down the determinants of scientific productivity,

both at the level of the individual researcher and institutional level. However few studies

have combined both the researches publications output and factors that contribute to that

output.

This study explores factors that may explain why some faculty members are more

productive than others. Some of these factors explain or predict increased productivity,

educational administrators in Kenya might be able to implement policies to encourage

and support higher levels of research productivity.

1.2 The Statement of the Problem

The current academic climate in higher education in Kenya threatens the Kenyan

universities’ ability to sustain the conditions that support research achievements.

Increased demands on government and private funding, a deteriorating physical

infrastructure, increased pressure on undergraduate programs, module II, university

expansion strategies and general economic climate in the country have raised concerns

about the continued capacity of universities to maintain teaching, research productivity

and service to the state (Lertputtarak, 2008; Atwoli, 2008).

UNESCO, (2006) has raised serious concerns over the nature of university

education in the developing countries. It says that most universities are under immense

pressure to increase their enrolment in order to meet the human resource development

15

targets of their respective countries. This has led to teaching becoming their first priority

and often their only pursuit. Also, because of scarce financial resources, they are unable

to adequately equip and maintain their research facilities or replenish their libraries. In

addition, they are unable to recruit or retain well-qualified faculty with strong research

credentials who, for various reasons, prefer to move to developed countries (brain drain).

Other pertinent issues include (a) how much of the research carried out in universities in

developing countries is directly or indirectly relevant to the development needs of the

country, and (b) how much of the findings gets effectively transmitted to the relevant

users. These concerns need to be addressed urgently if the universities in the third world

countries have to make an impact in society.

In most developing countries universities are the main and often the only

institutions to undertake research, and if these falter, knowledge production for the

country as a whole will be seriously affected. Statistics show the very poor state of

research output of many developing countries, and the most disadvantaged region is Sub-

Saharan Africa (UNESCO, 2006).

The slogan “publish or perish” is commonly used in universities in the west in quest

for promotion (Mwamwenda, 1994). The Universities in developing world are also taking

into consideration publications by lecturers as a requirement for upward mobility. Many

of the academic staff in universities are not involved in productive research work while a

few of them are. This is what (Creswell 1985) referred to as a ‘puzzle’ why some

faculties produce research year after year while others do not conduct any research at all.

In spite of all these concerns, and the demand by universities for academic staff to

publish, there has never been any concern or understanding of the circumstances under

16

which the academic staff operate. There have never been deliberate efforts to understand

the problems that academic staff faces in their quest to publish. In this regard, there are

very few studies done in Kenya to analyze the factors that influence research productivity

in institutions of higher learning. The published literature in Kenya to date on the factors

influencing research productivity among university academicians in Kenya is limited.

Considering that many studies have been done in various countries of the world,

particularly in the developed countries, it is therefore important that a study is conducted

in a developing country to compare notes with those studies from other countries of the

world.

Therefore, this study sought to establish the factors that influence research

productivity among academic staff in selected public and private universities in Kenya.

1.3 Research Questions

The following research questions guided study;

1. What is the status of research productivity in selected public and private

universities in Kenya?

2. What are the individual/institutional factors that hinder/encourage

research productivity among academic staff in selected public and private

universities in Kenya?

3. What is the attitude of university academic staff in Kenya towards

research and publishing?

4. How can research productivity among academic staff in selected public

and private universities in Kenya be enhanced?

1.4 Hypotheses

17

1. There is a significant relationship between academic staffs’ type of university and

research productivity.

2. There is a significant relationship between academic staffs’ age groups and


3. There is a significant relationship between academic staffs’ gender and research

productivity.

4. There is a significant relationship between academic staffs’ rank and research

productivity.

5. There is a significant relationship between academic staffs’ highest degree

obtained and research productivity.

6. There is a significant relationship between years since last highest degree was


7. There is a significant relationship between academic staff’s university and


8. There is a significant relationship between academic staff s’ gender and attitudes

towards research and publishing.

9. There is a significant relationship between academic staff s’ type of university and

attitudes towards research and publishing.

10. There is a significant relationship between academic staff s’ age groups and


11. There is a significant relationship between academic staff s’ rank and attitudes


18

12. There is a significant relationship between academic staff s’ highest degree

obtained and attitudes towards research and publishing.

13. There is a significant relationship between academic staff s’ years since last

highest degree was obtained and attitudes towards research and publishing.

14. There is a significant relationship between academic staff s’ university and


1.5 Significance of Study

The purpose of this study was intended to stimulate thought, and to recommend

specific actions about management of research output in Kenya. The general aim of the

study is to provide information that can assist in the design, development and formulation

of institutional research policies in the changing global situation, and in particular to

highlight those factors that should be emphasized in order to further encourage academic

lecturers to increase their research productivity.

Reward differentials, promotions and rankings among men and women in higher

education are outstanding issues that have not been fully addressed and results of this

study can be useful in addressing this problem.

The results of this study may provide important information about how

universities, research funding organisations and even government can implement policies

or develop strategies to foster lecturers’ creative research productivity and other scholarly

activities.

According to Henry and Burch (1974), most decision makers continue to use

published research as the primary indicator of academic quality. Similar to corporations

who measure "success" by bottom line profits or market share, academic institutions use

19

research productivity as the index to their overall reputation and as a means to strengthen

their national and international stature. Published research forms the best available

criterion for evaluating the quality and quantity of individual faculty members and of

their departments and institutions.

It is therefore prudent to carry out some form of audit like what the current study

has done so that the authorities know the true picture of the research system in the

country.

1.6 Scope and delimitation of the study

For the purpose of this study, research productivity of university academic staff in

Kenya has been limited to research output in journals, conference papers and books

authored between 2004 – 2008. The study focused in the academic staff in both private

and public universities in Kenya. There were 5 and 6 private and public universities

respectively.

Academic staff’s demographic information and their attitude towards research and

publication were studied.

Seventeen Heads of department also participated in the study.

1.7 Theoretical Framework

This study has been grounded on a model developed by Victor Vroom (1964).

The model is known as expectancy motivation theory. This is due to the conviction that

to determine factors influencing someone to do something, they must have a motivation

behind it.

20

This theory has been modified by various scholars over the years. The most recent

scholar to modify this model was Lertputtarak (2008) who used it for her doctoral

dissertation on determinants of research productivity in Thailand.

Business dictionary (2009) has defined motivation as internal and external factors

that stimulate desire and energy in people to be continually interested in and committed

to a job, role, or subject, and to exert persistent effort in attaining a goal. Motivation is

the energizer of behavior and mother of all action. It results from the interactions among

conscious and unconscious factors such as the (1) intensity of desire or need, (2)

incentive or reward value of the goal, and (3) expectations of the individual and of his or

her significant others.

1.7.1 Expectancy Theory of Motivation - Victor Vroom

Expectancy theory relates choices to outcomes. Individuals assess the probability

of success if a certain behavior is performed and choose to act based on the probability of

certain outcomes, which may be intrinsic or extrinsic (Nadler & Lawler, 1977). Self

determination theories are developed through the choices individuals make and the

behavior toward the interaction with and mastery of one’s environment. Self

determination is the capacity and need to choose and to have these choices be

determinant of one’s actions (Deci and Ryan, 1991).

According to this theory, effort, performance, and outcomes determine

motivation. High motivation develops when individuals believe a realistic amount of

effort will result in successful performance leading to desired outcomes. When an

individual does not expect performance to bring a desired result, then effort will not be

worth expending and motivation will be low. Thus, the key to motivation is to understand

21

those elements that will enhance the linkages between effort and performance and

outcomes (Nahavandi, 1997). As a cognitive choice theory, expectancy theory focuses on

the manner in which decisions are made regarding allocation of effort, highlighting key

components of the motivation process, such as effort-performance and desired outcomes,

and how they work together intrinsically and extrinsically as the basis for such decisions

(Mowday & Nam, 1997).

An individual is motivated to behave in a certain manner because (a) he or she has

a strong desire for a certain task outcome and a reasonable expectation of achieving that

outcome and (b) because he or she also expects that the achievement of the task outcome

will result in reward in terms of pay, promotion, job security, or satisfaction of individual

needs - physiological, safety, esteem and so on. Therefore, Vroom would maintain that

we do things in our jobs in order to achieve second level rewards:

Vroom (1964) indicated that if a worker sees high productivity as a path leading

to the attainment of one or more of his or her personal goals, he or she will tend to be a

high producer. Conversely, if he or she sees low productivity as path to the achievement

of his or her goals, he or she will tend to be a low producer.

1.7.2 Rationale for Motivation Theories

This motivation theory was selected for this study because the motivated

environment drives staff to produce more research outcomes. Kuh and Whitt (1988)

stated that academic environments and cultures or climates generally provide both

socializing and reinforcing organizational norms, values and expectations concerning

22

research. Therefore the environment under which researchers do their work is very

critical in realizing increased research productivity.

Starbuck, 2006; 84) recognizes the critical role of researchers and notes;

Since research is an occupation that involves prestige and salaries, one

should expect to see career-oriented behaviour, and one does. Social

scientists seem to be more concerned with producing papers than with

producing knowledge.

This quote draws attention to the fact that university research, which is Starbuck’s

focus in his book, is not just knowledge production, but is also, more mundanely, paid

work with reputation and career opportunities attached to it. The image of the brilliant yet

isolated mind, locked up in an ivory tower and striving to enhance knowledge just for the

sake of enhanced knowledge has been subject to erosion (Barry et al., 2001). A clear

indication here is that academic staff in Kenya and elsewhere is also concerned about

developing their careers and reaping maximum output out of it. They are therefore

motivated to work just like any other employees in an organization.

Nadler and Lawler (1977) summarized the four assumptions of expectancy

theory:

1. Behavior is determined by forces that exist within the individual and their

work environment.

2. Individuals make decision about work behavior based on examining whether

they are part of the group (membership) plus their effort to perform the task

for ‘how hard to work, how much to produce, and at what quality’.

23

3. People have different needs, desires and goals.

4. People make decisions among a variety of choices based on their expectations

that a particular behaviour will lead to desired output.

In conclusion, expectancy theory appears suitable for this study as it views motivation

and performance as critical aspects to concepts such as research productivity. The

following Table summarizes studies that have been done focusing on research

productivity and employing the expectancy model in their studies.

Table1.1: Summary of related studies on research productivity employing the

expectancy model.

Name(s) of researcher(s) Topic of study Theories Employed

Butler and

Cantrell (1989)

Extrinsic reward valence

and productivity of

business faculty: A within

and between subjects

decision modeling

experiment

Expectancy theory

Tein and

Blackburn

Faculty rank systems,

research motivation and

Reinforcement theory,

Cognitive evaluation,

24

(1996) faculty research

productivity measure

refinement and theory

testing.

Expectancy

theory

Blackburn and

Lawrance

(1995)

Faculty at Work:

Motivation, Expectation,

Satisfaction.

Reinforcement theory,

Personality and career

development theories,

Dispositional theories

Expectancy theories,

Attribution Theories, Efficacy

theories.

Information-processing

theories

Williams

(2003)

A mediated hierarchical

regression analysis of

factors related to research

productivity of human

resource education a

workforce development

postsecondary faculty

Expectancy theory,

Efficacy theory.

Chen, Gupta,

and Hoshower

(2006)

Factors that motivate

business faculty to conduct

research: An expectancy

theory analysis. Journal of

Expectancy theory

25

Education for Business.

Lertputtarak S. (2008) An Investigation of Factors

Related to

Research Productivity in a

Public

University in Thailand: A

Case Study

Expectancy theory

Mowday, R., and Nam, S.

(1997),

Expectancy Theory

Approaches to Faculty

motivation.

Expectancy theory

Tien F.F. (2000), To What Degree Does the

Desire for Promotion

Motivate Faculty to

Perform Research? Testing

the Expectancy Theory.

Expectancy theory

Modified from Lertputtarak (2008)

The above Table has given a summary of various studies that have been done

since 1989 to as recent as 2008 and all have employed the expectancy theory in their

work. This is a justification for this study to employ this theory in this study.

Efficacy Theory

26

Perhaps this is the most recent modification to the expectancy model. Besides

expectancy theory, efficacy theory is important to this thesis. Although efficacy theory is

not included in process or content motivation theories, efficacy theory was mentioned in

the research by Blackburn and Lawrence (1995), and William (2003) that studied

research productivity. In regards to expectancy and value, efficacy theory is closely

related to expectancy theory (Bandura and Locke 2003). Gist and Mittchell (1992)

suggested that the significance of self-efficacy for motivation and performance in work

settings has been well demonstrated and also used in the technical repertoire of human

resource management professionals. In his social cognitive theory, Bandura (1997:3)

introduced the construct of self-efficacy. He describes self-efficacy as;

‘Confidence in one’s capabilities to organize and execute the courses of

action required to produce given attainments’

As a consequence, he suggests that efficacy theory plays an important role in a

person’s self-regulation processes (Bandura 1991). In this theory, a person’s behaviour is

motivated and regulated by self-evaluation reactions to their own actions, and therefore,

self-directedness partially determines the course of one’s behaviour. People will

participate in and try to deal with situations that they have ability to handle, but avoid

situations that they perceive as being beyond their capabilities.

Self-efficacy theory helps us to demonstrate how much effort people will expend

and how long they will persist in the face of difficulties (Bandura 1977), and helps us to

27

predict how a person’s level of effort and persistence on a task will vary in relation to

their level of goal commitment.

This suggests that the higher a person’s perceived self-efficacy, the greater is the

potential for performance related accomplishments (Bandura, Reese & Adams 1982).

Self-efficacy is different from self-esteem and self-concept, which tend to be more global

assessments of the self across several situations. Self-efficacy is task-specific and

varies in relation to experience, learning, and performance feedback (Bandura1982)

Bandura (1977) indicated that efficacy is derived from four major sources:

performance accomplishment, vicarious experiences, verbal persuasion and physiological

arousal. Furthermore, expectations of personal efficacy appear to determine coping

behaviour, that is, initiation, effort expended and sustained effort. In this regard, Bandura

(1977:191) postulated that:

Cognitive processes mediate change but that cognitive events are induced

and altered most readily by experience of mastery arising from effective

performance...psychological changes can be produced through other

means than performance accomplishments’.

He also stated that behaviour patterns are formed through observation of others

and that these observations later serve as a guide for action. These research findings

indicate that people who view themselves as highly efficacious act, think and generally

feel differently than people who perceive themselves as inefficacious (Bandura 1986),

suggesting that personal accomplishments require both skills and belief in what they can

do or the ability to use their skills and knowledge.

28

Phillips and Russell (1994) found a statistically significant correlation between

research self-efficacy and research productivity (r=0.45) and between self-efficacy and

the research training environment (r=0.39). A study by Taylor, Locke and Gist (1984)

demonstrated that self-efficacy is directly linked to performance of academic research

productivity. This accorded with the work of Landino and Owen (1998), who found that

faculty’s research productivity was positively correlated with self-efficacy (r=0.17), and

Vasil (1992), who found that when self-efficacy perception increased, academic research

productivity also increased. Another related study by Blackburn et al. (1991), who

conducted a study of 3,930 faculty members from all institution types across the United

States, found that self-efficacy accounted for a significant proportion of explained

variance in research productivity (r=0.44).

1.8 Conceptual Framework

The following figure illustrates how this study has been conceptualized by the

researcher. This figure has been modified to suit the current area of study.


29

Behaviour

High Research Productivity

Institutional Characteristics

Low Research Productivity

Individual Characteristics

This conceptual framework states that the Individual and institutional

characteristics work together to influence one’s behaviour towards research productivity.

Both institutional and individual characteristics work together in influencing the

behaviour of an academic staff towards research productivity. In this case, one’s

behaviour can lead to high or low research productivity, high research productivity in this

case means that an academic staff will produce more books, articles, patents, conference

papers etc. Low research productivity on the other hand means that an academic staff,

who is not affected by institutional and individual characteristics, will produce less and

hence have low research productivity.

30

It shows the interrelationship at play between individual and institutional factors

to form a basis for a productive, research oriented institute. In this case a university. This

model has been modified from Bland, Seaquist, Pacula, Center and Finstad (2002) who

synthesized literature on faculty research productivity and finally developed a model that

asserted high research productivity to be strongly associated with both institutional,

individual and leadership characteristics. In the Bland et al (2002) model, faculty research

productivity is highest when a faculty member has specific individual qualities works in

an institution that is highly conducive to research and supported by able institutional

leadership.

The model went further to make a hierarchical order of the factors and how they

influence research productivity. That is the individual characteristics are essential but

they have more or less power in assuming faculty research productivity depending on

how research productive the faculty members’ institution is.

In this model, the final output has been provided as articles, books, conference

papers, Patents etc. These are products of a research endeavour. When all these products

are divided by the number of the academic staff, then research productivity is realized. It

is important to note that a higher value realized means that the research productivity is

higher and vice versa.

1.9 Operational Definition of Terms

Research Productivity

For the purpose of this study, research productivity has been limited to all forms

of output from a research endeavor. This is in the form of research papers, conference

presentations, research publications in journals, and books authored. This can be

31

expanded to mean any scholarly research produced by academic faculty members that

contributes to the knowledge base of a discipline. For example; a research publication in

refereed journal, academic book or book chapter. (Creswell, 1986; UNESCO, 2006)

Institutional factors

Institutional factors are those factors that directly emerge from the institution’s

structure, such as the type of institution, institution policy for promotion, research policy,

and faculty collaboration toward a community of scholars, workload, salary, mentorship,

recruitment and selection of staff, resources, and material support.

Individual Factors

Demographic factors used in this study were derived from socio-demographic

factors of Blackburn and Lawrence (1995). These demographic factors include age,

gender and marital status, and these were included in individual factors. They have been

used to find out whether they interfere with an academic staff member’s ability to carry

out research. Others are attitude toward conducting research, academic qualifications,

advanced degree earned, research experience, skills and training, and rank status.

Measuring Research Productivity

Research productivity is conventionally measured as the ratio of total publications

to number of lecturers. Publication analysis of journal articles and books is clearly the

most common measure of such research performance (Olson, 1994). The principal

dependent variable for research productivity in the current study is the number of total

articles/books published per average faculty member. Publication data gathered from

self-reported information was found to be a reliable indicator. Allison and Stewart (1974)

32

found that self-reported response from chemists was correlated with publication counts

obtained from Chemical Abstracts (r = .94).

1.10 Organization of the study

This thesis has been organized into five chapters,

Chapter one concentrates on introduction which forms the preliminaries to the

whole thesis. The background to the study, research problem, the research questions and

hypothesis are presented here. Definitions of terms used in this study are also given in

this chapter.

Chapter two presents the Literature review which is a highlight of previous work

done by other scholars in areas related to the present study. This chapter mainly

concentrates on work done on research productivity across the globe. The most relevant

models to this study are also reviewed. A Summary of the literature is given.

Chapter three explains the research design and methodology used in this study.

Chapter four concentrates on presentation, interpretation and discussion of the

findings.

Chapter five makes summary, conclusions and recommendations of the study.

CHAPTER TWO

33

LITERATURE REVIEW

2. Introduction

The following literature review has mainly been done on the area of research

productivity at various levels from diverse parts of the world but mainly from the west.

The keywords that were used to search for the related literature were “publication

productivity”, and “research productivity.” Some issues have been highlighted that have

been borrowed by the present study and finally a summary of the same has been done.

This literature review has been grouped into the following sub headings;

Theories Related to Research Productivity

Research Productivity and Publications

Theories of Motivation

Determinants of Research Productivity

Research Productivity Measurement

Research Productivity and Type of Institution

Factors that Influence Research Productivity

Factors associated with Personal Career Development

Research Productivity and Academic Disciplines

Research Productivity and Technology Transfer/Patents

Research Productivity and Teaching Effectiveness

Summary of Literature Review

2.1 Research Productivity and Theories

Based on the rationale of expectancy theory, Tien (2000) examined the degree to which

the desire for promotion motivates faculty to do research. Using Taiwanese faculty

34

survey data, the study found that faculty members, who showed higher motivation for

promotion, also displayed better research performance than their colleagues who showed

lower motivation for promotion. The study also found that different kinds of rewards had

different motivating effects on various types of faculty research performance. After

controlling for the effects of demographic, educational, and institutional variables, the

results of logistic regressions showed that faculty who thought that promotion and the

satisfaction of curiosity were important, tended to publish articles; faculty who wanted to

demonstrate their mastery of their disciplines tended to publish books; and faculty who

cared about personal income were more likely to seek and receive the National Science

Council Research Outcome Grant.

Bean (1982) developed a model known as a causal model of faculty research

productivity. This model proposes two general types of variables which are assumed to

affect individual research productivity: institutional variables and individual variables.

Institutional variables are as follows: level of research emphasis at the institution,

granting of advanced degrees at the institution, institutional reputation, and size of

institution, degree of affluence, degree of centralization, and degree of autonomy of the

institution. Individual variables are as follows: level of research goals, number of

research colleagues, degree of undergraduate teaching responsibilities, level of research

resources, level of perceived equity of rewards, level of alienation, perceived level of

legitimacy in one's research, level of expectancies, level of need for personal growth,

level of publication in graduate school, period of time as a faculty member after

beginning as a productive academic in research, academic rank at an institution with a

research emphasis, and level of individual autonomy for individuals with high levels of

35

research goals. Several variables that have received mixed support in empirical research,

and which are not included in this model are individual ability, sex, field of study, career

stage, and prestige.

The model indicates the pathways through which these variables are expected to

produce variations in the dependent variable. These variables act through a set of

individual variables in influencing individual productivity which is the dependent

variable. These variables are expected to have multiplicative/interactive effects on the

dependent variable. The model also indicates that organizational factors affect individual

behaviour which results in a faculty member being more or less productive in the area of

research.

A number of studies have proposed models to account for researcher productivity.

Finkelstein (1984) proposed seven variables to predict the publication rates of academics.

However, this model did not include the institutional factors that impact on researcher

productivity. The current study has brought both institutional and individual factors on

board. Creswell’s (1985) model recognized the importance of the institution and its

research culture in influencing an individual academic’s productivity. Dundar and Lewis

(1998) developed a statistical model from US multi-disciplinary data that found research

productivity to be associated with both individual and environmental attributes. Bland,

Center, Finstad, Risbey and Staples (2005), empirically tested the model of Bland, et al

(2002) using data from a medical school and quantitative methods. They concluded that a

combination of individual and institutional factors, facilitated by effective leadership,

influenced research productivity in the school.

36

Two models linked to research productivity were identified that have been applied

to a business context. The first tailored a theoretical model developed to assess the

scholarly output of economists (Burke, Fender and Taylor 2007) to academic

accountants, using multivariate statistics. In a second model, Chen, Gupta and Hoshower

(2006) applied Vroom’s expectancy theory model with an aim to better understand the

motivation of business related academics from eight main disciplines, including

accounting.

Lotka's law, named after Alfred J. Lotka (1926) is one of a variety of special

applications of Zipf's law. It describes the frequency of publication by authors in any

given field. It states that the number of authors making n contributions is about 1 / na of

those making one contribution, where a nearly always equals two. More plainly, the

number of authors publishing a certain number of articles is a fixed ratio to the number of

authors publishing a single article. As the number of articles published increases, authors

producing that n publications become less frequent. There are 1/4 as many authors

publishing two articles within a specified time period as there are single-publication

authors, 1/9 as many publishing three articles, 1/16 as many publishing four articles, etc.

Though the law itself covers many disciplines, the actual ratios involved (as a function of

'a') are very discipline-specific.

The general formula says:

37

http://en.wikipedia.org/wiki/Zipf's_law

http://en.wikipedia.org/wiki/Alfred_J._Lotka

XnY = C

Or

Y = C / Xn,

Where X is the number of publications, Y the relative frequency of authors with X

publications, and n and C are constants depending on the specific field (Lotka, 1926).

This law is believed to have applications in other fields for example in the

military for fighter pilot skills.

Illustration For 100 authors, who on average write one article each over a specific

period, we also have those making one contribution, i.e. a power law, where a is often

nearly 2. It is an empirical observation rather than a necessary result. This form of the law

is as originally published and is sometimes referred to as the "discrete Lotka power

function”

Table 2.1: Illustration of Lotka’s Law

Number of articles written Number of authors writing that number of articles

10 100/102 = 1

9 100/92 ≈ 1 (1.23)

8 100/82 ≈ 2 (1.56)

7 100/72 ≈ 2 (2.04)

6 100/62 ≈ 3 (2.77)

5 100/52 = 4

4 100/42 ≈ 6 (6.25)

3 100/32 ≈ 11 (11.111...)

38

http://en.wikipedia.org/wiki/Power_law

2 100/22 = 25

1 100

Source (Lotka 1926)

There is need for a study on research productivity in Kenya to work out and see

whether the output in Kenya confirms the Lotka power function. This law has been

applied elsewhere, this was done in Nigeria. Gupta (1987) generated a bibliography of

entomological research in Nigeria, 1900–1973 in total, 1720 publications were analysed

to study the author productivity patterns and to test the applicability of Lotka's law for the

obtained distributions. Four different files were generated, one for the publications of all

the authors, second for the publications by first authors, third for single authors and

fourth for coauthors.

Lotka's law, in its original form as inverse square law, was found not applicable to

any of the four data sets. However, it was found to apply in its generalized form with the

calculated values of characteristic exponent . The values of were found to be 1.9, 1.8,

2.2 and 2.4 for the four different data sets. K-S statistical test was applied to test the

applicability of generalized form of Lotka's law. The maximum difference in the

observed and estimated values of the proportions of authors was found to be highly

insignificant at 0.01 level of significance in each of the four cases

Nwagwu (2006) carried out a study on Bibliographic data on biomedical

literature of Nigeria drawn from articles listed in Medline Journal covering the period

1967–2002, and numbering 6820. These articles were analysed to study the pattern of

39

productivity of various author categories using Lotka’s law. The total of 2184 authors

who wrote the papers was divided into four different files, namely all authors, first

authors, non-collaborative authors and co-authors. The hypothesis of this study was that

the productivity patterns of each of the categories of authors differed from Lotka’s

inverse power law. The results showed that only the co-author category differed from the

inverse power version of the law, while the other categories did not, although they

yielded various exponents.

Both Nwagwu (2006) and Gupta (1987) are in agreement with Lotka’s law. This is

attributed to the fact that their exponential ranges within the limits of 1.8 and 2.6.

Bland et al (2002) synthesized the literature on faculty research productivity into a

model that asserts high research productivity is strongly associated with eight individual

characteristics, fifteen institutional characteristics, and four leadership characteristics.

This model has evolved through its application in several studies, as noted earlier. In the

Bland et al. (2002) model, faculty research productivity is highest when a faculty member

has specific individual qualities, works in an institution that is highly conducive to

research, and is led by someone who possesses essential leadership qualities and uses an

assertive–participatory management approach.

Figure 2.1 displays the model and briefly describes the individual, institutional,

and leadership characteristics in the model. Further, the Bland et al. (2002) model

suggests a hierarchical order to these three sets of qualities. That is, the individual

characteristics are essential, but they have more or less power in assuring faculty research

productivity depending on how research-conducive the faculty member’s institution is.

40

Finally, the impact of the institution is mediated by the qualities and style of the leader.

This study has picked on the interaction of the individual and institutional factors on the

influence the overall research productivity of the university academic staff in Kenya.

2.2 Research Productivity and Publications

Price (1963) who studied the growth of scientific literature went on to generalize

that 50% of scientific publications was produced by 6% of the scientific community and

that the average scientist published about three papers in his lifetime. In another study,

Bottle, et al (1994) compared publication counts produced by chemical professors,

readers and senior lecturers in the United Kingdom and those in the United States (1981-

1991) and found no significant difference in their publication productivity.

Reskin (1977) studied a random sample of 238 academic chemists between 1955

and 1961 and found that 7.5% published nothing in the first decade following the receipt

of their degree and 11% published 1 article. Although the average rate of publications

achieved was low, the variations of publication productivity between the scientists were

high (Blume & Sinclair, 1973). Lotka (1926) analysed papers published in physics

journal and found the distribution of publication was highly skewed. This indicated that a

small minority of scientists produced the bulk of the papers.

Kyvik (1990) in a study noted that productivity differences were the least in

natural science (women published 20% fewer articles than men) whilst women in

medicine, social science and humanities were 30-35% less productive than men.

Academic rank was found to be important in relation to productivity. Professors were

more productive than associate professors, and since there were fewer women in senior

41

positions, the difference in productivity between ranks had consequences for average

productivity between male and female researchers. Tower, Desai, Carson and Cheng

(2006) reached the same conclusions in a large scale study of Australian accounting

academics. Interestingly, Kyvik (1990) observed that women published less than men in

the same positions but that they were more productive than men in lower positions. Thus

female associate professors published more than male associate professors, and female

associate professors published more than male assistant professors.

There was a relationship between age and productivity and this connection was

upheld for both men and women. High age was negatively related to productivity for both

men and women. Women were more productive in the age group 50-54, while men were

more productive in the age group 45-49. Considering all researchers, productivity was

highest in the 45-49 age groups. For both men and women, married and divorced persons

were more productive than single persons. Women with children were more productive

than women without children. (Tower, G; Plummer, J; and Ridgewell, B 2007)

This study has used publications in refereed research journals as a surrogate for

research productivity. This approach is supported by the literature. Radhakrishna and

Jackson (1993) reported that publishing in refereed journals was ranked as the most

important factor when agricultural and extension education department heads were asked

to rank the importance of 13 factors in the evaluation of faculty. In a related study,

Radhakrishna, Yoder and Scanlon (1994) concluded “Publications (refereed articles in

journals and paper presentations in conferences) are considered to be a very important

component of faculty productivity” (p. 17). In Kelly and Warmbrod’s (1986) study, most

of the variance (84.1%) in their research productivity score was explained by publications

42

in refereed journals, with the remaining variance explained by seven other variables. The

decision to use refereed journal articles as a surrogate for research productivity was based

on the studies cited here.

Wagner, Hornsby, Talbert, Hobbs, Brown, & Kenrick (1994) undertook a study whose

purpose was to quantify research publication productivity of family medicine

departments in selected family medicine and interdisciplinary journals. A 5-year journal

search was conducted to identify original research articles published by family medicine

department faculty. Publication productivity of all departments was ranked, and

regression analysis was used to identify predictors of publication productivity. The

departments leading in publication productivity published more than 25 articles over the

5-year period. The number of faculty and mean dollar value of family medicine

department establishment grants were the strongest predictors of publication productivity.

When adjusted for departmental size, some departments were found to have high

publication productivity per faculty member, even though total numbers of departmental

publications were low in comparison to other departments. This study identified one way

of comparing departments in terms of publication productivity. Large departments, and

those with more developmental grant support, had the highest publication productivity.

Quantification of research output is not enough as this study attempted to do. The present

study has gone beyond quantifying the research productivity and found out reasons that

propel higher productivity of research output among university lecturers in selected

public and private universities in Kenya.

The counting of total or average publications achieved is therefore a common and

popular method used to assess research productivity; it is also easier to obtain such

43

bibliographic data (Martin, 1996). This study has used publication data gathered from

self-reported information from University lecturers in Kenya. It was easier to obtain this

information from the lecturers themselves than to the journal publications. This is

because most of the journals are published out of the country.

Generally, the concept of productivity is considered as "units of output per units

of time" (Waworuntu & Holsinger, 1989). When applied to research, Print and Hattie

(1997) stated that research productivity is the totality of research performed by academics

in universities and related contexts within a given time period. Then, research

performance indicators can be devised by measuring that productivity in order to provide

a basis for making judgments about research quality.

Zainab (1999) states that the outputs of research comprised of intangible and

tangible outcomes. The intangible outcomes are more complex, which include new

scientific knowledge and awareness of new methodologies, and theories. On the other

hand, the tangible outputs of research are published research findings such as research

report or publication in refereed journals which has achieved national or international

recognition, or communicated at conferences. Researchers grant different forms of

recognition based on their contribution to the field, which include citations, positive

ratings and rankings by peers, award of honors and prizes.

Publication counts became units for measuring output. Publication count is an

indicator of research productivity, which may also include patents, inventions and

awards. Publication counts are used to rank faculties and academics. Institutions can be

44

ranked based on the total of publications and the ratio of publications to full-time faculty

(Toutkoushian, Porter, Danielson and Hollis, 2003).

Lange (2001) indicates that quantitative science indicators are essential indicators

for evaluation purposes. They are used for the allocation of funds, scholarships, and

tenures. Apart from publication lists, the most frequently used quantitative indicators for

scientific performance, are the citations which scientists, journals, or scientific

institutions receive. Author productivity, together with the type of publication and the

rank of author, can be used to assess the output of a researcher (Tsay, 2004).

The number of papers published by a group, institution or nation is a partial

indicator of its size and productivity, which give an indication of the research activity in a

particular discipline. Therefore, the publication produced in a particular discipline need to

be determined in order to assess its productivity (Gu and Zainab, 2001).Research

performance and publication productivity by faculty members of an institute could be

used as indicators for ranking institutions.

In their study, “Academic Ranking of World Universities – Methodologies and

Problems”, Liu and Cheng (2005) have ranked more than 1000 universities worldwide by

several indicators of academic or research performance, including alumni and staff

winning Nobel Prizes and Fields Medals, highly cited researchers in twenty-one broad

subject categories, articles published in Nature and Science, articles indexed in Science

Citation Index-Expanded(SCIE) and Social Science Citation Index (SSCI), and academic

performance with respect to the size of an institution. It would be impossible to rank the

quality of university education worldwide due to the huge differences of universities in

different countries and the technical difficulties in obtaining internationally comparable

45

data. Therefore, Liu and Cheng ranked research universities in the world by their

academic or research performance based on internationally comparable data.

Generally, research publication is used to assess the qualifications for promotion

and tenure. Therefore, scientists do research in order to get promoted to higher rank

among their colleagues. Although they preferred teaching as one of the criteria used for

evaluation process for tenure and promotion, but the emphasis was placed on research

(Ali, Young and Ali, 1996). Thus, scientists prefer to collaborate with other researchers

in order to be more productive and to produce better quality research.

Published literature has reported a number of studies that used the quantity of

publication to assess research productivity. Blackburn, Behymer and Hall (1978) used

total articles published over two years, total career publication and total book published

from self-reported data to assess the productivity of 1,216 academic staff members from

4-year colleges and 7,484 staff from universities in the United States.

The instrument used was the questionnaire. Publication data gathered from self-

reported information was found to be a reliable indicator. Allison and Stewart (1974)

found that self-reported response from chemists was correlated with publication counts

obtained from Chemical Abstracts (r = .94). Publication counts have not only been used

to provide productivity counts but also used to assess research trends in certain

disciplines. David, Piip and Haly (1981) used total number of publication counts in

textile research to identify trends in specific areas of research and found a decline in basic

research at the expense of applied textile research. Though this study was done many

years ago, the present study has borrowed the use of self reported data and the

questionnaire in its study format.

46

Onsongo (2005) published an article entitled, The Role of Research and

Publication in the Promotion of Academics in Kenyan Universities based on a study that

was carried out in one of the Kenyan public universities between January and April 2000

to investigate the role of research and publication in the promotion of academics in

Kenyan universities. Data were collected from 14 academic men and 13 academic

women through questionnaires/interview guides and document analysis. Documents such

as promotion criteria and academic staff lists were used to analyse the promotion criteria

and determine the ranks occupied by the academic staff. Findings from the study revealed

that academic promotions were strongly linked to research and publications. Academic

staff had limited access to research and publication as shown by their low involvement in

research and low publication rates. Academics were found to face a number of obstacles

such as inadequate funding, lack of information on available resources, absence of

support systems to enhance research and publications, and inadequate time. Perhaps this

is the only study carried out in Kenya on the area of research productivity. The sample

population used here was not representative of the university academic staff at the time.

The present study increased the sample population so as to realize generalizable findings.

Maske, Durden and Gaynor (2003) examined the factors that cause disparity

between male and female publications. They found 41.3% of the difference between male

and female article production was explained by experience, number of courses taught,

type of university orientation, and other control factors. They argued that the unexplained

difference may be related to discriminatory practices in the publication process. Other

contributory factors showed that women were more involved in community service

activities at the expense of research. Their statistical regression results showed that

47

females had 12.2 years experience whereas males had 17.2 years experience; the

marginal year of experience was associated with an increase of 0.99 papers for males and

0.45 for females. Other significant predictive factors included a negative relationship

with time devoted to administration, teaching or working in a teaching-focused

institution.

This study dwelt on the factors causing disparity in research performance

between male and female researchers. This is not enough; it should have proceeded to

find out those factors that promote or hinder research productivity of all sexes. That is

what the present study has strived to accomplish.

Oppenheim and Ellerslie (2008) carried out an investigation whether a

relationship existed between motivation and publication productivity of UK academic

Information Scientists. A motivational questionnaire survey was performed, and citation

analyses undertaken to determine the publication and citation count of the 45

respondents. Findings of this study demonstrated significant differences in motivational

levels and publication counts by age, gender, caring responsibilities and hours spent on

research. The paper concluded that those likely to produce more publications were older

males without responsibilities who did 6-15 hours research per week. The conclusions of

this study cannot be so useful in academic circles. The present study came up with

tangible conclusions on the way forward to motivate university lecturers to work even

harder in their academic endeavours.


48

Source: Bland et al (2002)

This model has been borrowed and modified to fit the conceptual framework for this

studyit forms a perfect interaction of the various factors at play in the research

productivity efforts of the academic staff.

2.3 Theories of Motivation

There are a number of different views as to what motivates workers. The most

commonly held views or theories are discussed below and have been developed over the

last 100 years or so. Unfortunately these theories do not all reach the same conclusions!

(Wikipedia)

Taylor

Frederick Winslow Taylor (1856 – 1917) put forward the idea that workers are

motivated mainly by pay. His Theory of Scientific Management argued the following:

49

Workers do not naturally enjoy work and so need close supervision and control

Therefore managers should break down production into a series of small tasks

Workers should then be given appropriate training and tools so they can work as

efficiently as possible on one set task.

Workers are then paid according to the number of items they produce in a set

period of timepiece- rate pay.

As a result workers are encouraged to work hard and maximize their productivity.

Taylor’s methods were widely adopted as businesses saw the benefits of increased

productivity levels and lower unit costs. The most notably advocate was Henry Ford who

used them to design the first ever Production line, making Ford cars. This was the start of

the era of mass production.

Taylor’s approach has close links with the concept of an autocratic management

style (managers take all the decisions and simply give orders to those below them) and

Macgregor’s Theory X approach to workers (workers are viewed as lazy and wish to

avoid responsibility).

However workers soon came to dislike Taylor’s approach as they were only given

boring, repetitive tasks to carry out and were being treated little better than human

machines. Firms could also afford to lay off workers as productivity levels increased.

This led to an increase in strikes and other forms of industrial action by dis-satisfied

workers.

Mayo Elton

Elton Mayo (1880 – 1949) believed that workers are not just concerned with

money but could be better motivated by having their social needs met whilst at work

50

(something that Taylor ignored). He introduced the Human Relation School of thought,

which focused on managers taking more of an interest in the workers, treating them as

people who have worthwhile opinions and realizing that workers enjoy interacting

together.

Mayo conducted a series of experiments at the Hawthorne factory of the Western

Electric Company in Chicago He isolated two groups of women workers and studied the

effect on their productivity levels of changing factors such as lighting and working

conditions.

He expected to see productivity levels decline as lighting or other conditions

became progressively worse. What he actually discovered surprised him: whatever the

change in lighting or working conditions, the productivity levels of the workers improved

or remained the same.

From this Mayo concluded that workers are best motivated by:

Better communication between managers and workers (Hawthorne workers were

consulted over the experiments and also had the opportunity to give feedback)

Greater manager involvement in employees working lives (Hawthorne workers

responded to the increased level of attention they were receiving)

Working in groups or teams. (Hawthorne workers did not previously regularly

work in teams)

In practice therefore businesses should re-organize production to encourage

greater use of team working and introduce personnel departments to encourage greater

manager involvement in looking after employees’ interests. His theory most closely fits

in with a paternalistic style of management.

51

Maslow

Abraham Maslow (1908 – 1970) along with Frederick Herzberg (1923- )

introduced the Neo-Human Relations School in the 1950’s, which focused on the

psychological needs of employees. Maslow put forward a theory that there are five levels

of human needs which employees need to have fulfilled at work.

All of the needs are structured into a hierarchy and only once a lower level of

need has been fully met, would a worker be motivated by the opportunity of having the

next need up in the hierarchy satisfied. For example a person who is dying of hunger will

be motivated to achieve a basic wage in order to buy food before worrying about having a

secure job contract or the respect of others.

A business should therefore offer different incentives to workers in order to help

them fulfill each need in turn and progress up the hierarchy (see below). Managers should

also recognize that workers are not all motivated in the same way and do not all move up

the hierarchy at the same pace. They may therefore have to offer a slightly different set of

incentives from worker to worker.

Herzberg

Frederick Herzberg (1923- ) had close links with Maslow and believed in a two-

factor theory of motivation. He argued that there were certain factors that a business

could introduce that would directly motivate employees to work harder (Motivators).

However there were also factors that would de-motivate an employee if not present but

would not in themselves actually motivate employees to work harder (Hygiene factors).

Motivators are more concerned with the actual job itself. For instance how

interesting the work is and how much opportunity it gives for extra responsibility,

52

recognition and promotion. Hygiene factors are factors which ‘surround the job’ rather

than the job itself. For example a worker will only turn up to work if a business has

provided a reasonable level of pay and safe working conditions but these factors will not

make him work harder at his job once he is there. Importantly Herzberg viewed pay as a

hygiene factor which is in direct contrast to Taylor who viewed pay, and piece-rate in

particular Herzberg believed that businesses should motivate employees by adopting a

democratic approach to management and by improving the nature and content of the

actual job through certain methods. Some of the methods managers could use to achieve

this are:

Job enlargement – workers being given a greater variety of tasks to perform (not

necessarily more challenging) which should make the work more interesting.

Job enrichment - involves workers being given a wider range of more complex,

interesting and challenging tasks surrounding a complete unit of work. This

should give a greater sense of achievement.

Empowerment means delegating more power to employees to make their own

decisions over areas of their working life.

Reinforcement theory

Reinforcement theory was developed by Skinner (1953). In reinforcement theory,

behavior can be explained by environmental conditions. The theory relies on the concept

of the ‘law of effect’, which demonstrates that positive or pleasant behaviors are more

likely to be repeated (Thorndike 1911).

There are four types of reinforcement: positive reinforcement, negative

reinforcement, extinction and punishment. Positive and negative reinforcement intend to

53

increase behavior, while extinction and punishment aim to decrease behavior. Due to

reinforcement theory, people learn several things during the process of reinforcement.

Although the reinforcement theory is a powerful influence tool, the theory contains some

limitations (West Virginia University 1996); (1) it is difficult to identify rewards and

punishment. Finding good rewards and punishments requires a great deal of experience

and insight. (2) It requires control all sources of reinforcement, (3) internal changes can

be difficult to create. It works best with the heuristic thinker, not requiring systematic

thinking. It needs to maintain steady reinforcement cues to maintain the desire actions.

(4) Punishing is difficult to do well.

Adam's Equity theory

Adam's Equity theory is a motivation theory that points out the fact that the

managers should seek a fair balance between the employees' inputs (effort, loyalty, hard

work, sacrifice, etc) and their outputs (recognition, status, salary, status etc), in order to

motivate employees (Adams, 1965). He also stated that it is very vital to make the

employee feel that he is treated fairly if the managers are to achieve positive outcomes

and motivate the employees effectively.

McClelland’s Need for Achievement

The one single motivating factor which has received the most attention in terms of

research is the need for achievement (n-ach). Much of this knowledge is due the work of

David McClelland of Harvard.

Individuals with a high n-ach have a number of distinctive characteristics which

separate them from their peers. First of all, they like situations where they can take

54

personal responsibility for finding solutions to problems. This allows them to gain

personal satisfaction from their achievements.

A second characteristic of high n-ach people is that they like to set moderately

high goals for themselves. These goals are neither so low that they can be achieved with

little challenge, nor so high that they are impossible.

A third distinctive characteristic of high achievers is that they want concrete

feedback on their performance. Only certain types of jobs provide this kind of feedback,

however, and so some kinds of jobs are unattractive to high achievers.

Expectancy Theory of Motivation - Victor Vroom

Victor Vroom (1964), of Carnegie-Mellon in Pittsburgh, has challenged the

assertion of the human relationists that job satisfaction leads to increased productivity.

(This theory has been called the contented cow approach to management.) The

assumption is that if management keeps employees happy, they will respond by

increasing productivity. Vroom defines motivation as: "A process governing choices,

made by persons or lower organisms, among alternative forms of voluntary behaviour."

In organizational terms, this concept of motivation pictures an individual,

occupying a role, faced with a set of alternative voluntary behaviours, all of which have

some associated outcomes attached to them. If the individual chooses behaviour 1,

outcome A results; if 2 then B results and so on.

Expectancy theory relates choices to outcomes. Individuals assess the probability of

success if a certain behavior is performed and choose to act based on the probability of

55

certain outcomes, which may be intrinsic or extrinsic (Nadler & Lawler, 1977). Self

determination theories are developed through the choices individuals make and the

behavior toward the interaction with and mastery of one’s environment. Self

determination is the capacity and need to choose and to have these choices be

determinant of one’s actions (Deci and Ryan, 1991).

According to expectancy motivation theory, effort, performance, and outcomes determine

motivation. High motivation develops when individuals believe a realistic amount of

effort will result in successful performance leading to desired outcomes. When an

individual does not expect performance to bring a desired result, then effort will not be

worth expending and motivation will be low. Thus, the key to motivation is to understand

those elements that will enhance the linkages between effort and performance and

outcomes (Nahavandi, 1997). As a cognitive choice theory, expectancy theory focuses on

the manner in which decisions are made regarding allocation of effort, highlighting key

components of the motivation process, such as effort-performance and desired outcomes,

and how they work together intrinsically and extrinsically as the basis for such decisions

(Mowday & Nam, 1997).

Knowing that individuals choose behaviours in order to obtain certain outcomes is

nothing new. The question is why they choose one outcome over another. The answer

provided by the other motivational theories (by Maslow, Herzberg, McClelland) is that

the choice reflects the strength of the individual's desire or need for a specific outcome at

a certain time.

However, Vroom makes the point that task goals (productivity, quality standards

or similar goals attached to jobs) are often means to an end, rather than the end in itself.

56

There is a second level of outcomes which reflect the real goals of individuals and these

may be attained, in varying degrees, through task behaviour.

An individual is motivated to behave in a certain manner because (a) he or she has

a strong desire for a certain task outcome and a reasonable expectation of achieving that

outcome and (b) because he or she also expects that the achievement of the task outcome

will result in reward in terms of pay, promotion, job security, or satisfaction of individual

needs - physiological, safety, esteem and so on.

Vroom would maintain that we do things in our jobs in order to achieve second

level rewards:

"If a worker sees high productivity as a path leading to the attainment of one or more of

his or her personal goals, he or she will tend to be a high producer. Conversely, if he or

she sees low productivity as path to the achievement of his or her goals, he or she will

tend to be a low producer" Vroom (1964)

Certainly Vroom has hit on an important aspect of motivation. We do not attempt simply

to satisfy a need or even a set of needs in a straightforward, "If I do this, then I will

achieve that" manner. We work with a chain of goals and rewards, where goals in one

area are only a means of achieving goals in another.

In conclusion, the tasks of the managers to motivate the employees are indeed not

that easy. This is because each and every employee has got their very own needs that tend

to motivate them. However, the managers need to have some sort of acknowledge that

will help them to understand the employees well and think of better ways of motivating

them. This is where the motivational theories come into consideration. It is these theories

that provide an explanation of how to motivate them based on what motivates them.

57

The Hierarchy of needs theory and Hertzberg's two factor theory deals with explaining

how the employees are to be motivated by way of looking into their needs. On the other

hand, Adams theory also helps the managers to understand that a fair balance between

inputs and outputs of employees are important. The Expectancy theory too shows that

rewards tend to motivate the employees. However, overall all motivation theories do state

that rewarding and recognizing employees are important in order to motivate employees

thus acting as the foundation to motivate employees.

Choice of Expectancy Model

This study is going to be guided by the expectancy model that has been outlined

above. This justification is due to the fact that several studies have been conducted in the

area of research productivity and most of them have based their studies on expectancy

model. For example; Williams (2003) made very significant studies and in each case, he

used the expectancy model effectively. These studies were; research productivity of

nursing faculty and a mediated hierarchical regression analysis of factors related to

research productivity of human resource education workforce development of post

secondary faculty.

Expectancy theory appears suitable for this study as it views motivation and

performance as critical aspects to concepts such as research productivity. Nadler and

Lawler (1977) summarized the four assumptions of expectancy theory:

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Behavior is determined by forces that exist within the individual and their work

environment.

Individuals make decision about work behavior based on examining whether they

are part of the group (membership) plus their effort to perform the task for ‘how

hard to work, how much to produce, and at what quality.

People have different needs, desires and goals.

People make decisions among a variety of choices based on their expectations that

a particular behaviour will lead to desired.

One well-established research productivity theory, Life-Cycle theory (Hu and

Gill, 2000), suggests that, in general, the research productivity of a researcher rises

sharply in the initial stages of a career, peeks at the time of tenure review, and then

begins a decline.

2.4 Determinants of Research Productivity

Taking a slightly wider view, research productivity can include research

publication in professional journals and in conference proceedings, writing a book or

chapter, gathering and analyzing original evidence, working with post-graduate students

on dissertations and class projects, obtaining research grants, carrying out editorial duties,

obtaining patents and licenses, writing monographs, developing experimental designs,

producing works of an artistic or creative nature, engaging in public debates and

commentaries (Creswell 1986).

However, research is typically a private and self-mastered activity, and it can be

difficult for university staff members to balance an effective project agenda with the

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demands of teaching, service and life in general. According to Boice (1987), productivity

should emerge from hard work, and a fair schedule for research activity should utilize a

benchmark that encourages a struggling researcher to relate to their current level of

activity. For example, Boice (1987) found that a new faculty member who could find

only one hour per weekday to work on their research, generally managed to submit about

1.5 manuscripts per year, which is then consistent with the expectations for a pay rise and

higher tenure status. Furthermore, faculty members who adopt a regimen of brief daily

periods for research projects typically experience less stress in managing their time and

their lives (Boice 1987).

The development of clear measures for research productivity will be a significant

influence in the nature of the service sector. Research productivity has been defined as

the relationship between the outputs generated by a system and the inputs provided to

create those outputs. It may also include the term ‘efficiency’ and more importantly

‘effectiveness’, which measures the total output or results of performance (Turnage

1990).

However, in combining the two words as ‘research productivity’, a simple

definition becomes more difficult in a research environment because different people

have very different attitudes about its meaning. Whilst productivity is very important in

industrial circles, public concern over competitiveness and productivity in universities

enters virtually every policy discussion, whether the subject is education, the budget

deficit or national politics (Krugman 1991).

It is important to define the term research productivity. For the purposes of this

study, it is important that the notion of ‘research productivity’ be carefully defined, since

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it is a key element in the development of the research questions. To begin, ‘Research’

means the careful study or investigation, especially in order to discover new facts or

information (Oxford University 1995). ‘Productivity’ means the total production

compared with inputs or consumption over the same period of time, which serves as a

measure of whether the producer’s production processes are working efficiently (Witzel,

1999).

Print and Hattie (1997) define research productivity as ‘the totality of research

performed by academics in universities and related contents within a given time period’

(p.454), and research efficiency has been defined as the productivity of research per unit

of input resource (Kostoff 1995). Research productivity is an outcome measurement of

scholarly effort (Jacobs, Hartgraves & Beard 1986; Kurz et al. 1989), and has two

components that are; (i) knowledge creation (research) and (ii) knowledge

distribution(productivity) (Gaston 1970). For the most part, the ‘product’ of academic

lecturers’ research is scholarly publication (Carnegie Foundation 1991). The importance

of this definition of research productivity is that it enables faculty members to share

insights, demonstrate academic scholarship, gain recognition for creative thinking, and

finally to develop a reputation for expertise in a specialty area (Rhodman 2002).

2.4.1 Research Productivity Measurement

The most frequently used measure of the quantity or amount of research

productivity is a numerical publication count or the journal article count over a certain

time period. The activities included in measuring productivity range from a narrow

perspective of ‘number of research articles published’ to a broad interpretation which

consists of presentations, both formal and informal, number of graduate students that a

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staff member is advising, publications of any type and proposals submitted for funding.

Moreover, it also includes counts of the number of editorial duties, conference deliveries,

licenses, patents, monographs, books, experimental designs, and works of an artistic or

creative nature, public debates and commentaries (Creswell 1986). Rotten (1990) stated

that a common approach to measuring research productivity was to count the number of

books, articles, technical reports, bulletins, and book reviews published, as well as

presentations given and grants received through reviewing curriculum vitae or other print

materials.

Price (1963) who studied the growth of scientific literature went on to generalize

that 50% of scientific publications was produced by 6% of the scientific community and

that the average scientists published about three papers in his lifetime. In another study,

Bottle, et al (1994) compared publication counts produced by chemical professors,

readers and senior lecturers in the United Kingdom and those in the United States (1981-

1991) and found no significant difference in their publication productivity. The counting

of total or average publications achieved is therefore a common and popular method used

to assess research productivity since it is easier to obtain such bibliographic data (Martin,

1996).

Allison and Stewart (1974) found that self-reported response from chemists was

correlated with publication counts obtained from Chemical Abstracts (r = .94). Braun,

Glanzeland and Schubert (1990) used publication data from the corporate index files of

the Science Citation Index (SCI) database for the period 1981-1985, to assess the

publication productivity of authors from 10 major OECD countries. Budd (1995)

addressed the level of publishing productivity of academic staff members from a number

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of universities for the years 1991 and 1993 who were also members of the Association of

Research Libraries. The publication data was collected from the three citation indexes of

the SCI. The universities were ranked by the number of publication and per capita

publication achieved (total publication by number of academic staff).

Publication counts have not only been used to provide productivity counts but

also used to assess research trends in certain disciplines. David, Piip and Haly (1981)

used total number of publication counts in textile research to identify trends in specific

areas of research and found a decline in basic research at the expense of applied textile

research. Reskin (1977) studied a random sample of 238 academic chemists between

1955 and 1961 and found that 7.5% published nothing in the first decade following the

receipt of their degree and 11% published 1 article. Although the average rate of

publications achieved was low, the variations of publication productivity between the

scientists were high (Blume and Sinclair, 1973). Lotka (1926) analysed papers published

in physics journal and found the Distribution of publication was highly skewed. This

indicated that a small minority of scientists produced the bulk of the papers.

Fielden and Gibbons (1991) pointed out that within the business faculty, many

lecturers emphasize articles published in refereed journals and trivialize all other

measures of productivity. Clement and Stevens (1989) found that management

administrators put greater weight on scholarly research and less on trade and newspapers

articles than their non-management business peers. Radhakrishma and Jackson (1993)

reported that publishing in refereed journals was ranked as the most important factor in

research productivity, and Radhakrishma, Yoder and Scanlon (1994, p.17) noted that

‘publication (in refereed articles in journals and paper presentations at a conferences) are

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considered to be very important component of faculty productivity’. This statement was

supported by Kotrlik, Bartlett, Higgins, & Williams (2002) in reference to Personal

Communication from William J Cooper, former Dean of the Louisiana State University

Graduate School.

Kotrlik et al. (2002) quoted William Cooper as stating that ‘the only magic

number is zero; if you haven’t published in refereed journals, then publications in

research conference proceedings, books and other publications are meaningless’ (p.3).

This statement clearly gives credence to publishing in refereed journals. Going by the

state of poor journal development in developing countries, it now becomes quite

challenging for researchers from the developing countries to publish their works. Most

universities do not have the financial ability to maintain these journals for long.

The quantity of research productivity can be measured as number of published

pages in journals (Malhotra & Kher, 1996; Hoverstad 1991, cited by Babber et al., 2000),

as number of articles published (Stahl, Leap & We 1988; Hadjinicola and Soteriou,

2006); or through a combination of both methods (Grover et al., 1992; Babber et al.,

2000). Such studies that have used a combination of both methods have aimed to rank

institutions as well as individuals based on their research output. This study has employed

such a combination was beyond the capacity of this study, so it focused on the number of

articles published.

Published literatures have reported a number of studies that used the quantity of

publication to assess research productivity. Blackburn, Behymer and Hall (1978)used

total articles published over two years, total career publication and total book published

from self-reported data to assess the productivity of 1,216 academic staff members from

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4-year colleges and 7,484 staff from universities in the United States. The instrument

used was the questionnaire. Publication data gathered from self-reported information was

found to be a reliable indicator.

2.4.2 Webometrics

Webometrics or cybermetrics is the study of the quantitative aspects of the

construction and use of information resources, structures and technologies on the Web

drawing on bibliometric and informetric approaches (Almind and Ingwersen, 1997) or the

study of web-based content with primarily quantitative methods for social science

research goals using techniques that are not specific to one field of study (Wikipedia).

Generally webometrics are concerned with the uploaded materials to the internet.

Since 2004 the Webometrics ranking of world universities has been offering

information about more than 6,000 universities ranked according to indicators measuring

Web presence and impact (link visibility).The Webometrics Ranking is a new

measurement of research productivity. Webometrics is devoted to the quantitative

analysis of the Internet and Web contents specially those related to the processes of

generation and scholarly communication of scientific knowledge. Most universities

across the globe are encouraging their staff to upload all manner of content to the internet

with the aim of increasing the visibility of their respective universities. Some of the

content upload include class notes, class schedules, course outlines, and other related

instructional materials.

The aim of these rankings is to provide extra motivation to researchers

worldwide for publishing more and better scientific content on the Web, making it

available to colleagues and people wherever they are located. The new innovations in

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education in terms of open and distance education have greatly benefitted from this

resource. The Web indicators used are based and correlated with traditional

Scientometrics and bibliometric indicators and the goal of the project is to convince

academic and political communities of the importance of the web publication not only for

dissemination of the academic knowledge but for measuring scientific activities,

performance and impact too.

2.4.3 Research Productivity and Type of Institution

Holcomb, Bartel and Thomson (1988) were concerned that little was known

about the scholarly productivity of faculty members who taught in respiratory care

programs. They studied the scholarly activities of respiratory care faculty members in

southern academic health centers via a mailed survey. An analysis of the responses (n =

33, 86.8%) revealed: (1) The respondents' principal scholarly activity was the reporting

of research findings in refereed journals, with a productivity index (number of

articles/years on faculty) of 0.25, or one published article for every 4 years of

employment in higher education, which was significantly less than that of other allied

health faculty (productivity index 0.69, P less than 0.05). (2) Less than a majority of

respondents had presented a paper at a professional meeting during the 3 years preceding

the survey. (3) Only a small percentage of respondents had been involved in research. (4)

Promotion opportunities and academic preparation were the primary factors that

encouraged scholarly pursuits, and heavy teaching responsibility was the primary

discouraging factor. (5) Scholarly activity is perceived as an important consideration in

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academic promotion decisions. In conclusion they said, respiratory care program faculty

and administration should take steps to increase the scholarly production of faculty

members. This study focused on one department only. The present study has captured

more faculties in this study.

Ho (1998) conducted a study on measurement of research output among the three

faculties of business, education, humanities and social sciences in six Hong Kong

universities. Data was collected from the 1990-95 annual reports of research and

publication outputs of each university. In order to have a fair comparison of publication

outputs of each academic, rank, faculty and university, a framework was developed from

practical experience and from literature to investigate the problem. Results indicated that

the publication outputs of academics in Hong Kong were about the same as other

countries in many aspects.

The recommendation from this study, therefore, was that Hong Kong academics

should not be motivated to do more research. Pressing these academics for more research

publications could raise the figure in the start, but would not necessarily increase the

output in the long run. I do not agree with these findings. Researchers should be

motivated to continue working hard in their areas of specialization. If laxity creeps in

then it will be hard to overcome it again. This study relied on annual reports only. It

could have been better to seek more information from other sources. The present study

has used both lecturers and heads of department as main respondents in this study.

Under this sub heading, studies conducted at different levels are considered. For

example, Adams (1996) in a study entitled research productivity in a system of

universities in the USA explored some efficiency aspects of the university system. The

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findings suggested that leading schools had lower average and marginal costs of

performing research than lesser institutions, and that leading institutions had a

comparative advantage at generating higher quality, more highly cited research.

Brocato (2005) carried out a study to determine the research productivity of

faculty in family medicine departments at U.S. medical schools, as well as the individual

and environmental characteristics and prior socializing experiences predictive of research

productivity. In the year 2000, a 43-item questionnaire was mailed to 796 faculty to

obtain descriptive data toward formulating a conceptual model of the research

productivity of family medicine faculty. Prior to model construction and testing through

full-model regression, the model's factors were reduced through factor analysis. A total of

474 questionnaires (63%) were returned. Eighty percent of respondents spent a half-day

or less per week on research; on average they produced less than one scholarly product

per year. Few had research experience, nor could identify a research agenda or current

research project.

Mixed messages were perceived related to research, both at institutional and

disciplinary levels. In testing a conceptual model, psychological and cognitive

characteristics were most predictive of research productivity, along with time spent on

research. Psychological and cognitive factors included enhancing research skills,

establishing a definable research agenda, fostering research networks, having multiple

research projects underway, maintaining in-depth knowledge of a research area, and

clearly understanding research expectations for promotion and tenure. The clinical and

academic demands on family medicine faculty reduced the likelihood that they will

engage in research. These demands prevent the development of a critical mass to provide

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mentorship and networking necessary for research productivity. The study concluded that

resources were needed to recruit faculty with an interest in research and to provide

faculty development in research skills, mentorship, and networking. This study did not

perform a triangulation to find out what the heads of departments had to say about the

research productivity. The present study has filled this gap by getting the input of the

heads of departments. In one way or the other, they are in agreement with the lecturers in

terms of research productivity in Kenya.

Waller, Wyatt, and Karni (1999) undertook a study to describe the research and

scholarly productivity of faculty in four-year college and university clinical laboratory

science (CLS) programs. To identify meaningful scholarship, to assign values to that

scholarship, and to list the top 15 CLS programs according to faculty research

productivity. In 1996, a national study involving 127 college and university CLS

programs was conducted to determine whether faculty was participating in research. A

questionnaire was distributed to 505 faculty members. Data from 286 respondents (57%

response) representing 114 of 127 (90%) CLS programs were analyzed. The study took

place at The Ohio State University with collaboration from the University of Tennessee-

Memphis and the University of Minnesota. All CLS faculty within a four-year university

or college sponsoring a CLS program were invited to participate. Research productivity

included time spent in research, numbers of publications and presentations, and

grantsmanship.

Data indicated that those faculties who possess earned doctorates and are

employed by research universities have higher levels of research productivity. While 46%

of the CLS faculty held doctorates and 50% were tenured, 42% of all CLS faculty

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members had not published a research paper or abstract since 1990. On the other hand,

23% of the faculty responding had published six or more articles or abstracts since 1990,

46% were successful in obtaining external funding, and 15% of faculty members had

been awarded grants larger than $100,000. Conclusions arrived at indicate that the top

10% of clinical laboratory science faculty researchers are performing approximately one-

half of all scholarly activities. The top fifteen research programs in CLS were identified,

and not surprisingly, they were located in research universities. Results showed that CLS

faculty had made progress in scholarship including highest degree obtained, publications,

presentations, and grantsmanship. A questionnaire used in this study was borrowed and

adjusted to be used in the present study.

Barhyte and Redman (1993) undertook a study to establish Department-level

measures of productivity. These were constructed by using information reported by 180

nursing deans of schools with graduate (master's and doctoral) programs. Productivity

was calculated in three ways: total (net), publications, and grants. The scores for each

school were derived from nine categories of faculty scholarly activities. The following

variables were examined for their contribution to productivity: three measures of

environmental support budgeted and doctorally prepared faculty, students (Masters,

doctoral), all graduate students-faculty ratio, scholarship time, and private faculty offices.

The regressions of log-transformed variables yielded R2 = .59 for total (net) productivity,

.54 for publications, and .50 for grants productivity. This study was limited in that it

delved on departmental measures of productivity. This study has considered individual

factors too. Barhyte’s study concentrated on the departmental level factors only.

Individual factors have been brought on board in the current study. The findings of the

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current study as can be seen in the later chapters of this thesis is that both individual and

institutional factors play a role in the influence on research productivity in Kenya.

Marchiori (1998) undertook a study to investigate the research productivity of

chiropractic college faculty and identified parameters associated with increased peer-

reviewed publication. A survey was administered to collect data in a cross-sectional

design. Data were collected through a survey administered to all full-time chiropractic

college faculties working in the United States. Although the survey addressed many

scholarly activities, the criterion variable selected for this study was the number of peer-

reviewed journal articles published over the past 3 years. Three groups of faculty had

more publications: those primarily assigned to research, those with either a D.C. degree

or a D.C. and Ph.D. degree, and finally those with a rank of full professor. Faculty age

and gender were not associated with the reported number of publications.

The majority of faculty members (72.2%) had not published a single peer-

reviewed article in the last 3 years. Less than 2% of the faculty members had published

10 or more peer-reviewed articles in the last 3 years. Conclusion arrived at indicated that

many faculty were not involved in research activities. Conclusion here was that; Faculty

development and incentive programs needed to be implemented to stimulate these

individuals. This study took into consideration one department only, the present study has

brought on board more faculties and departments as can be seen in chapter four of this

work.

Ramsden (2005) describes results from a study of academic productivity in

Australian higher education. It estimated the output (in terms of quantity of publications)

of individual staff and academic departments across different subject areas and types of

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institution. Concerning research productivity, Australian academics resemble their

colleagues in other countries: the average is low, while the range of variation is high.

Most papers were produced by few academic staff. Several potential correlates of

productivity, including level of research activity, subject area, institutional type, gender,

age, early interest in research, and satisfaction with the promotions system, were

examined.

A model linking departmental context to personal research performance through

department and personal research activity was developed and tested. The results

supported earlier views that structural factors (such as how academic departments are

managed and led) combined with personal variables (such as intrinsic interest in the

subject matter of one's discipline) to determine levels of productivity. There was evidence

also that research and teaching do not form a single dimension of academic performance.

The present study has borrowed much from this study particularly in investigating

research activity, subject area, institutional type, gender, age, early interest in research

and such. It was interesting to see how these factors reflect in institutions of higher

learning in developing countries like Kenya.

Milgrom, Heima, Tomar, Kunzel (2008) carried out a study to describe the

research productivity of the members of the International Association for Dental

Research (IADR) Behavioral Sciences and Health services Research Group and

examined personal and professional factors related to greater productivity. The findings

from previous studies suggested there could be gender discrimination in opportunities for

women faculty. Members on the active membership list for this IADR group were

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surveyed by email. Most were dentists, and three-quarters had external funding for their

research. The primary outcome measure was the number of self-reported published

articles in Pub Med in the preceding twenty-four months. The mean number of these

publications was 4.9 (SD=5.1). Gender and time in research were the best predictors of

research productivity of this population. There was no difference in time for research

between the men and women in this study. Controlling for gender, the best single

predictor of research productivity remained percentage time spent in research. Overall,

the members of the IADR group spent almost three times as much time in research and

were more than twice as productive as faculty members as a whole as described in earlier

studies.

In view of the current emphasis in many countries on addressing the social and

behavioral determinants of oral health disparities, the productivity of this area of dental

research is very important. Trends toward clinically oriented, non-research-intensive

dental schools in the United States and reductions in time and funding available to

conduct research should be of concern. This study ignored institutional factors. The

present study recognizes the critical role played by institutions in promoting research

productivity, the institutional factors have therefore been considered as critical players

affecting academic staff’s research productivity.

Dyrbye (2008) undertook a study to determine the research productivity related to

required research experiences during medical school. The authors studied the research

productivity of the 998 graduates at Mayo Medical School who had participated in a

required third-year medical school research experience (21, 18, or 17 weeks long)

between 1976 and 2003. Outcomes were verified by published research reports and

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abstracts, and presentations at scientific meetings. Research reports and abstracts related

or unrelated to the required research were distinguished. Seventeen of the graduates were

excluded when considering authorship of research reports (ambiguous data). Four

hundred (41%) of the remaining 981 graduates published one or more research reports

related to their required research experience, 176/998 (18%) published one or more

abstracts related to their required research project, and 375/920 (41%) presented research

findings at an extramural meeting at least once.

Graduates who published a research report or abstract related to their required

research or presented research at a scientific meeting published more research reports

unrelated to their required research than did their peers who did not publish or present

their required research (all P < .05). More graduates in the 21-week group were first

authors (203/584; 35%) than were those in the 17/18-week group (60/336; 18%, P

= .001), but other outcomes were similar for different durations (P > OR = .17). The

study concluded that required medical school research experiences facilitate tangible

research products and may promote subsequent research productivity. Shorter

experiences seemed to yield outcomes similar to longer experiences. Dyrbye’s study

focused on one discipline only. This could not give a true picture of the research activities

in the universities. This study in Kenya has brought on board more faculties and

departments so that it gives a true picture of the situation in Kenya today.

2.5 Factors that Influence Research Productivity

Cole (1979) conducted a study on a scientist's productivity in regard with age.

The study revealed that research productivity did not vary with age. The finding

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conflicted with accepted wisdom and with an earlier literature epitomized by Lehman's

Age and Achievement, that had found that scientists' productivity often reached a peak

relatively early in life and then declined steadily and significantly. The study used

academicians who eventually became professors in highly ranked departments. Another

related study found that researchers were productive, in terms of publishing, between 30

and 79 years old, reaching a peak of 1.76 papers per year by the time they are 53 years

old Gonzalez-Brambila &Velosob (2003). This study used a unique data set of Mexican

researchers to explore the determinants of research productivity. The study findings

confirm a quadratic relationship between age and productivity.

However, productivity peaks when researchers were approximately 53 years old,

5 or 10 years later than what prior studies had shown. These results suggested that age is

not very important in terms of research productivity. Results also show great

heterogeneity across areas of knowledge. Interpretations of other aspects, such as gender,

country of PhD, cohort effect, among others are also discussed. The present study has

studied both gender and age of the participating respondents.

Ostmoe (1986) carried out a study to identify factors which were important in the

publication productivity of university nurse faculty. Two central research questions were

addressed: 1) what relationship exists between selected professional, educational, and

career variables and the publication productivity of university nurse faculty members? 2)

What is the typical publication productivity profile of university nurse faculty? The

population consisted of 422 full-time tenure tract nurse faculty teaching in seven nursing

schools that offered baccalaureate, masters and doctoral programs and were located in

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public Research Universities. All data were obtained through the use of a questionnaire.

Completed questionnaires were received from 80% of the respondents. Faculty not

meeting the criteria for the study and all instructors were eliminated from analysis. Data

were ultimately analyzed for 261 subjects.

Thirty-two variables were found to have a significant relationship to faculty

publication productivity. Eleven of these variables (highest degree, years since first

master's, age, rank, teaching responsibilities, time spent teaching, time spent in research,

hours of clinical instruction, teaching and research preferences, journals received, beliefs

about the desirable relationship between publication and promotion and tenure) and five

motivational variables were included in a regression analysis. These 16 variables grouped

into three clusters, accounted for .4845% of the total variation in university nurse faculty

publication productivity. Current job socialization factors and motivational factors

accounted for a significant amount of variation in faculty publication productivity even

when highest degree, years since first master's, age, and rank were controlled.

This study focused on research universities only. It was not representative of the

universities. The present study has sought to investigate factors influencing research

productivity in public and private universities in Kenya. The results, conclusions and

recommendations are presented in chapter five of this study.

Richards (1987) conducted a study to explore patterns and correlates of research

productivity of members (N=2,713) of the Population Association of America (PAA).

Five measures of research productivity for the years 1981 to 1985 were examined: (1)

number of presentations at PAA meetings; (2) number of times appeared as an author in

the population journal "Demography"; (3) number of times served as editorial consultant

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for "Demography"; (4) number of publications listed in "Population Index"; and (5)

number of times listed as principal investigator in the federal inventory of population

research. The results revealed that the five criteria of research productivity were related to

predictors, including demographic characteristics, educational and work background, and

past productivity. Measures of research productivity were highly skewed. The best

predictor of research productivity was previous productivity in the same area.

Productivity was also associated with younger age, a research-oriented educational

background, and perhaps a greater commitment to the population field. The results

illustrate the usefulness of data from public records for research on the psychology of

scientists. This study focused on one study only. It was also carried out in the late 80s.

The present study has provided up to date information.

Wilson and Wilson (1989) undertook a study on home economics education

administrators' attitudes of factors contributing to research productivity of faculty. One

hundred and sixty administrators responded to the questionnaire. Twenty-three percent

came from public land-grant institutions, 50% from public non-land-grant institutions,

and 26% from private institutions. Logistic regression models were used to analyze the

data. Findings of the study showed that a majority of the administrators in all three types

of institutions believed that the research productivity level of their faculty was not high.

Results also indicated that administrators in the study perceived faculty morale, faculty

development, expertise in writing for publication, and teaching productivity as factors

contributing significantly to faculty research productivity. The present study went way

beyond administrators and focused on the university academic staff.

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Waworuntu & Holsinger (1989) carried out a study on factors influencing the

research productivity of Indonesian faculty in public higher education. These factors were

divided into three broad categories corresponding to: (1) ascriptive, (2) achievement and

(3) organizational variables. As the validity of the productivity concept is difficult to

ascertain, three different measures were employed. These were: (1) a simple summative

count of all self-reported scholarly writing, (2) a summative scale with each separate item

weighted, and (3) a subjective self-evaluation measure. Data was collected for the

specific purpose of this study at the end of 1982. From the population of 20,945 tenured

faculty in Indonesia, 11,269 representing 54% were interviewed using an extensively

pretested interview schedule.

Using multiple regression analysis, models were specified for each of the three

dependent variables. Following initial breakdown analysis of means, a multiple

regression analysis was conducted. The three groups of predictor variables did not have

equal influence on research productivity. The ascriptive variables had the smallest

association with all three dependent variables. The achievement block of variables had

almost three times as many significant coefficients than did the organizational block in

the two objective count measures of productivity. In the specification for self-evaluation,

the organizational block was a slightly larger number of significant coefficients. This

study was carried out in the late 80s; the present study presents investigated current

factors influencing research productivity. The use of a simple summative count of all

self-reported scholarly productivity has been borrowed to be used in the present study.

Yu (1998) carried out a study on Sex differences in research productivity. The

study found empirical evidence based on a systematic and detailed analysis of data from

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four large, nationally representative, cross-sectional surveys of postsecondary faculty in

1969, 1973, 1988, and 1993 in the USA. The research yielded two main findings. First,

sex differences in research productivity declined over the time period studied, with the

female-to-male ratio increasing from about 60% in the late 1960s to 75 - 80% in the late

1980s and early 1990s. Secondly, most of the observed sex differences in research

productivity were attributed to sex differences in personal characteristics, structural

positions, and marital status. This study undertook a study on one variable only. The

present study has sought to find out all those factors that influence research productivity.

Jones (1998) undertook a study whose purpose was to determine factors that were

associated with increased individual research productivity among clinical faculty in 67

United States and Canadian schools of dentistry. Individual faculty research productivity

was defined as the total number of articles in refereed journals and book chapters

published during an academic career. The 328 respondents represented a response rate of

62.8% from a 25% stratified random sample of faculty who (1) had full-time

appointments and held at least a degree in dental surgery. Or foreign equivalent, (2)

taught in a clinical department of the dental schools, and (3) were not department

chairpersons and did not hold administrative positions (assistant dean, associate dean, or

dean) within the dental school.

Respondents reported a mean of 9.9 years in full-time dental education, a mean of

10.8 publications, and a mean of 7.5 hours spent in research per week. Forward addition

multiple regression analysis demonstrated that five predictor variables, from a total of 20

variables evaluated, accounted for 59.9% of the variance in individual faculty research

productivity. These predictor variables were total dollar amount of past research funding,

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career age, training status, colleague utilization in conducting research, and conducting

research from planned goals. This study was narrow since it was concerned with

individual factors that determine research productivity. The present study has

investigated the institutional factors that influence research productivity.

Babu (1998) designed an investigation with the sole purpose of establishing

factors which determine the productivity of scientists in India. Nearly 200 variables

influencing research productivity were collected through relevant literature, analysis of

biographies of great scientists, and discussion with eminent scientists. Finally, through a

critical examination, 80 variables were selected for the use of Q-sort technique. The

sample for the study consisted of a cross section of scientists ranging from Fellows of

Indian National Science Academy to young agricultural scientists. Mailed questionnaires

and personal interview methods were used for collecting data. Out of a total of 912

respondents, reply was obtained from 325. On the basis of Q-sorted data, 26 variables

were selected for further analysis and they were subjected to principal component factor

analysis. The results indicated eleven factors affecting research productivity of scientists.

They were: persistence, resource adequacy, access to literature, initiative, intelligence,

creativity, learning capability, simulative leadership, and concern for advancement,

external orientation, and professional commitment.

This study delved on sole purpose of establishing factors which determine the

productivity of scientists in India. Other data like demographic factors could have offered

some meaningful insights into his study. The present study has investigated other factors

like demographic factors to inform this study.

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Jones (1998) undertook a study to evaluate individual faculty research

productivity with respect to gender among clinical faculty in 66 United States and

Canadian schools of dentistry. A comprehensive survey instrument was developed to

collect information on factors associated with research productivity of individual faculty.

The investigation focused on time spent per week in various work related activities

(teaching, administration, research, and private practice), external grant money obtained

for research, and responses to 12 variables evaluating the subject's attitudes of their

research background, work environment, attitude and outcome effects from publishing,

and the use of colleagues in conducting research.

The 833 respondents represented a response rate of 69.4% (833/1200) from a 50%

stratified random sample of faculty who (1) had full-time appointments and held at least

the DMD or DDS degree or the foreign equivalent, (2) taught in a clinical department of

the dental school, and (3) were not departmental chairpersons or administrators. The

majority of subjects responding to the survey were male (705 males, 84.6%; 128 females,

15.4%). Respondents reported a mean of 10.3 years (males = 10.8, females = 7.5) in full-

time dental education and a mean of 10.1 career publications (males = 11.6, females =

6.5; P < 0.001).

Although there was no significant difference in weekly hours devoted to academic

responsibilities between males and females, several factors did demonstrate significant

gender differences (external grant money obtained for research purposes, P < 0.03;

feeling that the departmental chair did not emphasize research, P < 0.05; feeling a lack of

autonomy within their institution, P < 0.007; and feeling a lack of available colleagues for

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research purposes, P < 0.001). The implications of the findings are discussed concerning

strategies for improving research productivity for females in academic dentistry.

These results suggest that sex differences in research productivity stem from sex

differences in structural locations and as such respond to the secular improvement of

women's position in science. Basically this study was on gender and present study may

not wish to go into the issue of gender in research productivity, this can be made as a

recommendation for future studies.

Fox (2005) carried out a study in the United States on the relationship between

marriage, parental status, and publication productivity for women in academic science,

with comparison to men. Findings indicated that gender, family characteristics, and

productivity were complex considerations that went beyond being married or not married,

and the presence or absence of children. For women in particular, the relationship

between marriage and productivity varied by type of marriage: first, compared with

subsequent marriage and occupation of spouse (in scientific compared with non-scientific

occupation). Further, type of family composition was important: women with preschool

children had higher productivity than women without children or with school-age

children. Women with preschool children were found to be a socially selective group in

their characteristics, particularly in their allocation of time. Marriage and parental status

were narrow factors to be used for a study. The present study has gone beyond gender

and investigated other factors that influence research productivity.

Sax, Hagedorn, Arredondo and Dicrisi (2004) came up with a study that explored

the role of several family-related factors in faculty research productivity for a large,

nationally representative sample of university faculty members. The role of marriage,

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children, and aging parents was examined after controlling other personal and

environmental factors, such as age, rank, department, and intrinsic motivations to conduct

research that previous research had shown to influence research productivity. Analyses

were conducted on a sample of 8,544 full-time teaching faculty (2,384 women and 6,160

men) at 57 universities nationwide in the United States. Results showed that factors

affecting faculty research productivity were nearly identical for men and women, and

family-related variables, such as having dependent children, exhibited little or no effects.

The present study did not go into details or characteristics of the family.

Dundar and Lewis (1998) summarized results of several studies carried out in the

United States on research productivity. In this summary they found individual and

organizational attributes to affect research productivity. Individual attributes included:

innate abilities - such as IQ, personality, gender and age - and personal environmental

influences. Inconsistent results were reported when these variables were investigated.

They further identified as personal environmental factors the quality and culture of

graduate training and the culture of the employing department. They argued that most

research studies have found a positive correlation between departmental culture and

research productivity. The departmental culture referred to shared values and attitudes

within the academic unit. Faculty and administrators who learned to place a high value

on research as graduate students tended to foster a research-oriented culture throughout

their professional lives. This study was about summarizing what other studies had found

out.

Campion and Shrum (2004) carried out a study to find out why women were

finding it more difficult to pursue research careers than men. Based on a survey of 293

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scientists in Ghana, Kenya, and the Indian state of Kerala, the study examined gender

differences on a variety of individual, social, and organizational dimensions. The results

showed small or nonexistent differences between women and men in individual

characteristics, professional resources, and the organizational conditions under which

research was conducted. There was definitely a need to conduct comparative studies in

research productivity but this has not been handled by the current study. The fact that this

study was done in the developing world atmosphere is an assurance that more studies

need to be done in this same line just as what the current study has done.

Bozeman (2005) conducted a study based on the curricula vitae and survey

responses of 443 academic scientists affiliated with university research centers in the

USA, an examination of the longstanding assumption that research collaboration had a

positive effect on publishing productivity was done. Since characteristics of the individual

and the work environment were endogenously related to both collaboration and

productivity, the study focused on the mediating effect of collaboration on publishing

productivity. By using the two-stage least squares analysis, the findings indicated that in

the presence of moderating variables such as age, rank, grant, gender, marital status,

family relations, citizenship, job satisfaction, perceived discrimination, and collaboration

strategy, the simple number (‘normal count’) of peer-reviewed journal papers was

strongly and significantly associated with the number of collaborators. However, the net

impacts of collaboration were less clear. When the same model was applied and

examined, productivity by ‘fractional count’, dividing the number of publications by the

number of authors, it was found that number of collaborators was not a significant

predictor of publishing productivity.

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In both cases, ‘normal count’ and ‘fractional count’, significant effects of research

grants, citizenship, collaboration strategy, and scientific field was found. This led to a

belief that it is important to understand the effects of the individual and environmental

factors for developing effective strategies to exploit the potential benefits of

collaboration. This study concentrated on research collaboration and did not involve the

institutional and individual factors responsible for research productivity. These have been

well covered in the present study.

Bland et al (2005) conducted a study to test the ability of the Bland et al. (2002)

model-based on individual, institutional, and leadership variables influencing faculty

research productivity-to explain individual and group (department) research productivity

within the context of a large medical school. This study used data from the University of

Minnesota Medical School. Twin Cities vitality survey conducted in 2000 had a response

rate of 76% (n = 465 faculty). A statistical software package was used to conduct t tests,

logistic regressions, and multiple regressions on these data. The validity of faculty,

department, and leadership characteristics identified in the Bland et al. (2002) model

were confirmed as necessary for high levels of research productivity. Faculty productivity

was influenced more by individual and institutional characteristics; group productivity

was more affected by institutional and leadership characteristics.

The study made two conclusions. First, that the characteristics and groupings

(individual, institutional, and leadership) in the Bland et al. (2002) model predict faculty

research productivity. Secondly, research productivity is influenced by the interaction of

the three broad groupings, and it is the dynamic interplay of individual and institutional

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characteristics, supplemented with effective leadership, that determines the productivity

of individuals and departments.

Hadjinicola (2006) carried out a study to investigate factors that promote research

productivity of Production and Operations Management (POM) groups of researchers in

US business schools. In this study, research productivity of a POM group was defined as

the number of articles published per POM professor in a specific period of time. The

paper also examined factors that affect research quality, as measured by the number of

articles published per POM professor in journals, which had been recognized in the POM

literature as an elite set. The results of this study showed that three factors increased both

the research productivity and the quality of the articles published by professors of a POM

group. These factors were (a) the presence of a POM research center, (b) funding

received from external sources for research purposes, and (c) better library facilities.

Doctoral students do assist in improving research quality and productivity, but they were

not the driving force.

These results had important implications for establishing research policy

guidelines for business schools. For example, real-world problems were funded by

external sources and had a higher probability of publication. Furthermore, schools could

place more emphasis on external funding, as most engineering schools do, since groups

receiving external funding are more productive in terms of research. This study did not

look at factors that hinder research productivity. The present study investigated both

factors that promote and hinder research productivity in selected public and private

universities in Kenya and brought on board more faculties as basis of this study.

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Cepero (2007) undertook a study in the USA to expand knowledge about faculty

productivity and the institutional and individual factors that may contribute to increased

levels of faculty scholarly productivity. A sample of full time faculty from the restricted

1999 National Study of Postsecondary Faculty database was used to address the two main

research questions. The first research question related to procedures that can be used to

statistically model faculty productivity, and was answered using Structural Equation

Modeling (SEM) techniques such as Confirmatory Factor Analysis (CFA), Multiple-

group CFA, Latent Profile Analysis and CFA with covariates.

The second research question related to the factors associated with high levels of

faculty productivity, and was investigated using a set of explanatory models using

Multilevel Logistic Regression Modeling. Three factors (inflation, sole productivity and

joint productivity) were used to model the productive behaviors of the sample. Several

independent variables, such as gender, rank, and type of institution were positively and

negatively associated to the three dependent variables. These variables differed across the

three factors in the model of scholarly faculty productivity. These outcomes suggested

some level of independence that existed among the variables associated with each of the

factors in the model of productivity developed as part of the study. The results of this

research had important implications for university administrators and agency directors

who develop policies designed to foster faculty professional development. This study also

provided valuable data for researchers in the field of gifted education and talent

development related to fostering creative productivity and talent development in

adulthood.

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A recent scholar noted the following; Future research should include a more

comprehensive set of indicators of faculty productivity that also address other dimensions

of faculty scholarly responsibilities such as teaching productivity. These future studies

may lead to more accurate understandings of a broader conception of scholarship in

higher education institutions (Cepero, 2007; 2). The present study has identified

comprehensive indicators influencing faculty research productivity.

Tower, Plummer, and Ridgewell (2007) noted that differences in terms of sex

research productivity can contribute to differences in earnings. Scholarly production is

typically a significant factor in determining earnings and promotions, and many authors

note that women faculty members publish less on average than their male counterparts.

Thus gender differences in publication rates explain, at least partially, differences in

average earnings and promotion rates between men and women. Numerous studies have

shown that research production in Africa is highly skewed; South Africa accounts for one

third of Africa's publication output, Egypt and Nigeria jointly account for another one

third, result that is congruent with the findings of previous studies. (Tijssen, 2007; Paraje

et al, 2005; Uthman & Uthman, 2007)

Tower et al (2007) examined top six journals in the world and found no difference

between women and men productivity when the percentage of women participating in the

academic work force is factored in. He noted that women had a 30-35% participation rate

in academic university positions and represented almost 30% of the authors in the top

tiered journals. There were also no significantly statistical differences in Journal Impact

Factor ratings between men and women. These findings were found to be consistent

across all the major disciplines, science, business and social science. Other trends were

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noted such as the significantly higher number of authors in science journals and the

different trends between US and non-US authors. Science authors’ quality (as measured

by Journal Impact Factor (JIF of 31.9) was significantly higher than non-science authors

(JIF 6.5); thus differences in quality were discipline specific not a gender issue. The

implications were that academic women’s research contribution matches that of a men’s

productivity. This study relied on journal publications only for analysis. The present

study carried out an investigation involving the lecturers themselves.

Hassan, Tymms and Ismail (2008) carried out a study whose purpose was to

explore the perspectives of Malaysian academics in relation to academic productivity and

some factors affecting it. A large scale online questionnaire was used to gather

information from six public universities. The most productive role in the eyes of the

academics was found to be teaching, with research and administration coming second and

third, respectively. Several factors were found to be related to productivity and some of

these had policy implications. The universities themselves differed markedly and

research productivity was related to the amount of time available, and linked negatively

to the teaching load. Hassan’s study concentrated on public universities only. The present

study sought to investigate factors that influence research productivity in selected private

universities in Kenya.

Usang, Udey, & Akuegwu (2007) carried out a study that examined academic

staff research productivity in Universities in South-South zone of Nigeria. Ex post facto

design was adopted for this study. The sample size comprised of 480 academic staff

drawn from a population of 3120. Data collection was carried out using a researcher –

constructed instrument called Academic Staff Research Productivity Inventory which

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was validated and pilot tested. The data obtained were treated statistically using

Independent t-test and contingency Chi-square (X2) analyses. Results indicated that male

and female academic staff differed significantly in their research productivity; married

and single academic staff differed significantly in their research productivity and there is

a significant influence of areas of specialization on academic staff research productivity.

It was recommended that academic staff in universities should be encouraged to

carry out research work irrespective of their gender, marital status and areas of

specialization. It is interesting to note that the current study has not found significant

difference on research productivity in terms of gender.

Lertputtarak (2008) carried out a study in Thailand on one of the public

Universities. The conceptual framework for her research was chosen to integrate

empirical research findings on faculty role performance and productivity with two

existing motivation theories, namely Expectancy Theory and Efficacy Theory. The

research methodology used a qualitative research approach, based on in-depth interviews

with eleven representative respondents from ‘The Noble University’. Lertputtarak came

up with five factors that impact on academic research productivity. These were

environmental factors, institutional factors, personal career development factors, social

contingency factors, and demographic factors. According to the findings of this study,

these five factors were divided into three main groupings which were termed the essential

factors, desirable factors, and side-affect factors. Each of these factors, she claimed,

needed resolution, in a sequential way, by administrators of the university. This study did

not provide room for triangulation. The present study has employed quantitative method

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though it has also had to involve some qualitative aspects particularly when analyzing the

open ended items in the questionnaires for triangulation purposes.

Perhaps the newest study in research productivity was conducted by Ogbogu

(2009) who examined the research output of female staff and the factors that affect their

research productivity in the Nigerian university system. The study was carried out with a

view to promoting strategies that could enhance productivity and increase the research

output of female staff in Nigerian universities. The study adopted a survey research

design. The purposive sampling method was used in administering questionnaires to 381

female academic staff from twelve randomly selected universities in the six geo-political

zones of Nigeria. The study revealed that female research output was generally low: 59.5

per cent of female academics published one paper annually; 23.6 per cent published up to

two papers; 1.1 per cent published three papers; and 15.8 per cent did not publish on an

annual basis. Although most female academic staff published annually, most wished to

increase their publication rate.

The study's results found that marital status, religion, academic position and

number of hours of lectures per week had an impact on their ability to carry out research

and publish the results. The study concluded that female academics made contributions

that are more significant to teaching than research and that the Nigerian university system

needs to develop strategies to enhance female research output. Religion is a sensitive

issue and the present study has not used it as a factor influencing research productivity.

However other factors such as gender, age group, rank have been used in the present

study with very interesting results.

2.5.1 Factors associated with Personal Career Development

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Fulton and Trow (1974) observed that 29% of the full professors, 20 percent of

the associate professors, percent of the assistant professors and 2 percent of the

instructors has published five or more articles in a two-year period. This work accords

with the findings of Bailey (1992) who pointed out that rank is a significant predictor of

research productivity. Dundar and Lewis (1998) found that departments with higher

ranked faculty resulted in higher research productivity (Vasil 1992).

Beehr, Walsh and Taber (1976) indicated that role stresses can interfere with the

way in which a person interprets the notion that working hard and effectively will bring

about the satisfaction of higher order needs. These authors also suggested that role

stresses may adversely affect workers who strongly value the task attributes of enriched

work. In a similar study, Pfeffer and Langton (1993) reported job satisfaction was

positively related to productivity, and noted that staff opinions of their personal

circumstances may influence productivity, whether it is an opinion of job satisfaction,

research/ training environment, funding adequacy or the freedom to collaborate. It has

been suggested that interest in research can be the best predictor of research productivity.

However Blackburn et al. (1991) found this variable not to satisfactorily predict

productivity. The top academic institutions generally produce a high level of research

productivity because high-status universities enjoy advantage in terms of financial

resources and research support that encourage publication. Reskin, (1977) pointed out

that a process of ‘homosocial’ reproduction is common within business schools, so that

graduates of high-status universities are hired by other high-status institutions.

Homosocial is also referred to as cumulative advantage. This refers to a staff

member’s prior academic and professional training. The attribute of accumulative

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advantage makes it easier to achieve success in publishing because of prior research

project experience, research membership, development of research skills, and

collaboration on research project and research sponsorship (Creswell 1985; Fox 1996).

According to Finkelstein (1984), academic rank is a significant predictor of publication

success because the academic lecturers in higher ranks generally have more control over

their workload assignment, allowing faculty of higher rank to produce more research than

those of a lower rank.

Personal career development factors are those factors that come from the

academic and personal qualifications of academic lecturers themselves. These factors

include such items as an individual’s ability and interest, attitude toward conducting

research, academic origin, the type of advance degree earned, research experience, skills

and training, rank and tenure status. In a similar way, a staff member’s attitudes and

commitment to scholarly work relates closely to their research productivity (Lertputtarak,

2008)

2.6 Research Productivity and Academic Disciplines

Studies assessing the research productivity of departments in academic

institutions or individual researchers have been conducted in various disciplines such as

in general business (Niemi, 1998); Baden-Fuller et al. 2000), management (Stahl et al.

1988), marketing (Niemi,1988), finance (Klemkosky and Tuttle,1977; Ederington

1979;Niemi, 1987) accounting (Jacobs et al. 1986), management information systems

(Vogel and Wetherbe 1984; Grover et al. 1992), and economics (Laband, 1985). These

studies ranked departments according to the number of articles published in the refereed

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journals of their relevant fields. They did not provide factors that influence research

productivity of lecturers that is what the present study did.

Biglan (1973) found that researchers from natural sciences and engineering

published more journal articles, in contrast, researchers from social sciences and

education tended to publish more monographs, results that seem to be consistent with

more current studies (Brooks, 2006). In addition, Biglan found that researchers in applied

fields (e.g., engineering, education, and economics) produced more technical reports than

scholars in no applied fields. Brooks (2006), in a study that compared three cohorts of the

NSOPF (1988, 1993, and 1999), found that social science and natural science/engineering

faculty members were expected to demonstrate higher rates of research dissemination

than faculty members from the field of humanities. In contrast, faculty members from

humanities had a tendency to publish higher numbers of books and chapters in edited

books.

Hannington (1986) undertook a study to identify the characteristics of the active

research producers within dental schools. A survey was mailed to 4,901 full-time faculty

members in 53 U.S. dental schools, of which 1,481 (31%) were returned. Faculty

Research Productivity (FRP) was defined as the number of publications generated by a

faculty member during his or her academic career, as reported in response to a survey

question. Seventeen characteristics of faculty members also were obtained from

responses to survey items. Using stepwise multiple regressions, five variables predicted

38% of the variance in FRP: Interest in Research, Earned Ph.D., Number of Journal

Subscriptions, Consulting Time per Week, and Research Time per Week. While the

relationship between FRP and student contact time was linear (the more articles produced

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the less student contact time), time spent in teaching per week did not enter the prediction

equation. The present study has focused on active researchers and has sought to get views

from them with a view to finding out the factors that influence their research

productivity.

Kohlenberg (1992) carried out a study to identify the relationship between faculty

research productivity and organizational structure in schools of nursing. This was

necessitated by the fact that nursing research had been widely recognized by members of

the nursing profession, yet comparatively few engaged in conducting research. He argued

that although contextual variables that facilitate or inhibit nursing research had been

investigated, the relationship between organizational structure and nursing research

productivity had not been examined. This problem was examined within the context of

the Entrepreneurial Theory of Formal Organizations. A survey methodology was used for

data collection. Data on individual faculty research productivity and organizational

structure in the school of nursing were obtained through the use of a questionnaire. A

random sample of 300 faculty teaching in 60 masters and doctoral nursing schools in the

United States was used.

The instruments for data collection were Wakefield-Fisher's Adapted Scholarly

Productivity Index and Hall's Organizational Inventory. The data were analyzed using

Pearson Product-Moment Correlation Coefficients and multiple correlation/regression

techniques. The study found out that the overall relationship between faculty research

productivity and organizational structure in schools of nursing was not significant at

the .002 level of confidence. The present study has also studied individual factors

responsible for research productivity.

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Mills, Zyzanski, Flocke (1995) carried out a study whose objective was to

describe residency research productivity and identify the set of independent factors that

best characterize programs at various levels of productivity. A 23-item survey was mailed

to 226 randomly selected family practice residency directors. The survey included items

on program demographics, mentoring, resident and faculty research activities, and

program research resources. Factor and discriminant analyses were performed to identify

the major independent factors associated with productivity. A total of 154 completed

surveys were received for a response rate of 68%. Based on a cross tabulation of grants

per program and publications per faculty, 22% of programs had high productivity, 46%

had medium productivity, and 32% had low productivity. The significant factors of

mentor support, amount of research activity, and program size contributed independently

to the classification of programs by relative level of research productivity. These

associations remained significant when university programs were excluded. The study

concluded that Family practice residencies with relatively higher research productivity

are more likely to have three characteristics than lower productivity programs:

availability of a research mentor, more faculty research activities, and larger program

size. This study focused on directors instead of targeting the researchers themselves. This

study sought information from both researchers and their heads of departments.

Hemlin & Gustafsson (1996) explored the main factors influencing the research

productivity in the arts and humanities in Sweden. A questionnaire was constructed to

identify and assess the effects of various factors important for the productivity of the

individual researcher as reflected in the number of papers and PhD’s produced. First,

respondents were given the opportunity to list in their own words a number of important

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factors influencing research productivity. Secondly, they evaluated on rating scales the

importance of a number of pre-selected factors (e.g. individual characteristics,

organisational features, external factors) assumed to be important for research

productivity. 50% of a sample of 256 researchers in the humanities responded.

Ratings were grouped to produce a number of indices and these were subject to

multiple regression analyses. The main results showed that the production of papers was

predicted by the number of Ph.D.'s produced and inversely related to the importance of

organisational factors. The production of Ph.D.'s was dependent on the year of the Ph.D.

and the position of the respondent as well as on the number of papers s/he produced. A

number of conclusions were drawn: a) there was support for the academic social position

effect also in the humanities; b) organisational factors apparently played a minor role in

comparison to individual characteristics in the humanities than in the sciences and; c) the

differences in productivity of papers were also related to gender, but not to size, area or

language of publications. The present study went beyond studies in humanities and social

sciences and incorporates other faculties in the university setup. This has indeed yielded a

more representative type of information from the universities.

Bonzi (1992) collected data from the curriculum vitae of 411 senior faculty at

Syracuse University which was analysed to uncover trends in productivity over time.

Results showed that productivity was related to status and academic discipline. Overall,

productivity earlier in a career was a good indicator of later productivity. Increase in

productivity among females was greater than among males, but males were more

productive overall. Humanities and science/mathematics faculty increased productivity to

a greater extent than did social scientists and professional school faculty. Journal articles

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were cited much more than are book chapters, so faculties not tending to publish in

journals did not show a high citation count. In addition, citation counts were biased

toward older works, since they had greater exposure than more recent works. Focusing on

senior faculty only could not give objective results to a study. This study has been all

inclusive in terms of lecturers who were selected to take part in this study.

Wanner, Lewis and Gregorio (1981) conducted a comparative study on Research

Productivity in Academia in the US. He noted that a significant number of studies of

scholarly productivity were accumulated in the past and a majority focusing on limited

samples of specialists in one or only a few scientific disciplines, making it difficult to

generalize findings across dissimilar academic disciplines. Wanner et al’s paper tested a

model incorporating both academic and nonacademic factors as determinants of scholarly

productivity with samples of physical and biological scientists, social scientists, and

humanists taken from the 1972-73 in the American Council on Education survey of

faculty at U.S. institutions of higher learning.

The study found considerable variation in the process determining productivity

both across the broad disciplinary categories as well as within categories when article and

book productivity were compared. The study also examined the relative influence of the

disciplinary context and attributes of scholars on productivity. The evidence found

suggested that the decisive edge that physical and biological scientists enjoy over social

scientists and humanists in article productivity was largely the result of the nature of

work or a favorable disciplinary milieu, while the lower rate of productivity among

humanists was more heavily determined by their attributes.

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Kotrik (2002) conducted a study to determine the factors that explained the

research productivity of agricultural education faculty in colleges and universities. In this

study, publications in refereed journals were used as a surrogate for research productivity.

The study described the research productivity of agricultural education faculty, their

attitudes of the organizational culture that exists in their department to support research

productivity, and their self-assessment of their research competency. The population for

the study included all full-time, professorial rank faculty employed by colleges and

universities in the United States that offered agricultural education.

The regression analysis was used and it revealed that three variables explained

50% of the variance in research productivity. These variables included number of

doctoral students advised to completion in the last five years, faculty members’ attitudes

of their research confidence, and the number of graduate assistant hours allocated to the

faculty member. The variables that did not explain a significant proportion of the

variance were percentage of the faculty member’s time allocated to research, salary,

organizational culture and support of research, age, gender, rank, number of master’s

students advised to completion in the last five years, and number of years they had held a

tenure track position. This study gives an indication of the variables to consider in my

present study.

Borokhovich et al (1995) examined differences in finance research productivity

and influence across 661 academic institutions over a five year period from 1989 through

1993. They found that 40 institutions accounted for over 50% of all articles published by

16 leading journals over the five year period; 66 institutions accounted for two-thirds of

the articles. Influence was more skewed with as few as 20 institutions accounting for

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50% of all citations to articles in these journals. They further found that the number of

publications and publication influence increased with faculty size and academic

accreditation. Prestigious business schools were associated with high publication

productivity and influence. The present study has zeroed in on both academic staff and

institutions.

Print and Hattie (1997) carried out a study to demonstrate, via the use of the

discipline of Education, a procedure to identify and weight the importance of various

indicators of research productivity which in turn have become significant components in

determining quality within and between universities. The methodology allows for the

identification of indicators that are most important, and ascertains if there are differences

among academics as to the relative weighting of the various research indicators.

Highly valued indicators of research productivity amongst the Education

academics were refereed journal articles, peer reviewed books, and major competitive

research grants. Refereeing was critical in the determination of quality in research

productivity, and the findings generalized across many academics regardless of their own

personal productivity. It is recommended that the methodology can serve to determine the

tacit weights that academics within and across disciplines attach to various research

products. At least, this method makes academics and administrators aware of the

weightings they are actually using when making decisions about the quality of academic

departments.

Hasselbacka, Reinstein, Schwanc (2000) conducted a study which made use of

comprehensive data on both the quantity and quality of research productivity of 3878

accounting faculty who earned their accounting doctoral degrees from 1971 to 1993.

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Publications in 40 journals were used to measure faculty publication quantity. Journal

ratings derived from a compilation of the rankings of five prior studies and co-authorship

were used to measure publication quality. Choosing benchmarks for an individual faculty

required users of the data to determine four parameters: (1) what credit to give a faculty

member for co-authored articles; (2) what level of journal quality was appropriate, (3)

choosing appropriate levels of performance and (4) deciding the emphasis to place on the

number of years since the doctoral degree was earned. It was discovered that the average

number of authors per article was significantly correlated with time and growing at a pace

of 0.017 authors per article per year.

Wierzbicki (2002) conducted a study to investigate the research productivity of all

staff in chemical pathology in the United Kingdom. Chemical pathologists or

biochemical scientists were identified from publicly available sources. All journals, their

impact factors (IFs), and individual publications over the period of 1995 to 1999 were

identified from electronic databases. Each publication was sub classified with respect to

type of publication, number and position of author, and subspecialty to which the article

referred. Results of this study indicated that research output over the period comprised

6162 articles, originating from 1399 individuals, 264 of who were medically qualified.

Specialty initiated research accounted for 26% of the total publications and 80% of the

research was performed in teaching hospitals.

Research output was highly skewed because 49% of individuals published a letter

or more, 20% published one original piece of research over five years, but only 4% were

research active, as defined by one publication each year. International standard research,

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defined as one paper each year in journals with IF > 4, was achieved by 1% of the

profession, mostly aged > 55 years. Skewed distributions of publication rates were found

in all age deciles. The possession of higher research degrees correlated with higher output

in all age deciles.

The conclusions formed from this study indicate that those working in chemical

pathology are active in initiating and conducting research, although at a low level.

Because long-term activity in research correlates with the possession of higher research

degrees and the opportunity to carry out research from early in career pathways, priority

should be given to encouraging research in training. The present study has gone beyond

Chemical pathologists and investigated more researchers in different disciplines.

Chen (2004) surveyed 320 faculty members in a study from 10 business schools

to examine the intrinsic and extrinsic rewards that motivate faculty to conduct research.

Of the thirteen rewards studied, receiving or having tenure was the most important

reward, while getting a possible administrative position was the least important. There

were significant differences in the importance of these rewards between tenured-

untenured and between male-female faculty members. Faculty perceives a strong link

between research productivity and the attainment of the rewards of tenure and of

promotion.

However, in the minds of the faculty, the link between publications and the

reward of salary increases was not strong. Associate professors reported lesser

importance than either full professors or assistant professors on nine of the thirteen

rewards and perceived a weaker link between research productivity and achieving the

reward. This implies that the associate professors were the least motivated faculty rank to

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perform research. There was no significant difference in the number of journal articles

either published or accepted for publication within the past 24 months by tenure status,

gender, or faculty rank. Rewards are not the only factors that can influence research

productivity. Even institutional working conditions can increase research productivity.

The present study has investigated more factors that can influence research productivity.

Dakik (2005) analysed the quality and quantity of scientific publications of the

medical faculty at the American University of Beirut (AUB) during a six year period

(1996–2001). The study included all faculty members in the medical school of AUB in

the year 2001. A Medline search inclusive of the years 1996–2001 was done for each

faculty member and a total number of 881 publications were obtained. The faculty

consisted of 203 members. Their average productivity rate (mean (SD)) was 1.24 (1.38)

publications/faculty member/year (PFY), with a mean impact factor of 2.69 (4.63).

Eighteen per cent of the faculty did not have any publication in the six year study period,

and only 20% had two or more publications per year. There was a significantly higher

publication rate among newly recruited faculty members (0.93 (1.40) PFY for those

appointed before 1990, 1.45 (1.24) PFY for those appointed during 1990–1995, and 1.67

(1.43) for those appointed after 1995, p = 0.007), and among those who were younger in

age (p<0.01).

Collaboration with international investigators resulted in more original

publications than work done only at AUB (65% v 35%, p<0.001), and a higher journal

impact factor for the publications (3.20 (3.85) v 1.71 (2.36), p<0.05). This was one of the

first studies that analysed the research productivity of the medical faculty in a university

setting in a developing country. It showed a wide variation in the research productivity of

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the faculty members that seems to be related to individual as well as institutional

characteristics. This study did not elaborate on the factors responsible for research

productivity of researchers. That is what the present study has strived to realise.

Ramona, Lee, Skakun, & Lorne (2007) carried out a study to determine whether

the sequence of training to obtain MD and PhD degrees was associated with different

career paths for physicians who had their PhD before medical school and those who

obtained it after their MD, and to explore the factors that encouraged or dissuaded

Canadian dual-degree physicians in pursuing a research career. In 2003, questionnaires

from the University of Alberta, Edmonton, Canada, were sent to all 734 Canadian

physicians having MDs and PhDs; they were identified through the Canadian Medical

Directory. Data collected were gender, year and country of MD, sequence of obtaining

degrees, portion of time on clinical, research, teaching, and administrative duties, number

of publications and grant amounts held, and perceived incentives and disincentives to

research careers. Two focus groups were held with a subset of physicians to further

explore themes.

The response rate was 64%. On the basis of the timing of the PhD relative to the

MD, physicians were designated early PhDs (26%), concurrent PhDs (12%), or late PhDs

(62%). Late PhDs spent more time in research and less time on clinical practice than the

other two groups and spent more time teaching and had published more papers than the

early PhDs. Grant amounts were highest for late PhDs. Lack of time and resources were

the major disincentives to research, and noteworthy incentives were the opportunity for

intellectual challenge and creativity, and previous research experience. The study

concluded that Physicians who obtained a PhD after an MD had a more research-focused

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career than those who entered medical school with a PhD. Sequence of training was a

narrow way of analyzing research productivity. The present study has employed more

factors and how they influence research productivity.

Wills et al (2008) carried out a study on a meta-analysis of international studies

from accounting and related business fields, published between 1988 and 2008, the study

examined factors influencing the research productivity of academics. In this study, more

than 70 factors were identified from 25 studies, which were then reduced to clusters of

factors, or themes. A data-driven approach to thematic analysis was used to identify the

factors and to allocate them to nine themes. The study examined the relationships

between the themes and proposed a model of how they were linked. The study suggested

that three hierarchical clusters of factors at government, institution and individual levels

influenced the research output of accounting academics. This study concentrated on

business subjects. The present study has involved other disciplines

2.7 Research Productivity and Technology Transfer/Patents

Recently, researchers have proposed other indicators of creative productivity such

as the number of patents, presentations at conferences, or textbooks published (Antony

and Raveling, (1998). Brooks (2006) insisted “we know that these indicators [traditional

indicators of scholarship productivity] reflect only a narrow slice of an institution’s

overall research and scholarship, and do not accurately represent the full variety of

scholarly activity of faculty in all disciplines”. The main problem in only using traditional

indicators of productivity is that this omission might introduce systematic bias against

those disciplines characterized by different type of indicators Brooks (2006)

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Donald (2003) undertook a study in the United Kingdom on assessing the impact

of university science parks on research productivity. This was against a background that

University science parks stimulate technological spillovers. Results of this study

indicated that there was virtually no empirical evidence on the impact of these facilities

on research productivity. This was done by examining whether companies located on

university science parks in the United Kingdom had higher research productivity than

observationally equivalent firms not located on a university science park. The results

showed that there was no relationship between the science parks and research

productivity. The development of Science parks are at the infancy stage in Kenya.

Jean and Schankerman (2004) carried out a study on Patent Quality and Research

Productivity: Measuring Innovation with Multiple indicators. They analyzed the

determinants of the decline in research productivity using panel data on manufacturing

firms in the US for the period 1980–93. They focused on three factors: the level of

demand, the quality of patents and technological exhaustion. They developed an index of

patent 'quality' using detailed patent information and showed that using multiple

indicators substantially reduced the measured variance in quality. They further found out

that research productivity at the firm level was inversely related to patent quality and the

level of demand, as predicted by theory and patent quality. In this case, patent quality was

positively associated with the stock market value of firms. This confirmed the fact that

research productivity positively influences quality of a patent. This present study has not

explored levels of patent applications with KIPI, ARIPO or WIPO but has made

suggestions for further research to explore research productivity in Kenyan Universities

in regard to patent applications.

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Torrez (2006) reports about a study by the Milken Institute, an independent think-

tank based in Santa Monica in the US, which sought to determine the universities

worldwide that were doing the best job at technology transfer and commercialization of

their discoveries and inventions in biotechnology. In their study titled Mind to Market: A

Global Analysis of University Biotechnology Transfer and Commercialization, the

institute found that the University of California system was the most successful university

in licensing income from its discoveries and inventions, a total average of about $100

million per year, followed by Stanford University ($50 million) and the Massachusetts

Institute of Technology ($33 million).

The study highlighted the importance of research to a university's bottom line and

its positive economic effects on its region. It also stressed the importance of a technology

transfer office to a university and of a campus's proximity to clusters of biotech firms that

can fund research. The present study recognizes the critical roles played by patent

institutions in Kenya and in the region; however, this study has concentrated on

publications by university lecturers as a yardstick for research productivity.

Welker (2006) conducted a survey of senior university research administrators on

a broadly defined set of their institution’s research activities. In spring 2005, the senior

research administrator at each of the 250 Carnegie classified doctoral/research

universities-Extensive and Intensive was identified (Shulman, 2001). They were

contacted by email and asked to answer an on-line survey. The purpose of the survey was

to gain a better understanding of national trends in research planning and priority setting,

research culture, research publicity, economic development, and technology transfer.

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Ninety-five senior research administrators completed the survey, forming a response rate

of 38%. Responses came from 35 states.

Two-thirds of the respondents were from Doctoral/Research Universities -

Extensive and one-third was from Doctoral/Research Universities -Intensive. Two-thirds

represented universities with undergraduate enrollments of over 10,000; 72% were public

and 28% were private institutions.

The survey was intended to stimulate thought, not necessarily to recommend

specific actions, and to provide insights into the management of research at U.S.

universities.

2.8 Research Productivity and Teaching Effectiveness

Kenneth (1987) made an analysis review of the research that had been done on the

connection between research productivity or scholarly accomplishment of faculty

members and their teaching effectiveness (as assessed by their students). On average, it

was found that there was a very small positive association between the two variables. The

association between research productivity and teaching effectiveness was explored

further by considering whether its size and direction varied by career stage of faculty

members, their academic discipline, and the type of college or university in which they

taught. The current study has not gone into making comparisons between the teaching

and research aspects of faculty members but has looked at the contribution of both

lecturers and students in terms of research output.

Centra (2005) investigated the relationship between research productivity and

teaching effectiveness to shed light on the long-debated question of whether performance

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in one area enhanced performance in the other. The academic field and the stage of a

faculty member’s career were both considered in the analysis. Two samples — one of

2,973 and the other of 1,623 faculty members from a variety of institutions, were studied.

In considering results of both analyses, teachers of social science courses were the only

group for which there were consistent though modest relationships between the numbers

of published articles and student ratings of instructor effectiveness. Thus spillover effects,

or a general ability factor, or other reasons for a possible link between research and

teaching performance were not totally supported. The relationship between performance

in the two areas was either nonexistent or, where it appeared, it was too modest to

conclude that one necessarily enhanced the other. This study did not indicate the method

of data analysis used.

2.9 Summary of Literature Review

The literature review done in this work is in the area of research productivity at

various levels. The research work done range from groups of institutions, individual

institution, fields of specialization to individual variables that contribute to research

productivity. From this literature review, it is clear that few studies have combined the

analysis of both institutional and individual factors, it is clear that most studies have not

used the university heads of departments to get information for triangulation purposes.

This study has tried to cover this gap. It is also clear and evident that most of the work

has been done in the developed countries and very little has been done in the developing

world.

The literature also indicates that most of the work has been done in the recent past

meaning that this is a new emerging area that developing countries should embrace to

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gauge the productivity of their research capacity. Furthermore, very little work has been

done in Africa and in Kenya in particular in this area. Perhaps this is the first

comprehensive study to be carried out in Kenya targeting on research productivity; this is

evident from the literature reviewed. This by itself is a justification for this work to be

carried out in Kenya.

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CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

3 Introduction

This chapter presents and justifies the research design and methodology employed

in this study. It describes the research design, target Population, sample and sampling

procedures, description of research instruments, test for validity and reliability,

description of data collection procedures, description of data analysis procedures.

3.1 Research Design

In this study, survey research design was employed. The survey design collects

data at a particular point in time with the intention of describing the nature of the existing

conditions, indentifying the standards against which existing conditions can be compared

and for determining the relationship that exists between specific events (Orodho, 2004).

This design is preferred because the researcher was interested in describing the existing

phenomenon without any manipulation. This method was appropriate for this study

because it enabled the researcher to collect much information in less than one month’s

time. It was also possible to make a wide coverage of both public and private universities

in Kenya in that short duration. This design has also been used because the findings of

the study would be generalised to the whole population. Therefore survey research design

was found to be appropriate for this study.

3.2 Target Population

This study targeted academic staff in Kenya drawn from selected public and

private universities. This study focused on research productivity of the university

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academic staff; this is because lecturers are mandated to teach and to conduct research in

their areas of specialization. Therefore the university academic staff drawn from selected

faculties in the mentioned universities formed the population of this study. Others were

heads of departments drawn from the selected universities.

3.3 Sample and Sampling Procedures

Stratified random sampling was used in this study. This is because the population

studied was heterogeneous. There are differences among the lecturers in terms of gender,

rank and academic disciplines. A total of 400 questionnaires were administered to the

university academic staff in the ratio of 70 to 30 for male and female academic staff

respectively. On the other hand, simple random sampling was used to target university

heads of departments. This sampling procedure was preferred because only those who are

charged with the said responsibilities as heads of departments were used.

The sampled universities consisted of five private and six public universities. For

ethical issues the universities were randomly assigned letters A, B, C, D, E, F, G, H, J, K,

and L. Letter I was skipped in this naming. Public universities were A, B, C, D, J, K

while the private universities were E, F, G, H and L. The names of faculties and

departments remained the same. This confidentiality was maintained due to the nature of

sensitivity associated with performance of universities. Other universities in the sample

had also requested for the confidentiality of the findings.

3.4 Description of Research Instruments

This study employed the questionnaires and document analysis in data collection.

3.4.1 The Questionnaire

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The questionnaire was the main research instrument used for this study. It was

employed to collect information from the university lecturers and heads of departments.

It had both open and closed ended items. This study mainly used the questionnaire as the

instrument for data collection. This was partly because most of the studies done in this

area have used this instrument for data collection. It has also been proven that self-

reported data has correlated positively with the information collected from journal

publications. The instrument was developed and pilot tested to ensure that it was valid

and reliable.

3.4.2 Questionnaire for Lecturers (Appendix 1)

The questionnaire was used to collect six types of information from lecturers

which were required for the purposes of this study.

The questionnaire was structured as follows:

Demographic information: contained information of the lecturers with variables such as

name of university, faculty, department, gender, age group, rank, highest degree obtained,

and work load.

Information on publications: information sought here included research publications

done by the lecturer between the years 2004-2008.

Individual factors: a wide range of questions specifically targeting the individual factors

were presented to the lecturers. The respondent was supposed to tick against the

appropriate responses. The response was a four level scale ranging from 1 – 4, 1- Large

Extent; 2 – Some Extent; 3 – Little Extent; 4 – No Extent.

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Institutional factors: questions here centred on institutional factors influencing research

productivity. The respondent was supposed to tick against the appropriate responses. The

response was a four level scale ranging from 1 – 4, 1- Large Extent; 2 – Some Extent; 3 –

Little Extent; 4 – No Extent.

Attitude on research and publications: this contained a five point Likert type scale SA

– Strongly Agree; A – Agree; U - Undecided/No opinion; D – Disagree; SD – Strongly

Disagree.

Personal opinions on problems and possible solutions: this section had questions that

sought information on any factors affecting research productivity that had not been

covered in the items above. The lecturers were also asked to give possible solutions to the

problems/factors they had identified.

3.4.3 Questionnaire for Heads of Departments (Appendix I1)

The questionnaire was structured as follows:

Demographic information: contained information of the lecturers with variables such as


and work load.

Information on publications: information sought here included research publications

done by the lecturer between 2004-2008.

Individual factors: a wide range of questions specifically targeting the individual factors

were presented to the lecturers. The respondent was supposed to tick against the

appropriate responses. The response was a four level scale ranging from 1 – 4, 1- Large

Extent; 2 – Some Extent; 3 – Little Extent; 4 – No Extent.

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Institutional factors: questions here centred on institutional factors influencing research

productivity. The respondent was supposed to tick against the appropriate responses. The

response was a four level scale ranging from 1 – 4, 1- Large Extent; 2 – Some Extent; 3 –

Little Extent; 4 – No Extent.

Personal opinions on problems and possible solutions: this section had questions that

sought information on any factors affecting research productivity that had not been

covered in the items above. The heads of department were also asked to give possible

solutions to the problems/factors they had identified.

The scales and items used in the instruments was selected after a review of the

literature and grounded in the theoretical base of this study. Each of the questionnaires

had a short introductory letter signed by the researcher attached to it.

During the data analysis, the items on the five –point Likert scale were reversed in

scoring negative questions. Hence the higher the percentage or the mean, the more

important the factor was perceived to be.

Document Analysis

This was the other document used in the data collection exercise. The instrument

was used in collecting information on the research policies for the institutions that had

developed them. Six universities were sampled here. Four public universities and two

private universities were used in this case. Main concern here was the weight given by

individual universities towards personal career development of the individual academic

staff. It yielded interesting results in this regard.

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3.5 Piloting/Pre-Testing

The purpose for pre-testing was to ensure that items in the questionnaire were

stated clearly and had the same meaning to all respondents. The questionnaire was pilot

tested with 10 university faculty members who did not take part in the study.

3.6 Validity and Reliability of Instruments

3.6.1 Validity

Nachmias and Nachmias (2005) observes that validity is about answering a

question, “Am I measuring what I intend to measure?” Coolican (1996) observed that

validity of the instrument is the extent to which it measures what it is intended to

measure. In this case, three experts in the faculty of education CUEA assisted the

researcher in validating this instrument so that it was able to measure what it was

intended to measure. They looked at them independently and ascertained that they could

solicit the kind of information they were to collect. Their views on the content and

structure were incorporated in the final draft of the instrument. Peer review was also used

to enhance face and content validity. At this stage the items on attitudes of the academic

staff to research and publishing were added to the instrument. Appropriate changes were

also made during validation and were incorporated into the instrument. Other changes

were done in the wording of items, the design of scales, and in the instructions for

completing the instrument. The suggestions from the experts and peer reviewers helped

to achieve the content and face validity of the instruments.

3.6.2 Reliability

Reliability refers to the extent to which the measuring instrument contains

variable errors, that is the errors that appear inconsistently from observation to

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observation during any one measurement attempt or that vary each time a given unit is

measured by the same instrument (Nachmias and Machmias,2005; 170). Instruments used

in Social sciences yield more errors than those used in physical variables. This reliability

measure varies between 0 – 1. An instrument with a figure closer to 0 has more errors

than one which is closer to 1 (Nachmias & Nachmias, 2005; 172).

In this case the researcher employed the Split half method. The questionnaires

were administered to a group of lecturers in pilot testing. Received responses were split

into two by using odd and even numbers. Cronbach Alpha Coefficient yielded a

coefficient which indicated that the questionnaires were reliable. If the coefficient

realized was too low, some work had to be redone on the questionnaire until it yields

satisfactory coefficient. In most cases, a value lying between 0.70 – 1.00 is commonly

accepted (Nachmias & Nachmias, 2005).

The reliability index for closed ended questions for academic staff was calculated

using SPSS for Windows (version 15). The split half technique was used to analyse the

ten questionnaires. A total of ten (10) questionnaires were used for this calculation. For

this case the Spearman –Brown coefficient was calculated and it yielded a coefficient of

0.796. According to Nachmias and Nachmias (2005) this coefficient is enough to justify

the instruments use.

The realized coefficient indicated that the instrument was reliable enough to

collect information from the academic staff in Kenya.

On the other hand, six questionnaires of the Heads of Departments’ was subjected

to Cronbach Alpha Coefficient and it yielded 0.753 reliability index. This coefficient was

regarded as qualifying research instrument to be used in this study.

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3.7 Description of Data Collection Procedures

The Department of Postgraduate studies in Education at the Catholic University

of East Africa provided the researcher with a clearance letter. This letter made it possible

to make an application for a research permit from the National Council for Science and

Technology (NCST), in the Ministry of Higher Education, Science and Technology. The

researcher used this permit to contact the university vice chancellors for clearance to

conduct research in their respective institutions. Permission was granted from all the

universities that were contacted. The only problem was that some of the vice chancellors

took long to grant approval letters.

The questionnaires were administered to the selected university academic staff by

the researcher and research assistants. The questionnaires were self administered while

others were sent by email. Due to the large scope of this study, involving universities

outside Nairobi and the fact that university academic staff are scattered in offices across

the university, it became prudent to engage the services of research assistants. All the

research assistants were trained by the researcher and were assigned to specific

universities. The package to each lecturer contained a cover letter explaining the purpose

of the study and the questionnaire. The same applied to the Heads of Departments

questionnaires.

Research policies from six universities were collected and analysed according to

the preset criteria. The main criteria here was to find out whether the issue of career

professional development, among academic staff, had been sufficiently taken into

account and catered for in the university research policies.

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3.8 Description of Data Analysis Procedures

Descriptive and inferential statistics were used to analyse the data. Quantitative

data from responses to closed ended type of questions in the questionnaire were coded in

the computer by applying the Statistical Package for Social Sciences (SPSS) version 15.

Several statistical analysis were performed, the quantitative data analysis was performed

to generate frequencies and percentages. Cross tabulation analysis of various independent

variables like age group, gender, type of university, rank and highest degree obtained

with the participants’ number of publications were performed. Information obtained from

the attitude scale was used to test hypothesis using one way ANOVA at 0.05 level of

significance. If the p- value was less than or equal to 0.05 level of significance the

hypothesis was rejected. If the p-value was greater than 0.05 level of significance the

hypothesis was not rejected. The one way ANOVA was used to test all the hypothesis

presented in this study.

Factor analysis was employed in data reduction. It reduced large number of

factors influencing research productivity into a small number of factors that explained

most of the variance observed in a much larger number of variables (Obure, 2002; child,

2006).

The results were presented in frequency and percentage Tables, graphical

representations and pie charts. Means and standard deviations were also computed to

determine the respondents’ attitudes towards research productivity. Data collected from

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the open ended items were analysed by grouping similar answers together across

respondents in order to form emerging themes/factors. The theme that had many

respondents thus formed the main factor influencing research productivity. Other themes

attracted very few responses and were therefore regarded as minor factors. These factors

were sorted in a descending format for ease of interpretation and presented in a tabular

form.

Besides the demographic information and the last section of the of the Heads of

Department questionnaires other parts of the questionnaires had many missing variables

and was therefore excluded from analysis. It looked like most HoD’s were not willing to

commit themselves on the issue of publications made in the department in the last five

years, they were not willing to give their views on the close ended questions on

individual and institutional factors affecting research productivity, nevertheless they

contributed meaningfully to the open ended questions presented to them. These items

have been analysed in the last section of chapter 4 and they are indeed invaluable input in

this study.

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CHAPTER FOUR

PRESENTATION AND DISCUSSION OF THE FINDINGS

4.1 Introduction

This chapter presents the study findings, interpretations and analysis including

discussions based on the structure of the research questions. Deliberate effort has been

made to address the pre specified study questions with relevance to factors influencing


4.2 Demographic characteristics of participants

The demographic information is derived from item one to ten of both the

Universities' academic staff and heads of department questionnaires. These items were


and work load.

4.2.1 Academic staff’s demographic information

Presentations, discussions and analysis in this section are founded on the

perceived participants’ background variables. These, the researcher deemed to be

important indicators and determinants of their attitudes in regard to the factors hindering

or promoting research output among Kenyan university academic staff. This information

was obtained from question one to ten of the university academic staff questionnaire

(Appendix 1)

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The participants were asked to place a tick or a mark in an appropriate variable

that best presented their status i.e. Name of university and faculty, age group, gender

academic rank, years since last degree obtained.

Item nine and ten required the participants to fill in hours time spent on teaching

load and research work per week respectively.

The questionnaire response rate obtained from academic staff was 69.3%. Male

participants constituted 70.2% while 29.8% was made up of female academic staff. Sixty

nine percent of the questionnaires were responded to by academic staff in public

universities while 31.0% were filled by academic staff in private universities as shown in

the Tables below.

Table 4.1: Frequency Distribution of academic staff by Gender

Gender Frequency Percent

Male 193 70.0

Female 82 29.6

No Response 2 0.7

Total 277 100.0

n=277

Table 4.1 above indicates the composition of the sample. It is spread in the ratio

of 70:30 for men to women academic staff. Somehow it was a satisfactory ratio given that

the number of academic staff and even students is in that range.

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Table 4.2: Distribution of academic staff by type of university

Type of University Frequency Percent

Public university 191 69.0

Private university 86 31.0

Total 277 100.0

n=277

Table 4.2 indicates that more respondents were drawn from the public university

than from the private Universities. This is a true reflection of the University enrolment in

Kenya today where there are seven Public universities with 11 constituent colleges and

several campuses all forming the public Universities. On the other hand, there are over

twenty private universities with smaller enrolment.

Table 4.3: Distribution of academic staff by Age group

Age Group Frequency Valid Percent

20-29 30 10.8

30-39 75 27.1

40-49 104 37.5

50-59 54 19.5

60-69 14 5.1

Total 277 100.0

n=277

Table 4.3 above, illustrates a higher number of academic staff i.e. 37.5% who

belonged to the 40-49 age group while 5.1% of the respondents were in the 60-69 age

group. This implies that most of the respondents were lecturers in the 40-49 age category.

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This is in agreement with the analysis done below which indicates that most of the

respondents were those in the lecturer category.

Table 4.4 indicates that most of the participants held lecturer positions; this is

represented by 39.7% of the respondents. 17.4% were senior lecturers while 17.0% were

assistant lecturers. Seven point six percent were graduate assistants and 7.2% were

tutorial fellows. About six percent represented associate professors while only 3.3% were

full professors as shown in Table 4.4. The fact that many of the respondents were

lecturers is satisfying in the sense that this is the group that is struggling to climb the

academic ladder of the university to such positions as senior lecturers and professors. At

least this was an ideal group to be interviewed and to see how they are fairing on in terms

of research publications. Some studies reviewed had eliminated the use of the graduate

assistants and tutorial fellows from their analysis. The argument they were positing was

that the graduate assistants, tutorial fellows and assistant lecturers had not published

much and therefore they could not be taken into account. But for the case of this study,

the researcher found this group to have published though not much. Secondly, the nature

of Kenyan Universities is really changing. Most of the Universities are resorting to part

time academic staff. They recruit lecturers at those lower levels as part time lecturers

with the intention of minimizing on costs. Others are recruited but take long to move up

the ladder. Lastly, the current study is not very much into the output of the academic staff

but their attitudes and views on factors that influence research productivity. This was the

basis for retaining this group of academic staff in the study. The study also captured

about 9% of the respondents in the rank of associate professors and above.

124

Table 4.4: Distribution of academic staff by Position Held in Their respective

Universities.

Position held Frequency Percent

Graduate Assistant 21 7.6

Tutorial Fellow 20 7.2

Assistant lecturer 47 17.0

Lecturer 110 39.9

Senior Lecturer 48 17.4

Associate Professor 17 6.2

Professor 9 3.3

No Response 1 0.4

Total 277 100.0

n=277

Figure 4.1 indicates that a majority (53.4%) of the academic staff had Masters

Degrees, 37.9% had PhD’s while 6.9% had bachelor’s degrees. This was a good

representation for the academic staff in Kenya. It is a situation that is gradually changing.

It has been known over the years, for example at some of the oldest university public

universities in Kenya, a majority of the PhDs are found in the so called life and basic

sciences. The other areas like humanities and social sciences have had very few PhDs.

This has partly been explained by the abundance of university scholarships targeting

these fields. Secondly, there were no opportunities for PhD courses at the PhD level for

125

these members of staff. This is however changing due to more universities offering PhD

programmes across the country.

Figure 4.1: Distribution of academic staff by Highest Degree Obtained

n=277

It was further observed that 45.8% of the academic staff participants had obtained

their last highest degree in less than 5 years, while 25.6% obtained degrees between 6 –

10 years ago. 17.0% represented participants who obtained their highest degree between

11 – 15 years ago. 5.4% obtained their last highest degrees between 16 – 20 years ago

while only 4.0% obtained theirs more than 20 years ago as shown in Table 4.5. This

information is basically indicates that a sizeable number of the academic staff in Kenyan

Universities have masters degrees.

126

Table 4.5: Distribution of academic staff by years since last highest degree was

obtained

Years since last degree was obtained Frequency Valid Percent

<5 Years 127 45.8

6-10 Years 71 25.6

11-15 Years 47 17.0

16-20 Years 15 5.4

More than 20 Years 11 4.0

No Response 3 1.1

Total 274 100.0

The participants were asked to place a tick or a mark in an appropriate variable

that best suited their status i.e. Name of university and faculty, age group, gender

academic rank, years since last degree obtained.

The response rate obtained from heads of department was 60.7%. 76.5% of the

questionnaires were responded to by male participants while 23.5% were filled by female

participants. 88.2% of the questionnaires were responded by heads of departments in

public universities while 11.8% were filled by those in private universities as shown in

Tables 4.6 and 4.7.

Table 4.6: Distribution of Heads of departments by gender

Gender Frequency Percent

Male 13 76.5

Female 4 23.5

Total 17 100.0

127

n=17

Table 4.7: Distribution of Heads of departments by type of university

Type of University Frequency Percent

Public 15 88.2

Private 2 11.8

Total 17 100.0

n=17

Many of the heads of departments i.e. 41.2% belonged to the 40-49 age group,

just like the lecturers, while 37.3% belonged to the 50-59 age group and only 17.6% in

the 30-39 age group. Basically most of these HoDs are over 40 years of age as shown in

Table 4.8 below.

Table 4.8: Distribution of Heads of Departments by Age Group

Age group Frequency Percent

30-39 3 17.6

40-49 7 41.2

50-59 6 37.3

No response 1 5.8

Total 17 100.0

n=17

Table 4.8 illustrates that most of the HoDs were above 40 years of age.

Majority of the head of departments held lecturer positions; this was represented

by 59%. About 23% were associate professors while 12% were senior lecturers. One was

a professor as shown in Figure 4.2 below

Figure 4.2: Distribution of heads of departments’ by Position held in university

128

n=17

Approximately 35.3% of the heads of departments had masters degrees, 58.8%

had PhD’s while only 5.9% had bachelors degrees as shown in the Table below.

Table 4.9: Distribution of heads of department by highest degree obtained

Highest degree obtained Frequency Valid Percent

Bachelors degree 1 5.9

Masters degree 6 35.3

PhD 10 58.8

Total 17 100.0

n=17

It was further observed that 52.9% of the head of department participants had

obtained their last highest degree between 6 - 10 years ago, while 17.6% obtained less

than 5 years ago and between 16 – 20 years ago. Only 11.8% obtained their last highest

degrees between 11 – 15 years ago as shown in Table 4.10.

129

Table 4.10: Distribution of heads of departments by years since last highest degree

was obtained

Frequency Percent

<5 Years 3 17.6

6-10 Years 9 52.9

11-15 Years 2 11.8

16-20 Years 3 17.6

Total 17 100.0

n=17

Table 4.10 shows that most of the HoDs finished school in more than 9 years ago.

This means that they are persons who have been in the university system for long. The

responses that they provided to the rest of the items in the questionnaire reflected this

maturity.

4.3 Nature of Research Productivity among public and private universities in

Kenya between 2004-2008

This section analysed data that helped to answer research question number one on

the nature of research productivity among public and private universities in Kenya.

4.3.1 Average publication output by university between 2004 - 2008

The information presented below is a calculation of the average publication

output of the academic staff in Kenya. The calculation was done by dividing the total

publications from self reported data in this study by the number of academic staff in the

sample. This was done for all the universities under study in each of the five years under

study.

Table 4.11: Average publication output By University 2004-2008

130

UNIVERSITY 2004

Mean %

2005

Mean %

2006

Mean %

2007

Mean %

2008

Mean %

Total

Mean %

A 0.41 13.1 0.45 11.7 0.55 12.0 0.60 11.2 0.71 12.6 2.72 12.1

B 0.57 18.3 0.74 19.3 0.89 19.4 0.89 16.6 0.90 16.0 3.99 17.7

C 0.38 12.2 0.47 12.2 0.38 8.3 0.52 9.7 0.19 3.4 1.94 8.6

D 0.23 7.4 0.23 6.0 0.31 6.8 0.23 4.3 0.15 2.7 1.15 5.1

E No

data

No

data

No

data

No

data

0.08 1.7 0.17 3.2 0.33 5.9 0.58 2.6

F No

data

No

data

0.16 4.2 0.25 5.5 0.38 7.0 0.38 6.7 1.17 5.2

G 0.19 6.1 0.28 7.3 0.28 6.1 0.31 5.8 0.56 10.0 1.62 7.2

H 0.21 6.7 0.35 9.1 0.36 7.8 0.46 8.6 0.46 8.2 1.84 8.1

J 0.67 21.5 0.67 17.4 0.75 16.3 0.88 16.4 0.88 15.6 3.85 17.1

K 0.29 9.3 0.33 8.6 0.24 5.2 0.43 8.0 0.57 10.1 1.86 8.2

L 0.17 5.4 0.16 14.2 0.50 10.9 0.50 9.3 0.50 8.9 1.83 8.1

TOTAL 3.12 100 3.84 100 4.59 100 5.37 100.1 5.63 100.1 22.55

100

National Average = 2.05

Table 4.11 summarizes average publication outputs of academic staff in the

eleven Universities under study between 2004 – 2008. This can also be referred to as the

research productivity of the said institutions. Here, the total research publications were

divided by the total number of academic staff in that institution to yield this figure. The

highest production, according to this table, was done in 2008 by university B which

131

produced 0.90 publications per academic staff per year. This means that each academic

staff produced 0.90 publications in that year. The publication outputs fluctuate between

these five years and vary considerably across different universities and also across

different years for the same university. From the information presented in this Table, the

Universities can be divided into two groups, that is; high and low performers. Those

above the ten percent average mark can be considered as a high group (B, A, J) while

those with a performance less than ten percent can be grouped as low performing group

(C, D, E, F, G, H, K, L). It is evident that all the high group universities are public

universities whereas the low group has accommodated all the private universities in the

sample with a few public universities. This may be partially due to the higher number of

senior lecturer and above in the older universities unlike the private universities. Funding

levels might be different too in these institutions.

It is interesting to see here that B and J had a higher average rate of publications.

One thing that is quite clear of University B is the large size of the institution and also

being the first university to be set up here in Kenya as a branch of the University of East

Africa. This university enjoyed this privilege for too long till 1984 when the second

public university was established after the recommendations of the Mackay report of

1982. These findings are in disagreement with the findings by Ho (1998) who found that

the average production rate for the universities he studied in Hong Kong was above 7.0

mark for the five year study he undertook the study. On the other hand, Milgrom et al

(2008) conducted a study with the International Association for Dental Research (IADR)

Behavioral Sciences and Health services Research Group whose primary outcome

132

measure was the number of self-reported published articles in Pub Med in twenty-four

months.

The mean number of the publications he found was 4.9 (SD=5.1). Studies done in

Beirut by Dakik (2005) analysed the quality and quantity of scientific publications of the

medical faculty at the American University of Beirut (AUB) during a six year period

(1996–2001). Their average productivity rate was found to be 1.24 publications/faculty

member/year (PFY). Eighteen per cent of the faculty did not have any publication in the

six year study period, and only 20% had two or more publications per year. For the case

of the present study, no single university managed to produce a single digit in terms of

research productivity of its individual members per year. The highest was 0.90 by

university B in the year 2008.

The conditions under which academic staff was working in Ho and Milgrom’s

studies are different from the situation in Kenya and these differences in research

productivity might have arisen due to that.

However, these findings resonates the findings of Lee and Bozeman (2004) who

used the number of papers published in five years to calculate the research productivity

of various countries around the world. For their sample of U.S. academicians, they found

an average ranging from 14.40 papers for assistant professors to 25.75 papers for full

professors. Their study used self-reported data too, the self-reported sum of articles in

national and foreign journals was 4.5 for both academic and government researchers.

both the African and Indian respondents in their study published at a significantly lower

rate than Bozeman’s sample of U.S. scientists. Within developing areas; however, there

133

were significant differences. The mean number of total articles (foreign and national

journals) ranged from 7 articles in Kerala, to 3.6 in Ghana and 2.5 in Kenya.

134

Table 4.12: Frequency distribution of those who Published by rank

Graduate

Assistant

Tutorial

Fellow

Assistant

lecturer

Lecturer Senior

Lecturer

Associate

Professor

Professor Total

2004 0 1 4 41 32 16 8 103

0% 1.0% 3.9% 39.8% 31.1% 15.5% 7.8% 100.0%

20052 4 9 50 38 16 9 129

1.6% 3.1% 7.0% 38.8% 29.5% 12.4% 7.0% 100.0%

20063 7 14 58 38 15 9 145

2.1% 4.8% 9.7% 40.0% 26.2% 10.3% 6.2% 100.0%

20073 9 25 64 37 15 9 162

1.9% 5.6% 15.4% 39.5% 22.8% 9.3% 5.6% 100.0%

20087 11 29 70 40 16 9 182

3.8% 6.0% 15.9% 38.5% 22.0% 8.8% 4.9% 100.0%

The scenario presented in Table 4.12 shows that the position of lecturer and senior

lecturer were the most productive of all the academic staff under study. The contribution

by graduate assistants, tutorial fellows and professors was not as high. This is an expected

scenario since most lecturers are aspiring to grow professionally. They are being

motivated to produce more so as to climb the academic ladder. They have the energy

because they fall under the most productive bracket of 40-49 years old. They have also

gained enough experience to propel them to writing more publications. This is a

135

confirmation that the experience gained by the academic staff is crucial or instrumental in

increasing ones productivity in terms of publishing.

Table 4.13 Frequency distribution of those who did not publish by rank

Graduate

Assistant

Tutorial

Fellow

Assistant

lecturer

Lecturer Senior

Lecturer

Associate

Professor

Professor Total

2004 19 43 70 16 1 1 0 150

12.7% 28.7% 46.7% 10.7% 0.7% 0.7% 0% 100.0%

2005

19 16 38 61 10 1 0 145

13.1% 11.0% 26.2% 42.1% 6.9% 0.7% 0% 100.0%

2006 18 13 33 53 10 2 0 129

14.0% 10.1% 25.6% 41.1% 7.6% 1.6% 0% 100.0%

2007 19 11 22 47 11 2 0 112

16.9% 9.8% 19.6% 42.0% 9.8% 1.8% 0% 100.0%

2008 14 9 18 41 8 1 0 91

15.4% 9.8% 19.7% 45.1% 8.9% 1.1% 0% 100.0%

Table 4.13 is a confirmation of the analysis in Table 4.12. Here, it is quite evident

that a smaller number of lecturer and above is not engaged in publishing. A majority of

the non publishers fall in the category of lecturer and below. There is an interesting

scenario for the categories of assistant lecturer and lecturer between the years 2004-2008

The assistant lecturers started on the wrong footing with about 70 of them not publishing

anything in 2004 but this figure continued to rise. This can be attributed to experience

and the realization of the importance of publishing and publications. Unfortunately, the

136

reverse is the case for the position of lecturer. This can be explained by the motivation by

the lecturers to move to higher rank hence an increase in publication output.

It is interesting also to note that there was no single professor who had not

published in the five year period. They had made publications in their fields of

specialization and were therefore were recognized as producers. There are many reasons

that can be associated with this. The first one is the experience of the professors to attract

funding, ability to allocate funds for research to their teams, ability to use graduate

students to develop collaborative papers and of course the trust the donor community has

on people with experience to conduct their research and evaluations. Most donors or

projects usually require that the Principal Investigators be people who have handled work

of same magnitude before.

The findings of this study are in agreement with Kelchtermans et al (2005) who

observed that most of the studies to date aim at explaining average productivity profiles,

ignoring the often skewed distribution of research productivity, with many researchers

non-active and a few researchers accounting for the bulk of the publications. This issue of

persistence of research productivity profiles is underexplored in the literature. Indeed the

above Table 4.13 indicates that it is only the professors who have managed to publish

consistently over the five year period. They have been able to realize the benefits of

publishing whereas the majority of the academic staff have not had that privilege.

137

Table 4.14: Average publication output by gender 2004-2008

2004

Mean %

2005

Mean %

2006

Mean %

2007

Mean %

2008

Mean %

Total

Mean %

Male 0.39 54.2 0.46 48.4 0.51 48.6 0.60 52.6 0.69 53.9 2.65 51.6

Female 0.33 45.8 0.49 51.6 0.54 51.4 0.54 47.4 0.59 43.1 2.49 48.4

TOTAL 0.72 100 0.95 100 1.05 100 1.14 100 1.28 100 5.14 100


Table 4.14 indicates that the publication output among gender fluctuates between

these five years. It is evident that there is some difference between the publication levels

when categorized by gender. The difference is however small. Though this table (Table

4.14) shows that women published fewer papers than their male counterparts between

2004 – 2008, the difference is not statistically significant (0.722) at the 95 percent level

as can be seen in Table 4.21.

This is in agreement with Blackburn et al. (1978), who observed that gender does

not appear to influence research output after other influences such as rank and discipline

are controlled. The national average at 2.57 is however quite low and needs to be boosted

across the board. Studies on gender and research productivity have been studied with

mixed results. Bailey (1992) reported a higher level of research productivity by male

faculty members. Other researchers have noted that female faculty members are lagging

behind experienced male faculty members in research productivity (Smith, Anderson,

Lovrich, and Nicholas, 1995). Blackburn et al. (1991) stated that the relationship between

gender and research productivity had been addressed in many studies and that little if

138

any, and sometimes contradictory, correlations have been found. Therefore for this case

in Kenya, the information realized is satisfactory.

Gender differences in scientific productivity have been given attention by many

researchers in the recent past. Several studies have found that female scientists publish at

lower rates than male scientists. Using a sample of American biochemists, Long (1993)

finds that sex differences in the number of publications and citations are bigger during the

first decade of the career but are reversed later. He attributes the lower productivity of

females to their overrepresentation among non-publishers and their under representation

among the extremely productive. Definitely this has not been supported by the present

study.

139

Table 4.15: Average publication output By Rank 2004-2008

2004

Mean %

2005

Mean %

2006

Mean %

2007

Mean %

2008

Mean %

Total

Mean %

Graduate

Assistants

0.05 2.2 0.10 2.7 0.14 3.5 0.14 3.2 0.33 6.7 0.76 4.0

Tutorial

Fellows

0.2 8.6 0.2 5.4 0.35 8.8 0.45 10.3 0.55 11.2 1.75 9.1

Assistant

Lecturers

0.87 37.5 0.19 5.2 0.30 7.5 0.53 12.2 0.62 12.6 2.51 13.0

Lecturers 0.29 12.5 0.45 12.3 0.53 13.3 0.58 13.3 0.64 13.1 2.49 12.9

Senior

Lecturers

0.33 14.2 0.79 21.5 0.79 19.8 0.77 17.7 0.83 16.9 3.51 18.2

Associate

Professors

0.47 20.3 0.94 25.6 0.88 22.1 0.88 20.2 0.94 19.1 4.11 21.4

Professors 0.11 4.7 1.0 27.2 1.0 25.9 1.0 33.0 1.0 20.4 4.11 21.4

TOTAL 2.32 100 3.67 100 3.99 100 4.35 100 4.91 100 19.24 100


Table 4.15 presents data on the average publication output among various ranks

held by the academic staff in Kenyan Universities between 2004-2008. This productivity

index was calculated by dividing the total published data per year by the total number of

academic staff in the sample. From this information, it is clear that the publication output

increases with the rank held by the academic staff. For example the graduate assistants

140

had 0.76 as their total mean for the 5 year period while the professors had 4.11 as their

total mean for the same period. This may be due to the exposure of older academic staff

to research funding opportunities, experience and professional networks in research.

There is an obvious decline of publication output from the higher cadres of professorship

to the lower cadres. Bailey (1992) found that rank is a significant predictor of research

productivity. Dundar and Lewis (1998) found that departments with higher ranked faculty

had higher research productivity.

Vasil (1992) reported that rank was a significant predictor of research

productivity. This gives the same explanation for the current study. Some argue that the

professors have low teaching load and can therefore manage to have plenty of time to

publish. Others argue that they are mainly in the administrative positions and that they

can manage to maneuver research funds to their research teams. According to Finkelstein

(1984), academic rank is a significant predictor to publication success because the

academic lecturers in higher ranks generally have more control over their workload

assignment, allowing faculty of higher rank to produce more research than those of a

lower rank. Fulton and Trow (1974) found that 29 percent of full professors, 20 percent

of associate professors, 13 percent of assistant professors, and 2 percent the instructors

have published five or more articles in a two-year period.

141

Table 4.16: Average publication output by Age group 2004-2008

2004

Mean %

2005

Mean %

2006

Mean %

2007

Mean %

2008

Mean %

Total

Mean %

20 – 29 0.07 3.3 0.1 4.0 0.17 6.5 0.2 6.8 0.33 10.2 0.87 6.7

30 – 39 0.16 7.5 0.27 10.9 0.4 15.3 0.51 17.5 0.68 21.1 2.02 14.7

40 – 49 0.41 19.2 0.53 21.4 0.58 22.1 0.62 21.2 0.65 20.2 2.79 20.4

50 – 59 0.63 29.6 0.72 29.0 0.76 29.0 0.80 27.4 0.77 23.9 3.68 26.9

60 – 69 0.86 40.4 0.86 34.7 0.71 27.1 0.79 27.1 0.79 24.5 4.01 29.3

TOTAL 2.13 100 2.48 100 2.62 100 2.92 100 3.22 100 13.7 100


Table 4.16 above illustrates that older age groups generate more publications than

younger age groups. From this Table it is clear those persons who are 50 years and above

(about 25% of the academics) account for more than a half of the average publications for

the entire five year period. Again this shows that older academics in Kenya are producing

more than younger academics. The life-cycle model (Diamond, 1986) predicts that

faculty research productivity will decline as an individual’s academic experience

increases. Unfortunately this study was not able to sample those lecturers’ above 70 year

142

maybe this could have given us an insight into this life cycle model. Age has been

included in several studies with conflicting results.

Bland and Berquist (1997) observed that the average productivity of faculty

seemed to drop with age, however, many senior faculty members remained quite active in

research activities and their products were comparable to those of younger faculty

members. They also reported that there was no significant evidence that age determined a

drop in productivity, but increased workloads and shifting emphasis was to blame.

Gorman and Scruggs (1984) reported that age was related to research productivity.

Blackburn et al. (1991) stated that the relationship between age and research productivity

had been addressed in many studies and that little if any, and sometimes contradictory,

correlations were found.

Table 4.17: Average publication output By University Type 2004-2008

2004

Mean %

2005

Mean %

2006

Mean %

2007

Mean %

2008

Mean %

Total

Mean %

Public 0.47 75.8 0.56 68.3 0.63 68.5 0.67 65.0 0.74 60.7 3.07 66.6

Private 0.15 24.2 0.26 31.7 0.29 31.5 0.36 35.0 0.48 39.3 1.54 33.4

TOTAL 0.62 100 0.82 100 0.92 100 1.03 100 1.22 100 4.61 100


Table 4.17 is an indication that the private universities are lagging behind in terms

of publications output than their counterparts, the public universities. An encouraging

feature here is the steady progress of the increase in the productivity index between 2004-

143

2008. Though this table (Table 4.17) shows that private universities published fewer

papers than their counterparts, the public universities, between 2004 – 2008, this

difference is not statistically significant (0.55) at the 95 percent level as can be seen in

Table 4.19. However, there are many reasons that can account for the fewer number of

papers published by the private universities in Kenya in the said period. This may be

partly due to the fact that most of them were awarded charters more recently. These

universities also have comparatively more newly recruited academic staff than the public

universities. This can also be attributed to the higher number of graduate students in older

universities than in the newer universities.

The public universities have also benefitted a lot from central governments for a

long time. This dependence on government enabled them to invest in infrastructure that is

serving them well to date. Again, being the oldest universities in the country, all donor

funding projects were targeted at them. They have continued to enjoy this good will and

they have built on it. The facilities they built have been converted for use during this

season of module II. In the process they prove to have a niche over the others who did not

have this opportunity. Ho (1998) conducted a study on measurement of research output

among the three faculties of business, education, humanities and social sciences in six

Hong Kong universities. In order to have a fair comparison of publication outputs of each

academic, rank, faculty and university, a framework was developed from practical

experience and from literature to investigate the problem. Results indicated that the

publication outputs of academics in Hong Kong were about the same as other countries in

many aspects. Here, average publication output ranged from 7.0, 7.3 and 8.9 for faculties

of business, social sciences and education respectively.

144

The recommendation from this study, therefore, was that Hong Kong academics

should not be motivated to do more research. According to Ho (1998), more motivation

could just increase initial output but decline in the long run. On the other hand, Milgrom

et al (2008) found out the research productivity of members of international dental

association to be 4.9. These two scenarios presented indicates that the universities in

Kenya have to work extra hard to redeem themselves from this underperformance.

Table 4.18: Average publication output by highest degree obtained 2004-2008

2004

Mean %

2005

Mean %

2006

Mean %

2007

Mean %

2008

Mean %

Total

Mean %

Masters 0.15 45.5 0.26 23.6 0.36 29.8 0.48 36.1 0.56 38.9 1.81 33.5

PhD 0.18 54.5 0.84 76.4 0.85 70.2 0.85 63.1 0.88 61.1 3.6 66.5

TOTAL 0.33 100 1.1 10 1.21 100 1.33 100 1.44 100 5.41 100


Table 4.18 presents data on the publications output by the highest degree

obtained. It is clear that the PhD holders are producing more research publications than

the Masters Degree holders. This is a confirmation on the publication output by age and

rank held in the university. This is a challenge for universities in the country to increase

the number of PhDs in their universities if they are to realize higher publication outputs.

145

4.3.2 Test of Hypotheses

The purpose of this section is to test the results discussed above. The One way

ANOVA has been employed in this exercise to test the results presented above.

The Ho was tested at 0.05 level of significance.

Decision rule: When the P Value is less than or equal to .05, the null hypothesis is

rejected meaning there is significant difference between the variables under study. If,

however, the P Value is greater than .05 level of significance, the null hypothesis is not

rejected meaning that there is no significant difference between the variables being

tested.

Ho1 There is no significant difference between the means for academic staffs’ type of

university and research productivity.

Ha1 There is significant difference between the means for academic staffs’ type of


Table 4.19: One way ANOVA test of difference in the mean score of academic staff‘s

type of university and research productivity.

ANOVA SUMMARY TABLE

Source of variation SS DF MS F Sig.

Type of university Between

Groups9.34 36 .259 1.47 .055

Within Groups 30.9 175 .177

Total 40.2 211

146

In the One way ANOVA Table above, the P-value for type of university is 0.055. This is

greater than 0.05 level of significance.

Decision: The null hypothesis is not rejected.

Conclusion: Thus there is no significant difference between the means for academic

staffs’ type of university and research productivity.

There were two types of universities in this study, the private universities

and the public universities. The results obtained here indicate that there is no significant

difference in terms of research productivity by the academic staff from whichever type of

university. This is very positive to the private universities in Kenya in the sense that most

of them are new and have entered the arena to compete with the public universities. The

public universities have been around for long and have many advantages associated with

that. These advantages range from the large number of qualified senior staff, long term

association with the state for funding and other resources, and well established

infrastructure.

Ho2 There is no significant difference between the means for academic staffs’ age groups

and research productivity.

Ha2 There is significant difference between the means for academic staffs’ age groups


147


age group and research productivity.

ANOVA SUMMARY TABLE


Age group Between

Groups68.1 36 1.89 2.34 .000


Total 209.5 211

Conclusion: Thus there is significant difference between the means for academic staffs’

age groups and research productivity.

This is indeed confirmed from the available data. The older age groups are

producing more than the younger age groups. This is indeed true for most of the

organization. One key factor that comes to play here is the issue of experience. The more

the years spent in performing a particular task, the skills of doing the same work are

sharpened and improved. For this case, the university senior lecturers, associate

professors and above are no doubt producing more in terms of research publications than

the younger age groups.

Ho3 There is no significant difference between the means for academic staffs’ gender and


Ha3 There is significant difference between the means for academic staffs’ gender and



gender and research productivity.

148

ANOVA SUMMARY TABLE


Sex Between

Groups6.29 36 .175 .843 .722


Total 42.6 211

In the One way ANOVA Table above, the P-value for type of university is 0.722.

This is greater than 0.05 level of significance.



staffs’ gender and research productivity.

Basically this result indicates that the research output of both males and females

in the university setup in Kenya is the same. This is a small confirmation that given the

chance and opportunity, the female academic staff in Kenya is able to compete favorably

with their male counterparts, if not better. This can be a reason to promote them to

administrative positions in the universities to fulfill the 30% requirement for women in

leadership positions in the universities as advised by Kenya Government (GOK, 2005).

Ho4 There is no significant difference between the means for academic staffs’ academic

rank and research productivity.

Ha4 There is significant difference between the means for academic staffs’ academic rank


149


rank and research productivity.

150

ANOVA SUMMARY TABLE


Rank Between

Groups138.1 36 3.84 2.85 .000

Within Groups 235.6 175 1.35

Total 373.7 211

In the One way ANOVA Table 4.22, the P-value for type of university is 0.000. This is

lower than 0.05 level of significance.

Decision: The null hypothesis is rejected.

Conclusion: There is significant difference between the means for academic staffs’

academic rank and research productivity.

This is a confirmation of the earlier analysis of the academic staff in terms of age

groups. This result indicates that the higher the rank, the higher the research productivity

in the University set up in Kenya. This is quite true in that the senior lecturers and the

professors fall in this category. For all obvious reasons, the senior members of the

academic staff will tend to produce more than the junior members of the academic staff.

This does not apply to Kenyan universities only; it is the same in most universities in the

world. (Ho, 1998) confirmed this when he studied the research output of academic staff

in Hong Kong, he found out the senior members of the academic staff were writing more

than the junior members of staff. The senior members of staff may also be involved in

research in their universities due to their ability to draw funding for research. Most

funding organizations prefer to deal with persons who have wide experience in research.

Again, these are the people in university leadership positions, they have added

151

advantages in securing internal resources meant for research. In most universities they

utilize the graduate students in writing joint books, journals, and even conference papers.

Many studies illustrate a correlation between academic rank and a scientist’s

productivity. In a study sample of American academics, Blackburn and Al [1978] show

that full professors publish at a higher average rate than associate professors and research

staff. Dickson [1983] and Kyvik [1990] have illustrated the same effect of professional

role on scientific productivity in their respective studies of Canadian and Norwegian

universities. Academic rank was studied by Bailey (1992), Dundar and Lewis (1998),

Gottlieb et al. (1994), Teodorescu (2000) and Vasil (1992). Each found rank to be a

significant predictor of research productivity. Several studies have found seniority of

academic rank to be correlated with research performance.

Ho5 There is no significant difference between the means for academic staffs’ highest

degree obtained and research productivity.

Ha5 There is significant difference between the means for academic staffs’ highest degree



highest degree obtained and research productivity.

ANOVA SUMMARY TABLE


Highest degree Between

Groups

22.8 36 .634 2.414 .000

152


Total 68.8 211


lower than 0.05 level of significance.


Conclusion: There is significant difference between the means for academic staffs’

highest degree obtained and research productivity.

It goes without saying that the higher the academic qualifications of one, the

higher the research output. In this case, the highest academic qualifications are the doctor

of philosophy. From the data available in this study, this group’s output is comparably

higher than that of the masters degree holders. The PhD holders are no doubt the leaders

in publishing in Universities due to their long experience in the field of research.

Ho6 There is no significant difference between the means for academic staffs’ years since

last highest degree was obtained and research productivity.

Ha6 There is significant difference between the means for academic staffs’ years since last

highest degree was obtained and research productivity.

Table 4.24: One way ANOVA test of difference in the mean score of academic staff

‘s years since last highest degree was obtained and research productivity.

ANOVA SUMMARY TABLE

153


Years since last highest

degree obtained

Between

Groups67.1 36 1.86 1.40 .079





staffs’ years since last highest degree was obtained and research productivity.

Here, the period since last highest degree was obtained does not seem to matter much in

terms of research output from the universities in Kenya.

Ho7 There is no significant difference between the means for academic staffs’ university


Ha7 There is significant difference between the means for academic staffs’ university and



university and research productivity

154

ANOVA SUMMARY TABLE


Lecturer’s university Between

Groups560.1 36 15.6 1.18 .244

Within Groups 2314.8 175 13.2

Total 2874.9 211




Conclusion: Thus there is significant difference between the means for academic staffs’


In this study it was found out that some academic staff from particular individual

universities was doing better in terms of research productivity than others. There are

many reasons associated to this. Some of the universities are quite new and have just

begun. Some are old public universities that have enjoyed the resources from the state for

far too long. These differences in the university setups will definitely reflect in the

manner o research output that they produce. There might be efforts placed by individual

universities to promote research and many more reasons can account for these

differentials in terms of research output.

Gibbs and Locke (1989) insisted that research productivity was the most

important criterion for making promotion and tenure decisions after surveying 59 chairs

and committees in 93 universities. This increase in emphasis on research and decrease in

importance of teaching and service has been recognized by faculty members since the

155

1980s (Cargile & Bublitz 1986; Schultz, Mead & Hamana, 1989). It is therefore clear

why staff in traditional universities, where research has always featured more

significantly in promotion and development of status, is expected to maintain and

possibly increase research output. It should be clear, though, that a balance needs to be

struck between these noble duties of the university, teaching, research and service

4.4 Lecturers’ views on individual and Institutional factors influencing research

productivity

In this section, the views of universities academic staff have been explored in line

with the research question on factors that promote or hinder research output among

Kenya university academic staff.

4.4.1 Lecturers views on individual factors influencing research productivity

Table 4.26: Individual Factors influencing research productivity

Large Extent Some

Extent

Little

Extent

No Extent

f % f % f % f %

1 By self motivation 175 63.2 49 17.7 28 10.1 22 7.9

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2 Socialization with colleagues 49 17.7 125 45.1 55 19.9 45 16.2

3 Research content knowledge 124 44.8 82 29.6 37 13.4 28 10.1

4 Research skills gained 136 49.1 77 27.8 34 12.3 24 8.7

5 Job satisfaction 97 35.0 77 27.8 58 20.9 39 14.1

6 Simultaneous research projects 42 15.2 100 36.1 78 28.2 45 16.2

7 Parenting responsibilities 48 17.3 77 27.8 72 26.0 75 27.1

8 Early orientation to research

work

106 38.3 86 31.0 52 18.8 26 9.4

9 Personal work discipline 154 55.6 65 23.5 32 11.6 23 8.3

From the Table 4.26 above the frequency distribution for each participant per item

was calculated in an SPSS for Windows version 15. The information obtained can be

summarized as follows; self motivation (63.2%) Personal work discipline (55.6%)

Research skills gained (49.1%) Research content knowledge (44.8%) and early

orientation to research work (38.3%) received a rating of large extent. This means that

these are the individual factors that influence research productivity to a large extent. This

is in agreement with the findings of the Principal Component Analysis that shall be

discussed later in this study. These findings indicate that even within the individual factor

influencing research productivity, there are key factors that are making a big contribution

than the rest. In this case, self motivation is seen as a key mover in the individual factors

category. This is closely followed by personal work discipline. This basically means that

the decision to publish or not to publish lies with individual academic staff. If he is

157

motivated and disciplined and focused in that direction, then he will achieve his objective

of publishing.

4.4.2 Lecturers views on Institutional factors influencing research productivity

Table 4.27: Institutional Factors Influencing Research Productivity

Large Extent Some Extent Little Extent No Extent

f % f % f % f %

1. Resources available for research 183 66.1 49 17.7 23 8.3 21 7.6

2. Rewards for research output 117 42.2 83 30.0 51 18.4 24 8.7

3. Good salary 80 28.9 78 28.2 71 25.6 43 15.5

4. Sufficient work time 126 45.5 87 31.4 34 12.3 26 9.4

5. Clear research coordination goals 92 33.2 108 39.0 44 15.9 24 8.7

6. Mentorship among colleagues 78 28.2 114 41.2 51 18.4 26 9.4

7. Communication with professional networks 93 33.6 119 43.0 42 15.2 20 7.2

8. Library facilities 132 47.7 74 26.7 41 14.8 25 9.0

9. Size of the university 34 12.3 74 26.7 74 26.7 88 31.8

10. Recruitment and selection of academic staff 38 13.7 79 28.5 79 28.5 76 27.4

11. Positive group climate 69 24.9 109 39.4 58 20.9 37 13.4

12. Research emphasis by university 112 40.4 78 28.2 52 18.8 28 10.1

13. Access to relevant journals 125 45.1 88 31.8 36 13.0 26 9.4

14. teaching load 147 53.1 65 23.5 20 7.2 32 11.6

15. Availability of technology e.g. internet and computers 149 53.8 71 25.6 26 9.4 22 7.9

16. Equipment for research 158 57.0 65 23.5 24 8.7 22 7.9

17. Number of graduates students supervised 68 24.5 86 31.0 60 21.7 51 18.4

Table 4.27 clearly indicates that particular institutional factors have a direct

influence on research productivity of academic staff in Kenya. These factors are assumed

to be provided by the respective institutions where the academic staff work. Topping the

list is resources for research (66.1%), equipment for research (57%), availability of

158

technology equipment (53.8%), large teaching load (53.1%) and rewards for research

(42.2%). Contrary to general opinion, good salary, size of the university, recruitment and

selection of academic staff have not been rated highly. These findings are in agreement

with the Principal Component Analysis to be discussed later in this study. Several studies

have reported the relationship between research productivity and salary (Jacobsen, 1992;

Pfeffer & Langton, 1993; Tornquist and Kallsen, 1992). Since salary often reflects

research productivity levels, this was expected. Paying attractive salaries in return for

performance may serve as an incentive for higher productivity from faculty members.

Higher salaries may also attract productive faculty while at the same time minimizing the

possibility of losing active faculty to other institutions (Pfeffer & Langton, 1993). Closer

home, the University of Transkei in South Africa tried this and they succeeded. The

number of publications shot up since early 1990s and the tempo has remained

(Mwamwenda, 1994).

4.4.3 Heads of Departments’ views on Individual factors influencing research

productivity

159

Table 4.28: Individual factors Influencing research productivity as perceived by

Heads of Departments


f % f % f % f %

1 Self motivation 9 52.9 5 29.4 No

data

No

data

2 11.8

2 Socialization with colleagues 4 23.5 3 17.6 6 35.3 3 17.6

3 Research content knowledge 8 47.1 4 23.5 No

data

No

data

3 17.6

4 Research skills gained 10 58.8 2 11.8 3 17.6 1 5.9

5 Job satisfaction 6 35.3 6 35.3 2 11.8 2 11.8

6 Simultaneous research projects 4 23.5 5 29.4 4 23.5 3 17.6

7 Parenting responsibilities 2 11.8 4 23.5 6 35.3 2 11.8

8 Early orientation to research work 6 35.3 3 17.6 3 17.6 3 17.6

9 Personal work discipline 6 35.3 6 35.3 3 17.6 1 5.9

4.4.4 Heads of Departments’ views on Institutional factors influencing research

productivity

Table 4.29: Heads of Departments attitudes on institutional factors affecting

academic staff research productivity


f % f % f % f %

1. Resources available for research 12 70.6 3 17.6 1 5.9 1 5.9

2. Rewards for research output 11 64.7 3 17.6 3 17.6

3. Good salary 5 29.4 4 23.5 4 23.5 3 17.6

160

4. Sufficient work time 12 70.6 1 5.9 2 11.8 2 11.8

5. Clear research coordination goals 8 47.1 6 35.3 1 5.9 2 11.8

6. Mentorship among colleagues 4 23.5 4 23.5 9 52.9

7. Communication with professional networks 4 23.5 5 29.4 7 41.2 1 5.9

8. Library facilities 10 58.8 1 5.9 2 17.6 3 17.6

9. Size of the university 2 11.8 7 41.2 8 47.1

10. Recruitment and selection of academic staff 5 29.4 6 35.3 3 17.6 2 11.8

11. Positive group climate 1 5.9 10 58.8 4 23.5 2 11.8

12. Extent to which research productivity is affected

by research emphasis by university

9 52.9 2 11.8 3 17.6 3 17.6

13. Access to relevant journals 1 5.9 9 52.9 5 29.4 2 11.8

14. Teaching load 11 64.7 5 29.4 1 5.9

15. Availability of technology e.g. Internet and

computers

6 35.3 7 41.2 1 509 3 17.6

16. Extent to which research productivity is affected

by equipment for research

10 58.8 2 11.8 3 17.6 1 5.9

17. Number of graduates students supervised 6 35.3 5 29.4 1 5.9 4 23.5

The heads of departments postulate that resources available for research (70.6%),

sufficient work time (70.6%), rewards for research output (64.7%), teaching load

(64.7%), library facilities (58.8%) and equipment for research (58.8%) play a critical role

as institutional factors influencing research productivity. This is very clear that without

the requisite resources, no meaningful research output will be realized in any institution.

The point that these HODs are making is that lack of financial support will lead to poor

performance in terms of research output and that the more the resources for research the

better for an institution to carry out research mandate. This researcher’s long association

with the university system in Kenya, both public and private university setups has

161

experienced situations where many theses, of complete researches have been lying in

shelves without being published. This is testimony that in most cases, financial resources

are a small component for publishing to take place. However, it can also be argued that

financial resources are needed to run a journal or to print books for that matter.

However, it is interesting to note that the heads of departments concur with

lecturers that a good salary will not influence research productivity.

4.4.5 Principal Component Analysis

This part of the study also used Principal Component Analysis (PCA) to make an

analysis of the factors that influence research productivity. PCA is one type of factor

analysis. Factor analysis attempts to identify underlying variables or factors that explain

the pattern of correlations within a set of observed variables. It is mainly used in data

reduction to identify a small number of factors that explain most of the variance observed

in a much larger number of manifest variables. (Obure, 2002; child, 2006) in this way the

most important variable accounting for the variability in a set of data can be identified.

This method has been used by various studies focusing on research productivity in

the recent past. Babu (1998) designed an investigation with the sole purpose of

establishing factors which determine the productivity of scientists in India. Nearly 200

variables influencing research productivity were collected, 26 variables were selected for

further analysis and they were subjected to principal component factor analysis. The

results indicated eleven factors affecting research productivity of scientists. Brocato

(2005) carried out a study to determine the research productivity of faculty in family

medicine departments at U.S. medical schools, as well as the individual and

162

environmental characteristics and prior socializing experiences predictive of research

productivity. Cepero (2007) undertook a study in the USA to expand knowledge about

faculty productivity and the institutional and individual factors that may contribute to

increased levels of faculty scholarly productivity.

This study found Principal Component Analysis to be the best statistic to reduce

the variables in this study and obtain variables that contribute a higher variation to the

dependent variable.

4.4.5.1 Selecting the Variables for PCA

The literature review done for this study initially extracted 33 variables that have

been found to influence research productivity. This has been discussed in Chapter 2 of

this study. These factors were broadly classified into institutional and individual factors.

After several iterations (repetitions of analysis based on the criteria guiding the PCA)

fourteen (14) variables were extracted. These variables were found to have high factor

loadings and therefore contributed more variation to the dependent variable. These

variables were divided into three categories namely institutional, individual and personal

career development factors. These 14 variables are;

1. Age of the academic staff

2. Academic rank

3. Highest degree obtained

4. Years since last highest degree

5. Self motivation

6. Research content knowledge

7. Research skills gained

8. Early orientation to research work

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9. Personal work discipline

10. Resources for research

11. Rewards

12. Teaching load

13. Availability of technology

14. Availability of equipment for research.

Table 4.30: Rotated Component Matrix (a)

Component

1 2 3

Age group of respondent -.014 .072 .850

Position held in the University -.035 .033 .895

Highest degree obtained .016 -.043 .808

Years since you obtained last highest degree .055 -.013 .771

Extent to which research productivity is

affected by self motivation.807 .133 .054


affected by research content knowledge.858 .096 .005


affected by research skills gained.830 .163 .053

164


affected by early orientation to research work.655 .109 .005


affected by personal work discipline.720 .181 -.078


affected by resources available for research.141 .766 .141


affected by rewards for research output.097 .683 .025


affected by teaching load.089 .722 -.156


affected by availability of technology e.g.

internet and computers

.229 .744 .031


affected by equipment for research.129 .803 .013

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

A Rotation converged in 4 iterations.

The factor loadings of the variables of the three components are presented on

Table 4.30. This table shows the factor loadings of each variable. Those variables that

have the highest weights are the most important variables accounting for highest

variations in the Principal components. For example, research content knowledge

accounts for a high variation to the first Principal component. This is followed by

research skills gained then the rest in that order. For principal component two, highest

variations are accounted for by equipments for research, availability of technology in that

165

order. In the last principal component, highest variation is provided by position held in

the university, age group and highest degree obtained.

The highest variations in this set are the academic position held in the university

or the rank of the academic staff. These ranks are graduate assistants, lecturers, senior

lecturers, professors and such. The result presented here basically confirms that the rank

of the academic staff contributes highest to research productivity.

Table 4.31: Correlation Matrix and Rotation

Extraction Sums of Squared Loadings

Components Total % of Variance Cumulative %

1 4.061 29.0 29.0

2 2.818 20.1 49.1

3 1.964 14.0 63.2

A further assistance was given in the interpretation of the three components, this

involved performance of several rotations. The three components explained a total of

63.2% of the variance in the data with the first, second and third components contributing

29%, 20% and 14% respectively. The factor loadings of the variables of the three

components are presented on Table 4.30.

The eigenvalues extracted were 4.06, 2.82 and 1.96 for components 1, 2 and 3

respectively. These values were above the acceptable eigenvalue of 1. This is a clear

indication that the data were sufficient for this analysis. It is these values that were used

to construct the scree plot.

Table 4.32: KMO and Bartlett's Test

166

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .770

Bartlett's Test of Sphericity Approx. Chi-Square 1506.680

df 91

Sig. .000

The Kaiser-Mayer-Olkin (KMO) measure of sample adequacy is .770 indicating

that the data matrix has sufficient correlation to justify principal component analysis.

Furthermore the Bartlett’s test of sphericity produced a high value and statistically

significant which also meant that the data matrix was sufficient for PCA.

Figure 4.3: Scree plot for principal component analysis

167

Component Number1413121110987654321

Eig

enva

lue

5

4

3

2

1

0

Scree Plot

On the other hand the scree plot indicated a clear break after the fourth component. This

confirmed the existence of the three components extracted (Figure 4.3).

4.4.5.2 Further Interpretation of the PCA

A total of 33 variables were used to extract the three components that affected

research productivity in this study. After several removals of variables that did not meet

the requirements, 12 variables were finally selected and used to extract three components

that reflect various diversity of research productivity. They formed the basis to judge the

factors influencing research productivity among academic staff in Kenya.

168

The first component can be interpreted as personal career development factors

that have contributed much in this component number one. This is in agreement with a

study presented by Blackburn and Lawrence (1995). This component explained the

highest variance of 29% in the data. The second component, contributing 20%, can be

interpreted as institutional factors.

Third factor, contributing 14% can be interpreted as demographic or experience of

the individual researcher to publish. Component one and three are closely related in this

analysis. This may mean that it is the individual’s self determination, commitment,

motivation, and stamina that do count in establishing whether an individual researcher is

able to publish or not. The institutional component, explained 20% of the variance in the

data involves all those resources, equipment or rewards that are supposed to be supplied

by respective institutions.

Ramsden (2005) argues that factors associated with high research performance in

the study that he carried out, indicated that the strongest personal (i.e., individual)

correlates were early interest in research, involvement in research activity, and seniority

of academic rank. All these indicate that it was the individual factors that were coming

out strongly to influence research productivity. This is in agreement with the findings of

this study. Williams (2003) investigated the factors related to research productivity of

human resource education and workforce development in the postsecondary faculty, and

as a result classified related factors into three categories: environmental factors,

institutional factors and individual interest and ability factors. This has also been

reflected in this study.

169

A study by Suwanwala (1991) investigated attitudes of research productivity of

academic lecturers in Chulalongkorn University, the most famous institution in Thailand,

and found that many lecturers did not realize the importance of conducting research, and

many of them lacked the knowledge, skills, experience and resources to do research. This

is basically a confirmation of the findings of this study. This study is quite clear that the

extent of research mastery is a problem that needs to be solved first and foremost. This

combines both lack of research skills and research content.

Extent to which research productivity is affected by availability of technology e.g.

internet and computers was one of the components raised in the PCA table above. It

attracted a factor weighting of 0.744, this was quite significant in such a study. Internet is

the new vehicle for access and delivery f information. Just like a vehicle, it is so useful to

the person who knows how to use it. Wagner et al (1994) estimated that only 10% of

academics at institutions with access to the Internet actually used it. He suggested that

30% of the users only used it for e-mail. Possible reasons for this lack of use were

unawareness of available information sources on the Net and the lack of skills in locating

the information needed.

Adams and Bonk (1995) attributed barriers to use amongst academics to the lack

of time, and lack of training on how to use. As observed by Lazinger,Barllan and Peritz

(1997), most of the studies on Internet use by academics only focused on the users,

leaving the reasons for non-use unexplored. Lazinger,Barllan and Peritz reported about

80.3% (371 out of 462) of academic staff members from the University of Jerusalem

were Internet users. Similarly, White (1995) found that between 72% and 73% of the

academics sampled consulted the Internet for their information needs. Other reported

170

scholars have mixed response of the effect on the use of the Internet. This was an

expected result in the early years of electronic networks when the interface was not so

user- friendly.

Cohen (1996) indicated that academic staff’s main use of CMC (Computer

mediated communications) is for the e-mail facilities to and from other faculties, both on-

and off-campus. Other usages of the Internet include FTP (File transfer protocol), Telnet

and Gopher. The study also found low use of the electronic journals. In the United States,

McClure, et al. (1996) reported on the impact of networking on the academic institution.

Abel, Liebscher and Denman (1996) reported on academic scientists and engineers’ use

of electronic networks mainly for e-mails, electronic discussion groups, access to

databases, running programs and file transfer.

Lazinger, Barllan and Peritz found that 362 outof 371 respondents used the

Internet for e-mail and most e-mail correspondences were research related. White (1995)

found that the younger academic staff tended to use the Internet more than those older. A

reason for this may be that, the former group participated in electronic discussion groups

and were less dominated by those higher in status. Cohen (1996) investigated the use of

computer networks by 888 academic staff, and found higher use of the computer

networks by younger academics. Applebee, Clayton and Pascoe (1997), however, did not

find age a significant factor in the use of the Internet and proposed that’ older’ academics

have ‘caught on’ the use of computers in their work. The effect of age on computer use

therefore is inconclusive, especially in the current situations where all academics

regardless of age are dependent on the computers for their teaching, research and

administrative work. The effect of gender on computer use has also been investigated.

171

A study by Ruthand Gouet (1993) found greater number of female academics

using the computer networks. White (1995) discovered that female academic staff makes

significantly higher use of the Internet than their male counterparts. In contrast,

Applebee, Clayton and Pascoe (1997) found a higher number of male academics from the

University of Canberra used the e-mail than the women. Cohen’s study (1996) also

indicated a greater proportion of females using the network than males.

Most of these studies are confirming that most lecturers at the university level do

not have the capacity to use the internet for research work. However, most of these

studies were done in the 90s when the usage of the internet was not wide spread. Efforts

may be needed to train the university academic staff on the more ways in which the

internet can be more useful for academic work than the emails use only.

4.5 Attitudes of academic staff on Research and Publications in their institutions

The lecturers’ attitudes were measured on a five point Likert scale where strongly

Agree = 5, Agree = 4 Undecided = 3, Disagree =2 strongly Disagree = 1. The scale was

reversed in cases where the question was negatively constructed. The total score of

frequencies for all responses from all respondents was calculated. This was used to

calculate the mean attitude score. It is this mean attitude score that was used by SPSS

Version 15 to run the one way ANOVA against the demographic variables identified.

4.5.1 Lecturers total attitude scores on research and publications.

Table 4.34 displays the total frequency distribution of the lecturers’ scores on

their attitude towards research and publications. It gives a general picture on their

attitude towards the listed statements. From this information it can be seen that most of

172

the lecturers fall in the category of undecided. Therefore, the analyses that follow will

give a true picture of the lecturers’ attitudes towards research and publishing.

The analysis of this part was done in a 2 scale nominal scale that categorized the

attitude scale into two. All the statements that scored more than 50% were categorized in

another group.

Under the strongly disagree, there were several statements. Notable ones were,

publishing is important for any lecturer aspiring to grow professionally 70%; research

and publishing increases the visibility of the university 64.6%. These two statements

clearly indicate some tone of pessimism on the part of the academic staff. It gives a

feeling of despair and hopelessness in regard to research and publishing. On the other

hand, the academic staff are in agreement that academic staff should spend family

resources in research and publishing. This is ironical, the statement made here is that

research is important and therefore family resources can be used to conduct researches,

on the other hand the academic staff are saying that publishing is not important for

anybody striving to grow professionally. Perhaps the message here is that the academic

staff are not seeing a contribution of publishing as a criteria for professional promotion.

They are saying that publishing is crucial but it should not be used as a criterion for

promotion. Some academic staff in this study had indeed indicated that research and

publishing alone should not be used as a yard stick for promotion. Some argument was

that senior university academic staff should also be promoted on the basis of research and

publishing in the areas of specialization.

Another notable issue here is the case where the academic staff are implying that

money meant for research should be used for academic staff salaries (50.2%). This is

173

perhaps the first and only incidence in this study that the academic staff have raised the

issue of salaries. This contradicts their individual suggestions in table 4.49 where the

issue of salaries was raised by a paltry 4.0%. Perhaps this is because they had captured it

in this section. The Kenya’s UASU has done a good job in agitating for better salaries for

its staff and it is hoped that will translate to better research output.

Table 4.33: Frequency distribution of lecturers’ attitude scores on Research and

Publications in their institutions

Attitude Statements Strongly

Disagree

Disagree Undecided Agree Strongly

Agree

Mean

s

SD

f % f % f % f % f %

1 My department should offer additional

pay for publishing

128 46.2 64 23.1 19 6.9 33 11.9 25 9.0 3.88 1.37

2 Lecturers should be offered less

teaching load for publishing in refereed

journals.

88 31.8 85 30.7 26 9.4 37 13.4 38 13.7 2.46 1.41

3 Universities should offer monetary

rewards as an incentive for publishing

in refereed journals.

118 42.6 77 27.8 15 5.4 35 12.6 29 10.5 2.20 1.38

4 Universities should spend money

meant for research on improving

salaries for lecturers

22 7.9 21 7.6 15 5.4 77 27.8 139 50.2 4.06 1.26

5 Promotions should be based on

publishing alone

20 7.2 26 9.4 14 5.1 123 44.4 88 31.8 3.86 1.19

6 Teaching alone without research is

important

52 18.8 45 16.2 22 7.9 59 21.3 88 31.8 2.68 1.55

7 Lecturers should spend family 5 1.8 7 2.5 19 6.9 90 32.5 152 54.9 4.38 0.87

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resources on research and publishing

8 Research and publishing increases

visibility of a university

179 64.6 57 20.6 10 3.6 14 5.1 15 5.4 1.65 1.13

9 Publishing is important for any lecturer

aspiring to grow professionally

195 70.4 54 19.5 6 2.2 8 2.9 13 4.7 1.51 1.02

10 Universities should be ranked purely on

the basis of their research productivity

57 20.6 73 26.4 17 6.1 82 29.6 44 15.9 2.94 1.43

11 Teaching is more important than

publishing

18 6.5 16 5.8 35 12.6 104 37.5 97 35.0 3.91 1.15

12 I'll rather spend my time as part time

lecturer than on writing articles

10 3.6 25 9.0 23 8.3 109 39.4 108 39.0 4.02 1.08

13 Appointment to senior university

management should be based on

academic writing

52 18.8 89 32.1 29 10.5 64 23.1 41 14.8 2.83 1.37

14 Lecturers who do not publish have no

business teaching in university

50 18.1 52 18.8 40 14.4 74 26.7 61 22.0 3.16 1.43

15 It is advisable for lecturers to spend

personal resources on research and

publishing

91 32.9 43 15.5 30 10.8 61 22.0 49 17.7 3.02 1.43

16 Developing countries need research

more than developed countries

72 26.0 66 23.8 19 6.9 57 20.6 61 22.0 2.89 1.54

n=277

4.5.2 University Academic Staffs’ Mean Attitude Score towards Research and

Publishing

The mean score for each question was worked out. All negatively states items

were reversed so that all the statements became positive. The responses were assigned

scores as follows;

SA – 5

A – 4

U – 3

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D – 2

SD – 1

The minimum total that a respondent could score was 16 assuming that he scored

SD = 1 in all the 16 items). On the other hand, the maximum score was 80 (assuming that

the respondents strongly agreed in all the 16 items in the questionnaire). It is these totals

that were used to calculate the mean scores for each of the following independent

variables.

Table 4.34: Academic Staff gender mean attitude score

Gender n Mean Std. Deviation

Male 169 49.8 9.83

Female 74 49.2 7.75

Total 243 49.6 9.23

From Table 4.34 above, both male and female academic staffs tend to have equal

attitude mean scores towards research and publishing. Both sexes are in agreement

concerning their attitude towards research and publishing. However, these means have to

be tested in order to make conclusions.

4.5.2.1 Hypotheses testing

The Ho was tested at 0.05 level of significance.

Decision rule: When the P Value is less than or equal to .05, the null hypothesis is

rejected meaning there is significant difference between the variables under study. If,

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however, the P Value is greater than .05 level of significance, the null hypothesis is not

rejected meaning that there is no significant difference between the variables being

tested.

Ho8 There is no significant difference between male and female attitudes towards research

and publishing.

Ha8 There is significant difference between male and female attitudes towards research

and publishing.

Table 4.35: ANOVA test of difference in the gender mean attitude scores towards

research and publishing

ANOVA SUMMARY TABLE

Gender

Sum of

Squares df Mean Square F Sig.

Between Groups 22.844 1 22.8 .267 .606

Within Groups 20606.078 241 85.5

Total 20628.922 242

In the One way ANOVA Table above, the P-value for gender is 0.606. This is greater

than the set 0.05 level of significance.


Conclusion: thus there is no significant difference in academic staff’s attitude towards

research and publishing. Therefore both male and female academic staff have almost

same attitude towards research and publishing. This is supported by the actual figures

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produced by these two groups of academic staff in Table 4.14 and the total attitude mean

scores presented in Table 4.34 above. Their output for the 5 year period was about the

same. In the one way ANOVA Table 4.35, the lecturers’ attitudes on research and

publishing when categorized by gender is .606. This is higher than the P Value of .05.

This hypothesis is therefore not rejected. This means that there is no significant

difference in attitudes of university academic staff on research and publishing when

categorized by gender.

In other words, both male and female academic staff has same views on research

and publishing in the universities in Kenya. On the other hand, Bonnett (2004) analyzed

900 research articles in nine major Evolutionary Ecology Journals in order to examine

how gender influences research output. The study found that women and men differed in

areas of research, with women much more likely to conduct projects on behavior rather

than evolution or ecology. Studies on differences between gender and research

productivity has been done by various scholars. These studies have tended to have

contradictory findings. The findings of the present study has made a blanket finding

among Kenyan scholars that there is no significant difference among them when

categorized by gender.

Table 4.36: Academic Staffs’ types of university mean attitude score

Type of university n Mean Std. Deviation

Public university 171 50.3 10.4

Private university 74 48.1 5.58

Total 245 49.7 9.22

From Table 4.37 above, public university academic staffs have a mean attitude

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score which is higher than that of the private universities. This is in agreement with the

age-group category’s attitude towards research and publishing. This can be explained by

the large number of older academic staff teaching in public universities than at the private

universities and the length of time that these institutions have been in operation since

inception. Most public universities are older than most private universities in Kenya. .

Ho9 There is no significant difference between public and private universities academic

staff’s attitude towards research and publishing

Ha9 There is significant difference between public and private universities’ academic

staff attitude towards research and publishing

Table 4.37: ANOVA test of difference in academic staffs’ type of university mean

attitude scores towards research and publishing

ANOVA SUMMARY TABLE

Type of university

Sum of


Between Groups 244.288 1 244.3 2.895 .090

Within Groups 20505.222 243 84.4

Total 20749.510 244


greater than the set 0.05 level of significance.


Conclusion: The null hypothesis indicates that there is no significant difference in

academic staff’s type of university and attitude towards research and publishing. In other

179

words the academic staff from both the public and the private universities has no

differing attitudes towards research and publishing.

This means that there is no significant difference in attitudes of university

academic staff on research and publishing when categorized by the type of university.

Here there were two categories of universities, the public and the private Universities.

This finding therefore indicates that there is no significant difference in terms of research

output of those lecturers from the public or private universities in Kenya. Therefore the

type of university does not influence the amount of research output to be done by the

university academic staff.

Table 4.38: Academic Staffs’ age group mean attitude score

Age group n Mean Std. Deviation

20-29 26 45.7 7.58

30-39 70 47.0 7.52

40-49 90 49.8 7.94

50-59 48 53.5 10.5

60-69 11 58.2 14.9

Total 245 49.7 9.22

From Table 4.38 above, it is clear that the older the age group of a lecturer, the

more attitude score he/she has towards research and publishing. This is a reflection of the

appreciation that one develops as he/she grows old in the profession. This is a common

phenomenon even in other fields where young professionals are not fully satisfied with

their jobs. They keep on searching for better jobs or the so called greener pastures.

The life-cycle model (Diamond, 1986; Hu and Gill, 2000) predicts that faculty

research productivity will decline as an individual’s academic experience increases.

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Sometimes, the estimated regression coefficient of the variable years of academic

employment is negative. One plausible reason for this decline in research productivity in

the developed countries is the decline of extrinsic motivation as a result of attainment of

tenure and promotion and the proximity of retirement (Diamond, 1986). Another factor

may be that senior faculty members tend to have more service and administrative

responsibilities, which may hinder their research productivity. Overall, this cannot be

concluded for the case of Kenya because it was not supported by data. Another possible

reason is that there was no respondent above the 70 year mark.

Ho10 There is no significant difference between age groups’ attitude towards research and

publishing.

Ha10 There is significant difference between age groups’ attitude towards research and

publishing.

Table 4.39: ANOVA test of difference in academic staffs age group mean attitude

scores towards research and publishing

ANOVA SUMMARY TABLE

Age group

Sum of


Between Groups 2414.014 4 603.58 7.899 .000

Within Groups 18335.496 240 76.39

Total 20749.510 244


This is less than 0.05 level of significance.

181


Conclusion: Thus there is a significant difference in lecturers’ age group and attitude

towards research and publishing. Thus one age-group has differing attitude towards

research and publishing.

This means that there is a significant difference in academic staff’s attitudes on

research and publishing as categorized by the age group of the respondents. It also means

that each age group has differing opinions on the management of research and publishing

in the universities. This is in agreement with the earlier data where the older age groups

are seen to be more active in research and publishing than the other groups. In a recent

study, Turner and Mairesse (2003) analyzed the impact of research productivity relative

to age, gender and education of French condensed matter physicists. The study found that

there was a quadratic relation between the age of the scientists and the number of

publications, with researchers’ productivity increasing before 50 and then declining after

51. The results of that study, using citations, were not significantly different from those

obtained with publications.

Table 4.40: Academic Staffs’ rank mean attitude score

Rank n Mean Std. Deviation

Graduate Assistant 15 45.7 4.92

Tutorial Fellow 18 49.4 7.72

Assistant lecturer 42 48.8 8.43

Lecturer 102 48.9 8.17

Senior Lecturer 41 51.1 11.3

Associate Professor 14 52.1 13.2

Professor 8 57.1 13.4

Total 240 49.6 9.28

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Ho11 There is no significant difference between academic staff’s rank attitude mean scores

to research and publishing.

Ha11 There is significant difference between in academic staff’s rank attitude mean scores

to research and publishing.

Table 4.41: ANOVA test of difference in academic staffs rank mean attitude scores

towards research and publishing

ANOVA SUMMARY TABLE

Rank

Sum of


Between Groups 940.554 6 156.759 1.860 .089

Within Groups 19633.942 233 84.266

Total 20574.496 239


greater than the set 0.05 level of significance.


Conclusion: thus there is no significant difference in academic staff’s rank and attitude


This hypothesis is therefore not rejected. This means that there is no significant

difference in attitude of academic staff’s attitudes on research and publishing as

categorized by the rank of the respondents. It also means that rank of academic staff has

no differing opinions on the management of research and publishing in the universities.

This is not in agreement with the earlier data where the older age groups are seen to be

more active in research and publishing than the other groups.

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The other section in this study especially section 4.3.2.4 and table 4.22, there is a

clear indication that; rank of academic staff is significant in research productivity.

However, since this was just the attitude of the academic staff, the results indicated here

could just mean what it has measured in attitudes.

Table 4.42: Academic Staffs’ highest degree mean attitude score

Highest degree n Mean Std. Deviation

Bachelors degree 16 46.9 4.63

Masters degree 133 48.7 8.36

PhD 91 51.5 10.8

Total 240 49.6 9.29

From Table 4.42 above, it is evident that Bachelors degree holders teaching at the

university have a lower attitude score towards research and publishing. On the other

hand, the masters and PhD holders have a higher attitude score towards research and

publishing. However, the PhD holders have a higher attitude score than their masters

counterparts. This is a repeat of earlier scenarios where the persons who have served in

the university environment for long, have favourable attitude to research and publishing

than those who have been in the university in a shorter period.

Ho12 There is no significant difference between highest degree obtained and attitude


Ha12 There is significant difference between highest degree obtained and attitude towards


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Table 4.43: ANOVA test of difference in academic staffs highest degree mean

attitude scores towards research and publishing

ANOVA SUMMARY TABLE

Highest degree

Sum of


Between Groups 558.867 2 279.434 3.297 .039

Within Groups 20085.128 237 84.747

Total 20643.996 239




Conclusion: Thus there is a significant difference in lecturers’ highest degree obtained

and attitude towards research and publishing. . This means that there is significant

difference in attitudes of university academic staff on research and publishing when

categorized by highest degree obtained. In other words, both PhD and Masters Degree

holders have differing views on research and publishing in the universities in Kenya

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Table 4.44: Academic Staffs’ years since highest degree was obtained mean attitude

score

Years since last highest

degree n Mean Std. Deviation

<5 Years 113 47.1 7.18

6-10 Years 63 50.5 10.7

11-15 Years 43 52.9 9.01

16-20 Years 12 51.8 9.91

More than 20 Years 8 55.6 12.7

No response 3 51.7 5.86

Total 242 49.6 9.16

Ho13 There is no significant difference between years since highest degree obtained and

attitude towards research and publishing.

Ha13 There is significant difference between years since highest degree obtained and


Table 4.45: ANOVA test of difference in academic staffs’ years since highest degree

was obtained mean attitude scores towards research and publishing

ANOVA SUMMARY TABLE

Years since last

highest degree

Sum of


Between Groups 1608.602 5 321.720 4.083 .001

Within Groups 18596.703 236 78.800

Total 20205.306 241

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Conclusion: Thus there is a significant difference in lecturers’ years since last highest

degree was obtained and attitude towards research and publishing.

Table 4.46: Academic Staffs’ University mean attitude score

Name of university n Mean Std. Deviation

A 34 46.8 6.2

B 67 55.4 11.9

C 19 51.1 9.70

D 12 42.6 11.3

L 3 47.3 6.41

E 11 47.3 3.98

F 8 45.9 5.06

G 26 49.7 5.64

H 26 47.7 6.15

J 22 47.1 5.49

K 17 45.9 5.74

Total 245 49.7 9.22

From Table 4.46 above, it is evident that University B has a higher attitude towards

research and publishing than the other universities in the study. A majority of the

universities had an attitude score less than 50 towards research and publishing.

Ho14 There is no significant difference between universities and attitude towards research

and publishing.

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Ha14 There is significant difference between universities and attitude towards research

and publishing.

Table 4.47: ANOVA test of difference in academic staffs university mean attitude

scores towards research and publishing

ANOVA SUMMARY TABLE

Name of university

Sum of


Between Groups 3781.416 10 378.142 5.215 .000

Within Groups 16968.095 234 72.513

Total 20749.510 244




Conclusion: Thus there is a significant difference in different universities attitudes


4.6 Lecturers perceived factors hindering research productivity

The data presented below was synthesized from the last section of the lecturers’

questionnaires. This data were collected by an open ended question that sought the

lecturers’ views on the problems and possible solutions facing academic staff in their

pursuit to publish. The qualitative data were grouped and analysed into emerging themes.

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Manual tallying was employed in this section. It is important to note that the qualitative

data in this section supports the quantitative data presented earlier.

This is the only section that has highlighted the issue of individual and

institutional factors that hinder research productivity in this study. The lecturers have

given about 30 issues that they feel are hindrance to research productivity in this country.

The data collected was grouped into three main categories in terms of their percentages.

These groups are presented below;

0 – 8% = 1

9 – 17% = 2

18 – 22% = 3

Table 4.48: Academic staff views on problems hindering research productivity

Possible Problems Frequencies Percentage GROUPS

1. Inadequate research fund 100 21.1 1

2. Heavy teaching load 76 16.0 2

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3. Inadequate facilities and equipments 56 11.8 2

4. Lack of research motivation 46 9.7 2

5. Lack of enough time for research 34 7.2 3

6. Poor salaries 19 4.0 3

7. Inadequate research knowledge in some lecturers 18 3.8 3

8. Lack of institutional support 13 2.7 3

9. Poor attitude towards research 13 2.7 3

10. Lack of journals specifically African or Kenyan 11 2.3 3

11. Engagement in Consultancy services 9 2.0 3

12. Poor leadership/politics 9 2.0 3

13. Low recognition of research publications 8 1.7 3

14. Long procedures in accessing research funds 7 1.5 3

15. Lack of research monitoring systems 6 1.3 3

16. Poor promotion criteria 5 1.1 3

17. Lack of implementations of research findings 5 1.1 3

18. Research proposal writing problem 5 1.1 3

19. Lack of research coordination 5 1.1 3

20. No mentorship and encouragement by seniors 5 1.1 3

21. Poor terms of employment 4 0.8 3

22. Expensive publication process 3 0.6 3

23. Poor family support/family responsibilities 3 0.6 3

24. Inadequate exchange programs in universities 3 0.6 3

25. Commercialization of university education 3 0.6 3

26. Delay in disbursement of research funds 2 0.4 3

27. Lack of research supervision 2 0.4 3

28. Laziness 2 0.4 3

29. Promotion not based on merit and performance 1 0.2 3

30. Misuse of research fund 1 0.2 3

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Under category 3 there is only one problem presented here and this has a percentage

of 21.1% this was explained as inadequate research fund. The academic staff who said

this constituted slightly less than a quarter of all the respondents in this study. Their

argument is that due to lack of financial resources to conduct research is an hindrance to

research efforts in this country however, one respondent, from the open ended items

opposed this and said that “there is too much money outside there for research, it is only

that our academic staff are not willing to write winning proposals”. Others said that the

little available financial resources are not well administered; instead it takes too long to

disburse the research funds. Available evidence indicates that most universities in the

study have research funds of some sort, but the administration of these funds could not be

established by this study. Efforts are needed to find out the problem facing research

funding in this country. Others indicated that there was gross misuse of research funds. It

was not clear whether this was on the part of the fund administrators or it was the

research fund recipients.

Under group two, there were three issues raised that hinder research productivity,

these were heavy teaching load (16%), inadequate research facilities (11.8%) and lack of

research motivation (9.7%), when combined, this forms about a third of the total

respondents. Arguments placed under heavy teaching load are that the academic staffs are

allocated many teaching subjects that make them less effective in discharging their

research mandates. This can also be due to the many lessons that academic staff take

especially when teaching other colleges. This is the so called moonlighting. The solution

provided by themselves here is employment of more academic staff. The issue of

191

employment of academic staff in Kenya is a responsibility of individual universities

councils.

The highest administrative organ in the university set up). In this case it is not

easy to manipulate all these institutions to effect a change in their employment policies.

This has led to recruitment of staff in part time basis across most universities across the

country. This is in a way to cut costs and the overheads that come along with hiring

permanent staff. The second factor under this category is the issue of motivation. They

are putting forward an argument that they are not well motivated to conduct research in

their respective universities. Again this is an issue that can be well handled by their

respective university councils. It is true that any worker needs positive motivation to

enable him/her perform better in his/her place of work.

The first category comprises of 25 items or issues that are attracting percentages

of between 0.2% to 7.2%. The top one in this list is lack of enough time to conduct

research. This problem is quite related to the issue of teaching load. The assumption that

the academic staff are making is that heavy teaching load is reducing or denying them

enough time to conduct research. This is followed by poor salaries. This is in position six

from the top. This attracted 4% of the respondents. It is evident here that most academic

staff in Kenya is not keen on salary increase but rather enabling environment to conduct

research. The next item in this group is lack or inadequate research knowledge with some

academic staff. This problem has also been raised by the Heads of departments in the

section that follows. This is true because any job requires regular updates on the changing

ways and techniques of performing the tasks involved. In most organizations this is the

issue of refresher courses and seminars to shape skills. The area of proposal and article

192

writing is competitive especially when the publications are to be made in journals outside

the country. It is interesting to see that this problem has been raised by the academic staff

themselves.

Other issues raised by the academic staff are; lack of institutional support. This

goes along with poor institutional leadership, politics and poor promotion criteria. It is no

secret that some universities have been accused of poor leadership. This weak leadership

transcends to all sectors of the university even the acquisition of journals for the

university library may be hampered. This also demoralizes the academic staff in their

endeavor to publish.

A poor promotion criterion was also highlighted. The argument put forward here

indicated that some promotions were done irregularly hence demoralized the academic

staff. One academic staff member mentioned the issue of promotion with fake

publications. I think the point being made but the publications are not properly

authenticated.

Expensive publication process was also mentioned by the academic staff. The

argument fronted by these academic staff was that it was expensive to publish an article

in local university publication centers. Some journal publishers request the contributors to

raise some fee for their publications to be made. It is indeed expensive to run a journal

regularly. The duration taken before a publication is finally printed might be long too.

These are the challenges that face academic staff in Kenya in trying to publish.

Issues raised in this section by the academic staff mainly hinge on lack of the

number of the academic staff who feel that the little finances available should be well

managed and administered.

193

Table 4.49: Heads of Department views on problems hindering research

productivity

194

Possible Problems Frequencies Percentage

1. Inadequate research funding 4 16.7

2. Lack of research motivation 4 16.7

3. Inadequate facilities and equipments 4 16.7

4. Heavy teaching load 4 16.7

5. Poor salaries for lecturers 2 8.3

6. Lecturer engagement in consultancy work 1 4.2

7. Inadequate research knowledge among some lecturers 1 4.2

8. Lack of enough time for research 1 4.2

9. Lack of implementations of research findings 1 4.2

10. Low recognition of research publications 1 4.2

11. Poor attitude towards research 1 4.2

From the above Table 4.49 it can be seen that four key factors influence research

productivity among university academic staff in Kenya as observed by the heads of

departments. These factors are; inadequate research funding, lack of motivation,

inadequate research equipment and heavy teaching load. Some of these factors have also

been highlighted by lecturers in this study. Basically this is a confirmation to the

concerns of lecturers. It is interesting to note that the issue of salaries has not featured

prominently by both lecturers and heads of departments as a factor hindering research

productivity.

Inadequate research knowledge among some lecturers has also been identified

here by the heads of departments. This is confirmation by the findings of the PCA

195

analysis. The issue of poor attitudes towards research has also been raised by the

academic staff, the root causes of these attitudes have not been established in this study.

A revisit of this should be done so as to find out reasons that make the academic staff to

have a poor attitude towards research.

4.6.1 Enhancement of research productivity.

The following section deals with the last research question that sought to establish

how research productivity among academic staff in the selected universities could be

enhanced. After studying the problems that face or hinder academic staff in their quest to

publish, the academic staffs and Heads of Departments gave some possible solutions to

tackle the problems at hand. These solutions that were provided, forms a basis for

enhancing research productivity of academic staff. Therefore, this section reviews the

possible solutions to the problems raised.

4.6.1.1 Academic staff views on possible solutions to problems hindering research

productivity

The information presented below (Table 4.50) provides possible solutions to the

problems perceived to influence lecturers’ ability to engage in productive research

activities. This information was analysed and grouped into emerging themes. The

information provided clearly indicates that it is a true reflection of the possible solutions

to the factors that influence research productivity as perceived by the lecturers. The

196

Principal Component Analysis identified individual factors to be the main component

that is affecting research output among academics in Kenya. The key among the

individual factors was the content knowledge and skills gained in engaging in meaningful

research. This is the area that needs to be addressed if the academics in Kenya are to

make meaningful contribution to research output.

Table 4.50: Academic staff views on possible solutions to problems hindering

research productivity

Possible Solutions Frequencies Percentage

1. Allocation of more funds for research 79 18.5

2. Employ more teaching staff 48 11.2

3. Reward researchers by promotions and money 42 9.8

4. Buy modern equipments for research 32 7.5

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5. Universities to allocate less teaching hours 28 6.5

6. Revise lecturers pay 28 6.5

7. Engage private sector in funding research 19 4.4

8. Create training in proposal and paper writing 18 4.2

9. Develop research policies 17 4.0

10. Build modern facilities for research 16 3.7

11. Provide clear institutional support 11 2.6

12. Encouragement by the old professionals 10 2.3

13. Consider other research contributors when giving

promotions

10 2.3

14. Give research and teaching equal time 10 2.3

15. Promotions strictly on productivity and junior staff should

not be in administration

10 2.3

16. Implement research findings 9 2.0

17. Establish credible journals in Kenya 8 1.9

18. Clear guidelines in ones role in research 8 1.9

19. Link and exposure to funding organizations 6 1.4

20. Employ lecturers that are on contract permanently 4 0.9

21. Universities should enter into exchange programs with

other universities

4 0.9

22. Universities to establish part time lecturers positions and

pay for their services

3 0.7

23. Reduce the number of enrollment for students in

universities

3 0.7

24. Early orientation in research work 2 0.5

25. Initiate external research supervision 2 0.5

26. Family to support research 1 0.2

The lecturers have given the solution to this problem as trainings in proposal and

paper writing skills. Ironically, this has only been proposed by 4% of the respondents.

The focus of lecturers’ possible solutions has mainly dwelt on institutional solutions to be

198

provided by their respective institutions. The key among the institutional factors raised

was the issue of inadequate research funds. The academic staff are of the opinion that

there is no enough funds allocated to do research in this country. But they are also

arguing that the available funds are mismanaged, come in too late and is not enough.

Over 18% of the academic staff is of the opinion of boosting the research fund

with the aim of increasing research productivity at the university level. This is an issue

that has been raised by both the academic staff and the Heads of Departments. It is indeed

true that no meaningful research can take place without financial inputs.

The academic staff are also suggesting that more teaching staff should be

employed to reduce the existing teaching load among the teaching staff.

Rewards and recognition of outstanding scholars or researching academic staff

has also been proposed by the academic staff. Any organization must employ a rewards

system for the best performers in their organizations. This will indeed boost the morale of

the writing academic staff and also encourage the rest of the academic staff to be engaged

in research work.

The issue of buying modern laboratory equipments for research has also been

used here. There is need to heed this call because there is no way a lecturer will conduct

research if he doesn’t have the requisite tools to enable him/her too perform the work at

hand. Most of these equipments in Kenyan universities are archaic and need and

overhaul. The changing times needs better equipment. The national polytechnics in

Kenya have gotten a facelift from a number of countries, particularly Italy to revamp

their laboratory equipments to acceptable standards.

199

The private sector has been proposed as a key partner in research. The private

sector has been engaged in government business of late in what is referred to as the

Public Private Sector Partnership (PPSPs). This has been effectively carried out under the

auspices of the Kenya Private Sector Development Strategy (KPSDS) whose aim is to

reduce the wide gap between the mainstream research institutes in the country. Basically

there is need to engage the private sector in the management of research in this country.

They will provide the funds, and the academic staff will provide the expertise.

Another possible solution raised by the academic staff is the issue of training the

academic staff is the issue of training the academic staff on proposal and paper writing

skills. This has been discussed in separate section of this study. This is an issue that can

be implemented within a short period of time. The senior members of staff should be

encouraged also to write training manuals and handbooks on topics such as, writing

wining proposals, research techniques and methods and such. Such materials together

with on the job training will help the academic staff to engage more in research work.

Establishment of academic journals in Kenya was another solution offered. Here,

the argument was that there are no credible journals in Kenya. This may not be true; there

are a number of journals being produced by different faculties in different universities in

Kenya. The only problem is that some of the journals do not publish regularly. They are

not up to date. This is mainly due to financial crisis or lack of articles to publish. The

Chairman, Kenya National Academy of Sciences confided with this researcher that they

did not have enough articles to publish in their three national journals. This is partly due

to the fact that most academic staff prefers to publish in higher notch journals that are

based outside the country.

200

Implementation of research findings is another issue raised. This is possible only

if the policy makers are able to agree with the findings and push for their implementation.

This is a complicated issue for a developing economy like Kenya. The Royal Society in

the UK has researchers and scientists attached to some of the members of parliament.

They provided a link between policymakers and the academic world. This is working so

well too far for Britain. It is called MP- Scientist pairing scheme, this has started to be

implemented in Kenya in a small way. This will maybe enable the implementation of the

science findings in Kenya.

These views are in agreement with Doyle (2006) who suggested that reward and

promotion system in colleges in which teaching has been the primary role of faculty

should be changed. These changes in priorities will allow increases in the incentive

system to encourage scholarly productivity, especially in higher education institutions

which have not been traditionally focused toward research endeavors. A second group of

strategies has been focused on fostering publication outputs. McGrail, Rickard and Jones

(2006) examined 17 studies that described strategies used by higher education strategies

to foster faculty publication rates. The strategies identified by the authors were oriented

to fostering the writing abilities and motivation. There were three types of intervention

identified by the authors: writing courses, writing support groups, and writing coaches

(McGrail et al., 2006). McGrail et al. (2006) found that all three models can be beneficial

to increasing publication rates.

The third strategy is to establish support systems for research in addition to

writing. Goodwin et al. (2006) described an example of a center to support faculty

research established at the school of education at the University of Colorado at Denver.

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This center included services such as: consultation with research associates; literature

search, editing and manuscript preparation; data transcription; and funding for

conferences presentations. Goodwin et al. (2006) found in the analysis of the first year of

operation of the center that faculty publication increased. They suggest that it is important

to include services to foster processes involved in research grant-seeking. These issues

have been raised by the academic staff who participated in this study. It is important for

the academic staff to be involved in refresher courses on the main issues involved in


Researchers (Buchheit et al., 2001;Hu and Gill, 2000; Tien, 2000) have

previously shown that faculty research productivity was a result of the interaction among

many endogenous and exogenous variables, including individual personal characteristics;

academic discipline; educational background; previous employment; institutional

characteristics; and teaching, research, and service assignments. This basically calls for a

multi-dimensional approach to enhancing research productivity in Kenya. Effort is

needed in solving all problems hindering research productivity both at the institutional

level and at the individual level.

4.6.1.2 Heads of Departments’ views on possible solutions to problems hindering


Table 4.51: Heads of Department views on possible solutions for problems hindering


Possible solutions Frequencies Percentage

1. Allocation of more research funds 3 13.6

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2. Buy modern equipments for research 3 13.6

3. Employ more teaching staff 3 13.6

4. Reward researchers 3 13.6

5. Revise lecturers pay 2 9.1

6. Private sector intervention 1 4.5

7. Building modern facilities for research 1 4.5

8. Early orientation to research work 1 4.5

9. Implementation of research findings 1 4.5

10. Establish research refresher courses 1 4.5

11. Promotions on research productivity 1 4.5

12. Clear guidelines in ones role in research 1 4.5

13. Universities should enter into exchange programs 1 4.5

Table 4.51 presents possible solutions which are a true reflection to the issues

raised by both lecturers and heads of department. It is evident that research funding,

equipment for research, recruitment of more staff and rewards for publishing are seen as

possible solutions to the challenges facing researchers in Kenya. It is interesting to note

here that the respondents, in this case the heads of departments are recommending the

introduction of research refresher courses. A research fund is one of the issues that the

heads of departments have recommended. Indeed nothing much can be achieved without

the input of financial resources to any enterprise. The Universities must look for

alternative sources of funding to support research in their universities. One of the most

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talked about sources is to have an endowment fund associated with the alumni fund to

raise funds for research. Another one can be venture capital. This is whereby a discovery

or invention made by the university can be sold out or partnered with an entrepreneur

interested in the new created knowledge. Most of the universities in the west have

benefited from such kind of arrangement. Income generating projects by the universities

can also raise funds for universities to conduct research.

In this study, the heads of departments have indicated that research productivity is

not high because of lecturers’ perceptions of a lack of a motivating environment; for

instance, lecturers have insufficient equipment and materials to pursue research in a

satisfactory manner. Tools for doing the job so as to motivate the academic staff to work

hard in their areas of specialization. Efforts should be targeted towards this direction to

overhaul the equipments in the universities so as to reflect the modern society. At present

the National polytechnics are in the process of overhauling their archaic machinery and

equipment.

Both members of the academic staff and heads of departments are recommending

the employment of more staff in the universities. They are making a point that they are

over worked in their duties. This is a debatable issue that cannot be exhausted. This is

because due to the rapid expansion of the university in Kenya, particularly after the

introduction of the so called module II programmes in Kenya in 1998, there has been

pressure on lecturers to accept more teaching load. This has led some of them to

moonlight in several institutions across the country. It is therefore possible that the

academic staffs are now feeling the pressure of the heavy teaching load. The

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mushrooming of satellite campuses means that more admissions are being made to

university this increase in enrollment demands more fro the academic staff.

Respondents in a study conducted by Traynor and Rafferty (1998) identified 'the

need for an improved research capacity' as a major issue facing nurses in academia,

'Research capacity building' has been defined as, 'a general term for a process of

individual and institutional development which leads to higher skills and greater ability to

perform useful research' (Trostle, 1992). Levin and Stephan (2001) hypothesized that

receipt of federal research support would be a determinant of academic scientists’

research productivity. Although they do not report these specific results, they indicate

that their findings were consistent with their expectations. Some universities in Kenya,

like the Catholic University of East Africa, have about two hours once a week for

research seminars for its members of staff and the postgraduate students.

The academic staff also gave another recommendation to buy modern equipments

for research. This is in agreement with the findings by Dundar and Lewis (1998) who

developed and tested a more comprehensive model of faculty research productivity and

found that library expenditures represented one of the important institutional attributes.

Unequal facilities and funds are important since departments with more money and better

laboratories, libraries, and other facilities are better equipped to train their staff and

students, resulting in higher publication rates (Payne & Spieth 1935).

Ideally, the enhancement of research productivity in Kenya cannot be complete

without development of relevant research policies particularly at the institutional level.

The research policies will address issues such as;

Sourcing of funding for research

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Training of academic staff in research

Awards for outstanding institutional academic staff

Financing academic journals

Developing of institutional centres of excellence

Research partnerships and collaborations

IPR sharing of benefits

Ethical considerations in research

Regular review of the policy

The research policies will not be complete without the IPR policies too. These two

documents go hand in hand and by developing them, the academic staff will be in a

position to maximize their potential in research productivity in cases where the IPR and

research policies exist, there is need to review them so as to reflect on the challenges

raised by the academic staff.

4.7 Document Analysis.

Document analysis was done on the various research policies presented by a sample

of the universities selected for this study. Some universities were not wiling to share their

research policies. It is however interesting that most of the universities have research

policies. Some universities have gone to the extent of putting the in the internet. Those

that do not have the research policies are in the process to complete them. Other

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institutions are reviewing their policies. The research policies sampled here were

developed between 2001 and 2008.

At University C there was the training for research competence. Here, the university

recognized that ongoing research in the University was mainly individual effort with little

inter-discipline collaboration within the university and was usually developed as projects

as apposed to programs. In addition, there was limited capacity to write fundable

proposals and to manage projects when such proposals were funded. The university

therefore saw the need to develop the capacity of the academic and research staff to

develop skills for the development and management of inter-discipline research programs

through training workshops and courses including proposal writing, project management

and reporting, whole-system-in-the-room workshops, donor relationships, financial

management for non-financial managers etc.

A percentage of the research fund (10%) has been set aside for in-house training of

academic staff in such workshops and/or to send trainers to such training who will in turn

return to build capacity within the university. This percentage caters for ccapacity

building, awards, researchers/investigators, centre for research and development, product

development etc. These items seem to be too many for the allocated 10% of revenues

from the successful funding. There is need to have the resources for capacity building as

a stand alone item. The university also recognized an important aspect of the professional

development of all academic staff in attendance to seminars, workshops and conferences.

Other aspects have been well covered in this research policy and these includes

offices, committees and panels that support research chapter which contains documents

related to University C, its research mandate, and bodies governing the university

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research including, the Centre for Research and Development, the University Research

Review and Advisory Board, the Ethics Review, Inter-School Research Review

Committee, Institutional Animal Care and Use and, Intellectual Property and Copyrights

Committees. It also explains why University C needs a policy, the creation of a research

fund and how that fund will be disbursed for various actions and finally how research

coordination will take place in the University.

Academic policies chapter contains documents related to principles concerning

research, academic freedom, eligibility for Principal Investigatorship, openness in

research, scientific misconduct, authorship, retention of research data, establishment of

Independent Laboratories, student relationships with outside entities, and others.

Financial aspects of sponsored projects and administration chapter contains

documents related to fiscal responsibilities of PIs; indirect costs, their application to

different types of projects, and procedures for obtaining waivers; cost sharing; tuition

remission; property; and others

Conflicts of commitment and interest chapter contains documents related to conflict

of commitment and interest, and related forms; consulting policies for faculty and for

Academic Staff-Research & Extension; policies related to start-up companies and equity

acquisition. Intellectual property chapter contains documents related to patents,

copyrights, and tangible research property. Environmental health and safety chapter

contains basic policies, as well as specific requirements related to chemical hygiene,

radiological hazards, lasers, and biohazardous agents; emergency procedures.

Human subjects in research chapter is a description of University C's Human

Research Protection Program (HRPP), including assurance of compliance with Ministry

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of Health requirements, and special guidance on the use of women, students and

laboratory personnel.

Laboratory animals in research chapter are on University C's Assurance of

compliance with Public Health Services’ requirements, and other documents related to

the care and treatment of animals.

Non faculty research appointments chapter contains documents related to graduate

student research assistants, academic staff-research & extension, postdoctoral scholars,

visiting scholars and visiting researchers, and consultants.

This is indeed a brilliant idea by the university to recognize the need for staff

development. However, the funding for this has been pegged on the availability of

funding. It could have been better to start off the program by manpower training.

At University A, there were no deliberate efforts made towards academic staff

professional development. Instead, a recommendation was made to allocate an

unspecified amount of funding towards training of academic staff involved in any

successful research funding. Other areas dealt with under this policy include;

Research Funding,

Research Planning and Administration

Approval, Monitoring and Control of Research Projects

Contributions to the Research Administration costs

Sharing/Disposal of Research Projects Resources

Remuneration of Research Staff

Creating a Conducive Research Environment

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Dissemination of Research Results

Proprietorship of Research Outputs

Monitoring-of Research Impact

Ethical and Environmental Considerations

At University F there was a detailed research policy and particularly on the research

forum/seminars and awards for its academic staff who participates in contributing to

refereed journals. University F Research Forum aims at bringing the teaching scholars

and students of the University to come close to each other and interact in research

activities. The forum encourages inter-disciplinary research in different subject areas in

the University. All faculty members and students are encouraged to participate and be

involved in a healthy academic activity in order to take the University to new heights in

teaching and research activities. Seminar programmes are held every month, each

faculty/school/institute has a set day for a one hour session dedicated to give a seminar by

faculty member(s) and students. Research Week is organized once every year.

Concerning publications, Research articles presented by the academic community

and staff in seminars and research week are published in a monthly Bulletin of Research

Forum. Some articles will are selected for publication in reputed refereed international

journals. An Annual Research Report outlining all the activities for the whole year are

prepared and presented at the end of each year. Awards and incentives have been set up

for academic staff who have their findings published in reputable journals and presented

to international conferences and workshops.

For example, where a researcher has published at least one article in a recognised

reputed International Journal (University Journal included); the University will reward

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such a researcher with monetary reward of $200. Where a researcher has presented at

least one paper in International Conferences or workshops, the University will reward

such a researcher with monetary reward of $100 over and above financial support by the

University to attend such conferences and workshops. Awards for Research Excellence

will be given to the best research (individuals or groups) in the faculty/school/institute.

The award will be in the form of financial grant to further the research activities of the

group or individual.

University J has made strides in developing research policy in promoting research

work in the university. The University Council has established a University Research

Fund (URF) and ensures that significant amount of funds are annually set aside for

various research activities. The activities form part of core business activities of the

University that complements teaching and gives University national and international

recognition. The university council has also set up the Apportionment of Annual

Research Grant (ARG) which is used for:

(a) Training of Junior Researchers to be able to participate in competitive

research. The Junior Researchers will be exposed to research techniques

and methodologies through attendance of workshops and seminars

organized at the Universities. The professional researchers in various

fields shall also have junior researchers as a requirement attached to them.

This opportunity will prepare the Junior Researchers to participate in

bidding for competitive project proposals.

(b) Funding Competitive Research and/or Project Proposals Research

proposals will be considered bi-annually.

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(c) Awards. The University will reward the best publication arising from

research projects. The University will also reward innovators who have

achieved excellent transfer of technology or knowledge and are not

necessarily academic publications.

(d) Conference attendance.

(e) University’s Annual Research Conference. The University organizes an

annual research conference for the purpose of disseminating research

findings.

(f) Financing of University’s Refereed Journal(s) will support journals to

publish and disseminate research findings resulting from researches in and

outside the university.

The establishment and maintenance of research relationship with private

sector/industry has been identified.

University J’s IPR Policy has also been developed to take into consideration the

intellectual property rights of the academic staff.

University B has developed a research productivity framework . This is a 3-

dimensional matrix. The first dimension consists of the Administrative, Financial and

Human Resources roles and responsibilities that are necessary for the successful

implementation of research. The second dimension consists of the research career

progression methodology which will be applied to progressively develop a researcher

from postgraduate studies up to research mentorship role at Professorial level. The third

dimension consists of the main organs of the University which have a responsibility to

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carry out the roles and activities required to develop the researcher. These organs consist

of:

Academic staff members

The Department

The Faculty, Institute or School

The College

The Office of Deputy Vice-Chancellor (Research and Development)

The matrix will thus document the policies that guide the organs of the University

with the aim of increasing the research productivity of members of staff.

The research productivity conceptual framework has the following guiding

principles.

Encouragement of the implementation of research projects that are relevant to

Kenya and the world and that are carried out using sound methodology and honest

reporting in a resource-efficient and ethical manner.

Facilitation of research proposal drafting and sourcing of research funds.

Facilitation of the disbursement of research funds to the researcher in a

transparent and efficient manner.

Provision of cost-effective research incentives and management and auditing of

research activity without overburdening research funds.

Facilitation, automation and decentralization of research management, carrying

out all research management activity at the lowest possible organ.

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Establishment of common benchmarks for research activity, including methods

for monitoring and evaluating research progress.

Maintenance of a sustainable research system by ensuring a steady, successful

flow of postgraduate students into the research system of the University B.

The research policy aims to facilitate the development of research careers, assuming

that researchers will become increasingly productive and useful for the fulfillment of the

research objectives of the University B as they grow in seniority.

The table below classifies academics as postgraduate students, research trainees,

researchers or research mentors for administrative purposes and outlines the

qualifications, establishment positions, roles, research activities, research-to-teaching

load and expected outputs of each category.

Table 4.52: Research Career Development Framework

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Stage of

research

Postgraduate

student

Research

trainee

Researcher Research

Mentor

Qualifications Bachelo

rs or Masters

degree

Master

s. or Ph.D.

Ph.D. Ph.D.

Positions Masters.

or Ph.D.

student

Tutori

al Fellow or

Lecturer

Lecturer

or Senior

Lecturer

Associate

and Full

Professor

Role Qualify

with expected

degree.

Assist in

research.

Carry

out research

project.

Carry out

research.

Supervis

e other

researchers and

students.

Attract

and administer

research

funding.

Mentor

all research

activity.

Recruit

and supervise

other

researchers and

students.

Attract

and administer

large research

grants.

Research

activity

Postgrad

uate research

Doctor

al or

Successf

ully implement

Successfu

lly administer

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Stage of

research

Postgraduate

student

Research

trainee

Researcher Research

Mentor

project or

thesis activity.

postdoctoral

research.

funded

research

projects.

large grants.

Participat

e in research

administration

in consultation

with head of

dept.

Research /

teaching load

(for research institutes the general load is 70% research / 30%

teaching)

Academic departments shall observe the research and teaching load

shown in the table below, that is, 30% Research / 70% other mission-

oriented activities

Expected

Outputs

(indicative)

Dissertati

on, Thesis,

Conference

papers.

1 Thesis

in 4 years

2

conference

papers in 4

Thesi

s,

conference,

journal

papers.

1

conference

/ journal

paper per

Reports,

conference and

journal papers,

books.

1 journal

paper or 2

conference

papers per

annum

Reports,

conference and

journal papers,

books.

2 journal

papers or 3

conference

papers per

annum or 1

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Stage of

research

Postgraduate

student

Research

trainee

Researcher Research

Mentor

years annum 1 patent book every four

years.

Source University B Research Policy

Perhaps this is the most comprehensive research policy developed in Kenya that is

geared towards encouraging research productivity among academic staff.

University H has research capacity building funds, which may be used by the

University departments to develop the capacity of their staff to conduct research. The

funds may be employed for organizing short courses on research methods. The amount

available for each year is Kshs 100,000/- This amount is quite low and may not benefit

many of the academic staff who would be engaged in career development. The university

has organised research forums for engaging staff and graduate students in research

methods. This is an important forum where an exchange on information gained is shared

with junior researchers and graduate students.

4.7.1 Summary of Document Analysis

Most of the research policies covered here had made omissions on several issues that

are deemed to promote research productivity. Funding for research was an item that was

clearly outlined and mechanisms for the same spelt out clearly. Issues like career

development were highlighted by few universities. There is need therefore for the

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documents to be reviewed with the aim of refocusing them to research productivity of

their academic staff.

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CHAPTER FIVE

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1 Summary

The current academic climate in higher education in Kenya threatens the Kenyan

universities’ ability to sustain the conditions that support research productivity. Increased

demands on government and private funding, a deteriorating physical infrastructure,

increased pressure on undergraduate programs, university expansion strategies and

general economic climate in the country have raised concerns about the continued

capacity of universities to maintain teaching, research productivity and service to the

community. This calls for regular update on the level of research productivity in the

country.

There are very few studies done in Kenya to analyze the factors that influence

research productivity in institutions of higher learning. This study therefore, examined

the factors that influence research productivity among academic staff in selected public

and private universities in Kenya. The themes discussed included the status of research

output in Kenya between 2004-2008, nature of publishing infrastructure, individual and

institutional factors influencing research productivity, the attitude of university lecturers

in Kenya in regard to research and publishing and lecturers and heads of departments’

views of problems and possible solutions to addressing the same problems. The study

finishes with a discussion of some of the strategies that can be used to enhance research

productivity among academic staff in Kenyan Universities.

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Literature review was done from wide sources that had explored studies related to

research productivity. Areas covered under this included, research productivity and

related theories, research productivity and type of institution, factors influencing research

productivity, technology transfer and research productivity, the teaching nexus, research

productivity and publication and a summary of all reviewed literature was done.

A survey research design and document analysis were used in this study. The

target population was the university academic staff in selected 5 private and 6 public

universities in Kenya. These formed 11 universities in total. The sample was selected by

using simple random sampling. The sample size was 277 (70.2% male and 29.8% female)

for academic staff and 17 for the heads of departments. Data were collected using three

instruments namely the questionnaire for lecturers and the other one for heads of

departments. The questionnaires were self administered to the academic staff. Three

questionnaires were filled and sent to the researcher through email.

Data collected were analysed using quantitative procedures. It was coded and

analysed into frequencies and percentages with the help of SPSS for windows (version

15). The SPSS tool was employed in calculation of the means, standard deviation,

frequencies and percentages and the generation of the ANOVA tables. It also helped to

compute the principal components using the Factor Analysis method. Lastly the

summary, conclusions and recommendations of the study were done. There were five

alternative hypotheses that were tested using one way ANOVA. There is a significant

relationship between gender and research productivity, there is a significant relationship

between age groups and research productivity, there is a significant relationship between

academic rank and research productivity, there is a significant relationship between

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highest degree obtained and research productivity, there is a significant relationship

between years since last highest degree was obtained and research productivity.

The PCA used in this study revealed that there were three main factors that

influence research productivity of Kenyan academicians. The first component was

interpreted as personal career development which explained the highest variance of 29%

in the data. The second component, contributing 20%, could be interpreted as

institutional factors whereas the third factor, contributing 14% could be interpreted as

demographic or experience of the individual researcher to publish. Component one and

three are closely related in this analysis. This may mean that it is the individual self

determination, commitment, motivation and stamina that do count in establishing whether

an individual researcher is able to publish or not. The second component, institutional

factors, explained 20% of the variance in the data and involves all those resources,

equipment or rewards that are supposed to be supplied by respective institutions. Other

findings indicated that the research productivity of Kenya was below par, the output

among academic staff was skewed, and that there was differing views on academic staff’s


Document analysis dwelt on the analysis of the sampled research policies of the

selected universities. Information from these documents revealed that most universities

were not keen in the area of staff professional development.

Main recommendation of this study is for government and individual institutions

to try and come up research policies that can guide the research process in this country by

addressing the pertinent issues raised by academic staff. The issues raised here include

individual and institutional factors. And they should be addressed together. It is only by

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addressing the raised issues that the country can think of making strides in increasing

research productivity of this country.

5.2 Conclusions

On the basis of the findings of this study, the following are some of the

conclusions that can be drawn.

1. The nature of research productivity among public and private universities in

Kenya, between 2004 – 2008 is below par when compared with those of other

countries of the world.

2. The research productivity produced indicated skewness in production. The older

universities had more production in terms of publications compared to the younger

upcoming universities. The public universities seem to be better in terms of research

output than the private universities. It also emerged that the status of research

productivity in Kenya is still low compared to the developed nations.

3. There are differences in publication output between different age groups, ranks

and highest degree obtained. Publication output is high among the senior members of

the academic staff. The senior members of staff have more publications than the

younger members of staff. This is also reflected in the position held in the university

and the higher qualification s held by the individual lecturers. It was evident that many

of the academic staff below the rank of lecturer from the majority of those who did not

publish anything between 2004-2008. Majority of the publications were done by

professors.

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4. Data collected also revealed that there was no much difference between the

research output produced by academic staff when categorized by gender, type of

university, faculty and years since last highest degree was obtained.

5. It was established that a combination of individual and institutional factors acted

to influence the research productivity of academic staff in Kenya. Research content

knowledge and self motivation were the key factors among individual factors that had

the greatest influence on researchers. On institutional factors it was resources for

research, equipments and availability of technology based equipments like internet and

computers.

6. Academic staff’s attitude toward research and publication is significant with age

groups, highest degree obtained, years since last highest degree and by name of the

university. However the academic staff’s attitude to research and productivity is not

significant with gender, rank and type of university.

7. Academic staff and heads of department are in agreement on possible solutions to

problems affecting research productivity in Kenya. Some of these problems are

inadequate research fund, heavy teaching load, inadequate research motivation, poor

salaries, inadequate research knowledge among some academic staff, lack of

institutional support, poor attitude toward research and lack of appropriate journals.

8. The possible solutions they offer here include, allocation of more research funds,

employment of more academic staff, reward for researchers, purchase of modern

equipments for research, revision of salaries for academic staff, engagement of the

private sector in research, introduction of research refresher courses for lecturers,

development of appropriate research policies, pegging promotion on administration on

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publishing. The heads of departments had almost the same views on this. One issue

that comes out, though, is that the press for good salaries comes a distant sixth

position.

9. Document analysis on the analysis of the sampled research policies of the selected

universities revealed that most universities had not made sufficient provision for staff

professional development.

5.3 Recommendations

It is evident from this study that indeed there are challenges to be overcome if at

all research productivity are to be realized in the higher institutions of this country. There

is a need for the stakeholders in this area to have an all inclusive dialogue to address the

pertinent issues raised by the academic staff.

This calls for the development of a national research policy that shall cascade

down to the institutional level.

1. Review of universities’ research policies that shall lead to;

Providing adequate research funds for researchers

Staff professional career development

Providing research training courses to raise research productivity and for

publishing results worldwide.

Developing overall research management systems.

Providing and establishing effective research assessment systems.

Supporting and encouraging private organizations to participate in the

university’s research by establishing systems which increase the opportunities

for the university and private organizations to work collaboratively.

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2. Engagement of the private sector in supporting research in the so called private

public sector partnership (PPPs). KEPSA (Kenya Private Sector Alliance) is

functional and may need to be approached on this.

3. Provide an effective publication system to promote the research productivity of

academic staff and graduate students. Again this is in agreement with the

development of the research policies that will guide the entire research process in

each university and the country at large.

4. Create avenues for publication and dissemination of work which is lying in

university shelves. Most of the theses done by undergraduate and graduate

students are lying in the university shelves. It is high time that these materials

were published so that they advance the mission of the universities for

dissemination of knowledge.

5. Encouraging collaboration between universities and research institutions by

providing an information system that is able to show availability and access to

utilize research facilities and data. The collaboration can extend to institutions

outside the country. The linking of professionals in particular fields will promote

scholarship at the university level.

6. Creation of a fund for the publication of tertiary books and journals; provision of

special funding for and revitalization of university publishing houses. In the

modern world the idea of popularizing the electronic journals in the universities

will be a great idea.

7. Fostering of a climate of reward for academic excellence particularly for

international publishing. The University should also consider the possibility of

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increasing a researcher’s salary and rewards, ensuring that the income derived

from doing research is equal to or higher than income derived from teaching.

8. Rewards for staff who engage with research. Appropriate rewards can be

promotion, recognition, and money, providing a difference in terms of rewards

between staff with research productivity and those without, in order to encourage

research motivation. In addition, the University might consider a kind of special

leave for the development of research, allowing teachers to take leave for one

semester to be completely free from their workload.

5.4 Suggestions for further studies

1. The current study explored the most frequently used measure of the quantity or

amount of research productivity as a numerical publication count of ‘number of

research articles’. There is need therefore for future studies to examine, quality,

peer review rating and citation analysis as new tools to assess the value of the

contributions of research to the discipline.

2. There is need also to undertake research productivity analysis at the university,

faculty or departmental level.

3. There is a need for research that analyzes and describes the efforts currently used

by institutions to foster their academic staff’s productivity levels.

4. Need for research to find out why individual professional development is a major

motivator for research productivity.

5. There is need to find out how the universities have implemented their research

policies especially in regard to individual professional development.

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6. The current study focused on universities. There is need to carry out a study on

the factors influencing research productivity in other knowledge producing

institutions e.g. National research institutes.

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Adams, J. (1965). ‘Inequity in Social Exchange’ Journal of Abnormal and Social

Psychology, vol. 62, pp. 335-343.

Adams, J. D. & Griliches, Z. (1996). Research Productivity in a System of Universities.

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http://ssrn.com/abstract=225619.

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255

APPENDIX I

Joash Migosi (Mr.)

C/O Directorate of Research

Management and Development

PO BOX 30568 – 00100

Utalii house, 9th Floor Room 935

NAIROBI

[email protected]

[email protected]

+254 723 869 169

June 18th, 2009

Dear participant,

I am writing to kindly request you to give your views and experiences as a university

academic staff in Kenya. This questionnaire is designed to study aspects of research

output among university academic staff, it will help to obtain information in regard to

factors hindering or promoting research output among Kenya university academic staff.

The findings of this study might be used by university administration, and government, to

make decisions on the management of research in this country.

The information obtained from this survey will be used for this study only. All ethical

issues in regard to this study will be observed.

Thank you very much for your time and cooperation. I greatly appreciate you and your

institution’s help in furthering this research endeavor.

256

mailto:[email protected]


Joash Migosi

Postgraduate student,

Catholic University of Eastern Africa

UNIVERISTY ACADEMIC STAFF QUESTIONNAIRE

A. DEMOGRAPHIC INFORMATION

1. Name of your university___________________________________________

2. Name of your faculty _____________________________________________

3. Name of your department _________________________________________

4. Your age group (kindly tick appropriately)

20-29 Years ( )

30-39 Years ( )

40-49 Years ( )

50-59 Years ( )

60-69 Years ( )

>70 Years ( )

No response

5. Sex (kindly tick appropriately)

Male ( )

Female ( )

257

6. Teaching position/ rank held in the University (kindly tick appropriately)

a. Graduate assistant ( )

b. Tutorial fellow ( )

c. Assistant Lecturer ( )

d. Lecturer ( )

e. Senior Lecturer ( )

f. Associate professor ( )

g. Professor ( )

h. Any Other (please Specify) ( )

7. Highest degree obtained, (kindly tick appropriately)

a. Bachelors degree ( )

b. Masters ( )

c. PhD ( )

d. Any Other (please Specify) ( )

8. Years since you obtained last highest degree (kindly tick appropriately)

a. <5years ( )

b. 6-10 years ( )

c. 11-15 years ( )

d. 16-20years ( )

e. More than 20 years ( )

f. Any Other (please Specify) ( )

258

9. Kindly estimate time spent on teaching load per week__________________

10. Kindly estimate time spent on research work per week___________________

B. NUMBER OF PUBLICATIONS

Kindly enumerate your research publications done between 2004-2008 in the

Table below

Type of

Publication

2004 2005 2006 2007 2008 Total

number of

Publications

Book(s) Authored

Singly

Book(s) co-

Authored

Publications Made

in Refereed

Journals

Papers Presented

in Conferences

Total number of

Publications

259

C. FACTORS AFFECTING RESEARCH PRODUCTIVITY

1. Kindly tick the extent to which the following individual factors affect your research

output. Use the key: 1 –Large Extent; 2 – Some extent; 3 – Little Extent; 4 – No Extent

1 2 3 4

Self Motivation

Socialization with colleagues

Research content knowledge

Research skills gained

Job satisfaction

Simultaneous research projects

Parenting responsibilities

Early orientation to research work

Personal Work discipline

2. Kindly tick the extent to which the following institutional

factors affect your research output. Use the key: 1 –Large Extent;

2 – Some extent; 3 – Little Extent; 4 – No Extent

1 2 3 4

Resources available for research

Rewards for research output

Good salary

Sufficient work time

260

Clear research coordination goals

Mentorship among colleagues

Communication with professional networks

library facilities (Books, journals other materials)

Size of university

Recruitment and selection of academic staff

Positive group climate

Research emphasis by university

Access to relevant journals

Teaching load

Availability of Technology e.g. internet, computers

Equipment for research

Number of graduate students supervised

D. ATTITUDE ON RESEARCH AND PUBLICATIONS

1. Please indicate how strongly you agree or disagree with the statements

regarding the issue of publishing in your institution. (SA –Strongly Agree;

A-Agree; U- Undecided/No opinion-Disagree; SD-Strongly Disagree

Item

SA A

U D

SD

My department should not offers additional

pay for publishing

Lecturers should be offered less teaching

261

load for publishing in refereed journals.

Universities should offer monetary rewards as

an incentive for publishing in refereed

journals.

Universities should spend money meant for

research on improving salaries for lecturers

Promotions should be based on publishing

alone

Teaching alone without research is not

important

Lecturers should spend family resources on

research and publishing

Research and publishing increases visibility of

a university

Publishing is important for any lecturer

aspiring to grow professionally

Universities should be ranked purely on the

basis of their research productivity

Teaching is more important than publishing

I’ll rather spend my time as part time lecturer

than on writing articles

Appointment to senior university

management should be based on academic

262

writing

Lecturers who do not publish have no

business teaching in university

It is not advisable for lecturers to spend

personal resources on research and

publishing

Developing countries need research more

than developed countries

2. Kindly list factors hindering research productivity among lecturers in your institution

and suggest possible solutions to these problems. (Those factors not captured above)

Problem Possible Solution

263

THANK YOU FOR YOUR PARTICIPATION

APPENDIX II

Joash Migosi


PO BOX 62157 – 00200

NAIROBI

[email protected]

+254 723 869 169

June 18th, 2009

264


Dear participant,

I am writing to kindly request you to give your views and experiences as Head/Dean of

department/faculty in a Kenyan University setup. This questionnaire is designed to study

aspects of research output among university academic staff, it will help to obtain

information in regard to factors hindering or promoting research output among Kenya

university academic staff. The findings of this study might be used by university

administration, and government, to make decisions on the management of research in this

country.

The information obtained from this survey will be used for the purposes of this study

only. All ethical issues in regard to this study will be observed.

Thank you very much for your time and cooperation. I greatly appreciate you and your

institution’s help in furthering this research endeavour.

Joash Migosi

Postgraduate student,


265

UNIVERISTY HEADS OF DEPARTMENTS QUESTIONNAIRE

A. DEMOGRAPHIC INFORMATION

11. Name of your university__________________________________________

12. Name of your faculty_____________________________________________

13. Name of your department _________________________________________

14. Your age group (kindly tick appropriately)

20-29 Years ( )

30-39 Years ( )

40-49 Years ( )

50-59 Years ( )

60-69 Years ( )

>70 Years ( )

a. Any Other (please Specify) ( )

15. Sex (kindly tick appropriately)

Male ( )

Female ( )

16. Teaching position/ rank held in the University (kindly tick appropriately)

a. Graduate assistant ( )

b. Tutorial fellow ( )

c. Assistant Lecturer ( )

d. Lecturer ( )

e. Senior Lecturer ( )

f. Associate professor ( )

266

g. Professor ( )

h. Any Other (please Specify) ( )

17. Highest degree obtained, (kindly tick appropriately)

a. Bachelors degree ( )

b. Masters ( )

c. PhD ( )

d. Any Other (please Specify) ( )

18. Years since you obtained last highest degree (kindly tick appropriately)

a. <5years ( )

b. 6-10 years ( )

c. 11-15 years ( )

d. 16-20years ( )

e. More than 20 years ( )

f. Any Other (please Specify) ( )

19. Kindly estimate time spent on teaching load per week ________________

20. Kindly estimate time spent on research work per week________________

B. NUMBER OF PUBLICATIONS

Kindly enumerate research publications done by your staff between 2004-2008 in the

Table below

Type of

Publication

2004 2005 2006 2007 2008 Total number

of

Publications

267

Book(s) Authored

Singly

Book(s) co-

Authored

Publications Made

in Refereed

Journals

Papers Presented

in Conferences

Any Other

category of

publications.

(Kindly Specify)

Total number of

Publications

C. FACTORS AFFECTING RESEARCH PRODUCTIVITY

1. Kindly tick the extent to which the following individual factors affect your staff’s

research output. Use the key: 1 –Large Extent; 2 – Some extent; 3 – Little Extent; 4 – No

Extent

1 2 3 4

Self Motivation

Socialization with colleagues

268

Research content knowledge

Research skills gained

Job satisfaction

Simultaneous research projects

Parenting responsibilities

Early orientation to research work

Personal Work discipline

2. Kindly tick the extent to which the following institutional

factors affect your staff’s research output. Use the key: 1 –Large

Extent; 2 – Some extent; 3 – Little Extent; 4 – No Extent

1 2 3 4

Resources available for research

Rewards for research output

Good salary

Sufficient work time

Clear research coordination goals

Mentorship among colleagues

Communication with professional networks

library facilities (Books, journals other materials)

Size of university

Recruitment and selection of academic staff

Positive group climate

269

Research emphasis by university

Access to relevant journals

Teaching load

Availability of Technology e.g. internet, computers

Equipment for research

Number of graduate students supervised

2. Kindly list possible factors hindering research productivity among lecturers in your

department and suggest possible solutions to these problems.

Problem Possible Solution

270

THANK YOU FOR YOUR PARTICIPATION

APPENDIX III

Document Analysis

Institutional research policies will be scrutinized to find out the following information;

1. Name of University

2. Year of research policy publication

3. Contents of research policy

4. Deliberate efforts to promote research productivity as outlined in the research

policy.

271

APPENDIX IV

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273

274

275

276

277

278

279

280

281

282

283

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