Innovation as a heuristic to excellence: A study in Indian
context
THESIS SUBMITTED TO THE UNIVERSITY OF DELHI
FOR THE AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY
IN PSYCHOLOGY
By
SANJAY SINGH
(University of Delhi)
Supervised by
PROF. N. K. CHADHA
Head of the Department
Department of Psychology
University of Delhi, Delhi - 110007
Year 2012
i
ABSTRACT
The current research studies the relationship between innovation and business
excellence within bounded rationality framework in Indian context. The study
conceptualizes innovation as a intuitive decision strategy by a manager/entrepreneur
to select those idea, information and opportunities from his/her environment which
are ecologically rational and brings excellence in a fast and frugal way. Rather than
seeing innovation as mere an act of creating something new or value addition, the
current study tries to see it as a strategic intuitive mechanism of adaptation and
growth under uncertain business environment. Based on Manimala (1992) and
Gigerenzer (2000, 2002) a scale to measure the ability of managers/entrepreneurs to
use innovation as a heuristic was developed (α =.963, N = 203) and, subsequently,
factor analyzed to identify the major factors underlying innovation heuristic. A
principal component analysis revealed the emergence of two major factors, i.e. ability
of managers to use innovation as a search-and-adapt heuristic (SAH) which
contributed to 51.44% variance, and ability of managers to use innovation as a fast-
and-frugal heuristic (FFH) which contributed to 6.19 % variance in the sample data. A
mediation analysis showed that the effect of the two obtained factors on excellence is
mediated through their summative effect called heuristic intelligence. A structural
equation modeling (SEM), using Bollen-Stine bootstrap method, was carried out to
test the hypothesized relationship in which the effect of both the predictor factors (i.e.,
SAH, FFH) over business excellence is fully mediated by heuristic intelligence. Based
on the various indices of model-fit the hypothesized model was found to be fit, and
thus accepted.
Keywords: Business Excellence, Fast & Frugal Heuristic, Heuristic Intelligence,
Search & Adapt Heuristic,
ii
DECLARATION
I, Sanjay Singh, hereby declare that this thesis entitled “Innovation as a heuristic to
excellence: A study in Indian context” is of my own composition, and that it contains
no material previously submitted for the award of any other degree. The work
reported in this thesis has been executed by me, except wherever due
acknowledgement is made in the text.
Sanjay Singh
iii
CERTIFICATE
This is to certify that the thesis entitled “Innovation as a heuristic to excellence: A
study in Indian context” submitted for the degree of Doctor of Philosophy is original
to the best of our knowledge. The research work was carried out by Mr. Sanjay Singh
in the Department of Psychology, University of Delhi, under the supervision of Prof.
N. K. Chadha. This work has not been submitted in part or full to this or any other
University for the award of any degree or diploma.
Sanjay Singh
Prof. N. K. Chadha
Research Supervisor
Department of Psychology
University of Delhi
Delhi - 110007
Prof. N. K. Chadha Head of the Department
Department of Psychology
University of Delhi
Delhi - 110007
iv
“Our first endeavors are purely instinctive, promptings of an imagination vivid and
undisciplined. As we grow older reason asserts itself and we become more and more
systematic and designing. But those early impulses, though not immediately
productive, are of the greatest moment and may shape our very destinies. Indeed, I
feel now that had I understood and cultivated instead of suppressing them, I would
have added substantial value to my bequest to the world. …Instinct is something
which transcends knowledge. We have, undoubtedly, certain finer fibers that enable
us to perceive truths when logical deductions, or any other willful effort of the brain,
is futile.”
Nikola Tesla (1856 - 1943),
My Inventions: The Autobiography of Nikola Tesla (pp. 2/32)
v
DEDICATION
Dedicated to
Nikola Tesla, his over 300 patents1, and his penniless days
and, to all those who are striving hard to stay hungry and foolish.
1 For more details see Snežana Šarboh (2006).
vi
ACKNOWLEDGEMENT
There are many people whom I want acknowledge for their suggestions, help and
support during the course of the current research work. The first and foremost I want
to acknowledge the my mentor and supervisor Prof. N. K. Chadha for allowing me to
pursue a topic of my interest and rendering all the intellectual and moral support that
was extremely necessary to take this research work to its logical conclusion. Prof.
Chadha developed the interest of numbers inside me, and then the eccentricity for
one’s work. I have learned lot of things from him directly or indirectly, and without
his encouragement and support this work would never have been complete. Secondly,
I want to thank many students and entrepreneurs who agreed to become part of my
research sample without getting paid for it. Apart from this, I want to thank the
following:
Prof. Vijay Govindrajan, Professor of International Business at Tuck School of
Business, Dartmouth College, USA, for his prompt replies to my queries and
encouraging email communications, and his permission to allow me to publish the
figure of his ‘Three Box Model of Strategic Innovation’
Dr. Marta Sinclair, Griffith Business School, Australia, for asking “Do you address
intuition in your thesis as well?”, and her kind permission to republish a paragraph
from her celebrated article ‘Intuition: Myth or a Decision-Making Tool?’
Harvard Business School Publishing for their permission to republish ‘the model of
invisible innovation’ (Kumar & Puranam, 2012) in my thesis.
Dr. Fahri Karakas, researcher at Open University Business School, U.K., for his kind
permission to republish the contents from his article.
Finally, I want to thank to this time and context which proved as a fertile ground for
the ideas which I have attempted to diagnose through this research work, and many
entrepreneurial narratives that shaped my thinking and life.
SANJAY SINGH
vii
TABLE OF CONTENTS
Abstract………………………………………………………………….. i
Declaration………………………………………………………………. ii
Certificate………………………………………………………………... iii
Dedication……………………………………………………………….. v
Acknowledgement………………………………………………………. vi
Table of contents………………………………………………………… vii
List of figures……………………………………………………………. x
List of tables…………………………………………………………….. xi
List of exhibits…………………………………………………………… xii
List of Abbreviations……………………………………………………. xiii
CHAPTER 1 INTRODUCTION
1.1 Introduction………………………………………………………….
1.1.1 The changing paradigms in business………………………..
1.1.1.1 Innovation and the traditional paradigm on leadership……
1.2 Rationale of the study………………………………………………..
1.3 Objective of the study……………………………………………….
1.4 Definition of key terms………………………………………………
1.5 Characteristics of innovation………………………………………...
1.6 The process of innovation……………………………………………
1.7 Types of innovation………………………………………………….
1.8 Conclusion……………………………………………………………
1
3
3
8
10
11
12
13
14
15
16
CHAPTER 2 REVIEW OF LITERATURE
2.1 Innovation in Indian context: The case of Indovation……………….
2.2 The current scenario…………………………………………………
2.3 Innovation as a heuristic to excellence: A review of past literature…
2.4 Chapter summary…………………………………………………….
17
19
26
28
34
viii
CHAPTER 3 METHODOLOGY
3.1 Research design………………………………………………………
3.2 Variables measure in the research……………………………………
3.2.1 Description of the variables………………………………….
3.2.1.1 Innovation-as-a-heuristic…………………………….
3.2.1.2 Search & Adapt Heuristic…………………………. .
3.2.1.3 Fast & Frugal Heuristic……………………………...
3.2.1.4 Heuristic Intelligence………………………………..
3.2.1.5 Business Excellence…………………………………
3.2.1.6 Description of variables in SEM terminology……….
3.3 Procedure……………………………………………………………..
3.4 Sample………………………………………………………………..
3.4.1 Sample size…………………………………………………..
3.4.2 Criteria for inclusion and exclusion in sample………………
3.5 Measurement tools…………………………………………………...
3.5.1 Innovation-as-a-heuristic questionnaire……………………..
3.5.2 Measure of Organizational Excellence………………………
3.6 Chapter Summary…………………………………………………….
35
37
38
40
40
40
41
43
44
45
46
47
48
49
50
50
56
57
CHAPTER 4 DATA ANALYSIS & RESULTS
4.1 Missing value analysis……………………………………………….
4.2 Descriptive results……………………………………………………
4.3 Principal component analysis………………………………………...
4.3.1 Test for group differences and data sufficiency……………..
4.3.2 Scree plot…………………………………………………….
4.3.3 Summary of principal component analysis…………………
4.3.4 Component plot……………………………………………...
4.4 Correlational analysis………………………………………………..
4.5 Structural Equation Modeling……………………………………….
4.5.1 Mediation analysis…………………………………………..
58
61
62
64
64
65
66
69
69
70
71
ix
4.5.2 the Proposed model………………………………………….
4.5.2.1 Practical issues involved in the proposed SEM……..
4.5.2.1.1 Sample size and missing values…………….
4.5.2.1.2 Continuous scales…………………………...
4.5.2.1.3 Univariate and Multivariate normality………
4.5.2.1.3.1 Bootstrapping as an aid to nonnormal
data……………………………………………..
4.5.2.1.4 Linearity Assumption……………………….
4.5.2.1.5 Outliers………………………………………
4.6 The SEM output and estimates………………………………………
4.7 Indices of model fit…………………………………………………..
4. 8 Chapter summary……………………………………………………
73
73
73
74
74
75
76
76
77
79
81
CHAPTER 5 DISCUSSION
5.1 A case for innovation………………………………………………...
5.2 Discussion of principal component analysis results…………………
5.3 Discussion of Structural Equation Modeling………………………..
5.4 Chapter Summary……………………………………………………
84
84
86
97
103
CHAPTER 6 LIMITATIONS AND IMPLICATIONS OF STUDY
6.1 Limitations of the study………………………………………………
6.2 Implications of the study……………………………………………..
6.3 Chapter summary…………………………………………………….
104
106
109
111
REFERENCES 114
APPENDICES
Appendix A Innovation-as-a-heuristic Questionnaire 144
Appendix B The correlation matrix 152
x
LIST OF FIGURES
Figure 1.1 New emerging paradigms in business
Figure 2.1 The three box model of innovation
Figure 2.2 The model of invisible innovation
Figure 3.1 Initially conceptualized model (before Principal Component
Analysis)
Figure 3.2 The hypothesized structural relationship among variables (after
Principal Component Analysis)
Figure 3.3 The gender division of sample size
Figure 4.1 Scree plot
Figure 4.2 Component plot
Figure 4.3 Analysis of the type of mediation in the proposed model
Figure 4.4 The proposed model with output values after confirmatory analysis
xi
LIST OF TABLES
Table 3.1 Reliability analysis of innovation as a heuristic questionnaire
Table 4.1 Descriptive statistics for the sample characteristics
Table 4.2 KMO and Bartlett’s Test
Table 4.3 Summary of principal component analysis
Table 4.4 Correlation between extracted components and total excellence scores
Table 4.5 Direct effects (Two tailed significance values)
Table 4.6 Direct effects after mediation –Two tailed significance values
Table 4.7 Regression weights of various paths in the proposed model
Table 4.8 The covariance estimate between SAH and FFH
Table 4.9 Variance and estimate of SAH and FFH, and residual
Table 4.10 A summary of indices of fit for the proposed model
xii
LIST OF EXHIBITS
Exhibit 1.1 Definitions
Exhibit 1.2 Changing paradigm of Leadership (Karakas, 2007)
Exhibit 1.2 Types of innovation
Exhibit 3.1 Description of variables in SEM terminology
Exhibit 3.2 List of items included in innovation-as-a-heuristic questionnaire
Exhibit 3.3 List of items measuring innovation as a fast & frugal heuristic
xiii
LIST OF ABBREVIATIONS
BE Business Excellence
FFH Fast & Frugal Heuristic
HI Heuristic Intelligence
IAH Innovation as a Heuristic
MLE Maximum Likelihood Estimation
MNCs Multi National Corporations
R & D Research & Development
SAH Search and Adapt Heuristic
SEM Structural Equation Modeling
Chapter 1 Introduction
3
CHAPTER 1
INTRODUCTION
1.1 Introduction
In the context of economic stagnation in the developed countries and the grim struggle for
economic growth and social change in the developing world there has been growing interest
in modes of vigorous, innovative entrepreneurial management (Khandwalla, 1987). This
has resulted in organizations increasingly looking to create practices that nurture
innovation and taps creativity of their employees. The research community is also
increasingly focusing on the various aspects of innovation resulting in a sustained
increase in the literature on innovation and its related aspects. Innovation, more now than
ever, clearly tops the value chain in product and service lifecycles (Katragadda, 2009).
The relationship between innovation and excellence has been noticed by scholars and
management practitioners since long. It has been found that the innovative leadership or
entrepreneurship stimulates economic growth (Schumpeter, 1934) as it leads to effective
combination of various factors of production (Schumpeter 1950). Peters and Waterman
(1982) has identified 8 key features of excellence in the U.S. companies one of which is
the commitment to innovation and dynamic growth. Innovative organizations are more
profitable, grow faster, create more jobs and are more productive than their non-innovative
competitors, even in mature industries (Franco, 1989; Capone et al., 1992; Baldwin &
DaPont, 1993).
1.1.1 The changing paradigms in business
The nature of business has been witnessing a major shift world over brought by various
changes at the global level (Friedman, 2005), and India seems to have been one of the most
affected beneficiary of these global forces which are shaping the world in a new way. Some
fundamental changes in the nature of business brought out by the such forces, which have
been relevant in shaping the core ideas of the present study, are listed below:
4
Figure 1.1 New emerging paradigms in business
The new age corporation are moving from a fundamental business role of meeting the needs
of their customers to creating a need for their products in their customers. This may involve
sometime guessing what kind of product a customer may need in future, but companies
cannot just afford to bank upon their customers to have this realization because of the intense
completion. In the words of great innovator and entrepreneur Steve Jobs, as cited in Issackson
(2011), "Some people say, 'Give the customers what they want.' But that’s not my approach.
Our job is to figure out what they’re going to want before they do. I think Henry Ford once
said, 'If I’d asked customers what they wanted, they would have told me, "A faster horse!"'
People don’t know what they want until you show it to them. That’s why I never rely on
market research. Our task is to read things that are not yet on the page” (p. 806). The best
companies know how to figure out what their customers would want in future before even
they know that they may want it (Kahney, 2008). Akio Morita, co-founder of Sony Corp.,
once said that “we don’t ask consumers what they want. They don’t know. Instead, we
apply our brain power to [figure out] what they need, and will want, and make sure we’re
there, ready”1. So, the role of the modern corporations aspiring for excellence is not limited
1 cited from Chris Dixon (2010), retrieved from http://articles.businessinsider.com/2010-04-
25/strategy/30062996_1_enterprise-server-software-companies-sony
From Need-
Fulfillment
to
Need Creation
From Team (Networker)
to
Individual
(Nerds)
From Rationality
to
Intuition &
Heuristic
From Leadership
to
Innovation
From Management
to
Entrepreneurship
5
to fulfilling the need of their customers and satisfying. It also involves identify they possible
needs in future and fulfil them or sometimes developing a product that will create its own
need in customers.
The second major event in the domain of business world, especially in the software and
technology sector, has been the startling success of nerds. David Brooks (2008) considers the
mid to late 1970s as ‘The Decade of The Rise of Nerdism’ in modern America. According
to him 1980s marked the period of geek empowerment with the rise of Microsoft™ and the
digital economy. Nerds began making large amounts of money and acquired economic
credibility, which brought them tremendous social prestige as well. The information
revolution produced a parade of highly confident nerd moguls — Bill Gates and Paul Allen,
Larry Page and Sergey Brin, Marks Zuckerberg, Michael Dell, and so on. In India nerd
culture seems to rise to ascendency in the first decade of new millennium . Angela Saini
(2011) has termed India a geek nation with immense hunger and passion for science,
technology and innovation, especially among its newly educated youths. Also, India finds a
place in the Geek Atlas of the World (Graham - Cumming, 2009) as a place where science
and technology come alive. So, with the rise of nerdism in India we can say that the
individual initiative for innovation in India has begun. According to Schumpeter (as cited in
McCraw, 2007) individual entrepreneurship holds the key to economic growth of any
country.
Another remarkable event that has important implication for current research is the rise and
popularity of bounded rationality approach (Simon, 1956; Conlisk, 1996; Gigerenzer, 2000;
Kahneman, 2002) to cognition and its popularity in managerial decision studies (Gladwell,
2005; Ellison, 2006). There is a large amount of literature available on this issue so rather
than repeating it only its relevant aspects for the current research will be briefly presented in
the forthcoming paragraphs. The vision on human rationality can be classified into two broad
parts: one, we are demons having unlimited rational capacity or, second, we are humans with
a bounded rationality (Gigerenzer, Todd, & the ABC Research Group, 1999). Demons have
unbounded rationality and try to optimize under constraints while humans are satisficers who
make use of fast and frugal heuristics. A definition of related terminology is summarized in
Exhibit 1.1. on the following page. According to Gigerenzer & Brighton (2009) we are
Homo-Heuristicus making use of fast and frugal heuristics as an adaptive mechanism of mind
6
to satisfy our adaptive needs. The use of simple heuristics in our day to day behaviours can
make us smart leading to intelligent choices and outcomes (Gigerenzer & Todd, 1999).
The root of heuristics can be traced back to dual process theories which have been developed
since 1970s by researchers on various aspects of human psychology, including deductive
reasoning, decision making, and social judgment (Evans, 2008; Frankish & Evans, 2009).
According to dual process theories of cognition there are two contrasting type of thinking
processes called system 1 thinking and system 2 thinking processes. System 1 thinking
processes which are intuitive, nonconscious , fast, process information in parallel manner,
and are automatic, effortless, and associative, while system 2 thinking processes which are
based on reasoning, are slow, serial, controlled, effortful, rule-governed (Myers, 2002; Taleb,
2007; Frankish & Evans, 2009).
Exhibit 1.1 Definitions
Unbounded Rationality: Unbounded rationality encompasses decision-making strategies that
have little or no regard for the constraints of time, knowledge, and computational capacities
that real humans face.
Optimizers Under Constraints: While making decisions under constraints of time, money
and other resources we try to optimize the value of resources. We decide upon something
that gives the best value of our resources spent. We stop as soon as the cost outweighs the
benefit (The Stopping Rule). However, in real world situations optimal strategies are
unknown or unknowable (Simon, 1987)
Satisficing : In real world situations we adjust our aspiration level and end the search for
alternatives as soon we encounter with an alternative that exceeds our aspiration level
(Simon, 1956, 1990)
Fast & Frugal Heuristics: Fast and frugal heuristics are those simple heuristics that employ a
minimum of time, knowledge, and computation to make adaptive choices in real
environments (Gigerenzer, Todd, & the ABC Research Group, 1999). Fast and frugal
heuristics are simple to execute because they limit information search (because of their
satisficing property) and do not involve much computation. A heuristic is good to the extent
it is adapted to meet the structure of its environment., an attribute called ‘ecological
rationality’
7
Research evidences suggest that system 1 thinking is more powerful in comparison to
system 2 thinking (Thaler, Sunstein & Balz, 2010; Stroop, 1935) and many a times it leads to
equally better outcomes as compared to system 2 thinking processes (Gigerenzer, 2000;
Gladwell, 2005). The current research also studies the use and implications of system 1
thinking processes for the managerial and organizational innovation and excellence.
Apart from these, the two other noticeable shifts have been on a growing emphasis on
innovation in all aspects of the organizational practice including leadership, and an increasing
emphasis on entrepreneurial style of management (Khandwalla, 1987). Recently, a more
success approach to leadership views exercising leadership through innovation or innovative
means and styles (Menino & Maloney, 2003; Barsh, Capozzi, & Davidson, 2008). According
to Steve Jobs, as cited in Issackson (2011), innovation is what distinguishes between a leader
and a follower. Karakas (2007) has also reported that the new paradigm on leadership
emphasizes a shift from pure rationality to positive intuition, from certainty to uncertainty,
from command and control to flexibility and empowerment, etc., see the Exhibit 1.2 below:
Exhibit 1.2 Changing Paradigm of Leadership (Karakas, 2007)
Old Paradigm New Paradigm
Pure Rationality
(Actuality, Intellectual Stimulation,
Problems , Conservative)
Positive Intuition
(Potentiality, Emotional Arousal,
Opportunities, Creative)
Certainty
(Clarity, Order, Determinate,
Stability)
Uncertainty
(Ambiguity, Chaos, Indeterminate
Change)
Command & Control
(Top down, Controlling, Doubtful,
Domination)
Flexibility & Empowerment
Egalitarian, Inspiring, Trusting,
Collaboration)
Uniformity
(Hierarchical, Absolute, Selective
Simplicity)
Diversity
(Lateral, Contextualism , Inclusive
Complexity)
8
Profit Orientation
(Theory X, Competition, Economic
Profit Oriented)
Multiple Orientation
(Theory Z, Cooperation
Socioeconomic, Triple Bottomline)
Self Centered
(Ethnocentric, Individualistic
Authoritative, Short-term interest)
Community Centered
(Community oriented, Communitarian,
Collaborative, Service to community)
Old Science
(Newtonian, Linear, One truth
Reductive)
New Science
(Quantum, Nonlinear, Multiple Truths
Emergent)
Old Metaphor
(Mechanic, Static, Solid Ice
Building)
New Metaphor
(Organic, Dynamic, Emergent
Networking)
Reprinted by permission of Dr. Fahri Karakas (2007)
From The Twenty-First Century Leader: Social Artist, Spiritual Visionary, and
Cultural Innovator. Global Business and Organizational Excellence, 44,
doi:10.1002/joe.20143, Copyright © Author; All rights reserved.
These shifts underscore the importance of innovative and entrepreneurial style of leadership
over the traditional approaches to leadership.
1.1.1.1 Innovation and the traditional paradigms on leadership
Traditional theories of leadership have not made any explicit or detailed reference to
innovation and its role in achieving organizational excellence. The early scientific-
reductionist or structural-bureaucratic fascinations about leadership only led to a distorted
perception of leadership. The development of leadership theories are heavily influenced
by the three historically important studies in the area of organizational behaviour: the
Iowa, Ohio State & Michigan studies- and “unfortunately, they are still heavily depended
upon these studies, leadership research has not surged ahead from this relatively
auspicious beginning” (Luthans, 1998, p. 383).
9
The Great Man Theorists focus on individual traits of leadership that may lead to person
emerging as a leader irrespective of temporal or spatial considerations. This theory leaves the
scope for a creative or innovative person emerging as a leader but examples of great man
theory are heavily tilted toward public (especially political) personalities with little
reference to creative profiles in other areas like science, arts, technology or business
organizations. It’s difficult to trace a single leadership example whose leadership is
dominantly attributed to creativity or innovativeness. In contrast to this, some other
leadership approaches show a group approach where leadership is viewed more in terms of
leader’s behaviour toward a group/collectivity and how such behaviour affects and is
affected by the group of followers (Luthans, 1998). The acknowledgement of group as
an important leadership factor seems to b e t h e beginning of ‘shared leadership
approach’- an approach to leadership in which leadership is supposed to be co-created
through joint and continuous interaction between leader and followers. This approach
widenes the scope for creativity and innovation as generation of new ideas through
brainstorming or such other techniques had a better scope here, especially under a democratic
and participative leadership.
Another theoretical development was the situational approach which added temporal and
spatial dimensions to leadership. This approach posited that a person with particular
qualities or traits that a situation or time warrants will emerge as a leader, for example
Fiedler’s Contingency Model. According to Fiedler’s model in moderate situations (i.e.,
situations that are neither very favourable nor very unfavourable) the performance will be
higher if the leader is relationship oriented (Robbins, 2003). This also may be a right time
for leaders and his group to encourage innovation and experimentation in organization
coupled with strong people orientation and care for customers.
We can see that the task consideration which earlier formed one important aspect of
leadership in Behavioural Theories (for example, Ohio State Studies, University of
Michigan studies, The Managerial Grid of Blake & Moutan) is gradually loosing its
reference in modern theoretical constructions of leadership, and is partly being subsumed
into strong people or customer orientation (e.g., Peter & Austin , 2003), and partly being
replaced by a newer dimension which significantly make references to creativity,
10
innovation, experimentation and such other attributes. One of the striking feature of the
1990s was the remarkable surge of innovation (Lester & Piore, 2004) which now has
become biggest buzzword not only in industry but also increasingly finding strong resonance
in all walks of social and organizational life. This may warrant a search for ‘the third
dimension of leadership: the concern for creativity/innovation.
An important development in this respect have been the Scandinavian Studies which
propose a three dimensional model of leadership with ‘development-oriented behaviour’
being the third dimension of leadership. It proposes that earlier studies fail to capture the
more dynamic realities of today’s fast changing world, in which to achieve excellence a
leader has to show development-oriented behaviour like seeking new ideas, valuing
experimentations, originating new approaches to problems, encouraging members to start new
activities, generating and implementing change, etc. (Robbins, 2003). So, creativity and
innovation are gradually being acknowledged by researchers & management
practitioners as an independent leadership dimension and is being considered a critical
factor in achieving corporate excellence. The present research work tries to quantify and
measure the extent of criticality of innovation in achieving organizational excellence.
1.2 Rationale of the study
The present study is set in business organization context and aims to examine the role of
innovation in bringing business excellence. A large amount of research literature is available
on nature of innovation in relation to organizational excellence but studies on ‘innovation as a
heuristic’ is lacking. A growing body of researches (e.g., Kahneman, 2002; Gladwell, 2005;
Gigerenzer & Gaissmaier , 2011) suggest the dominance system 1 (intuitive-heuristic) thinking
processes in decision making, on the other hand, innovation has become an imperative for
business success and adaptation (Altshuller, 1999; Khandwalla, 2006). So, it is important to
study the exact nature of innovation as a part of system 1 thinking process. The present
research pursues this idea and tries to find the answer. Further, the old paradigm on system 1
thinking processes have received heuristics with negative connotations which, according to it,
lead to faulty or biased conclusions in decision making (Kahneman, 2002), but the current
research takes the alternative view that heuristics are mind’s adaptive mechanism
11
(Gigerenzer, 2000; Gigerenzer & Brighton, 2009) can be helpful in attaining and
sustaining business excellence. Considering innovation as a heuristic to excellence the
current research throws light on adaptive value of innovation for entrepreneurs and
managers working under resource constrained and increasingly uncertain environment.
Further, factorial study of ‘innovation as a heuristic’2 are lacking (Johannessen, Olsen,
Lumpkin, 2001; Aranda & Molina-Fernández, 2002), and the present study tries to
identify the major factors underlying innovation-heuristic through factor analytic method.
Further, a need was felt to explore the business model through which innovation heuristic
and excellence interact with each other. The present study tries to explore this model by
using structural equation modelling (SEM) technique.
1.3 Objectives of the study:
The current research has three major objectives:
1) To study whether innovation heuristic has a significant correlation with business
excellence;
The first basic objective of the present research was to see whether there is any
significant correlation between innovation heuristics and organizational excellence. The
propositions made regarding this objective are as follows:
Proposition 1: ‘Innovation heuristic’ is positively correlated to organizational
excellence.
More precisely, this proposition can be formulated in terms of following two
hypotheses3:
2 ‘Innovation as a heuristic to excellence’ has been measured by developing a scale based on Manimala
(1992). The scale is termed as ‘Innovation as a Heuristic Questionnaire’. It has been taken as an
independent variable and also referred as ‘innovation as a heuristic’ variable or simply ‘innovation
heuristic’. These phrases have been used synonymously and interchangeably in the thesis. 3 After developing the ‘Innovation as a Heuristic Questionnaire’ a factor analysis was carried out which
gave two factors called ‘search & adapt heuristic’, and ‘fast & frugal heuristic’. Later on based on the
feedback of experts an alternative name for ‘search and adapt heuristic’ was also considered: ‘adapt and
shape heuristic’. The name ‘search and adapt heuristic’ has been retained in the thesis and the explanation
for its alternative name has been given while doing factor naming. Further, ‘Heuristic Intelligence’ is a
12
Hypothesis 1a: There is a significant positive correlation between innovation as a ‘Search
& Adapt Heuristic’ and Business Excellence.
Hypothesis 1b: There is a significant positive correlation between innovation as a ‘Fast &
Frugal Heuristic’ and Business Excellence.
Hypothesis 1c: There is a significant positive correlation between ‘Heuristic Intelligence’
and ‘Business Excellence’.
2) To explore the factors underlying innovation heuristic through factor analysis method;
The second major objective of the current research was to identify factors underlying
Innovation Heuristic through factor analytic method.
3) To explore the structural business model through which Innovation Heuristic and
excellence interact with each other by using structural equation modelling (SEM)
technique.
The third, and final objective of the research was to identify the structural model through
which the two innovation heuristics and business excellence interact with each other.
1.4 Definition of key terms
Innovation is simply defined as an act of creating something new or finding new ways
to create value (Katragadda, 2009). It refers to an idea, practice or object that is
perceived new by an individual or other unit of adoption (Rogers, 1983). Innovation
includes the total set of activities leading to the introduction of something new, resulting
in strengthening the defendable competitive advantage of a company (Van der Meer,
1996). The two important psychological attributes of innovation are its emphasis
on behavioural dimension (i.e., action) and the perceived newness of the idea. Its
emphasis on behavioural part or action part differentiates it from its closest
counterpart i.e. creativity. Creativity consists of thinking new ideas and innovation
hypothesized variable measured as summative effect of ‘search & adapt heuristic’, and ‘fast & frugal
heuristic’. Business Excellence is the measured dependent variable.
13
consists of doing new ideas. Also, perception is very important for innovation. An
innovative idea may not be perceived innovative unless it proves its innovativeness by
significantly altering a domain function, and in fact, unless it does so it may remain
marginalized; for example the refusal of Yahoo!™
CEO to buy Google’s search
technology for US $1 million when he was approached by Google™ Guys (Larry Page
& Sergei Brin) at the beginning of Google™
, a story well described by David A. Vise
and Mark Malseed (2005) in their best seller The Google™ story. It was because
Yahoo!™
failed to perceive the innovativeness of Google™
search technology, and the
potential of search emerging as the biggest business on the internet in future. Now
Google™
is the biggest internet company in the world and has emerged as the biggest
threat to Yahoo! ™
and its closest rival Microsoft™
corporation.
Heuristic, on the other hand, are closely studied under the psychology of intuition as
intuitions are considered as the source of heuristics (Hogarth, 2001; Myers, 2002).
Intuition can be defined as a non-sequential and non-conscious mode of information
processing resulting into direct form of knowing with any conscious reasoning
(Sinclair, 2005, Epstein et al.,1996; Shapiro and Spence, 1997; Simon, 1987). Next, The
term ‘heuristic’ is of Greek origin meaning ‘to find out’ or ‘to discover’. This notion of
heuristics differs from approaches that define heuristics as rules of thumb or as irrational
shortcuts that result in decisional biases. Fast and frugal heuristics yield decisions that
are ecologically rational rather than logically consistent (Reimer and Rieskamp, 2007).
Some scholars consider heuristics as the mental strategies of problem solving that are
faster, more frugal and more accurate at the same time as compared to standard
benchmark strategies(Gigerenzer, & Todd, 1999). According to Katsikopoulos (2010)
heuristics refer to the models for making decisions, that rely heavily on core human
capacities, do not necessarily use all available information, and process the information
they use by simple computations, are easy to understand, apply, and explain.
1.5 Characteristics of Innovation
Rogers (1983) has offered a social analysis of innovation according to which the rate
at which different innovations get adopted by a member of social system vary strongly,
14
and depends, among other things, a number of characteristics of innovation itself. The
main features of innovation, according to Roger (1983) are relative advantage, which
refers to the extent to which an innovation is considered better than the idea, practice or
object that it is supposed to replace; compatibility, which refers to the extent to which
an innovation is consistent with existing values, previous experiences and the need of
potential users; complexity, which refers to the extent to which innovation is perceived
as difficult to understand and complex to use; trialability, which refers to the extent to
which an innovation can be tested and experimented on a limited scale; and,
observability, which refers to the extent to which the use and effect of an innovation are
visible to other members of the unit (for e.g., social system).
1.6 The process of Innovation
Tom Peters along with Nancy Austin (2003), in their book A Passion for Excellence,
has offered a model of the process of innovation according to which “It’s a messy
world…[and] in a messy world the only way to proceed is by constant
experimentation. If the constant experimentation is the only antidote to a messy
world then we need experimenters-or champions (skunks).…[and] if the messy-world-
experiment- champion-skunkwork paradigm makes sense, then we need to create a
climate that induces all the above to occur- a climate that nurtures and makes heroes of
experimenters and champions.” (p. 116).
Again, the authors (Peters & Austin, 2003) are of the view that the actual innovation
occurs in a zone where producers and consumers (or users) of a product or idea interact
with each other. According to them “analysis after analysis shows, in fact, that the great
majority of ideas for new products come from the users. Our own research confirms it,
not just in high technology but in the banking, health care and hamburger business as
well” (p. 156). After-the-fact-analysis for every industry, from blue jeans and hamburger
to mainframe computers and aircraft engines, shows that the products of 1995 will be
invented and prototyped ca. 1985 as some sort of trial involving a lead producer (more
often than not a small company) and a lead user (also often small), someone who thought
he could really take advantage of the new, untested technology” (p.158).
15
For example, in sophisticated industries (as most of them really are!) Peters & Austin
(2003) found that users, like a lot of other phenomena, are normally distributed. At
the front tip of the curve are those who are often as much as 10 to 15 years ahead of
their average peers (GM and Boeing for example, were far ahead in CAD use). They
are willing to take a risk in return for a new invention. Similarly, the lead producer
(particularly if he’s small) welcomes the lead users. So, innovation occurs in a small
zone where lead producers and lead consumers meet.
1.7 Types of Innovation
Innovation can classified in various ways depending upon the domain, context, and
purpose with which it is being studied. In the Exhibit 1.3 are summarized the two major
typological classification of innovation given by Torrance (1979), and Schumpeter (1934)4.
Exhibit 1.3 Types of innovation
Torrance5 (1979) Schumpeter (1934)
1. Fluency
It refers to the ability to produce a large number of
ideas or alternative solutions to a problem
1. Product Innovation
It refers to the introduction of
new product
2. Flexibility
It refers to the ability to see things from different
points of view; the ability to use many different
approaches or strategies while solving a problem
2. Process Innovation
It refers to the introduction of a
new method of production
3. Elaboration
It refers to the ability to enhance ideas or products
by providing more details or elaboration
3. Market Innovation
It involves finding a new
market
4 These typologies have been presented only in a tabulated manner and not elaborated in detail as they have
no direct bearing on the methodological aspects and the variables studied in the current research. 5 These typologies are essentially the types of creativity, and for practical purpose they have been
considered as the corresponding innovative typologies.
16
4. Originality
It refers to the ability to produce ideas that are
unique or unusual. Origination leads to inventions.
4. Input Innovation
It includes finding a new source
of supply
5. Organizational Innovation
It refers to the internal &
external changes in
organizations, including
mergers and acquisitions
1.8 Conclusion
Innovation is increasingly gaining saliency in the research and practice in the area of
management and organizational behaviour as it is being considered an important tool
of achieving leadership and excellence in a desired domain of corporate activity.
The innovation process emerges from a social instinct to excel and organize a messy
world championed by generally a small group of avant-garde people called skunks.
Initially innovation starts in a small zone where lead producers and lead customers
interact, and in some time (generally within a decade) it changes the way perceived
interactions occur in a domain or system for which it was originally intended. Thus,
it may act like a heuristic for achieving excellence as compared to other alternatives.
Traditional theories of leadership do not make explicit references to innovation as
a dimension of leadership and performance but the highly dynamic environment
of 21st
century business has necessitated not only the acknowledgement but also the
operationalization of innovation as a basic ingredient of new age leadership,
entrepreneurship and business excellence.
Chapter 2 Review of Literature
19
CHAPTER 2
REVIEW OF LITERATURE
The current research work is set in the context of Indian business organizations. This
chapter offers a review of various research works relevant to the current research topic.
Since, the research is aimed in the context of business organizations so general researches
and insights on relevant variables like innovation, heuristic and excellence have been
avoided and only research studies germane to research topic and domain under focus
have been presented in the following sections.
2.1 Innovation in Indian context: The case of Indovation1
According to Vedic Indian perspective the essential human nature is divine or Sat-
Chit- Ananda (The Existence Absolute- The Knowledge Absolute- The Bliss Absolute)
which subsumes in itself a continuous process of creation, maintenance and
destruction. This ancient view has further been elaborated in modern sense by Prof.
Vijay Govindarajan in his ‘Three Box Model of Strategic Thinking’ which he has
developed to facilitate strategic thinking in organizations. According to this model the
central task of an organization’s leaders is to balance managing the present with
creating the future (Govindarajan, 2006) at the same time while selectively
abandoning/destroying the past. Corresponding to this he has proposed three thinking
boxes wherein Box 1 thinking refers to managing the present while Box 2 and Box 3
relate to creation of future by adopting an innovative approach at all levels, see the
figure 2.1 on next page.
1 Indovation is a word coined by G. Katragadda (2009) which stands for Indian
Innovation.
20
According to Govindarajan (2006) many organizations restrict their strategic thinking to
Box 1. This tendency has been particularly acute in the past two to three years, as most
leaders have emphasized reducing costs and improving margins in their current
businesses. But strategy cannot be just about what an organization needs to do to secure
profits for the next year. Strategy must encompass Box 2 and Box 3. It must be about
what a company needs to do to sustain leadership for the next ten years. In fact, the
central task of an organization’s leaders is to balance managing the present with creating
the future.
Figure 2.1 The three box model of innovation (Govindarajan & Trimble, 2011)
Reprinted by permission of Prof. Vijay Govindrajan.
From The CEO's role in business model reinvention by Vijay Govindrajan, and Cristopher Trimble ,
Harvard Business Review, 9(1-2):108-14, Copyright © 2011 by Authors; all rights reserved.
21
According to him the examples of successful Box 2 and Box 3 initiatives are Dell™
computer’s direct model in the PC industry, Wal-Mart’s transformation of the
discount retailing industry, Apple’s introduction of iPod, etc. Further Govindarajan
(2006) has metaphorically used his Three Boxes as corresponding to three main Hindu
deities (the “Hindu Trimurti”): Vishnu, the god of preservation; Shiva, the god of
destruction; and Brahma, the god of creation. For Govindarajan (2006) the
correspondence between the three boxes and the three Hindu gods is clear.
Vishnu/Box 1 = preserving or managing the present; Shiva/Box 2 = destroying
or selectively abandoning the past; and Brahma/Box 3 = creating the future. He
further maintains that according to Hindu philosophy, creation-preservation-
destruction is a continuous cycle without a beginning or an end. The three gods play an
equally important role in creating and maintaining all forms of life.
Another major concept relevant for innovation in Indian context is the idea of Reverse
Innovation (Govindrajan & Trimble, 2012). Reverse Innovation indicates a recent trend
in business innovation by which multinationals and corporations first develop and test
their products in emerging/developing markets (like India) and then distribute/market
these products in developed worlds. The process is slightly counterintuitive and reverse
of earlier trend wherein innovative products were first conceptualized, designed,
developed, tested and marketed in and for developed world supposedly because of their
scientific advancement that fuelled innovation coupled with a rich consumer base that can
afford and experiment with these innovative products. So, traditionally the typical
innovation originated in developed world and then trickled down to developing world
gradually but the process of reverse innovation is opposite to this trend. The example of
reverse innovation are Tata NanoTM
cars which were primarily designed for pocket
constrained consumers of India but now Tata Motors plans to launch it in Europe and
American markets. Other examples of reverse innovation include Gatorade™ drink
popular among western sportsmen and body builders whose development was inspired by
a local carbohydrate drink given to patients of cholera in Bangladesh, Chicken tikka
22
masala became the number one favorite food in UK in the 1990s, commercialization of
Yoga in western world, AlivaTM
snacks which was initially produced to satisfy the Indian
consumers, Deere’s small 35 horsepower tractor initially developed to cater special
needs of Indian farmers who use tractors not only for cultivation but also for
transportation and commutation purpose as well, etc. The process of reverse innovation is
new which became significantly perceptible post-2008 financial crisis which significantly
reduced the growth in the western world and compelled it to explore innovative options
in emerging markets. According to idea of reverse innovation, for new age corporations
the future is far from home.
According to Chakraborty (1998) the fundamental principal of creation is gifting. He
cites the example of one India’s foremost creative genius Ravindranath Tagore. For
Tagore, creativity meant gifting, and gifting (or giving) is freedom and bliss, and such
freedom is the basis of ethics and growth. He is of the view that the entire divine plan
of the universe rests on gifting out of joy; the sun, the air, the water, the tree are all
gifts of joy from the creator for the created. Chakraborty differentiates between ego
and self and is of the view that the ‘ego grabs, the self gives’. So it is the True Self
(Sat-Chit-Ananda) whose nature is bliss and giving that essentially lies underneath
all creations and creative processes. Chakrakraborty’s ideas have parallels with the
Govindarajan’s ideas presented above as ego can be equated with the ‘Box 1’
(possession, profit= manage the present) and self can be equated jointly with ‘Box 2’
(renunciation= selectively abandoning the past) and ‘Box 3’ (giving= creating the
future).
Another important model of innovation discussing the nature of innovation in Indian
context is the ‘invisible model of innovation’ (Kumar & Puranam, 2012), according to
which although there is substantial amount of innovation coming from India but most of
it remain invisible to end users of
23
Figure 2.2 The model of invisible innovation (Kumar & Puranam, 2012)
Reprinted by permission of Harvard Business School Press.
From India Inside: The Emerging Innovation Challenge to the West by Nirmalya Kumar and Phanish
Puranam. Boston, MA, p.9.; Copyright © 2012 by the Harvard Business School Publishing Corporation; all
rights reserved.
customers around the world. According to the model, there are four major types of
innovation coming from India: one, globally segmented innovation mainly led by major
MNCs that have set of innovation and R & D centers in India; two, outsourcing
innovation by major Indian companies (working especially in the technology sector)
offered as a innovation on demand to support the new product development for the
consumers of the developed countries; three, process innovation coming from an
injection of intelligence where highly qualified staff doing routine jobs has invented
newer and better processes of completing the task; four, management innovation of the
global delivery model by reintegrating the globally distributed work coming from
different geographies and cultures in a innovative way.
Mohanty (2006) is of the view that with the advent of liberalization, privatization, and
globalization of economies, innovative organizations are emerging in India. He
Visible Innovation
Invisible Innovation
New products and services for
the end user
1. Globally segmented innovation:
Made in India, branded elsewhere
2. Outsourcing Innovation:
R & D on demand
3. Processes Innovation:
An injection of intelligence
4. Management Innovation:
The global service delivery model
24
has identified following six generic forces that have stimulated the emergence of
innovative organizations in India. These forces appears to have been instrumental in
adding a heuristic value to innovation viz-a-viz other means of achieving organizational
excellence.
Customer Power; the rising customer power demands a multi-
dimensional solution and knowledge based products. An innovative company
understands the context of customer power and envisions the space of
supplier -customer relationship.
Information Power; the information power enables the promotion of
knowledge networking, increases the speed of decision making, eliminates
bureaucracy, and strives to delight the customers.
Global Investor Power; due to accessibility to global investment and
development portfolio now organizations are able to invest in total
development by initiating global search for all resources including innovation.
Power of The Marketplace; the 21st
century open marketplace compels
an organization to understand real-time strategic changes and learn faster ways
for making quick innovations and acquisitions of competitive assets so as to
maximize value for the all stakeholders.
Power of Simplicity; this refers to the streamlining of systems and
procedures within the organizations and moving away from ritualistic
culture to an empowering and autonomous structure which
promotes innovation and excellence.
Power of the Organization; Organizational power rests in the capabilities of
an organization to quickly transform market opportunities into tangible
bottom-line results. This force leads to creation of smart and agile
structures and totally productive high-performance action teams (or innovative
teams).
25
Raghvan (2006), former executive VP, Ingersoll-Rand (India) Ltd., Banglore, while
commenting on how should the leadership/management of a MNC conceptualize and
implement their strategy, especially in respect to India, says that the India strategy of
a MNC has to be treated like a raga and has to be implemented in a disciplined way.
He sees a lot of similarity between strategy and the classical Indian music system
called raga. Indian classical music has a raga, which is a disciplined central theme for
a tune. Although in the middle of the raga one can improvise the tune, the musician
has to always ensure that he comes back to the main theme. He further says that
when he implemented global strategies for large MNCs in India, he ensured that the
main strategy or the tune or raga is definitely preserved but provided a lot of room for
improvisations to be introduced. The key to this approach is that the latitude
within which these improvisations can be allowed must be fixed. This seems to be an
important observation but Mr. Raghvan has not elaborated much on what constitutes
the centrality of this central theme, and its feasibility and broad implications while
dealing with a multi-cultural and multi-ethnic workforce like India. Also the latitude of
improvisations allowing the space for creativity and innovation has not been clearly
specified.
Prakash (2003) has offered a review of indigenous literature on understanding the
organizational behaviour in India. According to him experimentation and innovation
with the organizational system is necessary for growth and development of
organizations. He offers a model which can accommodate the elements of growth
from within as well as assimilation from the external influences. According to him this
model has “potential to creatively orchestrate the seemingly continuous as well as not
so continuous aspect of social reality” (p.1).
Chadha (1989) has offered a survey of definitions of creativity and discussed the
differences between creative/ innovative and conforming minds like unstructured,
unconventional, multidisciplinary thinking, free floating mind, desire to enter
uncertain and high risk arena, not appealing to common sense, etc. Many of these
26
attributes are shared by the Pioneering-Innovative leaders conceptualized by Khandwalla
(1985, 1987).
2.2 The Current Scenario
Doing Business Report published by World Bank (2012) has put India on 139th
rank on
the index of ‘ease of doing business’ (Total countries = 182, Singapore = 1, USA = 4).
The report cites that progressive elimination of ‘licence raj’ led to the 6 % increase in
the registration of new firms, with highly innovative and productive firms entering and
gaining the market while older unproductive firms either reinventing or quitting. This
makes clear that the stage for Schumpeterian type of innovation through the process of
creative destruction has started (the National Knowledge Commission Report on
Innovation In India, 2007, also corroborates this fact).
On Global Innovation Index, developed by INSEAD & WIPO (2012), India Ranks 62
with a score of 34.52 (Switzerland = 1, Score = 63.82). According to the report the major
areas of strength which are driving innovation in India are (numbers in parenthesis
indicate rank in respective areas) computer and communications service exports (4),
creative goods exports (9), gross capital formation (9), total value of stocks trade (13),
market capitalization1(19), legal rights strength to get credit (19) , growth rate of GDP
per person engaged (21), daily newspapers circulation (22), intensity of local
competition (27), and creative services exports (29).
According to Entrepreneurship in India Report (2008) published by National Knowledge
Commission “innovation has emerged as one of the drivers of India’s economic growth,
and is a factor in increasing competitiveness, profitability and market share as well as
reduced costs. ..The ‘Innovation Intensity’ (i.e. the percentage of revenue derived from
products or services which are less than three years old) has increased for large firms as
well as SMEs in India. The strategic prioritization of innovation has also intensified since
27
economic liberalization. Moreover, an interesting finding is that SMEs register a greater
increase in ‘Innovation Intensity’ than large firms. This could also indicate that smaller,
decentralized, creative and experimentation-oriented organizations could be the torch-
bearers of large-scale ‘disruptive innovation’ in the country”(p. 53).
According to the Innovation in India Report published by National Knowledge
Commission (2007) , in the growth of the Indian economy, Innovation is emerging as a
key driver, although this may neither be apparent nor readily visible. According to the
report :
• 17% of the large firms rank Innovation as the top strategic priority and 75% rank
it among the top 3 priorities.
• All the large firms agree (of which 81% strongly agree) that Innovation has
gained importance as being critical to growth and competitiveness since the start
of economic liberalization in India.
• All the large firms agree (of which nearly half strongly agree) that they cannot
survive and grow without investment in Innovation.
• An overwhelming 96% of large firms see Innovation spending increasing over
the next 3-5 years.
According to Govindarajan (2009) India has a very long history of over 5,000 years but
its economic history is rather short. India's economic history can be divided into three
phases: Prior to 1990, from 1990-2008, and 2008 and beyond. Prior to 1990, Indian
corporations were not intended to be efficient as if one could produce under the licence
raj, customers were lining up to buy. Firms were able to pass on their
inefficiencies to consumers. Post liberalisation, Indian corporates registered impressive
growth primarily by becoming more efficient, cutting unnecessary costs, reverse
engineering business models invented in the West, and benefiting from cost arbitrage.
This "efficiency" based game is now over. Going forward, innovation will be the key
to unlocking growth in India. Solving India's many problems - energy, health, water,
28
and education -would require fundamental business model innovation.
On the conclusion of Marico Foundation Innovation for India Awards, 2008, the
Mumbai Bureau of Economic Times (March 2008) notes that currently in India
innovation isn’t only the domain of high brow MNC’s but an idea that had
percolated down to smaller Indian companies and social sector. The motivation
behind groundbreaking innovation was often simply to make maximum social impact-
no wonder, over a 100 entries out of total 205 were about social impact. Tata’s Nano
car, to which Vijay Govindarajan (2009) has referred as a ‘social Innovation’ & R.
A. Mashelkar (2008) considered it a kind of ‘Gandhian engineering’, is a case in
point. The experts agree that if India has to make a mark at the global stage, innovation
has to be fostered at both business and social level. According to Dr. R. A. Mashelkar,
Director General of CSIR to grow we have to make innovation the way of life, the
behaviour definer, the soul, the society of this great nation. He further envisions that
Indians have to harness the dream of making India a laboratory for global innovation.
2.3 Innovation as a heuristic to excellence: A review of past literature
It has always been desirable, and imperative as well, for leadership and organizations
to strive for and achieve excellence in the respective area of their activity. In fact, in
the increasingly hypercompetitive business environment the organizational excellence
with all its primary and secondary manifestations is becoming basic minimum for
survival. Moid Siddiqui (2005) cites the research survey to show that only few
corporations live as long as the half of human life. Ethics, innovation, excellence, and
such other metaphors of the ilk are no longer the optional. They are the life force of
modern corporations. This is because innovation and its proper management can
contribute to the organizational excellence in the hyper competitive environment;
it can also increase a societies’ competitive position vis-à-vis other societies; and
it can contribute tremendously to improve the quality of life (Khandwalla, 2003).
But Kim & Mauborgne (2005) see beyond the competitive value of innovation. For
29
them innovation has a meta-competitive value as it can act like a tool through which
corporate can create new uncontested markets (‘the Blue Ocean’) rather the
competing or fighting for the existing, limited, markets (‘the Red Ocean’). So,
innovation acts like a heuristic to excellence in modern business context sometimes
acting as a tool to deal competition, sometimes by creating new completion and
sometimes making competition irrelevant by developing uncontested markets.
Carland & Carland (2009) consider innovation as the soul of entrepreneurship, an engine
of economic growth. The authors have studied and applied the Schumpeterian model
of innovation and entrepreneurship in two broad organizational fields, i.e., (Danish)
music and sports industry, and find that Schumpeterian model satisfactorily explain the
evolution of these industries. The innovation and the process of creative destruction is
linked with profits and good performance of businesses. In music industry, innovation is
routine and part of life, to be alive and kicking one has to innovate continuously.
However, in case of sports (football) industry it was ‘creative reconstruction’ rather than
Schumpeterian ‘creative destruction’ which led to the emergence of better performing
football clubs.
Mc Craw (2007) , in his book ‘Prophet of Innovation: Joseph Schumpeter and Creative
Destruction’, has highlighted the pioneering ideas of great economist Joseph Schumpeter
regarding innovation and its role in economic growth. According to Schumpeter (1934)
individual entrepreneurship holds the key to economic growth of any country. Initially
Schumpeter considered small firms to be more inventive (1909), however, later he
revised his position and maintained that innovation is not an option especially for firms
operating in a capitalist economy. Edwards and Gordon (1984) reported that small
businesses produced 2.4 times the innovations of their larger cousins and the pre-
eminence of small firms in innovation is still evident in a 2005 study conducted by
Baumol (2005). These trends also appear true for Indian businesses.
30
Khandwalla (2006) in his study suggests that to achieve corporate excellence becoming
much more innovative should be the high priority of business management and
leadership, especially for the Third world enterprises. He has discussed 16
management tools with real life application that can enable an enterprise to leapfrog to
a much higher plateau of innovativeness. They are: creativity training, innovation
training, creativity thinking networks, creative scenario building, creative surveys,
creative experiments, creative benchmarking, reverse brainstorming, exnovation,
multiplication of change agents, kaizen, creative overload, data mining, stakeholders
councils, intrapreneurship, and parallel groups. These tools deliver a number of value
propositions and facilitate an innovative mindset in the organization, a ‘stretch’
vision of future that can spur innovation, vital intelligence that stimulates
innovation, dumping of obsolete activities that creates the space for changes and
innovations, widespread changes and innovation throughout the organization,
continuous improvements and innovations, high potential new innovation leads, and
‘breakthrough’ innovations. He has also suggested that organization design that
facilitate innovations and management tools that help an enterprise generate a
continuing stream of successful innovations need to be incorporated in the core
management curriculums as it is extremely essential for achieving competitive-
edge and excellence.
Napier, Leonard and Sendler (2006) have found that leaders/ managers in global firms
are increasingly learning that creativity in management and marketing can be
widespread, both within and outside their firms. While focus has been on improving
technology and cost control, progressively more firms are looking to creativity and
innovation as ways to improve organizational performance and achieve excellence.
Understanding where the pockets of creativity are locked and what the strengths are
(and where weaknesses may lie) is an important baseline. These researchers
further maintain that to generate innovation, the leadership and management of a
company needs to be mindful and deliberate about establishing a culture and
31
programs that will encourage it. For example, 3M companies have long had a program
of ‘Genius Grants’ providing resource on a competitive basis- to scientists who wish
to pursue new ideas. More than 60 company scientists apply annually for some
$50,000 - $100,000 to pursue ideas that are outside of normal company projects, as
seed money for promising ideas.
Kim & Mauborgne (2005) in their book ‘Blue Ocean Strategy: How to Create
Uncontested Market Space and Make the Competition Irrelevant ’ raise a
question that why should companies waste time ‘breaking the competition’ when they
can ‘break away’ from the competition? In other words, why should leaders deplete
their attention span in an endless analysis and the tracking of the ‘competition’ when
can they choose the path of innovation instead? Kim and Mauborgne begin with an
elementary differentiation between the ‘red ocean’ and the ‘blue ocean’. The former
comprises ‘all the industries in existence today’, while the latter represents ‘all the
industries not in existence today’. The intensity of competition turns the market space
of the former into red, bloody oceans. The authors argue that it is time to move away
from the red waters of saturated markets in order to ‘create uncontested market space’
in the blue oceans of innovation since only innovation can actually ‘make the
competition irrelevant’ and lead to excellence.
While the red ocean of competition will not go away, the primary objective of the
authors is to set out a systematic strategy to make blue oceans possible since
innovations are not just ‘creativity’ or so called ‘value innovations’, but the ability to
‘align innovations with utility, price and cost positions.’ The creation of blue oceans
through the process of innovation, however, is extremely demanding. The authors set
out a framework comprising the different aspects of innovation so that practicing
managers can go about the task of value innovation in a systematic manner. The
proposition of Kim and Mauborgne couldn’t have been more timely, and taking a
lead from their research the present research will attempt to devise a way and an
32
example of how ‘the blue oceans’ can be created in a particular cultural context
by understanding the heuristic value of innovation in pursuit of organizational
excellence.
Miles et al. (2005) in their study found that the strategy of the most of successful
leaders rest on three basic principles: investing in people, supporting a
collaborative entrepreneurial culture, and finding and growing new markets around the
world through continuous innovation. The researchers cite the economist Joseph
Schumpeter (around 70 years ago) who first advanced the argument that innovation
is the primary driver of economic development. The value of ‘creative destruction’, as
Schumpeter describes the innovation process, has been confirmed recently by the
William Baumol, whose book ‘The Free Market Innovation Machine’ demonstrated
empirically that the firm and inter-firm ability to innovate explains why the
capitalist economies historically have the strongest growth. However, despite its
usefulness to firms innovation is not an easy task. Indeed, researchers further
maintain that, one survey found that CEOs believe that their firm utilizes only 15-25 %
of their innovation capacity.
Michael Dell (1999), founder and CEO of Dell Computer Corp., is of the view that
innovation and commitment are what takes any good company and make it great, and
men and women at Dell Computer Corporation continues to prove this. He attributes
the success of Dell Computer Corporation to a work culture that despises the status
quo. “We precondition our people to look for the breakthrough ideas, so that when
they are confronted with the big strategic challenges, they can rise to the occasion and
come up with the best solution- fast” (p.126). To teach people to be more innovative
two specific strategies adopted are: one, asking questions-which involves
approaching a problem, a response or an opportunity from a different perspective.
“By questioning all the aspects of our business, we continually inject improvement and
innovation into our culture” (p. 125). The second approach adopted is looking a
33
problem in a holistic sense. Dell Computer Corporation seems to be one of the most
glaring and most concrete example of achieving excellence through innovation and
customer service in the recent time especially in the 1990s. These two methods of
teaching innovation at Dell™ have an interesting parallel in Indian tradition:
asking questions and looking things in a holistic manner. So, such methodologies of
teaching/enhancing innovation can be easily implemented in Indian setting, and
important works by scholars like Khandwalla (2006) can be of great relevance in this
context.
Khandwalla (1992) considers creative excellence a major type of organizational
excellence among the 6 types of organizational excellence (Competitive,
Rejuvenatory, Institutionalized, Creative, Missionary, and Versatile) outlined by him.
The chief trait of creative organizational excellence is the commitment to
pioneering, innovation, experimentation, discovery and dynamic change. Such
organizations are in a constant state of flux, shedding or modifying current
activities, practices, and products and adopting new ones. A culture of creativity and
innovation prevails in such organisations sometimes coupled with the desire to dazzle
the world with breathtaking ideas.
Khandwalla (1983) in his study of 75 organizations, chiefly corporations, has sought
to identify the strength of causal relationship between a mode of management he
labelled as Pioneering – Innovative (PI) and four different dimensions of task
environment. His findings have indicated that while a scopeful environment may have
a stronger positive causal impact on the PI mode than vice versa, the PI mode has
much stronger impact on environmental complexity than vice versa. Thus
entrepreneurial type of management may be more suitable in turbulent, threatening
environment than in a complex environment, and professional management may be
more suitable in a complex environment than in a turbulent environment. He further
maintains that the rapid socio-economic changes (the Great Indian Renaissance of
34
1990s) generally imply both growing environmental complexity and turbulence,
especially for the societies’ larger organizations, fusion of entrepreneurial and
professional mode of management may be needed for most Indian public enterprises,
many large private sector enterprises, and other large development oriented
institutions. Given the importance of these two styles of management, it may
be useful for organizational psychologists to expand their conception of leadership
beyond those of structure and task orientation, consideration and nurturance,
participation, etc. to the leader’s commitment to risk taking, innovation,
professionalism, and operating flexibility. For example Khandwalla (1976-77), in his
study of 103 Canadian companies, found a significant association between the use of
the risk-taking style of management and the growth rate of the organization. He found
a similar association in his study of the PI mode of management in Indian organizations
(Khandwalla 1985)
2.4 Chapter Summary
As it may be easily deciphered from the review of above studies there has been an
increasing and wide acceptance of the role of innovation in achieving a
speedier competitive edge and excellence in present day work organizational
context. However the perception of innovation vis-à-vis its role in achieving corporate
excellence seems to be less explored. Also, competitiveness (“heuristic value”) of
innovation vis-à-vis other methods of excellence has not been explored. The present
study honestly attempts to fill this gap regarding the researches on innovation and
excellence. Important corollaries of these two contributions may further develop during
the course of current and future research work.
Chapter 3 Methodology
37
CHAPTER 3
METHODOLOGY
This chapter discusses the major methodological aspects related the current research
work. The chapter begins with an overview of the research methodology and then
discusses the research design, variables involved in the study, and the nature of sample
studied. After this, a discussion follows on the instruments used in the study along with
their development and organization in the form of final questionnaire.
3.1 Research Design
The current research follows a quantitative research methodology based on the principles
of positivistic paradigm of scientific research. The study adopts an objective approach for
studying the variables of interest. However, the some variables studied in the research
have been of latent type which have been computed with the help of two questionnaire.
The subjects were asked to self-report their beliefs and opinions on the items of the
questionnaire and the obtained data was later analyzed to meet the stated objectives of
research. Li (2006) has cited Neuman (1997) and Rundle-Thiele (2005), according to
whom, self administered questionnaire surveys can be deemed appropriate for
measuring self-reported beliefs and behaviours. The constructs of the study, for e.g.,
innovation-as-a-heuristic and business excellence have been measured based on the
belief of the sample related to these and related constructs by using self-report measures,
which satisfactorily capture them (Schmitt, 1994; Spector, 1994), developed by using
standardized procedure.
Further, the study follows a correlational research design which attempts to explore the
nature of relationship between innovation-as-a-heuristic variable and organizational
excellence. Correlational research design have been regarded as a major and widely used
38
research design in scientific research (Isaac and Michael, 1977; Fraenkel and Wallen,
1990). It is especially useful when the researcher is interested in finding the relationship
between two variables as this design helps in assessing the degree and direction
relationship between two variables. Further, structural equation modelling was carried out
to test the causal relationship among variables (Rippy, 2001).
3.2 Variables measured in the research
The two major types of variables studied in the current research were innovation-as-a
heuristic variable and business excellence. A casual relation was hypothesized between
the two variables where innovation-as-a-heuristic was conceptualized as independent
variable and organizational excellence as the dependent variable. At the beginning, it was
hypothesized that a factor analysis of the innovation-as-a heuristic variable will give n
factors whose effect over dependent variable i.e. business excellence will be explored
through structural equation modelling along with studying mediation effect if any (fig.
3.1). Later, after a factor analysis the final hypothesized model was as shown in figure
3.2:
Fig 3.1: Initially conceptualized model (before Principal Component
Analysis)
Business
Excellence
Mediation
Influence
Factor 1
Factor 2
Factor 3
Factor n
39
The major objective of the present research was to explore the structural relationship
between innovation heuristic and organizational excellence. The following kind of
structural relationship was hypothesized (see figure 3.2) between variables which was
later tested through structural equation modelling. In the final proposed model a linear
causative relationship was hypothesized from the two identified heuristics that emerged
after factor analysis, i.e., search & adapt heuristic, and fast & frugal heuristic, to heuristic
intelligence variable. Again, a linear causative path was hypothesized from heuristic
intelligence variable to the dependent variable, i.e. business excellence. In short, the
effects of ‘search & adapt heuristic’ and ‘fast & frugal heuristic’ over business excellence
was hypothesized to be mediated by ‘heuristic intelligence’ variable. The direct effects
‘search & adapt heuristic’ and ‘fast & frugal heuristic’ over business excellence were
also studied using mediation analysis and the full mediation model was preferred over the
partial mediation model1. The final accepted model is shown in figure 3.2 below:
Figure 3.2 The hypothesized structural relationship among variables (after Principal
Component Analysis)
1 For more explanation on it see discussion ‘3.2.1.4 Heuristic Intelligence (HI’ in this
chapter.
Business
Excellence
Fast & Frugal
Heuristic
Heuristic
Intelligence
Search & Adapt
Heuristic
40
3.2.1 Description of the variables
A brief description of all the variables studied in the current research is given below:
3.2.1.1 Innovation-as-a-heuristic (IAH):
The current study aimed studying innovation as a rule of thumb guiding managerial
decisions and seeing its impact on the organizational excellence. There are large body of
researches that suggest that in the wake of new economic and technological changes in
an uncertain world the use of heuristics and intuitions offers better decision outcomes
especially as compared to rational economic models (e.g., Agor, 1984; Goodman, 1993;
Tomer, 1996; Kuo, 1998, Eisenhardt 1999; Gigerenzer, 2000; Gigerenzer, 2002;
Sinclair, 2005). Further, there are scholars who have related intuitions/ heuristics with
innovation (Hogarth, 2001; Officer, 2005; Kaufman & Sternberg, 2010). The present
study attempts to study innovation-as-a-heuristic which is defined as using innovation as
a fast and frugal intuitive decision strategy (or a heuristic) for achieving organizational
excellence. To measure innovation-as-a-heuristic a questionnaire was prepared based on
Manimala (1992) and Gigerenzer (2000, 2002) which was later psychometrically
analyzed to develop a standardized scale. The details of the process of development of
questionnaire can be seen in the measurement tools section of this chapter. Further the
obtained data was factor analyzed and it was found that, after the factor analysis (and
deletion of 3 items) of the innovation-as-a- heuristic questionnaire, two broad factors
emerge from the factor analysis of the innovation as a heuristic scale i.e. innovation-as-a
search-and-adapt heuristic and innovation-as-a-fast-and-frugal heuristic. A sum of scores
on innovation-as-a-search-and-adapt heuristic and innovation-as-a-fast-and-frugal
heuristic was termed as heuristic intelligence of the manager/entrepreneur.
3.2.1.2 Search & Adapt Heuristic (SAH)
Search and adapt heuristic refers to the simple intuitive decision strategies (or rules of
thumb) that guide managers/entrepreneurs in searching the new inputs , information and
opportunities required for the successful growth and adaptation of their organizations.
41
The items measuring search-and-adapt heuristic are Item 1 to Item 17 in the innovation-
as-a-heuristic questionnaire attached in the Appendix A. All of these items have been
taken from the 19 innovation related heuristics given by Manimala (1992), two of which
were deleted after reliability analysis and factor analysis of the questionnaire. The
heuristic search and adaptation is of utmost value to the managers and entrepreneurs as
due to availability of vast amount of information they may experience an information
overload or, advertently or inadvertently, may end up using wrong information. It’s a
management truism that information is life blood of organization. It’s not information
per se but the ecologically rational use of right amount of information at right time that is
more important for decision makers, otherwise it is the wrong infusion of the same
lifeblood that kills organizations, says Bill Gates (Gates & Hemingway, 1999) in his best
seller Business@speed of thought. So, the seekers of the information must have an
intuitive stopping rule (Gigerenzer, 2000, 2002 ) which offer them a rule of thumb to
gives them an intuitive feeling that, in the given ecology, they have reached to the point
of right information and, that, they should stop their search an take action now. This
stopping rule is a characteristic of fast and frugal heuristics as by being fast and frugal
they keep a limit over the extent to which one can devote time and resources for
searching the new information or input. Researches show that greater amount of time
spent do not necessarily bring qualitative better or more profitable decisions (Gladwell,
2005). The name search and adapt heuristic was preferred as the factor structure of this
factor was heavily loaded with items emphasizing search for new and innovative ideas
and inputs and using them for better adaptation in one’s environment and ecology or the
creation of new excellence niche.
3.2.1.3 Fast & Frugal Heuristic (FFH)
The study of innovation as a fast and frugal heuristic is based on the idea of fast and
frugal heuristic as espoused by Prof. Gerd Gigerenzer and his research team at Max
Planck Institute for Human Development, Berlin, Germany. According to them fast and
frugal heuristics refer to simple, task-specific decision strategies that are part of a
42
decision maker’s repertoire of cognitive strategies for solving judgment and decision
tasks (Gigerenzer, Todd, & the ABC Research Group, 1999). Fast and frugal heuristics
can be important for decision makers especially when deciding about the new, hitherto
unencountered problems. For example, Reimer and Rieskamp (2007) assert that in many
environments fast and frugal heuristics can perform astonishingly well, in particular when
making predictions for new cases that have not been encountered before.
Taking a cue from this idea for the purpose of current research, innovation is
conceptualized as a fast and frugal heuristic which is defined as the perceived ability of
innovation as a simple, task specific intuitive decision strategy for achieving business
excellence in comparatively faster and efficient manner. Managers and entrepreneurs are
constantly confronted with the task of taking their enterprise at new heights amidst
growing competition, uncertainty and shrinking resources. In such situations innovation
acts as a fast and frugal way to achieve excellence by creating nonlinear or disruptive
outcomes (Schumpeter, 1934) in terms of new markets, new products or an improved
method of production. In comparison to this, the traditional means of increasing profit
and other barometers of organizational performance like investing more on
advertisement, leadership, or more and more practice and learning ( Ericsson, 1996), etc.,
are comparatively costly, slow and often inefficient. Internet’s biggest and worlds most
innovative company Google™ didn’t spent a penny in advertising itself (Vise &
Malseed, 2005) though later it did so for its browser Chrome™ during the era of great
browser war with Microsoft™ and Mozilla Firefox™
, but only to the extent of informing
its potential customers. A cursory glance at world’s best companies today like Google™
,
Apple™
, Facebook™
, Intel™
, Microsoft™
, Dell™
, etc. gives an impression that excellence
is increasingly becoming synonymous with innovative capability of firms. All these firms
have developed a choice architecture (Thaler & Sunstein, 2008; Thaler, Sunstein, &
Balz, 2010) that nudges them to use innovation to achieve excellence in fast and frugal
way.
43
3.2.1.4 Heuristic Intelligence (HI)
The variable heuristic intelligence is conceptualized as a summated measure of search &
adapt heuristic, and fast & frugal heuristic. It is named as intelligence because of two
reasons:
1) one, its contributory variables, i.e., search & adapt heuristic, and fast & frugal
heuristic, have been defined and measured as the ability of
managers/entrepreneurs to perceive and use innovation as a mechanism of
adaptation and growth in ones environment in a fast and frugal way. Thus, the
sum of two abilities can be again conceptualized as a type of ability.
2) Secondly, the variable, i.e., heuristic intelligence, itself has been conceptualized
as a type of ability. The heuristic intelligence has been defined as the intuitive
ability of managers to use those new information and inputs (from a large number
of available information and inputs) which are ecologically rational so that they
help in growth and adaptation in a specified task domain in a fast and frugal way.
The choice of these information and inputs are subject to performance outcomes
in a highly dynamic and uncertain environment so managers need to show the
ability of selecting and implement the best alternative first, or so, as compared to
his elements of his competitive sample. Alternatively, heuristic intelligence can be
defined as the ability of managers to use of innovation as a fast and frugal thumb-
rule in guiding their decisions to achieve organizational excellence.
One thing that needs to clarified here is why the preference was given to a joint effect of
search & adapt heuristic, and fast & frugal heuristic on business excellence rather than
relating then individually to the dependent variable i.e. business excellence? It was done
partly on the basis of the conceptual understanding of variables and partly on the basis of
guidance received from the data while doing the SEM analysis of the data. At conceptual
level, a joint effect of search & adapt heuristic, and fast & frugal heuristic is more
important as compared to their individual effects. Only search for innovative strategies is
44
not suffice. They all must be fast and frugal as well as ecologically rational at the same
time. There is no dearth of new things or innovative strategies while designing a product,
plan for a new business process, or some alternative form of innovation. What is
important is that they must be adaptive and efficient i.e. fast and frugal. And the ability of
a manager doesn’t lie only in choosing a thumb-rule which is new but adaptive and
efficient as well at the same time. Later, while doing the SEM analysis it was found that
both the heuristics (search & adapt heuristic, and fast & frugal heuristic) have a
significant covariance (111.812, p < .001) and do not fit the alternative models which
tried to explore their direct impact on business excellence. So, this means these heuristics
are intimately interlinked with each other and studying their joint effect will be more
fruitful as compared to seeing their effect in isolation.
3.2.1.5 Business Excellence
Business excellence in the context of current research has been conceptualized as it has
been discussed by Peters and Waterman (1982) and operationalized and measured by
Sharma et al. (1992). According to Peters & Waterman (1982) eight attributes of
organizational excellence are:
1) A bias for action : Excellent companies practice an active and action
oriented decision making process.
2) Close to the customer : Excellent companies are regularly in touch with
their customers, take feedback from them regularly, and build over it.
3) Autonomy and entrepreneurship : Excellent companies provide enough
personal space and autonomy which fosters creativity and innovation.
4) Productivity through people : Excellent companies see their staff as the
main source of profit who are the ultimate source of gain. World’s best
products are not created by machines but in minds.
45
5) Hands on, value driven : Excellent companies have organizational values
that guide their people, and promotes the growth oriented culture in
organizations.
6) Stick to the knitting : All excellent companies have a clear area of
expertise or core competency, and they stick to it. Even if these
companies diversify they preserve their core and stimulate the new
progress around it.
7) Simple form, lean staff : Excellent companies have simple organizational
structure with people working in small teams supported by an efficient
management.
8) Simultaneous loose-tight properties : Excellent companies have a
mechanism that strikes a right balance between central directions and
personal authority.
Sharma et al. (1992) have developed a 16 item scale based on these 8 attributes of
excellent companies with two item on each attribute. This scale has been used to measure
the business excellence variable.
3.2.1.6 Description of variables in SEM terminology
Further, structural equation modelling(SEM) has been done to see the structural
relationship among variables, so given below is a description of variables in SEM
terminology:
46
Exibit 3.1 Description of variables in SEM terminology
Types of
variable
Description Variable in dataset
Endogenous
Variable
- are those modelled as
dependent on other variables,
- They are regressed on exogenous
variables
- are receiver of arrowheads
- are the variables being predicted
Business Excellence,
Heuristic Intelligence
Exogenous
Variables
- Are modelled as independent and
influencing other variables
- sender of the arrowheads
- are the predictors (of endogenous
variables)
Search & Adapt
Heuristic,
Fast & Frugal Heuristic
Observed
Variables
(Measured
Variables)
- Are the variables that have directly
been measured
Search & Adapt
Heuristic,
Fast & Frugal Heuristic
Latent
Variables
(Inferred
Variables)
- They are not measured directly but are
inferred, defined and computed by the
researcher
Heuristic Intelligence
Residual
Variables
- residual are the difference between observed
and predicted values
r1, r2
3.3 Procedure
The objective of the research paved the way for the procedure . Once the objectives were
finalized the first task was to identify the sample and develop the measurement tools.
47
The sample (N=203) characteristics and other details of sampling procedure is discussed
in the following section in this chapter. One measurement tool, i.e. innovation-as-a-
heuristic questionnaire, was developed and its psychometric properties were established.
The second measurement tool was Excel questionnaire which was adopted from Sharma
et al. (1992). The details of these measurement tools are further discussed in this chapter
in ‘Measurement Tools’ section. Once tools were ready they were administered on
sample and data was collected. The collected data was analyzed by using SPSS 16.0 and
Amos 18.0. The obtained results are discussed in discussion chapter, and finally , the
future implications and limitations of the current study has been deliberated in the closing
sections of the thesis.
3.4 Sample
Due to limitation of time and other resources scientific researches are conducted on a
representative subset of a population under study. This representative subset of
population is known as sample. For the current research, the decision was made to
choose a sample which may help in realizing the objectives of the research . The
objective of the current research was to study innovation as a heuristic to excellence as
perceived and used by managers in Indian organizations. The sampling methodology
used was purposive sampling. Purposive sampling is a type of non-probability sampling
methodology which is characterized by the use of judgment or deliberate effort to obtain
representative samples by including presumably typical areas or group in the sample
(Kerlinger, 1973). Scholars (Babbie, 1998; Singh, 2006) have recommended the use of
purposive sampling in cases where the researcher wants to study a small subset of a large
population which he thinks includes typical or representative behaviour that he intends to
study. The aim of current research was to study innovation as a managerial heuristic so
the target population for the current research consisted of mangers working in Indian
organizational context. Of this population MBA students can be considered as a typical
and representative subset as they are qualified to be managers with work/internship
48
experiences in relevant areas. In consonance with this, 203 (Mean Age = 23.9, S.D. = 4.4)
final years MBA (Master of Business Administration) students from Delhi-NCR region,
who had done their internship, were selected.
3.4.1 Sample Size
The sample size of the current sample is 203. Li (2006) has cited a review of studies
indicating what should be the appropriate sample size for a scientific research study.
According to him “for SEM studies, a sample size of about 200 is typically considered as
adequate for small to medium structural equation models (Boomsma 1983; Loehlin 1992;
Ullman 2001). Other accepted rules of thumb include 5 cases per estimated parameter
(Bentler and Chou 1987), or 15 cases (Research Consulting 2001; Stevens 1996) per
measured variable” (p. 112). So, keeping in mind the objective of the study and number
of variables studied a sample size of 203 appears adequate, and wherever it has been
necessary, appropriate tests for measuring sampling adequacy has been computed; for
e.g., before doing principal component analysis (to identify the major factors underlying
innovation heuristic) Kaiser-Meyer-Olkin test (Kaiser, 1970, 1974) of sampling adequacy
was carried out.
The sample consisted of both males (N= 79), consisting of 38.9 % of the sample, and
females (N=124) consisting of 60.6% of the sample, as shown in the figure 3.3. Since, the
study didn’t aim to make any gender based comparisons so no attempt was made to
balance the gender ratio in the sample.
49
Figure 3.3 The gender division of sample size
3.4.2 Criteria for inclusion & exclusion in sample
The criteria of inclusion was that each member of the sample should be second year
Master level student of business administration with work/internship experience. Also,
full time managers employed in private/public limited companies were included in the
study as they also fall in the typical/representative cases. Twelve entrepreneurs working
in area of service sector were also included in the sample. MBA first year students or
second year students without internship were excluded from the study. Age and gender
were not the criteria of exclusion .
50
3.5 Measurement Tools
The two questionnaires were used in the study for measuring two constructs, i.e.,
‘innovation as a heuristic’ and organizational excellence. These tools are discussed below
along with their psychometric properties.
3.5.1. Innovation-as-a-heuristic questionnaire:
a) The major aim of current research was to measure innovation as a managerial
heuristic. Due to non-availability of any direct measure on this topic innovation
as a heuristic questionnaire was developed by Taking 19 innovation related
heuristics (out of total 186 heuristics that were being used by managers and
entrepreneurs in various business related decisions) given by Prof. Mathew J.
Manimala, IIM-Banglore (1992) after discussion with experts. These heuristics,
Manimala (1992) found that, were frequently used by Indian managers and
entrepreneurs as a rule-of-thumb guiding the management decisions involved in
the start-up and management of a new venture. According to Manimala (1992) the
“data on innovativeness and use of heuristics were collected from 138 published
undisguised cases on entrepreneurs, using the case-survey method that involved
the content analysis of these cases and quantification of the above variables. Case
data thus collected were verified against the field data collected from a
comparable group of 26 ventures” (p. 477 ). These innovation related heuristics
were arranged in a format of 7 – point Likert type rating scale where 1 denoted
‘strongly disagree’ and 7 denoted ‘strongly agree’. The list of items included in
the questionnaire are:
Exibit 3.2 List of items included in innovation-as-a-heuristic questionnaire
(based on Manimala , 1992)
1 Be a pioneer in the choice of products. Avoid highly competitive, low margin,
run of-the-mill products.
2 Ideas are the most important resource. Look for them everywhere .
51
3 Look for new (product) ideas among personal contacts (friends, hobby clubs,
professional associations, customer complaints, previous job contacts, etc.).
4 Look for new (product) ideas among technological developments abroad
especially among new, rare, or specialized products developed abroad.
5 Look for new (product) ideas among one’s own vision of the future, special
talents, and innovative research findings, or among the special skills of one’s
associates and staff.
6 Look for new (product) ideas among the components, substitutes, complements,
neglected ranges, supply gaps, deficiencies, and inadequacies of existing
products.
7 Look for new (product) ideas in others’ failures, commercialization gaps, their
half baked ideas, etc.
8 Look for new (product) ideas in the general environment (existing practices and
changes in the legal, political, religious, social, and cultural domains).
9 Be flexible in one’s ideas and plans.
10 Do not get stuck to one idea. Be prepared to leave it at the slightest indication of
failure, and develop new ideas.
11 Never be constrained by rigid plans and the narrow visions. Act according to
opportunities.
12 Treat personal problems/handicaps/ mishaps as indications to change one’s line
of thinking/occupation.
13 Never be complacent about successes, but keep on striving for excellence
through new ideas (Do not repeat success strategies until they fail).
14 Never stop searching for new ideas and opportunities.
15 Never set any geographical limits to one’s search for ideas and opportunities.
16 Introduce new products, modify existing products, and/or change strategies
periodically.
17 Keep the organization fresh and dynamic by periodically inducting young people
into it who have new ideas and the drive to implement them.
52
18 Launch new products on a trial basis, receive feedback, and slowly widen the
market.
19 Management is an art; play it by the ear. Rely on experience and intuition. Trust
one’s gut feelings more than formal analysis of data, trial runs, expert opinions,
etc.
b) 8 items were generated based on Prof. Gerd Gigerenzer’s idea of fast and frugal
heuristics (Gigerenzer, Todd, & the ABC Research Group, 1999; Gigerenzer,
2000; Gigerenzer, 2002) as it was hypothesized that innovation brings fast, frugal,
and drastic changes in performance and other competitive domains of business.
Many researchers have corroborated about the ability of innovation in bringing
non-linear, drastic or disruptive changes in business by improving performance,
beating competition, creating new markets and establishing market leadership
(Schumpeter, 1934; Kim & Mauborgne, 2005; Khandwalla, 2006). These
innovation related heuristics were arranged in a format of 7 – point Likert type
rating scale where 1 denoted ‘strongly disagree’ and 7 denoted ‘strongly agree’,
see Exibit 3.3 below:
Exibit 3.3 List of items measuring innovation as a fast and frugal heuristic
included questionnaire (based on Gigerenzer, Todd, & the ABC Research Group,
1999; Gigerenzer, 2000; Gigerenzer, 2002)
20 Innovation is the fastest way to create new market leadership.
21 Innovation is the quickest way to create an uncontested market and beat
competition.
22 The best innovative product/service in a domain is one that accomplish the domain
specific task in minimum number of steps and maximum simplicity.
23 Product/service improvisation means identifying and eliminating all unnecessary
steps in design and use.
53
24 Innovation is driving the market toward smaller but more efficient
products/services. The evolution of smart phones, tablets and nano-cars is case in
point.
25 When I make changes in my product I focus on how fast & simple it will become
for customers while adopting it.
26 I welcome all new ideas but ideas which are fast and frugal in bringing returns are
likely to be funded and supported first than an those which promise only long term
benefits.
27 A faster way to challenge and involve employees to give them time to explore new
ideas/products on their own.
c) The questionnaire was administered to the sample (N=203) and reliability analysis
was carried out whose result is shown below:
Table 3.1 Reliability analysis of innovation as a heuristic questionnaire
Item Corrected
Item-Total
Correlation
Be a pioneer in the choice of products. Avoid highly competitive, low
margin, run of-the-mill products.
.590
Ideas are the most important resource. Look for them everywhere . .737
Look for new (product) ideas among personal contacts (friends, hobby
clubs, professional associations, customer complaints, previous job
contacts, etc.).
.787
Look for new (product) ideas among technological developments
abroad especially among new, rare, or specialized products developed
abroad.
.670
Look for new (product) ideas among one’s own vision of the future, .716
54
special talents, and innovative research findings, or among the special
skills of one’s associates and staff.
Look for new (product) ideas among the components, substitutes,
complements, neglected ranges, supply gaps, deficiencies, and
inadequacies of existing products.
.705
Look for new (product) ideas in others’ failures, commercialization
gaps, their half baked ideas, etc.
.552
Look for new (product) ideas in the general environment (existing
practices and changes in the legal, political, religious, social, and
cultural domains).
.696
Be flexible in one’s ideas and plans. .777
Do not get stuck to one idea. Be prepared to leave it at the slightest
indication of failure, and develop new ideas.
.675
Never be constrained by rigid plans and the narrow visions. Act
according to opportunities.
.739
Treat personal problems/handicaps/ mishaps as indications to change
one’s line of thinking/occupation.
.561
Never be complacent about successes, but keep on striving for
excellence through new ideas (Do not repeat success strategies until
they fail).
.713
Never stop searching for new ideas and opportunities. .713
Never set any geographical limits to one’s search for ideas and
opportunities.
.629
Introduce new products, modify existing products, and/or change
strategies periodically.
.707
Keep the organization fresh and dynamic by periodically inducting
young people into it who have new ideas and the drive to implement
them.
.782
55
Launch new products on a trial basis, receive feedback, and slowly
widen the market.
.708
Management is an art; play it by the ear. Rely on experience and
intuition. Trust one’s gut feelings more than formal analysis of data,
trial runs, expert opinions, etc.
.711
Innovation is the fastest way to create new market leadership. .780
Innovation is the quickest way to create an uncontested market and beat
competition.
.675
The best innovative product/service in a domain is one that accomplish
the domain specific task in minimum number of steps and maximum
simplicity.
.712
Product/service improvisation means identifying and eliminating all
unnecessary steps in design and use.
.678
Innovation is driving the market toward smaller but more efficient
products/services. The evolution of smart phones, tablets and nano-cars
is case in point.
.588
When I make changes in my product I focus on how fast & simple it
will become for customers while adopting it.
.707
I welcome all new ideas but ideas which are fast and frugal in bringing
returns are likely to be funded and supported first than an those which
promise only long term benefits.
.663
A faster way to challenge and involve employees to give them time to
explore new ideas/products on their own.
.609
d) Inter-item correlations were also computed and it was found that none of these
correlations were less than .3, so the items can be accepted for final analysis (Field,
2010).
56
e) Further, items were factor analyzed through principal component analysis method
(oblique rotation), and it was found that two broad factors emerge, of which one was
related to search for new things and creative adaptation in ones business
environment (consisting of items mainly drawn from Manimala, 1992), and another
was related to fast and frugal characteristic of innovation as a heuristic. However, it
was found that item number 7 (i.e., ‘Look for new (product) ideas in others’ failures,
commercialization gaps, their half baked ideas, etc.’), 19 (i.e., ‘Management is an
art; play it by the ear. Rely on experience and intuition. Trust one’s gut feelings more
than formal analysis of data, trial runs, expert opinions, etc.’), and 20 (i.e.,
‘Innovation is the fastest way to create new market leadership.) are not falling
within any of the two factors, so they were deleted from the final questionnaire.
Thus, the final questionnaire consisted of 24 items (See the Appendix A).
f ) Reliability of total scale was found to be .963 (Cronbach’s Alpha = .963), which
shows the high reliability of the scale.
The innovation as a heuristic scale is based on the research in Indian organizations
(Manimala, 1992), so it is likely to offer the accurate measure of the extent to which
managers/entrepreneurs perceive innovation as a heuristic excellence . However, since
the scale has been constructed specifically to meet the objectives of current research
involving mainly managers with limited experience, so in future the use of this scale may
warrant more revisions leading to the more mature assessment of the innovation as a
heuristic.
3.5.2 Measure of Organizational Excellence
To measure organizational excellence the EXCEL Scale (Sharma et al., 1990a) was used
which is a 16 item scale designed to operationalize and measure 8 attributes of excellence
as espoused by Peters & Waterman (1982) in their book ‘In search of Excellence’. The
57
Excel scale consist of 16 affirmative statement type items with 2 items on each attribute
of excellence listed earlier in this chapter. The respondents of the current research were
asked to rate these 16 statements on a 7 point Likert type scale where 1 denoted strongly
disagree and 7 denotes strongly agree.
The Excel scale is a powerful measure , and so far best identified tool to directly assess
the 8 attributes of excellence as suggested by Peters & Waterman with verified reliability
and validity through independent researches (Caruana et al., 1995). The scale was
developed after following the rigorous procedures and paradigms of testing and
developing marketing constructs in business (Churchill, 1979). Sandbakken (2004) has
cited various researches which report the Cronbach´s Alpha reliability coefficient for the
Excel scale as .89/.90 (Sharma et al., 1990a), .92 (Caruana et al., 1995; Sandbakken,
2002) . Apart from this, 5 indicators of organizations performance were further added
after discussion with experts. On these indicators subjects were required to indicate their
responses on a 7- point Likert type scale. The total excellence score was computed by
adding the total scores of subjects on Excel scale and the 5 indicators of organizational
performance. The entire scale can be seen in the Appendix A.
3.6 Chapter Summary
Research is a creative exploration of some socially and academically significant issue
with a scientific temper. However, there is a danger associated with this creative
exploration that the researcher may get lost in the unknown wilderness of relative,
multiple and subtly changing social and scientific realities. So, it’s necessary to have a
guiding framework for the research along with sound scientific parameters which can be
used for testing the research findings. A sound research methodology is an imperative for
good scientific research. The modern scientific research is equipped with highly
sophisticated tools and packages which have been very helpful to the researcher and
58
scientific community in general in parsimonizing the entire research process. The current
research also tries to achieve its objectives by adopting a scientific framework to guide it
along with the use of analytic software packages like SPSS 16, and Amos 18.0. While
making an attempt to imbibe these points this chapter has discussed the research design,
nature of sample, measurement tools and other methodological details and procedures
followed in this research study.
Chapter 4
Data Analysis &
Results
61
CHAPTER 4
DATA ANALYSIS AND RESULTS
This chapter discusses the data analysis procedure used in the current research. The two
major software packages used for the analysis in the current research are SPSS
(Statistical Package for the Social Sciences) 16.0 and AMOS 18.0 . SPSS has been used
to do the preliminary descriptive analysis and principal component analysis . AMOS has
been used to do structural equation modelling and testing the proposed model fit.
4.1 Missing Value Analysis
At the outset the missing value analysis was carried to locate the missing values and the
apply the suitable statistical measures to replace these missing values. Missing value
analysis is important as it help address many concerns caused by the missing data.
Among the various available methods for missing value analysis expectation-
maximization (EM) method was used for conducting missing value analysis. In
expectation-maximization method all observed information about a parameter is used to
produce the maximum likelihood estimation of parameters (Acock, 2005). The missing
value analysis showed following result:
‘There are no missing values. EM estimates are not computed.’
The reason for this is that while administering questionnaires clear-cut instructions were
given to the subjects for filling all the details and before collecting the questionnaires it
was ensured that they have responded to all the items properly. Further, data from
improperly or incompletely answered questionnaires were not included in the data editor
(there were 16 such cases) so as to select responses of only from those participants who
were motivated enough to participate in the research process and complete the
questionnaire. It was considered necessary keeping the non-experimental nature of the
research inquiry.
62
4.2 Descriptive Results
The results of descriptive statistics for various variables are shown in the table 4.1 below.
The variables that were directly measured are age, gender, work experience, innovation
as a heuristic and excellence. The variables search and adapt heuristic, fast and frugal
heuristic and heuristic intelligence have been computed.
Table 4.1 Descriptive statistics for the sample characteristics
N Mean SD Skewness Kurtosis
Statistic Statistic
Std.
Error Statistic Statistic
Std.
Error Statistic Std. Error
age 203 23.92 .296 4.219 5.960 .171 45.486 .340
Work experience 203 2.05 .111 1.586 4.078 .171 22.473 .340
Search & Adapt Heuristic 203 89.09 1.373 19.556 -1.392 .171 1.476 .340
Fast & Frugal Heuristic 203 37.26 .549 7.825 -1.435 .171 2.329 .340
Heuristic Intelligence 203 126.34 1.815 25.853 -1.497 .171 2.064 .340
Innovation as a heuristic 203 141.95 2.052 29.237 -1.509 .171 2.144 .340
Excellence 203 108.96 1.490 21.232 -1.337 .171 1.431 .340
Valid N (listwise) 203
Although biographical variables like age, gender, and work experience were not required
for further analysis but there descriptive statistics have been presented to offer a complete
picture of the sample characteristics. Age and gender compositions have already been
discussed in the methodology chapter (under sample characteristics) so here only a brief
discussion will follow on them after which the descriptive characteristics of remaining
variables will be discussed. The total sample size included in analysis was 203 of which
79 (39.3 %) were males, and 124 (60.6 %) were females. Looking at this we can say that
females are a bit overrepresented as compared to males in the sample but gender based
comparison was not part of any of the three main objectives (see Chapter 1 Introduction,
for the detailed overview of objectives) of the current research, so the current sample
was accepted for further analysis. The mean age of sample is 23.92 (S.D. = 4.219) with
63
the average work experience of 2.05 years. The mean scores and standard deviations of
other variables are also shown in the table 4.1.
Another descriptive statistics measured were skewness and kurtosis. These two are the
measures of symmetry, or more precisely asymmetry, of the distribution. Skewness refers
to the extent to which a distribution departs from the symmetricity (Simpson & Kafka,
1971) or normal distribution. The range of skewness value varies from -3 to + 3 (Lomax,
2001). A positively skewed distribution will have positive skewness value, and the value
of mean will be greater than median which in turn will be greater than mode (i.e. mean >
median > mode), while a negatively skewed distribution will have negative skewness
value, and the value of mean will be less than median which in turn will be less than
mode (i.e. mean < median < mode). A symmetric distribution, such as a normal
distribution, has a skewness of 0. Kurtosis, on the other hand shows the “peakedness” of
distribution (Lomax, 2001). A distribution range from flat (platykurtic) shape to a
slender, narrow or highly peaked (leptokurtic) shape. In between the these two types lie
the bell-shaped normal distribution curve (mesokurtic).
According to Field (2009), z-scores can also be computed by dividing the skewness and
kurtosis scores with standard errors and their significance levels can be checked at the
desired level. According to him an absolute value greater than 1.96 is significant at p <
.05, above 2.58 is significant at p < .01, and absolute values above about 3.29 are
significant at p < .001. However, “large samples will give rise to small standard errors
and so when sample sizes are big, significant values arise from even small deviations
from normality. In case of large samples (i.e., 200 or more) it is more important to look at
the shape of the distribution visually and to look at the value of the skewness and kurtosis
statistics rather than calculate their significance” (p.139). Hence, z-scores were not
computed further. Again, Field (2009) has given a threshold value of 3.29 for these
measures and if the values of variables under scrutiny are below this threshold we can
proceed with further analysis. In present case the variables that were included in the
64
analysis to test hypothesis, i.e. search and adapt heuristic, fast and frugal heuristic,
heuristic intelligence, innovation as a heuristic and business excellence, all has values
below 2.58, and thus we can proceed with the further analysis.
4.3 Principal Component Analysis (PCA)
4.3.1 Test for group differences and data sufficiency
To explore the factors underlying innovation heuristic, a principle component analysis of
innovation as a heuristic (IAH) questionnaire was carried out. However, before
conducting factor analysis it was checked whether groups differ significantly on IAH
variable based on their gender or age. Further, an attempt was made to observe whether
the data was sufficient for doing factor analysis. To check the group difference a one way
analysis of variance (ANOVA) was performed whose results show that there is no
significant differences between the males and females (F (1, 200 ) = .665, p = .416), and
people across two age groups, i.e., one, less than 25 years old and, two, more than 25
years old (F (1,201) = .052, p = .82) for measured IAH variable. Further, KMO and
Bartlett’s tests were conducted to check the data sufficiency and suitability of the sample
for factor analysis. The results of these tests are shown on the next page:
Table 4.2 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .952
Bartlett's Test of Sphericity Approx. Chi-Square 3917.096
df 351
Sig. .000
The KMO test represents the ratio of the squared correlation between variables to the
squared partial correlation between variables. The KMO statistic varies between 0 and 1.
A value of 0 indicates that the sum of partial correlations is large relative to the sum of
65
correlations, indicating diffusion in the pattern of correlations (hence, factor analysis is
likely to be inappropriate). A value close to 1 indicates that patterns of correlations are
relatively compact and so factor analysis should yield distinct and reliable factors. Kaiser
(1974) recommends accepting values greater than 0.5 as barely acceptable, values
between 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between
0.8 and 0.9 are great and values above 0.9 are superb (Hutcheson & Sofroniou, 1999).
The KMO test value for current data .952 indicating the sufficient adequacy of sample
for factor analysis.
Bartlett’s test, on the other hand, examines whether the population correlation matrix
resembles an identity matrix. If the population correlation matrix resembles an identity
matrix then it means that every variable correlates very badly with all other variables and
in this case data is not factor analyzable. In our case Bartlett’s test is significant which
means our correlation matrix is significantly different from identity matrix and variables
correlate well with each other. Hence, cluster can be formed and factor analysis should be
performed to check this.
4.3.2 Scree Plot
Scree plot is a graph between each eigen value (Y - Axis) against the factor it is
associated. Scree plot is an important technique to determine whether or not an eigen
value is large enough to represent a meaningful factor. As cited in Field (2009), Cattell
(1966) has argued that cut-point for selecting the appropriate number of factors should be
at the inflexion point of the curve. In figure 4.1, the scree plot shows that the inflexion
point begins from component 2 onwards so meaningfully we can extract two factors.
According to Stevens (1992) with sample size as large as 200 scree plot provides a fairly
reliable criteria for selection of appropriate number of factors.
66
Figure 4.1 Scree Plot
4.3.3 Summary of Principal Component Analysis
A principal component analysis (PCA) was carried out on the 27 items of Innovation as a
heuristic questionnaire (based on Manimala, 1992; Gigerenzer, 2000; Gigerenzer, 2002)
with oblique rotation (direct oblimin). The Bartlett’s test of sphericity χ² (351) =
3917.096, p < .001, indicated that correlations between items were sufficiently large for
conducting PCA. An initial analysis was run to obtain eigen values for each component
in the data. It was found that two components had eigen values over Kaiser’s criterion of
1 and in combination explained 57.63 % of the variance. Given the KMO test of
sampling adequacy for the two groups, and the convergence of the scree plot and Kaiser’s
criterion the two components having eigen values 13.88 and 1.67 respectively were
retained in the final analysis. Further, to observe the internal consistency of these two
factors Chronbach’s Alpha was computed for the two factors which came out to be α =
.90 and α = .95 respectively. These components were names as search & adapt heuristic
(SAH), and fast and frugal heuristic (FFH). For a detailed discussion on factor naming
and related explanation see the discussion chapter.
Cut point of Eigen
value
67
Table 4.3 Summary of principal component analysis (N = 203)
SN Symbol Item Rotated Factor
Loadings
Search &
Adapt
Heuristic
Fast &
Frugal
Heuristic
1 Hi9 Be flexible in one’s ideas and plans. .897
2 Hi2 Ideas are the most important resource. Look for them everywhere . .885
3 Hi3 Look for new (product) ideas among personal contacts (friends, hobby
clubs, professional associations, customer complaints, previous job
contacts, etc.).
.866
4 Hi14 Never stop searching for new ideas and opportunities. .863
5 Hi16 Introduce new products, modify existing products, and/or change
strategies periodically.
.813
6 Hi4 Look for new (product) ideas among technological developments abroad
especially among new, rare, or specialized products developed abroad.
.774
7 Hi11 Never be constrained by rigid plans and the narrow visions. Act
according to opportunities.
.763
8 Hi17 Keep the organization fresh and dynamic by periodically inducting young
people into it who have new ideas and the drive to implement them.
.710
9 Hi5 Look for new (product) ideas among one’s own vision of the future,
special talents, and innovative research findings, or among the special
skills of one’s associates and staff.
.698
10 Hi10 Do not get stuck to one idea. Be prepared to leave it at the slightest
indication of failure, and develop new ideas.
.694
11 Hi18 Launch new products on a trial basis, receive feedback, and slowly widen
the market.
.671
12 Hi1 Be a pioneer in the choice of products. Avoid highly competitive, low
margin, run of-the-mill products.
.669
13 Hi15 Never set any geographical limits to one’s search for ideas and
opportunities.
.636
14 Hi8 Look for new (product) ideas in the general environment (existing
practices and changes in the legal, political, religious, social, and cultural
.616
68
domains).
15 Hi13 Never be complacent about successes, but keep on striving for excellence
through new ideas (Do not repeat success strategies until they fail).
.610
16 Hi6 Look for new (product) ideas among the components, substitutes,
complements, neglected ranges, supply gaps, deficiencies, and
inadequacies of existing products.
.577
17 Hi12 Treat personal problems/handicaps/ mishaps as indications to change
one’s line of thinking/occupation.
.527
18 Hi24 Innovation is driving the market toward smaller but more efficient
products/services. The evolution of smart phones, tablets and nano-cars is
case in point.
.839
19 Hi22 The best innovative product/service in a domain is one that accomplish
the domain specific task in minimum number of steps and maximum
simplicity.
.806
20 Hi23 Product/service improvisation means identifying and eliminating all
unnecessary steps in design and use.
.788
21 Hi27 A faster way to challenge and involve employees to give them time to
explore new ideas/products on their own.
.768
22 Hi25 When I make changes in my product I focus on how fast & simple it will
become for customers while adopting it.
.759
23 Hi21 Innovation is the quickest way to create an uncontested market and beat
competition.
.752
24 Hi26 I welcome all new ideas but ideas which are fast and frugal in bringing
returns are likely to be funded and supported first than those which
promise only long term benefits.
.722
Eigen Value
% of variance
Cronbach’s Alpha
13.88 1.67
51.44 6.19
.90 .95
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
Items with factor loading < .4 have been suppressed.
Factors with Eigen value < l & explaining less than 5% variance have been omitted
69
4.3.4 Component Plot
The component plot shows the emergence of two broad factors after PCA. The items that
cluster on component 1& 2 are shown in the figure 4.2. Excluding item no. 7, item 1 to
item number 18 cluster on component 1, while item no. 21 to 27 cluster on component 2.
Item no. 7, 19, and 20 were deleted as they are not related to any of these factors.
Figure 4.2 Component plot
4.4 Correlational Analysis
To study whether innovation heuristic has a significant correlation with business
excellence a correlation analysis was carried out among variables whose results are
summarized in the table 4.4 below. The two extracted factors were correlated with total
excellence scores and it was found that :
70
1) The Pearson’s correlation coefficient between ‘Search & Adapt Heuristic’ and
‘Business Excellence’ scores is r = .78 (p < .01); and
2) The Pearson’s correlation coefficient between ‘Fast & Frugal Heuristic’ and
‘Business Excellence’ scores is r = .65 (p < .01); and
Table 4.4 Correlations between extracted components and total excellence
scores
Search &
Adapt
Heuristic
Fast &
Frugal
Heuristic
Heuristic
Intelligence
Business
Excellence
Search & Adapt
Heuristic
1 .734**
.979**
.781**
Fast & Frugal
Heuristic
1 .858**
.647**
Heuristic
Intelligence
1 .787**
Business
Excellence
1
** Correlation is significant at the .01 level (2 - tailed)
3) The Pearson’s correlation coefficient between ‘Heuristic Intelligence’ and
‘Business Excellence’ scores is r = .79 (p < .01).
4.5 Structural Equation Modelling
To explore the structural model through which innovation heuristic and excellence
interact with each other structural equation modelling(SEM) was carried out using
AMOS 18.0. SEM is essentially a combination of exploratory factor analysis and
multiple regression analysis (Ullman 2001) techniques which is used for testing and
estimation of the causal relationship among variables within a proposed model. The
model tested in the current research consisted of observing the structural relationship
71
among two exogenous variables i.e., search & adapt heuristic and fast & frugal heuristic,
one latent variable i.e. heuristic intelligence, and one endogenous variable i.e. business
excellence (for a detailed description of the variables please refer to the methodology
chapter). Keeping in mind the problem associated with the multivariate normality and
continuity of the data bootstrap method was used (West et al., 1995; Yung & Bentler,
1996; Zhu, 1997).
4.5.1 Mediation Analysis
Further, To check whether the heuristic intelligence has any mediating effect between
exogenous variables (Search & Adapt Heuristic, and, Fast & Frugal Heuristic) and
Endogenous variables (Business Excellence) mediation analysis through Bootstrap
Method was carried out. However, before establishing any mediating effect it is
necessary to see whether exogenous variables (Search & Adapt Heuristic, and, Fast &
Frugal Heuristic) have any direct significant effect on endogenous variable (i.e.,
Business Excellence) . The results of the test of direct effect is shown on the following
page.
Table 4.5 Direct Effects (Two tailed significance values)
Fast & Frugal
Heuristic
Search & Adapt
Heuristic
Business
Excellence
.021* .001***
Since both values are significant (Table 4.5) so we can say that exogenous variables
(Search & Adapt Heuristic, and, Fast & Frugal Heuristic) have a direct significant effect
on endogenous variable (i.e., Business Excellence). So, the sample can further be
analyzed to see whether mediation effect is present. After this latent variable was
introduced, and it was found that when mediated by latent variable (i.e., Heuristic
Intelligence) the effects were again found significant (p < .001) . This means that
72
Heuristic Intelligence as a intervening variable plays a significant role, and, thus can be
included for further analysis. After this the proposed model was tested to see the type of
mediation (full vs partial) whose results was displayed below:
Figure 4.3 Analysis of the type of mediation (partial vs. full) in the proposed model
Table 4.6 Direct effects after mediation - two tailed significance values
Fast & Frugal Heuristics Search & Adapt Heuristic
Heuristic Intelligence .001*** .001***
Business Excellence .276 .033*
The result shows that path from both heuristics (i.e., Search & Adapt Heuristic, and Fast
& Frugal Heuristic) to latent variable (i.e., Heuristic Intelligence) is significant. Path from
Search & Adapt Heuristic to endogenous variable (i.e., Business Excellence) is
significant (p < .05), this means mediation effect of Heuristic Intelligence between
Search & Adapt Heuristic and Business Excellence is partial (i.e., partial mediation).
Further, path from Fast & Frugal Heuristic to endogenous variable (i.e., Business
Excellence ) is not significant (p – value = .276), this means mediation effect of
Heuristic Intelligence between Fast & Frugal heuristic and Business Excellence is Full.
73
74
4.5.2 The Proposed Model
After the initial analysis a model was proposed in which both SAH and FFH are fully
mediated through heuristic intelligence in bringing business excellence, the structural
equation output of the proposed model along with estimates are shown in the figure 4.4.
However, before performing the SEM some practical considerations were checked to see
the suitability of data for SEM analysis.
4.5.2.1 Practical issues involved in the proposed SEM
Before testing the proposed model the practical issues, like sample size and missing
values, continuity of chosen scales, univariate and multivariate normality, assessment of
linearity assumption, and test of outliers, related to the analysis were looked into. The
results related to practical issues are discussed below:
4.5.2.1.1 Sample Size and Missing Value
The data for the proposed model was collected from sample/cases of 203 participants.
There were 4 observed variables and 8 parameters to be estimated for the proposed
model. The ratio of sample size to the observed variable is 50.75: 1, and the ratio of
sample size to the number of parameters estimated is 25.36: 1. Bentler & Chou (1987) are
of the view that if these ratios are in the range of 10:1 or more, then we can consider the
sample size sufficient for a meaningful analysis. Stevens (1996) has considered 15 cases
per predictor/parameter as sufficient for meaningful analysis. The current sample
characteristics makes it suitable for SEM according to both the criteria.
Hair et al. (1995) has recommend a sample size of at least one hundred observations to
achieve adequate power in structural equation modelling. As we increase the sample size
above 100 the Maximum Likelihood Estimation (MLE) becomes increasingly sensitive to
the differences in the data, and for exceedingly large samples (N > 400 – 500) MLE
becomes too sensitive and even a small difference in data may turn all goodness-of-fit
indices showing poor fit (Hair et. al, 1995). The current sample size of 203 can be
75
considered appropriate (neither too small nor too big to turn MLE sensitive) for sufficient
power of SEM results. Further, the data was collected in a manner that each participant
have to submit the questionnaire only after completion along with eliminating any
dubious/incomplete questionnaire, so there were no missing values.
4.5.2.1.2 Continuous Scales
As cited in Li (2006), “whether we should treat such categorical scales as Likert-type or
semantic differential scales as continuous in statistical analysis has been a common
concern (Byrne 2001; Rundle-Thiele 2005). It has been suggested that this problem may
not be an issue when the number of categories is large (Byrne 2001)” (p. 136). In case of
current sample a 7-point Likert scale has been used to measure all the variables. Thus
the measurement meets the criteria of large categories as specified by Byrne (2001). Li
(2007) has also used a 7 – point Likert type scale in his model and consider it sufficient
as meeting the criteria specified by Byrne (2001) regarding the large number of
categories in the scale.
4.5.2.1.3 Univariate and Multivariate Normality
The tests of univariate normality has been already discussed under the descriptive
statistics section of this chapter and it was found that the variables that were included in
the analysis to test hypothesis, i.e. search & adapt heuristic, fast & frugal heuristic,
heuristic intelligence, innovation-as-a-heuristic and business excellence, have all their z
values below 2.58. Field (2009) has reported values up to 3.29 within the acceptable
limits for testing the normality assumption. Thus we can say that the univariate normality
characteristics of the sample falls within the acceptable limits. However, when tested
statistically both Kolmogorov –Smirnov test and Shapiro-Wilk test were significant for
all the observed variables (p < .001) showing all the observed variables being
significantly skewed.
76
However, according to West, Finch, & Curran (1995) the researcher should be concerned
about his results only if skewness > 2 and kurtosis > 7, and the skewness and kurtosis
values of all the variables in the current sample is well within these limits prescribed
limits. In view of these results it was considered safer to treat the data for controlling the
effect of multivariate non-normality . This is because univariate normality is necessary
but not sufficient condition for multivariate normality, i.e. even if the individual variables
are normally distributed (univariate normality) their joint distribution (multivariate ) can
be non-normal (Stevens, 1992; Field, 2009; Newsom, 2012).
4.5.2.1.3.1 Bootstrapping as an aid to nonnormal data
Byrne (2001) has considered bootstrapping as an important aid to deal with non-normal
data. Bootstrapping is an increasingly popular and promising approach to correcting
standard errors (Newsom, 2012). It is a kind of resampling procedure in which multiple
subsamples of the same size from the parent sample are drawn randomly, with
replacement by considering the original sample as its population (Byrne, 2001). The
researcher can assess the stability of parameter estimates with greater accuracy through
this large number of randomly selected subsamples generated by bootstrapping. The
Bollen–Stine bootstrap can be used to correct for standard error and fit statistic bias that
occurs in structural equation modelling(SEM) applications due to nonnormal data
(Enders, 2005). For the current sample 2000 bootstrap sub-samples were generated
through Bollen-Stine Bootstrap method (Bollen & Stine, 1992) and the obtained Bollen-
Stine Bootstrap p-value was .094 for the tested null hypothesis that model is correct.
Since the obtained Bollen-Stine p – value is not significant so we accept the null
hypothesis, i.e., there is no significant difference between the proposed model and sample
behaviour, i.e., the proposed model is correct. The other major model-fit indices are
presented in the forthcoming sections of this chapter which also indicate toward good
model fit.
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To sum up we can say that in actual practice in research nonnormnal data is quite
common, and in fact some authors believe that in practice normal data is more an
exception than reality, for e.g., Micceri (1989) has euphemistically termed normal curve
as an improbable creature. Although, it is reasonable to ask whether normality violations
are problematic for maximum likelihood analyses the research literature suggests that
non-normal data tend to have a minimal impact on the parameter estimates themselves
but can bias standard errors and distort the likelihood ratio test (Enders, 2010). To control
these distortions Bollen-Stine bootstrapping has been used in the current research whose
result indicate the model fit, so the problems arising out of non-normality has been
accommodated and rectified.
4.5.2.1.4 Linearity Assumption
Linearity among predictor variables (SAH, FFH, HI) and dependent variables (BE) were
assessed by both graphical (scatter plot) and statistical method (correlation method). The
scatter plots show a linear pattern and significant correlations among all the variables
under consideration i.e. SAH & BE (R squared linear = .611), FFH & BE (R squared
linear = .419), HI & BE (R squared linear = .612), SAH & HI (R squared linear = .946),
FFH & HI (R squared linear = .745). So, the linearity of relationship assumption has also
been satisfied by the current sample.
4.5.2.1.5 Outliers
An outlier refers to an observation that deviates markedly from rest of the observations
from the sample (Grubbs, 1969), and arouses suspicion that it was generated by a
different mechanism (Hawkins, 1980). Univariate outliers refer to the cases in the sample
having extreme values on a variable. Tabachnick & Fidell (Tabachnick & Fidell , 2001;
Tabachnick & Fidell , 2007) have considered a standardized z value over ±3.29 (p <
.001) as potential outlier. Further, AMOS computes Mahalanobis distance (Mahalanobis’
d-squared) to detect multivariate outliers, i.e extreme values coming from a combination
of two or more variables. Although 3 cases (observation no. 129, 47 & 165) were found
78
to be having large values of Mahalanobis distance (Mahalanobis’ d-squared) but the
relative distance among these values were not big so they were retained for further
analysis.
4.6 The SEM output & estimates
The output terms the current model as the recursive model. A recursive model is one that
specifies direction of cause from one direction only (Byrne, 2010), which is true for the
proposed model which is shown below:
Figure 4.4 The proposed model with output values after confirmatory analysis
The table 4.7 shows the various estimates for the path diagram for the hypothesized
model. The higher the value of estimate, the more variation it explains in the dependent
variable
Table 4.7 Regression weights of various paths in proposed model
Estimate S.E. C.R. p value
Heuristic_
Intelligence
<--- Search & Adapt
Heuristic
1.099 .012 94.727 ***
79
Heuristic
Intelligence
<--- Fast & Frugal
Heuristic
1.208 .029 41.644 ***
Business
Excellence
<--- Heuristic
Intelligence
.568 .032 17.862 ***
Looking at p values we can say all the paths in hypothesized model is significant.
Estimates tell that all the effects are positive. Both the heuristics lead to approximately
the same amount of variation in Heuristic Intelligence, for e.g., one unit change in
Search & Adapt heuristic would lead to 1.1 units change in Heuristic Intelligence, while
one unit change in fast & Frugal heuristics leads to 1.2 unit change in Heuristic
Intelligence. Similarly, 1 unit change in heuristic Intelligence leads to .58 unit increase
in Business excellence.
The standard error of estimate (S.E) is a measure of error of prediction. Values of SE are
low for the 3 paths, showing less error of prediction. Critical ratio (C.R.) is obtained by
dividing the estimate of a variable with its S.E. In large samples, CR can be referred as
standard normal distribution. Thus, a value of CR higher than ± 1.96 (at .05 level) and ±
2.56 (at .01 level) is considered as significant (Hox & Bechger, 1998). All the CRs are
significant for the 3 paths.
Covariance: The covariance between Search and Adapt Heuristic and Fast and Frugal
Heuristic is estimated to be 111.812 (p < .001) which is significant.
Table 4.8 The covariance estimate between Search and Adapt Heuristic
and Fast and Frugal Heuristic
Estimate S.E. C.R. P
Search &
Adapt
heuristic
<--> Fast & Frugal
Heuristic
111.812 13.292 8.412 ***
80
Variance: The variance scores of variables are shown in the table 4.9 on the next page.
All the variances are significant (p < .001). However, variance of SAH is much larger as
compared to FFH. This may be partly due to larger number of items on SAH (n=17) as
compared to FFH (n=8). Also, the nature of question were more varied in case of SAH
scale which may have further increased its variance. Further, a larger value of r2 (173.9)
as compared to r1 (4.77) indicates that sample-population difference in prediction of
Business Excellence can be higher as compared to Heuristic Intelligence.
Table 4.9 Variance estimate of Search and Adapt Heuristic and
Fast and Frugal Heuristic, and residuals
Estimate S.E. C.R. P
Search and Adapt
Heuristic
380.543 37.865 10.050 ***
Fast and Frugal
Heuristic
60.929 6.063 10.050 ***
r1 4.771 .475 10.050 ***
r2 173.903 17.304 10.050 ***
4.7 Indices of Model Fit
The proposed model was tested using AMOS 18.0 and various indices of model fit were
observed. The indices of fit that were used for testing the model along with their
obtained and desired values are shown in the table 4.10 on the following page. All the
indices except RMSEA show good model fit. A detailed interpretation and discussion of
these indices have been offered in the discussion chapter. In acknowledgement of
multivariate non-normality Bollen – Stine bootstrap values have been observed for the
Chi- square test of model fit. The major indices of model fit that have been reported
include Chi Square (χ2), χ
2 /df ratio, GFI (Goodness-of-fit Index), AGFI (Adjusted
Goodness-of-fit Index), PGFI (Parsimony Goodness of Fit Index), SRMR (Standardized
81
Root Mean square Residual), NFI (Normed Fit Index), TLI (Tucker – Lewis Index), CFI
(Comparative Fit Index), RMSEA (Root Mean Square Error of Approximation).
Table 4.10 A summary of Indices of fit for the proposed model
Index Obtained value Accepted range of values
for model fit
χ2 5.446 χ
2 is not significant (p =
.066, p Bollen - Stine = .094), so
we accept null hypothesis,
i.e., Ho: There is no
significant difference
between sample
covariance matrix and
population covariance
matrix. hence the default
model is acceptable .
df 2
P .066
(pBollen - Stine = .094)
χ2 /df ratio 2.723 1 to 3
GFI (Goodness-of-fit Index) .987 ≥ .95
AGFI (Adjusted Goodness-of-
fit Index)
.934 ≥ .95
PGFI (Parsimony Goodness of
Fit Index)
.193 < .5, should be less than .5
SRMR (Standardized Root
Mean square Residual)
.011 0 (perfect fit) to 1 (unfit)
NFI (Normed Fit Index) .996 ≥ .95
82
TLI (Tucker – Lewis Index) .993 ≥ .95
CFI (Comparative Fit Index) .998 ≥ .95
RMSEA (Root Mean Square
Error of Approximation)
.092 .00 to .1
4.8 Chapter Summary
The current chapter discussed the procedure applied for data analysis and the obtained
results have been presented. A missing value analysis showed no m-pattern as while
collecting data it was ensured each participant has answered all the items properly and
improperly answered questionnaires were excluded from the analysis. A principal
component analysis showed the emergence of two major factors underlying innovation
heuristic: search & adapt heuristic explaining 51.44 % of variance, and fast & frugal
heuristic explaining 6.19 % of variance. Jointly these two factors explain 57.63 % of
variance. A model was proposed and tested using Amos 18.0 which showed various
indices of model fit supporting the proposed model.
Chapter 5 Discussion
84
CHAPTER 5
DISCUSSION
The current chapter discusses the major findings of the study in light of the current
researches in area of innovation as a heuristic and their role in bringing business
excellence. Initially the results of the factor analysis are discussed which is followed by
an analysis of the nature of relationship among factors and dependent variable i.e.,
business excellence. After this the results of structural equation modelling have been
discussed, and the causative relationship among two innovation heuristic, i.e. SAH &
FFH, and business excellence has been analyzed and the role of heuristic intelligence as
a mediating variable has also been discussed.
5.1 A case for innovation
The current research was done on a sample consisting of 203 people (mean age = 23.92,
S.D. = 4.22) involving MBA students, managers and entrepreneurs. The preliminary
results show that the sample perceives a significant relationship between the use of
innovation heuristic and business excellence (r = .787, p < .01) irrespective of age,
gender and work experience. The research literature is replete with findings which attach
increasingly greater value to innovation as way of growth, adaptation and achieving
excellence. Although, innovation in modern India is an emergent concept but since
prehistoric times India commands a respectable place among pioneers and innovators. In
modern context innovation seems to have arrived in India through the ‘Reverse
Innovation’ process (Govindrajan, Trimble & Nooyi, 2012) and India today is not only an
outsourcing hub of backdoor noncreative function of western corporations rather and
active emergent hub of global innovation process.
However, some scholars still doubt the Indian creativity and innovation citing the lack of
sufficient creative freedom in India (Gupta, 2012), or its confounding with
argumentativeness (Dhandekar, 2010), and some even outrightly rejecting the idea of
85
Indian’s being creative (for e.g., de Bono, 2007). This is mainly because the major types
of innovation coming from India are invisible innovation, like innovation for business
customers, outsourcing innovation, process innovation and management innovation
(Kumar & Puranam, 2011). However, it’s wrong to assume that India lacks creativity, in
fact India possess a tradition of creativity. Looking at the ancient and prehistoric India we
find a rich example of creativity and innovation in various areas of human life. In the
similar vein the present sample has well appreciated the role of innovation as a heuristic
in bringing success and excellence in business context.
Indian managers and entrepreneurs are increasingly acknowledging and operationalizing,
what a growing body of research evidences suggest that innovation is a fruitful way for
firms to live long lives and prosper (Collins & Porras, 1994; Christensen, 1997; de Geus,
1997; Cobbenhagen, 2000; Tidd, Bessant & Pavitt, 2001)1. Therefore, the question is not
why to innovate, but how to innovate (van der Meer, 2007). Rasulzada (2007) further
builds the case for innovations by citing a review of studies as following:
“ According to researchers (e.g., Florida, 2002) the main source of growth in
21st centaury are not competition, knowledge or technology, rather than
fundamental drive to economic growth is identified as implemented human
creativity. We are experiencing changes more than ever before and to adapt
and to react to these changes creativity and innovation are considered as
necessary conditions of development (Csikszentmihalyi, 1996; Weisberg,
1999; Runco, 2004). For an individual creativity is associated with being
more productive (Amabile, 1983). Creative and innovative individuals are
thought to be happier, more committed and often strive to achieve self-
actualization (Csikszentmihalyi, 1997)” (p. 1).
Underscoring this importance of innovation the present study was set to understand it as a
intuitive mechanism and the factors underlying it. To study the underlying factors a
1 cited from van der Meer (1996)
86
principal component analysis of the obtained data was carried out whose results are
discussed in the following paragraphs. Further, the attempt was made to explore the
causal relationship among the factors underlying innovation heuristic and business
excellence by testing the structural equation model which has been discussed in the
ending section of this chapter.
5.2 Discussion of the Principal Component Analysis Results
A principal component analysis (PCA) of the 27 items of Innovation-as-a-heuristic
questionnaire (based on Manimala, 1992; Gigerenzer, 2000; Gigerenzer, 2002) with
oblique rotation (direct oblimin) displayed the emergence of two broad factors, i.e.
Innovation as a kind of search and adapt heuristic (SAH) and innovation as a kind of fast
and frugal heuristic (FFH) which jointly explain 57.63 % of variance. The correlation
matrix shows how each item is correlated to one another. The correlation matrix for the
current sample (attached in Appendix B) shows that all items have positive inter-item
correlation as well as item-total correlation. This is because all items were in form of
affirmative statements and none of the items were negatively worded. The inter-item
correlations ranged from .32 to .76 which is neither too high and nor too low and within
the acceptable range for further analysis (Field, 2009). The item-total correlations range
from .55 to .78 again indicating the absence of extreme and spurious correlation.
Before elaborating further on the results of PCA it will be worthwhile to discuss what is
meant by ‘innovation as a heuristic’? Innovation in traditional research literature has
been conceptualized as a sort of behavioural creativity (Schumpeter, 1934; Rogers, 1983;
Van der Meer, 2007; Katragadda, 2009) with related, generally positive, outcomes of
performance. However, in this thesis work innovation has been conceptualized as an
intuitive decision strategy by an entrepreneur/manager to select those idea, information
and opportunities from his/her environment which are ecologically rational and brings
87
excellence (profit) in a fast and frugal way2. Rather than seeing innovation as mere an act
of creating something new or value addition, the current research has tried to see it as
a strategic intuitive mechanism of adaptation and growth under uncertain business
environment3.
But before we study innovation as a heuristic its worthwhile to see if people in
organization are using intuitive heuristic decisions while deciding about key aspects of
their business. Marta Sinclair & Neal Ashkanasy (2005) has offered a comprehensive
review of literature which builds a case for increasing inevitability of heuristic or
intuitive decisions in organizations especially in comparison to rational choice models.
“Hayward and Preston (1998) argue that linear rational models do not perform
satisfactorily for businesses operating under rising pressure and ambiguity
(Andersen, 2000; Kuo, 1998). There have been many new factor that has forced
managers to question the viability of rational models and look for the intuitive
heuristic strategies like, high decision costs (Tomer, 1996), increased time
pressure (Kuo, 1998), inadequate information (Agor, 1984; Goodman, 1993),
fast-paced change (Andersen, 2000), along with other factors triggered by new
economic and technological forces since the 1980s (Hunt, 2000). According to
Langley et al. (1995) decision-making processes are partially driven by
emotion, imagination, and memories crystallized into occasional insights.
Eisenhardt and Zbaracki (1992) has stressed the importance of a
multidimensional approach to decision making encompassing bounded
rationality, as well as heuristics, insight, and intuition. Eisenhardt (1999) argues
that intuition seems to give managers a better grasp of the changing dynamics
2 The later part of definition is based on the work of Prof. Gerd Gigerenzer's (2000, 2002) idea of heuristics
as mind's adaptive tool-box. 3 In later part of thesis a combination of SAH and FFH has been conceptualized as ability of entrepreneur/
manager, and has been termed heuristic intelligence.
88
in which they have to operate nowadays.” (As cite in Sinclair & Ashkanasy,
2005, pp. 354)
(Reprinted by permission of authors.
From Sinclair, M., & Ashkanasy, N.M. (2005). Intuition: Myth or
Decision-Making Tool? Management Learning, 36 (3), 353–370.
Copyright © 2005 by authors; all rights reserved.)
Further, Intuitive decision strategies, i.e., heuristics have been found related to successful
execution of complex tasks, quick understanding of ambiguous circumstances and the
breakthroughs of discovery or innovation. (Hogarth, 2001). According to Officer (2005)
intuition or heuristic thinking is inextricably involved with innovation and personal
empowerment. Further, Officer has cited Hogarth (2001), according to whom there are
four basic skills demonstrated by the intuitively gifted, i.e., high capacity for
visualization, ability to acknowledge emotions and learn from them, willingness to
speculate and consider alternatives, and habit of testing perceptions, emotions and
speculations. These skills are also important for promotion of innovation (Officer, 2005)
and highly innovative people also score high on them.
Intuition is considered to be the core of creative functioning (Kaufman & Sternberg,
2010), and the above cited literature emphasizes that innovation is increasingly being
used by the business practitioners to achieve growth and excellence, and innovation is
intricately linked to intuition. In the decision choices of innovators, entrepreneurs and
managers innovation is reflected as various rules of thumb 19 of which were taken from
Manimala (1992) and 8 were generated based on Gigerenzer (2000, 2002). The total
variance was divided among the 27 possible factors out of which only two got eigen
value score over 1 as specified according to Kaiser. Kaiser's criterion is the most widely
known criteria for selecting factors because of its simplicity, objectivity, easy-to-apply
characteristics. Factors having eigen value over 1 have positive alpha reliabilities and,
89
hence, are generalizable factors under the assumptions of alpha factor analysis (Tinsley &
Tinsley, 1987).
The principal component analysis of the total 27 items gave two factors i.e. SAH
explaining 51.44 % of variance, and FFH explain 6.19 % of variance in the sample.
Jointly they explain 57.63 % of variance in the data. In the current research both the
heuristics have been conceptualized as a form of ability, essentially intuitive abilities.
Intuitive abilities are important since while operating in an uncertain environment the real
difference in performance will depend upon having the right intuitive ability if we keep
other factors constant. Both the factors are discussed below in the light of current
research objective.
Search and adapt heuristic (SAH) refers to the ability to search and act upon those idea or
information which are ecologically rational and have adaptive value. According to this
definition the chief characteristics of SAH is ecological rationality and its adaptive value,
both of which are essentially the same things. A behaviour is said to be ecologically
rational if it is adapted to one’s environment (Rieskamp & Reimer, 2007). Researches
show that ecological rationality and use of heuristics are related to good decisions and
accurate decision outcomes (Reimer, & Hoffrage, 2012; Katsikopoulos, & Reimer, 2012;
Reimer, & Katsikopoulos, 2011). Adaptive search for new information has been
documented as an important dimension of innovativeness (Wang & Ahmad, 2004; Afzal,
2009) as it is this search for new information that feeds the innovation process (Lundvall,
1985).
Search for information is a fundamental human behaviour and human beings have being
searching information from their environment (external as well as internal) since they
existed. This information have been used for various purposes like making sense of their
environment, to solve problems, to gather and store it for future reference, to increase
one’s knowledge or to meet some need (Afzal, 2009). In an uncertain and competitive
90
environment managers and entrepreneurs are also in search for the new information that
is ecologically rational and enhance the chances of their (business’) growth and
adaptation . However, after the advent of internet and other such networking and
information technologies the environment of the decision makers is full of lots of
alternatives and information. The new information age warrants a paradoxical situation
wherein at one hand information, if properly selected and managed, acts as life blood to
organizations by rejuvenating it with new ideas but if the same information, if wrongly
selected and poorly managed, can turn poisonous and kill the same organization (Gates
& Hemingway, 1999). Sometimes, the opposite may happen and the entrepreneur may
suffer with meagre or lack of adequate amount of information. In both the cases use of
heuristic-intuitive search mechanism may save lot of time, effort and resources giving
quick solutions. Even in case of meagre or no information heuristic search may give
equally good or even better results as compared to logical knowledge based search. The
idea of limited search (Simon, 1990), recognition heuristic (Goldstein & Gigerenzer,
2011; Pachur et. al, 2011; Volz et al., 2011; Pachur et. al, 2012) and less-is-more effect
(Goldstein & Gigerenzer, 2008; Katsikopoulos, 2010) is a testimony of this.
Looking at the table 4.3 (chapter 4 Data Analysis & Results) we find that 17 items have
positive factor loading on SAH factor. This factor was found to be loaded by the items
emphasizing on flexibility (Hi9: Be flexible in one’s ideas and plans, r = .8974; Hi 16:
Introduce new products, modify existing products, and/or change strategies periodically.,
r = .813; Hi 11: Introduce new products, modify existing products, and/or change
strategies periodically., r = .763; Hi 12: Treat personal problems/handicaps/ mishaps as
indications to change one’s line of thinking/occupation., r = .527). Another set of items
emphasized on search for new idea in various ways (Hi2: Ideas are the most important
resource. Look for them everywhere, r = .885; Hi 3: Look for new (product) ideas among
personal contacts (friends, hobby clubs, professional associations, customer complaints,
previous job contacts, etc., r = .866; Hi 14: Never stop searching for new ideas and
4 highest loading on SAH
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opportunities., r = .863; Hi 4: Look for new (product) ideas among technological
developments abroad especially among new, rare, or specialized products developed
abroad., r = .774; Hi 17: Keep the organization fresh and dynamic by periodically
inducting young people into it who have new ideas and the drive to implement them., r =
.710; Hi 5: Look for new (product) ideas among one’s own vision of the future, special
talents, and innovative research findings, or among the special skills of one’s associates
and staff., r = .698; Hi 10: Do not get stuck to one idea. Be prepared to leave it at the
slightest indication of failure, and develop new ideas., r = .694; Hi 15: Never set any
geographical limits to one’s search for ideas and opportunities., r = .636; Hi 8 : Look for
new (product) ideas in the general environment (existing practices and changes in the
legal, political, religious, social, and cultural domains)., r = .616; Hi 13: Never be
complacent about successes, but keep on striving for excellence through new ideas (Do
not repeat success strategies until they fail)., r = .610; Hi 6: Look for new (product) ideas
among the components, substitutes, complements, neglected ranges, supply gaps,
deficiencies, and inadequacies of existing products., r = .577 ). The final two items
loaded on factor SAH could be labelled as pioneering adaptation (Hi 18 : Launch new
products on a trial basis, receive feedback, and slowly widen the market., r = .671; Hi 1:
Be a pioneer in the choice of products. Avoid highly competitive, low margin, run of-the-
mill products., r = .669) .
So, based on the above analysis we can say that the major variables (as indicated by the
items) that are loading on factor 1, i.e., SAH, are flexibility in approach (4 items), search
for new ideas and information (11 items), and pioneering adaptation (2 items). This
shows that there are comparatively a larger number of items related to adaptive search for
new information have loaded on SAH factor as compared to other two identified
variables. Further, we can say that both flexibility in approach and pioneering adaptation
are essentially a type of adaptive reaction as the role of adaptivity is highly implied in
case of both the variables. Hence the factor was named as search & adapt heuristic.
Various researches that highlight the role of flexibility, search for new ideas and
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information and pioneering adaptivity in business success and growth has been discussed
in the following sections.
The alternative names for search and adapt heuristic were also considered, for example
adapt and shape heuristic. The idea behind using the word ‘adapt’ in factor nomenclature
has already been discussed in the preceding paragraphs so it will be parsimonious to
discuss the consideration of word ‘shape’ in the nomenclature of factor 1. The shaping
process is implied in the ‘flexibility heuristic’ just discussed above. A significant role of
innovation is to shape the environment based on innovators need. Innovation while
operating upon a particular ecology shapes it and get shaped by it. These two seemingly
opposing processes (i.e., adaptation and shaping) may appear one after another or
simultaneously depending upon the calls of innovators ecology and choice architecture.
Smithers & Blay-Palmers (2001) based on their study of Ontario soybean industry have
discussed various technological and other innovation in agriculture sector which have not
only been instrumental in adaptation of this sector to various climatic stresses but have
also shaped the practice of soybean cultivation in particular and farmers ecology in
general. According to the researchers the innovative seed varieties and farming practices
developed to cope with various climatic stresses and market requirements acted as a
means of accommodating spatial variation in average heat conditions in Ontario.
Another instance of innovation operating as an adaptation and shaping heuristic is the
Dell™
computers response highly dynamic nature to computer industry. In its initial stint
Dell™
, along with Microsoft
™ and Intel
™, through its innovative optimization of
cumbersome IBM™
computers shaped the nature of PC and computing industry leaving
IBM behind. Dell™
enjoyed a sort of leadership in PC industry and laptop market until
they failed to notice the rise of smart phones as an alternative laptops and PCs. Dell™
was left behind in a race that it created itself because it focussed only on its vision of
shaping the computing industry but failed to adapt it. Innovation is not only about
shaping the ecology in which one operates but also intelligently adapting to it. Nokia™
,
which once predicted the demise of Dell™
for its inability to adapt to the computing era
93
of mobile phones, was itself lagged behind of Apple™
and Samsung™
for its inability to
adapt to the requirements of the smart phone market. Nokia™
definitely shaped the way
people communicate by revolutionizing and making the mobile phones affordable for
everyone but it failed to take note of the pace at which the high end smart phones will
become a commodity of common consumption by replacing its essential mobile devices.
The shaping and adaptation in innovation are intricately interwoven and they are part of
the behavioural repertoire successful innovators. So, for the factor one ‘adapt and shape
heuristic’ could be also be considered as an alternative name but keeping in mind the
predominance of search related items and their better factor loadings the name ‘search
and adapt heuristic’ have been preferred ‘adapt and shape heuristic, and the following
paragraph concludes with a summary of the discussion on search and adapt heuristic.
The researches indicate that flexible thinking and innovativeness and entrepreneurial
behaviour are interrelated (Mueller & Thomas, 2001), and so is the openness to new
concept and ideas and business success (Lussier, 1995). According to Bird (1989),
creativity and the ability to discover innovative ways of protecting the firm from
competition may be key factors in the success of the venture. However, Ciavarella et al.
(2004) when explored the linkage between the entrepreneurs personality using ‘‘Big
Five’’ personality attributes—extraversion, emotional stability, agreeableness,
conscientiousness, and openness to experience – and success rate of ventures they found
a significant relationship between conscientiousness trait and business success but they
did not find a significant relationship between a traits of agreeableness and openness to
experiences and venture success. On the other hand, Cooper et al. (1995) found that
those who had no entrepreneurial experience, on the average, sought more and more
information. In particular, novice entrepreneurs searched less extensively in unfamiliar
domains, a behaviour consistent with bounded rationality. By contrast, experienced
entrepreneurs did not vary their search pattern. It was also found that entrepreneurs
having high levels of confidence sought less information, as expected. So, from these
researches we can conclude that search for adaptive information is a very important
94
factor in business success, however, the amount of information searched may depend
upon various personal and environmental factors as well.
Fast and frugal heuristic (FFH)
The second factor that was derived after that principal component analysis of innovation
as a heuristic (IAH) scale is innovation as a fast and frugal heuristic (FFH). A heuristic is
frugal when it does not require much information, and it is fast when it relies only on
simple computations. Fast and frugal heuristic are the simple mental models that are
based on the practical accounting of available mental resources of the decision maker.
They involve using the simple rules for making decisions that enable smart choices to be
made quickly and with a minimum of information by exploiting the way that information
is structured in particular environments. Despite limiting information search and
processing, simple heuristics perform comparably to more complex algorithms,
particularly when generalizing to new data—simplicity leads to robustness (Todd &
Gigerenzer, 1999).
In the current research there were 7 items (Hi 24, Hi 22, Hi 23, Hi 27, Hi 25, Hi 21, Hi
26)5 that loaded significantly on FFH. A closer look at the major themes underlying these
7 items show that out of 7, the three items (i.e., Hi 24, Hi 22, Hi 23) measure innovation
as a sort of scalar heuristic where it acts mainly by reducing the scalar properties of
products, like size (size of devices getting smaller while its power, efficiency and
features getting more enriched), steps (innovation acting as a process of eliminating
unnecessary steps while developing a product or process), cost, etc. The items measuring
innovation as a scalar heuristic are Hi 24 (Innovation is driving the market toward smaller
but more efficient products/services. The evolution of smart phones, tablets and nano-
cars is case in point, r = .8396), followed by Hi 22 (The best innovative product/service
in a domain is one that accomplish the domain specific task in minimum number of steps
5 all written here in decreasing order of factor loadings
6 item with highest loading on FFH.
95
and maximum simplicity, r = .806), and Hi 23 (Product/service improvisation means
identifying and eliminating all unnecessary steps in design and use, r = .788).
The remaining 4 items (i.e., Hi 27, Hi 25, Hi 21, Hi 26) measure, supposedly, the vector
properties of innovation, i.e., speed along with its ability to provide direction to
competition. These items are Hi 27 (A faster way to challenge and involve employees to
give them time to explore new ideas/products on their own., r = .768), Hi 25 (When I
make changes in my product I focus on how fast & simple it will become for customers
while adopting it., r = .759), Hi 21 (Innovation is the quickest way to create an
uncontested market and beat competition., r = .752) and Hi 26 (I welcome all new ideas
but ideas which are fast and frugal in bringing returns are likely to be funded and
supported first than those which promise only long term benefits., r = .722). This shows
that the major variables that are loading on factor 2 are innovation as a scalar mechanism
and innovation as a vector mechanism. As a scalar mechanism/strategy innovation leads
to the development of products that are lighter, simpler and cheaper, and as a vector
mechanism/strategy innovation gives a direction to firms by optimizing the competition
to the advantage of innovator in a fast and efficient manner. However, the scalar
strategies to assist and fuel the vector strategies as reducing size, steps or cost is not an
end itself unless and until they offer the firms the competitive advantage in a fast and
frugal way. Keeping these things in mind the second factor was named as innovation as a
fast and frugal heuristic or simply ‘fast & frugal heuristic’ (FFH).
The researches show that the new age innovations are incessantly bringing us products
which are lighter (less weight), simpler (less steps or complexity), cheaper (less cost), and
optimize the competitive market to the advantage of innovator in a fast and frugal way;
for e.g., Xia & O’Gorman (2003 ) studied the image capture device used in fingerprint
authentication. They found that in recent years due to remarkable innovations in these
devices their size and price have reduced and their performance have improved, for e.g.,
the better and more innovative devices came with smaller area, less cost and higher
96
resolution. The development of solid-state sensors “brought the size reduction from what
was brick-size for an optical device to postage size” (Xia & O’Gorman, 2003, pp.363).
Further integration on the electronic chips are enabling even smaller size devices . Again,
the authors mention that the cost of fingerprint capture devices have fallen from about
US$1500 to $30 since the early 1990s, and we can expect further reduction of price with
more technical innovations and with larger volume sales of the devices.
Evolution of Intel Microprocessors to feed from bulky mainframe computers to personal
computers, and then to sleek ultra books and smart phones offers another telling story of
using innovation as a fast and frugal strategy to offer lighter, simpler and cheaper
products that help in establish market leadership in a fast and efficient way. Intel® 4004
processor introduced in 1971 has initial clock speed of 108 KHz, consisting of 2,300
number of transistors based on 10μ manufacturing technology, which is to be contrasted
with Intel®
Core™ i7-3770T Processor introduced in 2011 having initial clock speed up
to 3.77 GHz, consisting of 2,270,000,000 number of transistors based on 45nm
8
manufacturing technology9. Although according to Moore’s law (Moore, 1965) number
of transistors on a chip would double about every two years innovation in manufacturing
technology has drastically reduced the size of transistors from 10μ to 45 nm resulting in
more number of transistors squeezed up in smaller area with better performance.
Innovations in manufacturing technology has acted as a speed heuristic by allowing us to
pack roughly twice as many transistors on a chip, making computers twice as fast as
compared to its precursor.
Adding to this, Schmidhuber’s speedup law says that the delay between each successive
radical invention in the field of computer technology decreases exponentially: each new
radical innovation comes twice as fast as the previous one (Schmidhuber, 2003). So,
innovation is intimately associated with speed sometimes acting as a agent of speed and
7 1 GHz = 1 x 10
9 Hz = 1 x 10
6 KHz
8 nm stands for nanometer where 1 nm =1 x meter = 1 x µ ; 1 µ = 1 x meter
9 Source: Intel website, Retrieved from http://download.intel.com/pressroom/kits/IntelProcessorHistory.pdf
97
excellence along with itself getting speeded up at an exponential rate. The fast and frugal
nature of innovation is infused to the process, designs and devices it is applied leading to
the development of, what great scientist Nikola Tesla envisioned as, the products with
intellect .
Michael Dell (1999) in his autobiography Direct from Dell has discussed how he used
innovation as a step reduction strategy to optimize the circuits of old IBM® PCs. Dell™
resold these improvised PCs again at a cheaper price to people through his innovative
direct selling model and finally beating IBM in their own game. A MEDEA+ report
(Catrene, 2008) considers process innovation key to reducing device size and cost.
Internet, as a unprecedented innovative technology (Peterson, 1997), has also acted as a
fast and frugal heuristic by eliminating the distance between an information seeker and
information, leaving redundant the slow transmission lines, and leading to the emergence
of fast paced business by offering secure electronic payments, and full-motion
demonstrations of the merchandise (Jarvenpaa & Todd, 1997; Hui & Wan, 2004). Thus
innovation is bringing fast paced changes in the nature of products and other areas where
it is being practised acting as a fast and frugal mechanism to increase one’s adaptability
and achieve excellence. It uniquely takes a top-down approach, creates new uncontested
markets (blue oceans) in a fast and efficient manner (Kim & Mauborgne, 2005).
5.3 Discussion of structural equation modelling (SEM)
Structural equation modelling (SEM) is a powerful multivariate method allowing the
evaluation of a series of simultaneous hypotheses about the impacts of latent
and manifest variables on other variables, taking measurement errors into account (Lee,
2007). As a multivariate modelling technique it is a popular tool for testing the causal
relationship among a set of independent and dependent variables, continuous or discreet
(Ullman, 2007). SEM is a confirmatory technique often used to test a proposed theory or
model. In current research the model was to be confirmed is termed as proposed model
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or default model. In the proposed model it was hypothesized that the two factors obtained
after exploratory factor analysis, i.e. SAH & FFH, come together to give rise to another
variable called heuristic intelligence which further brings business excellence. The
overall objective of the SEM was to establish that the proposed model has a close fit to
the sample data in term of the difference between the sample and model –predicted
covariance matrices (Dion, 2008). The proposed model was first conceptualized based on
theoretical understanding and then constructed in AMOS 18.0 to test whether the
obtained data (i.e sample) fits the proposed or default model.
AMOS calculates many indices of model fit in its output. Of these Greenspoon and
Saklofske (1998) has recommended the use of four fit indices to assess the model fits,
i.e., GFI, AGFI, and χ2. However, Shevlin, Miles, & Lewis (2000) has considered
Greenspoon and Saklofske’s (1998) criteria inadequate and have recommended the
assessment of model fit based on indices of overall fit (e.g. χ2 , GFI, SRMR), incremental
fit (e.g. NFI, CFI), the root mean square error of approximation (RMSEA) and Hoelter's
critical N (Shevlin,Miles, & Lewis, 2000). Cury et al. (1996) have used 5 indices i.e. χ2
,
Relative χ2
(i.e., χ2
/df), GFI, AGFI & SRMR to assess their model fit. The proposed
model showed a good-fit on most of these indices, and various indices of fit along their
interpretation are discussed as following:
Chi-square (χ2):
χ2 : For the current sample chi-square is not significant (χ
2 = 5.446, df=2, and p =
.066, p Bollen - Stine = .094) which indicates that obtained data fits with the proposed
model, and the proposed model cannot be rejected. When χ2
is not significant it
means that the hypothesized model adequately describes the sample data (Byrne,
2010).
χ2 /df ratio, or Relative Chi-square: it is another index of model-fit, and it is called
relative chi square. A value of relative chi square between 1 to 3 indicated
acceptable fit between hypothesized model and sample data (Joreskog, 1969;
Carmnines&McIver,1981). However, some researchers have recommended
99
relative χ2
value between 2 to 5 indicating a reasonable fit (Marsh &
Hocevar,1985). For the sample data χ2 /df ratio is 2.723 which is within
satisfactorily acceptable limits. So, we can further say that the hypothesized
model fits the data. Because Chi square is very sensitive to large sample size
additional indices of fit were also observed for testing the model which are
discussed below.
GFI (Goodness-of-fit Index), AGFI (Adjusted Goodness-of-fit Index) & PGFI
(Parsimony Goodness-of-Fit Index)
GFI & AGFI stand for the Goodness-of-fit Index and Adjusted Goodness-of-fit
Index whose values for the hypothesized model are .987 and .934 respectively.
The value of GFI & AGFI ranges from 0 to 1 where higher values show a better
fit. For a good and acceptable fit the value of these indices should be greater than
.95 (Byrne, 2010). For the current model both values indicate a good fit of
hypothesized model with sample data. AGFI differs from the GFI only in the fact
that it adjusts for the number of degrees of freedom in the specified model. As
such, it also addresses the issue of parsimony by incorporating a penalty for the
inclusion of additional parameters (Byrne, 2010). The GFI and AGFI can be
classified as absolute indices of fit because they basically compare the
hypothesized model with no model at all (Hu & Bentler, 1995).
PGFI stands for Parsimony Goodness-of-Fit Index. It takes into account the
complexity (i.e., number of estimated parameters) of the hypothesized model in
the assessment of overall model fit. PGFI subsumes the measure of the goodness-
of-fit of the model (as measured by the GFI) and the parsimony of the model
thereby providing a more realistic evaluation of the hypothesized model (Mulaik
et al., 1989; cited from Byrne, 2010). The PGFI seriously penalizes the model for
complexity, hence its value is considerably lower. As no threshold value of
parsimony fit indices have been recommended, there interpretation is not easy.
However, they can be seen along with other indices, for e.g., according to Mulaik
100
et al. (1989) if Goodness of fit indices (like GFI, AGFI) are in the range of .90s
we can expect the value of PGFI within .50s. The obtained value of PGFI for the
current sample is .197 which is less than .50 , indicating the harmony of indices
according to Mulaik et al.’s criteria.
SRMR (Standardized Root Mean square Residual)
SRMR stands for the Standardized Root Mean square Residual. It represents the
average discrepancy between the sample observed and hypothesized model. Its
value ranges from 0 to 1, where 0 denotes perfect fit and 1 denotes that sample
data is unfit with the hypothesized model. Generally for a well fitting model a
value of SRMR less than .05 is considered good (Byrne, 2010). For the current
model the value of SRMR is .0111 which indicates good model fit.
NFI (Normed Fit Index)
NFI was given by Bentler and Bonnet (1980), it is also known as DELTA 1. In
AMOS output it is shown under ‘baseline comparisons’ because it assesses the
model by comparing the χ2 values of the hypothesized model with a null model
(i.e., a model of random variables where correlation among variables is 0). NFI
varies from 0 to 1, where 1 shows perfect fit. NFI reflects the proportion by
which the researcher's model improves fit compared to the null model . For
example, a value of NFI =.65 means the researcher's model improves fit by 65%
compared to the null model. So, higher the value of NFI better the researcher’s
model improves the fit as compared to null model. Bentler (1992) considered a
value of NFI greater than .90 as representative of a well-fitting model , however
later (Hu & Bentler, 1999) revised cutoff value ≥ .95. For our default model NFI
= .996, indicating a well-fitting model.
TLI (Tucker – Lewis Index)
A major problem associated with NFI is that it is sensitive to sample size,
underestimating fit for samples less than 200 (Mulaik et al, 1989; Bentler, 1990).
So, according to Kline (2005) we cannot solely rely on NFI. This problem is
rectified in TLI . Also, Tucker-Lewis Index (Tucker & Lewis, 1973) prefers a
101
simpler model and penalizes the complex models. In this way it is again a better
index as compared to NFI. The value of TLI ranges from 0 to 1, and values close
to .95 indicates good fit (Hu & Bentler, 1999). In the default model , TLI = .993
indicative of good-fit.
CFI (Comparative Fit Index )
To address the problem associated with NFI, Bentler (1990) revised the NFI to
take sample size into account and proposed the Comparative Fit Index . CFI is
independent of sample size and is more appropriate measure of fit than NFI. The
value of CFI ranges from 0 to 1 , and values ≥ .95 indicate good-fit (Hu &
Bentler, 1999). In the default model CFI = .998, indicating good model-fit.
RMSEA (Root Mean Square Error of Approximation)
The degree of discrepancy (or the lack of fit) between the hypothesized model
and the population is known as the error of approximation. The RMSEA is a
standardized measure of error of approximation. According to Browne & Cudeck
(1993), the RMSEA provides a measure of the discrepancy per degree of
freedom for the model. The RMSEA values are classified into four categories:
close fit (.00–.05), fair fit (.05–.08), mediocre fit (.08–.10), and poor fit (over .10)
(Browne & Cudeck, 1993; MacCallum, Browne, & Sugawara, 1996).
In the current model the value of RMSEA = .092,which shows mediocre or
average fit . However, this value should be interpreted in the light of sample size
and degree of freedom for the default model. According to Kenny, Kaniskan,
and McCoach (2011) there is greater sampling error for samples having small df
and low sample size, especially for the former. Thus, models with small df and
low sample size (N) can have artificially large values of the RMSEA. The value
of RMSEA in models with small df and small sample size can be very
misleading. For these reasons, Kenny, Kaniskan, and McCoach (2011) has argued
to not even compute the RMSEA for low df models. So, if we exclude RMSEA
102
from our analysis, for the reasons stated above, all other indices discussed show a
good model fit.
The model discussed above is based on the idea that an organization’s ability to innovate
is recognized as one of the determinant factors for it to survive and succeed (Doyle, 1998;
Quinn, 2000). India is an emergent economy and modern Indian innovation is an
emergent construct (Kumar & Puranam, 2011) acting like a fertile ground for the
emergence of new explanatory theoretical model (Bhatti, 2012). The development of
above structural equation model should be seen in that context. The major constructs of
the proposed model find resonance in the pattern of innovations that India has
manifested after the end of ‘licence raj’10
in 1990s and subsequent liberalization of
Indian economy.
After opening of Indian economy coupled with the emergence of a flat world (Friedman,
2005), India has gradually emerged as a fountainhead for innovations, and most of the
innovations coming from India can be termed as frugal innovation because they seek to
minimize the use of material and financial resources in the complete value chain
(development, manufacturing, distribution, consumption, and disposal) with the objective
of reducing the cost of ownership while fulfilling or even exceeding certain predefined
criteria of acceptable quality standards (Tiwari & Cornelius, 2012). Indian innovation is
increasingly manifesting the heuristic intelligence through a combination of adaptive
ability and the ability of frugal thinking (Radjou, Prabhu & Ahuja, 2012) leading to the
development of ecologically rational products like low cost Nano® cars (Kevin, Freiberg,
& Dunston, 2010), cheap smartphones, and other products of consumption to suit the
local socio-economic needs of a large consumer base lying at the bottom of pyramid
(Prahalad, 2010).
10
a term coined by Indian freedom fighter and statesman Chakravarti Rajgopalachari (Erdman, 2007) to
refer to tape corruption in bureaucracy and public sector institution in India under British rule. The licence
raj was informally ended after the liberalization of Indian economy in 1990s.
103
5.4 Chapter Summary
Innovation has emerged as a main source of achieving growth and excellence in modern
business context. The two obtained components of innovation heuristic after factor
analysis, i.e. search & adapt heuristic, and fast & frugal heuristic, are found to be
integrated in theoretical literature on innovation as well as managerial and entrepreneurial
practices to achieve adaptive growth and excellence in a fast and efficient manner. The
proposed model, wherein effects of both SAH & FFH are fully mediated through
heuristic intelligence, is found to be fit based on various indices of fit. Finally, an
attempt has been made to locate this model in existing research literature and behavioural
practices of managers and entrepreneurs.
Chapter 6
Limitations &
Implications of Study
106
CHAPTER 6
LIMITATIONS AND IMPLICATIONS
The limitations of current research findings can be understood by looking at the
limitation of the methodological aspects involved in the research work, theoretical ideas
on which the research is based, and the validity and soundness of research findings in
predicting the future patterns. Methodological aspects of the research include the issues
related to the tools used for data collection, issues related to sampling, and the statistical
methods used to analyze the obtained data. Theoretically and conceptually the current
research is based on bounded rationality paradigm and effort has been made to look at
their implication for the organizational processes like innovation and excellence. The
strength and limitations of this and its sister outcomes might also become part of the
strength and limitation of the current research work. Finally, predictive power of the
current research findings in explaining the future trends in innovation will be important in
determining the significance of the current research work. All these evaluative aspects
have been discussed in detail in the following sections.
6. 1 Limitations of the study
There are two major tools used in the current research: one, Innovation as a heuristic
scale which was developed during the process of research; and second, the Excel Scale
developed by Sharma et al. (Sharma, Netermeyer, & Mahajan, 1990a). Although, the
innovation as a heuristic scale possess a good reliability coefficient (α = .963), and its
items were selected after adequate psychometric procedures but the results obtained by
the scale may be limited due to the reasons discussed below.
The scale is based on a set of 19 innovation related heuristic used by Indian entrepreneurs
chiefly based in and around Gujarat-Mumbai industrial area, given by Prof. Mathew J.
Manimala (1992). While identifying entrepreneurial heuristics the scale puts an
overwhelming emphasis on search of creative ideas and opportunities in one’s
107
environment as 11 out of 17 items1 selected in the final scale are related to an adaptive
search of new ideas and information in the business ecology of the entrepreneur/manager.
Further as compared to 17 items on SAH there are just 7 items on FFH subscale. This
may have two major implications: one, the search factor (SAH) may skew the result in its
favour as compared to fast and frugal factor (FFH). This could be the reason behind the
effect of SAH’s ability of having a direct bearing on the business excellence giving rise
to a partial mediation model; second, the large number of items on creative ideas (n = 11)
may have a confounding effect with the danger of SAH may turning into a measure of
creativity heuristic rather than innovation heuristic. Although creativity and innovation
are intimately related and creativity is a starting point of innovation but creativity
becomes innovation only when it is implemented (Amabile, 1996). However, enough
measures were taken before running the proposed structural equation model, and the
result (see figure 4.4, chapter 4) shows that the regression weight of SAH and FFH over
heuristic intelligence are 1.10 and 1.21 respectively. Hence, we can say that SAH has no
skewing effect on heuristic intelligence, and the role of both the heuristic in bringing
business excellence is fairly balanced when mediated by the heuristic intelligence.
Another limiting factor of the current research could be that innovation in Indian context
is an emergent concept (Kumar & Puranam, 2011) which may give rise to many alternate
theoretical propositions (Tiwari, & Herstatt, 2012). There could be alternative models
which may show an equal or better fit indices as there is no single best way through
variables may casually related to each other especially when they are studied under
presumed environmental uncertainties and limited choices faced by entrepreneurs and
managers. The one model that was also found to be fitting with data was the partial
mediation model suggested after the mediation analysis in which SAH was shown to
have a direct bearing on the business excellence along with its effect being mediated
through the heuristic intelligence variable. However, in structural equation modelling
technique it is not uncommon to see two or more alternative models that fit a specific
1 2 items were deleted after the doing reliability analysis and principal component analysis.
108
data set equally well, or, subject to certain restrictions, fit any data set meeting the
restrictions equally well (Spirtes et al., 1997). However, in the present case when the
possible competing models were tested only one model was found to be showing fit
which was discarded on the basis of conceptual grounds and the propositions of the
research, as in case of competing models the researcher may take the theoretical
proposition and his objectives as the direction for choosing the suitable model rather than
fall for arbitrariness (Spirtes et al., 1997; Byrne, 2001). In case of present research the
major theoretical model guiding the research framework was bounded rationality
approach (Gigerenzer et al., 1999; Gigerenzer, 2000; Gigerenzer, 2002) to cognition. An
Entrepreneur/manager, while innovating, will not and cannot go for an exhaustive search
to judge all possible alternatives and select the best one. So, the search heuristic has
meaning only when it is fast and frugal and adapted to its ecology (i.e., ecological
rational). Hence, a joint effect of SAH & FFH as hypothesized in the proposed model is
theoretically more sound as compared to their effect in isolation.
The Excel scale (Sharma, Netermeyer, & Mahajan, 1990a) is a standardized tool with
reportedly good Cronbach´s Alpha reliability coefficient ranging from .89/.90 (Sharma et
al., 1990a) to .92 (Caruana et al., 1995; Sandbakken, 2002). However, the scale is
explicitly based on 8 attributes of excellence given by Peters & Waterman (1982), and its
matter of further scrutiny how important are these attributes for managers and
entrepreneurs in the current business context. Also, the sample doesn’t include the real
life innovators so the obtained data might essentially reflect the perception of managers/
entrepreneurs regarding the nature of interaction between variables as presented in the
proposed model rather than innovation per se. Further, the validity of the current model
needs to be explored by confronting it with real life business events in past, present and
future. The ability to predict other (or future) data arising from the same latent process is
often seen as a mark of a model’s usefulness or quality, and it is commonly assumed that
a model’s fit to a given sample provides a good clue to this predictive ability (Preacher,
109
2006). The predictive ability of this model needs to be tested in future by testing its fit for
the samples manifesting the interplay of variables presented in the model.
Further, the two major multivariate techniques have been used in the current research
may bring in their own strengthening and limitationary influence to the research
outcomes. Although, factor analysis is a quite useful technique of ‘orderly simplification’
(Burt, 1940) through which we can condense and simplify the multivariate data (Kothari,
2008) but before use and interpretation of its results there should always be an
elaboration upon the quality of data, like sufficiency of sample size, normalcy of data,
etc., from which the factors have been derived (Child, 2006). These issues along with
their implications have already been addressed in chapter 3 (Methodology) and chapter 4
(Data Analysis & Interpretation). Although utmost care has been taken while naming and
interpretation of the two obtained factors but there is considerable subjectivity involved
in determining the number of factors and the interpretation of such factors (Tryfos, 1998).
To deal with this, the number of factors extracted have been based on the rigorous
statistical procedure but naming and interpretation of factors have been done after the
expert discussion and careful scrutiny of the research literature.
6.2 Implications of the study
Recently, the use of factor analysis to provide evidence for a theory has increased even
among those who earlier emphasized on its descriptive character (Pugesek, Tomer, & von
Eye, 2003). The theoretical implications of the obtained factors have already been
discussed in chapter 5 (Discussion), however, the proposed model have been tested using
structural equation model which brings its own bag of advantages and disadvantages
(Werner & Schermelleh-Engellike, 2009), like any other statistical method, which might
also influence the results of the study. The major advantages associated with SEM are the
availability of more valid conclusions because it uses several indicator (i.e., observed)
variables to predict a construct (unobserved variable). In current research two major
indicators (i.e., SAH & FFH), having good reliability coefficients ( αSAH = .9, & αFFH =
110
.95), augur well for the validity of conclusions derive from the model. Further, SEM
takes into account the measurement error and excludes it from the analysis which again
enhances the validity of its results. However, the parameter estimation process in SEM is
based upon maximum likelihood approach which is further based upon certain
assumptions like large sample size, multivariate normality, etc. Some assumptions, like
multivariate normality, are rarely achieved (Werner & Schermelleh-Engellike, 2009)
which might have its own implications for the obtained results. These issues have already
been addressed while discussing the practical issues involved in SEM analysis in chapter
4 (i.e., Data Analysis and Results).
The present study is a sincere attempt to study the nature of relationship between
innovation and excellence within the framework of bounded rationality paradigm. The
study could have both theoretical and practical implication. Theoretically, the study
posits two new factors, i.e. SAH & FFH, underlying innovation heuristic and their joint
influence measured as heuristic intelligence. These variables may require further
assessment and validation along with measuring their instrumentality in bringing
business excellence through other independent researches. The research literature is
replete of creative search and use of information (Lundvall, 1985; Lussier, 1995; Wang &
Ahmad, 2004; Afzal, 2009) to design fast and frugal products/processes (Xia &
O’Gorman, 2003; Catrene, 2008) to achieve excellence but the current research have
attempted to see these two factors in terms of abilities. As the research sample’s
responses might be based upon its perception, which could also be a limiting factor of
the study, the results of the study are indicating that people and organizations possessing
the ability of adaptive search (by practicing SAH) and offering frugal innovations (by
practicing FFH) are being perceived as more competent and capable of better growth and
excellence.
The idea of heuristic intelligence may be received critically among the researchers and
academicians but the intelligent use of heuristics is gradually finding a place among the
111
researchers of intelligence in the form of intelligence of intuition, gut or unconscious
mind (e.g., Simon, 1987b; Loftus & Klinger, 1992; Gladwell, 2005; Gigerenzer, 2007;
Kaufman, 2011). To what extent heuristic intelligence relates with other types of
intelligence , for e.g. practical intelligence (Sternberg, 1988, 1999), will warrant another
study and a detailed study exploring formally the ability of a person to use heuristics for
arriving at successful solutions along with the underlying sub-factors (i.e., heuristic
intelligence) will be of strong heuristic value per se.
On practical side, the one implication of study could be that it may help managers and
entrepreneurs to beat the rationality burnout caused by a heavy emphasis placed on to
appear rational while making decisions. However, this issue has long been explored
under the bounded rationality and behavioural economics research. The seemingly non-
rational-heuristic approach to decision making is itself a rational behaviour in a world of
constraints and uncertainty. The two identified factors , i.e. SAH & FFH, may nudge the
budding entrepreneurs and managers to exercise these heuristics in their various
behaviours like decision making, product designing, process improvement, redesigning
consumer experiences, selecting a new employee, creating future strategies to adapt and
achieve excellence in a fast and frugal way.
6.3 Chapter Summary
The chapter begins with highlighting the major limitations that might have crept in the
current research due various theoretical and methodological issues. The problem
associated at theoretical levels involves the locating the two extracted factors in the
research literature along with their validity issues. The model may require further
validation for different samples involving similar predictors and output variables. The
heuristic intelligence variable should be studied as a formal type of intelligence viz-a-viz
the formal literature on intelligence along with carrying out a comparative assessment.
The possible practical implication of the model for various managerial behaviours have
also been highlighted.
References
114
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Appendices
144
Name: Mr./ Ms.. …………………………………… Age: ……………… Years
Gender: ☐ M / ☐ F Designation: …………………
Name of Organization: ……………………………………. Total Work
Experience:……….Years
Dear Participant,
We are conducting a research study relating to various aspects of business
environment. The study contains a set of rating-type questions divided in four
parts and will take you just around 10 minutes to complete it. We are
sincerely thankful for your cooperation and time. Your responses will be kept
strictly confidential and will not be shared with anybody. However, if you
wish to know about the result of study after the completion of the study then
we’ll be happy to give you feedback if it can benefit you in anyway.
The purpose of research is educative only and the data you provide will be
used for the doctoral research (PhD) study. Please read the instructions at
each part of the study and proceed accordingly.
Thank You.
Yours Sincerely
Sanjay Singh, Doctoral Student
Department of Psychology
University of Delhi, Delhi -110007
Email: [email protected]
Prof. N.K. Chadha
Professor in Psychology
Department of Psychology
University of Delhi, Delhi -110007
Appendix A
145
PART 1 Instructions: Consider your decisions and approach as a manager/ entrepreneur. Read
the statements given below carefully and indicate the extent to which you agree or
disagree with each statement. You can show your level of agreement/ disagreement on a
7 – point rating scale ranging from 1 to 7 by putting a tick mark (✓) at the appropriate
number, where
1 = Strongly Disagree, 2 = Disagree, 3 = Slightly Disagree,
4 =Undecided, 5 = Slightly Agree, 6 = Agree, and 7 = Strongly
Agree
1 Be a pioneer in the choice
of products. Avoid highly
competitive, low margin,
run of-the-mill products.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
2 Ideas are the most important
resource. Look for them
everywhere .
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
3 Look for new (product)
ideas among personal
contacts (friends, hobby
clubs, professional
associations, customer
complaints, previous job
contacts, etc.).
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
4 Look for new (product)
ideas among technological
developments abroad
especially among new, rare,
or specialized products
developed abroad.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
5 Look for new (product)
ideas among one’s own
vision of the future, special
talents, and innovative
research findings, or among
the special skills of one’s
associates and staff.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
6 Look for new (product)
ideas among the
components, substitutes,
complements, neglected
ranges, supply gaps,
deficiencies, and
inadequacies of existing
products.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
146
7 Look for new (product)
ideas in the general
environment (existing
practices and changes in the
legal, political, religious,
social, and cultural
domains).
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
8 Be flexible in one’s ideas
and plans.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
9 Do not get stuck to one
idea. Be prepared to leave it
at the slightest indication of
failure, and develop new
ideas.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
10 Never be constrained by
rigid plans and the narrow
visions. Act according to
opportunities.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
11 Treat personal
problems/handicaps/
mishaps as indications to
change one’s line of
thinking/occupation.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
12 Never be complacent about
successes, but keep on
striving for excellence
through new ideas (Do not
repeat success strategies
until they fail).
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
13 Never stop searching for
new ideas and opportunities.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
14 Never set any geographical
limits to one’s search for
ideas and opportunities.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
15 Introduce new products,
modify existing products,
and/or change strategies
periodically.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
147
16 Keep the organization fresh
and dynamic by periodically
inducting young people into
it who have new ideas and
the drive to implement
them.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
17 Launch new products on a
trial basis, receive feedback,
and slowly widen the
market.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
18 Innovation is the quickest
way to create an
uncontested market and beat
competition.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
19 The best innovative
product/service in a domain
is one that accomplish the
domain specific task in
minimum number of steps
and maximum simplicity.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
20 Product/service
improvisation means
identifying and eliminating
all unnecessary steps in
design and use.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
21 Innovation is driving the
market toward smaller but
more efficient
products/services. The
evolution of smart phones,
tablets and nano-cars is
case in point.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
22 When I make changes in my
product I focus on how fast
& simple it will become for
customers while adopting it.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
23 I welcome all new ideas but
ideas which are fast and
frugal in bringing returns
are likely to be funded and
supported first than an those
which promise only long
term benefits.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
24 A faster way to challenge
and involve employees to
give them time to explore
new ideas/products on their
own.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
148
PART 2 Instructions: Think about your company/ organization and read the statements given below
carefully. Indicate the extent to which you agree or disagree with each statement in light of your
company/ organization’s outlook and approach in various areas. You can show your level of
agreement/ disagreement on a 7 – point rating scale ranging from 1 to 7 by putting a tick mark (
✓) at the appropriate number, where
1 = Strongly Disagree, 2 = Disagree, 3 = Slightly disagree, 4 =
Undecided,
5 = Slightly Agree, 6 = Agree, and 7 = Strongly Agree
1
The organization is
flexible and quick to
respond to problems.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
2 The organization is
flexible with employees
but administers discipline
when necessary.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
3 We have a small but
efficient management
team.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
4 The organization develops
products (and/or services)
that are natural extension
of its existing products and
services.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
5 The organization
concentrates on products
(and/or services) where it
has high levels of skills
and expertise.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
6 The organizations values
are driving force behind
our organization.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
7 In this organization we
instill a value system in all
our employees.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
8 It is the belief of the
management in this
organization that people
are of utmost importance
to the organization.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
149
9 The organization truly
believes in its people.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
10 In this organization we
encourage employees to
develop new ideas.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
11 This organization
believes in experimenting
with new product and
ideas.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
12 In this organization the
management creates an
atmosphere that
encourages creativity and
innovation.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
13 The organization believes
that listening to what
customers/ clients have to
say is a good skill to
have.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
14 This organization
considers the after the
sale services (and/or
follow up of customers
clients) as important as
making the sale itself.
1 Strongly
Disagree
2
Disagree
3
Slightly Disagree
4 Undecided
5
Slightly Agree
6
Agree
7 Strongly
Agree
15 We provide the
personalized attention to
all our customers/ clients.
1 Strongly
Disagree
2 Disagree
3 Slightly
Disagree
4 Undecided
5 Slightly
Agree
6 Agree
7 Strongly
Agree
16 This organization has a
small staff that delegates
the authority efficiently.
1 Strongly
Disagree
2
Disagree
3
Slightly
Disagree
4 Undecided
5
Slightly
Agree
6
Agree
7 Strongly
Agree
150
PART 3
Instructions: Again think of your company/organization’s conditions and indicate your response
on the each statement given below. The responses corresponding to each statement vary on a 7-
point rating scale ranging from – 3 to + 3, where:
Decreasing Rapidly = - 3, Decreasing Moderately = - 2, Decreasing Slowly = - 1, No
Change (in condition) = 0 , Increasing Slowly = + 1, Increasing Moderately = + 2,
Increasing Rapidly = + 3
1. The cash flow in your company is
Decreasing
Rapidly
(- 3)
Decreasing
Moderately
(- 2)
Decreasing
Slowly
(- 1)
No
change
(0)
Increasing
Slowly
(+1)
Increasing
Moderately
(+2)
Increasing
Rapidly
(+3)
2. The market share of your company
Decreasing
Rapidly
(- 3)
Decreasing
Moderately
(- 2)
Decreasing
Slowly
(- 1)
No
change
(0)
Increasing
Slowly
(+1)
Increasing
Moderately
(+2)
Increasing
Rapidly
(+3)
3. The sales growth of your company
Decreasing
Rapidly
(- 3)
Decreasing
Moderately
(- 2)
Decreasing
Slowly
(- 1)
No
change
(0)
Increasing
Slowly
(+1)
Increasing
Moderately
(+2)
Increasing
Rapidly
(+3)
151
4. The return on investment (RoI) of your company
Decreasing
Rapidly
(- 3)
Decreasing
Moderately
(- 2)
Decreasing
Slowly
(- 1)
No
change
(0)
Increasing
Slowly
(+1)
Increasing
Moderately
(+2)
Increasing
Rapidly
(+3)
5. The Net worth of your company
Decreasing
Rapidly
(- 3)
Decreasing
Moderately
(- 2)
Decreasing
Slowly
(- 1)
No
change
(0)
Increasing
Slowly
(+1)
Increasing
Moderately
(+2)
Increasing
Rapidly
(+3)
152
Inter-Item Correlation Matrix
Hi1 Hi2 Hi3 Hi4 Hi5 Hi6 Hi7 Hi8 Hi9 Hi10 Hi11 Hi12 Hi13 Hi14 Hi15 Hi16 Hi17 Hi18 Hi19 Hi20 Hi21 Hi22 Hi23 Hi24 Hi25 Hi26 Hi27 Item
total
Hi1 1 .590
Hi2 .507 1 . .737
Hi3 .566 .763 1 .787
Hi4 .445 .598 .699 1 .670
Hi5 .434 .643 .640 .632 1 .716
Hi6 .463 .502 .535 .581 .614 1 .705
Hi7 .347 .341 .367 .465 .505 .570 1 .552
Hi8 .458 .540 .570 .467 .477 .602 .437 1 .696
Hi9 .478 .662 .666 .580 .611 .579 .413 .581 1 .777
Hi10 .374 .535 .558 .468 .511 .439 .409 .495 .668 1 .675
Hi11 .452 .602 .644 .499 .511 .501 .403 .503 .733 .644 1 .739
Hi12 .466 .383 .479 .397 .374 .393 .395 .434 .489 .475 .512 1 .561
Hi13 .466 .495 .583 .450 .509 .515 .371 .518 .582 .480 .547 .537 1 .713
Hi14 .412 .635 .600 .557 .550 .533 .330 .465 .631 .520 .573 .358 .554 1 .713
Hi15 .375 .496 .485 .445 .485 .423 .319 .421 .456 .354 .437 .350 .626 .642 1 .629
Hi16 .474 .576 .579 .482 .459 .500 .327 .569 .603 .525 .582 .330 .511 .672 .557 1 .707
Hi17 .500 .656 .638 .511 .602 .531 .379 .556 .640 .534 .564 .426 .582 .600 .579 .652 1 .782
Hi18 .420 .574 .604 .438 .473 .508 .352 .578 .594 .493 .548 .320 .534 .542 .533 .627 .663 1 .708
Hi19 .412 .468 .536 .410 .488 .490 .447 .531 .559 .566 .557 .447 .530 .489 .446 .492 .652 .587 1 .711
Hi20 .431 .562 .603 .435 .546 .583 .434 .560 .609 .486 .638 .379 .544 .579 .451 .600 .624 .540 .642 1 .780
153
Hi21 .321 .432 .516 .323 .466 .469 .404 .485 .458 .462 .492 .413 .448 .432 .402 .439 .553 .455 .610 .691 1 .675
Hi22 .362 .434 .520 .442 .478 .479 .449 .486 .469 .397 .504 .359 .500 .476 .504 .474 .583 .517 .523 .689 .675 1 .712
Hi23 .379 .434 .497 .402 .466 .494 .411 .501 .435 .487 .453 .356 .460 .460 .406 .449 .517 .465 .474 .597 .588 .640 1 .678
Hi24 .358 .433 .385 .347 .375 .449 .429 .377 .357 .335 .421 .338 .444 .358 .318 .359 .434 .369 .383 .535 .459 .543 .606 1 .588
Hi25 .419 .501 .536 .492 .531 .485 .388 .453 .546 .426 .470 .343 .499 .454 .434 .433 .555 .516 .460 .579 .554 .670 .668 .618 1 .707
Hi26 .377 .402 .472 .450 .447 .484 .334 .489 .482 .452 .431 .367 .500 .423 .403 .454 .481 .477 .547 .570 .508 .568 .576 .553 .601 1 .663
Hi27 .307 .459 .454 .423 .423 .361 .348 .349 .439 .419 .411 .337 .452 .340 .394 .359 .428 .415 .455 .559 .574 .569 .478 .471 .601 .594 1 .609
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