UNIVERSIDAD DEL TURABO
SCHOOL OF BUSINESS AND ENTREPRENEURSHIP
KNOWLEDGE TRANSFER IN BUYER-SUPPLIER RELATIONSHIPS:
THE CASE OF MULTINATIONAL CORPORATIONS (MNCs)
AND THEIR LOCAL SUPPLIERS IN PUERTO RICO
By
Amarilis Delgado Santana, MBA
Dissertation Submitted in Fulfillment of the Requirements
for the Degree of Doctor in Business Administration
Gurabo, Puerto Rico
December, 2013
CERTIFICATION OF APPROVAL OF DISSERTATION
The dissertation of Amarilis Delgado Santana was reviewed and approved by the
members of the dissertation committee. The Doctoral Academic Requirements
Compliance form, signed by the dissertation committee members, has been deposited in
the Register’s Office and at the Graduate Studies & Research Center in the Universidad
del Turabo.
MEMBERS OF THE DISSERTATION COMMITTEE
Juan C. Sosa Varela, Ph. D. Universidad del Turabo Director
Marcelino Rivera López, Ed. D. Universidad del Turabo Member
Francisco Montalvo Fiol, Ph. D. Universidad Interamericana de Puerto Rico Member
©Copyright, 2013
Amarilis Delgado Santana. All Rights Reserved.
iv
KNOWLEDGE TRANSFER IN BUYER-SUPPLIER RELATIONSHIPS:
THE CASE OF MULTINATIONAL CORPORATIONS (MNCs)
AND THEIR LOCAL SUPPLIERS IN PUERTO RICO
By
Amarilis Delgado Santana, MBA
Juan C. Sosa Varela, Ph. D.
Director of Dissertation Committee
Abstract
We contend that factors such as expectation of relationship continuity, tacit knowledge,
quality of information exchanged and absorptive capacity can facilitate knowledge
transfer between multinational corporations (MNCs) and their local suppliers. In
contrast, factors such as trust, dependence and explicit knowledge are not as significant
for enabling knowledge transfer dynamics in this environment. We examine knowledge
transfer between multinational corporations and their local suppliers in Puerto Rico
through the analysis of empirical data collected from 110 suppliers in various industrial
sectors. We review relevant research related to the effects of knowledge transfer,
absorptive capacity, perceived trust, and supply chain management theory. Results
suggest that increases in expectations of relationship continuity, successful transfer of
tacit knowledge, quality of information exchanged, and absorptive capacity of local
suppliers can enhance vertical supply chain knowledge transfer. Our research could
hold significant contributions for both policy formulation and scholarly inquiry as a new
possible definition for knowledge transfer has emerged within the context of the results
of this research.
v
GIANCARLOS,
Thanks for being my
compass to move forward.
vi
ACKNOWLEDGEMENTS
First and foremost, I would like to thank God, who was my major source of
strength when I worked in my doctoral research and my family for their love and
patience.
I would like to extend my sincere thanks to Dr. Juan C. Sosa, Dr. Marcelino
Rivera, Dr. Francisco Montalvo and Dr. Ángel Ojeda for their mentoring and guidance
throughout the course of my doctoral research. I learned a lot from them.
I wish to thank my friends Maribel Pagán, Kateri Sosa, Carmen López and
Dr. Anidza Valentín for their continuous support and timely advice.
This thesis has also benefited from the involvement and cooperation of many
people, I express my appreciation to them.
Last but not least, I would like to thank Universidad del Turabo and Puerto Rico
Manufacturers' Association for their support during the data collection.
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TABLE OF CONTENTS
Page
LIST OF TABLES x
LIST OF FIGURES xi
LIST OF APPENDICES xii
CHAPTER 1. INTRODUCTION 1
1.1. Background to the Research 1
1.2. Research Problem, Research Question and Hypotheses 3
1.3. Justification for the Research 6
1.4. Methodology 7
1.5. Outline of the Research 8
1.6. Definition of Key Terms 9
1.7. Conclusions 9
CHAPTER 2. LITERATURE REVIEW 10
2.1. Introduction 10
2.2. Knowledge 10
2.3. Knowledge Transfer 12
2.4. Determinant Factors of Knowledge Transfer and Hypotheses 15
2.4.1. Trust 16
2.4.2. Dependence 19
2.4.3. Expectation of Relationship Continuity 21
2.4.4. Type of Knowledge Shared (Tacit and Explicit) 24
2.4.5. Quality of Information Exchanged 27
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2.4.6. Absorptive Capacity 30
2.4.7. Knowledge Transfer and Outcomes 32
2.5. Conclusions 35
CHAPTER 3. METHODOLOGY 36
3.1. Introduction 36
3.2. Methodology and Justification 36
3.3. Sampling Frame 37
3.4. Data Collection 38
3.5. Data Analysis 39
3.6. Ethical Considerations 39
3.7. Conclusions 40
CHAPTER 4. DATA ANALYSIS 41
4.1. Introduction 41
4.2. Description of the Population, Sample, Data Collection and Data Analysis 41
4.3. Demographic Information 42
4.4. Factor Analysis 56
4.5. Reliability and Validity 79
4.6. Values of R² 82
4.7. Collinearity 82
4.8. Testing for Hypotheses and Findings 84
4.9. Data Summary 87
CHAPTER 5. CONCLUSIONS 88
5.1. Introduction 88
ix
5.2. Discussion of Results 88
5.2.1. Determinant Factors that Facilitate or Inhibit Knowledge Transfer 89
5.2.2. The Effects of Determinant Factors 90
5.2.2.1. The Effect of Trust on Knowledge Transfer 92
5.2.2.2. The Effect of Dependence on Knowledge Transfer 94
5.2.2.3. The Effect of Expectation of Relationship Continuity
on Knowledge Transfer 96
5.2.2.4. The Effect of Explicit Knowledge on Knowledge Transfer 96
5.2.2.5. The Effect of Tacit Knowledge on Knowledge Transfer 98
5.2.2.6. The Effect of Quality of Information Exchanged on
Knowledge Transfer 99
5.2.2.7. The Effect of Absorptive Capacity on Knowledge Transfer 100
5.2.2.8. The Effect of Knowledge Transfer on the Outcomes 101
5.3. Conclusions 103
5.4. Contributions to Theory and Practice 104
5.5. Delimitations 105
5.6. Directions for Future Research 105
REFERENCES 107
x
LIST OF TABLES
Page
Table 1: Tacit and Explicit Knowledge 25
Table 2: Distinctions between Quantitative and Qualitative Researches 37
Table 3: KMO and Barlett's Test 56
Table 4: Communalities 57
Table 5: Total Variance Explained 60
Table 6: Rotated Component Matrix 62
Table 7: Overview Quality Criteria 79
Table 8: Fornell and Larcker Criterion 80
Table 9: Correlation between Variables 81
Table 10: Collinearity 83
Table 11: Path Coefficients and t-Values 86
xi
LIST OF FIGURES
Page
Figure 1: Research Structure 8
Figure 2: Modes of the Knowledge Creation 14
Figure 3: Conceptual Framework of Knowledge Transfer Between Buyer
Supplier 16
Figure 4: Distribution of the selected sample per gender 43
Figure 5: Distribution of the selected sample per age group 44
Figure 6: Distribution of the selected sample per educational qualification 45
Figure 7: Distribution of the years employed in the organization 46
Figure 8: Distribution of the role in the organization 47
Figure 9: Distribution of the employees in the companies 48
Figure 10: Distribution of the industry sector 49
Figure 11: Distribution of the primary functional responsibility 50
Figure 12: Distribution of the frequency of interactions 51
Figure 13: Distribution of the type of interaction 52
Figure 14: Distribution of the years worked with the buyers 53
Figure 15: Distribution of the overall knowledge (Firm's Perspective) 54
Figure 16: Distribution of the overall knowledge (Experience with these Buyers) 55
Figure 17: SmartPLS Structural Model Measurement with Loadings 84
Figure 18: SmartPLS Bootstrapping Model with Loadings 85
Figure 19: Revised Conceptual Framework of Knowledge Transfer Between
Buyer-Supplier 91
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LIST OF APPENDICES
Page
Appendix A: Constructs and Scale Development Sources 125
Appendix B: Survey Questionnaire 126
Appendix C: Summary of Demographic Information 132
Appendix D: Summary of Hypothesis Testing 135
1
CHAPTER 1
INTRODUCTION
1.1. Background to the Research
Knowledge is considered a source of sustainable competitive advantage
(Prahalad and Hamel, 1990; Peteraf, 1993; Argote and Ingram, 2000; Nonaka, Toyama
and Byosière, 2001) and one of the most important strategic assets (Winter, 1987; Ahnn
and Chang, 2004) in the organizations. According to Inkpen (2008), new knowledge
provides the foundation for new skills, which in turn can lead to competitive success.
While new knowledge is developed by individuals, organizations are playing a critical
role in articulating and amplifying that knowledge (Nonaka, 1994).
In today's business world, the value of the knowledge is being recognized by
organizations as they face an increasing competition driven by the technological
changes, economic challenges and globalization. As a result, organizations are
continuously deriving competitive advantages by creating and transferring knowledge
(Inkpen, 1996; Inkpen, 1998; Argote and Ingram, 2000; Ahn and Chang, 2004; Prevot,
2008; Easterby-Smith, Lyles and Tsang, 2008; Mathew and Kavitha, 2008; Muthusamy,
Hur and Palanisamy, 2008; Tseng, 2009; Lee and Wu, 2010; McNeish and Mann, 2010).
Over the past years, knowledge transfer has also drawn increasing attention as
knowledge fosters innovation and as an intangible asset creates sustainable competitive
advantage for the organizations (Joia and Lemos, 2010; Allameh, Harooni and
Borandegi, 2012). Some authors consider it as a central element in their studies (e.g.
Chen and McQueen, 2010; Lee and Wu, 2010; McNeish and Mann, 2010; Rashed,
Azeem and Halim, 2010; Wang and Noe, 2010; Zonooz, Farzam, Satarifar and Bakhshi,
2011; Reiche, 2011; Saari and Haapasalo, 2012; Worasinchai and Daneshgar, 2012).
2
Many researchers in the Knowledge Management Transfer (KMT) and
Technology Transfer fields highlighted the objectives for inter-firm knowledge transfer
(e.g. Dyer and Singh, 1998; Hall, 2000; Dyer and Nobeoka, 2000; Prevot and Spencer,
2006; Prevot, 2008). According to the latest findings of a research conducted by Prevot
(2008), the specific objectives behind the knowledge transfer process are summarized
below:
1) The improvement of knowledge of the source by feedback effect. For
example, after knowledge transfer is completed, the source benefits from
improvements made by the recipient while assimilating the knowledge
transferred. This is a bilateral process in which new knowledge is created.
2) The creation of knowledge in common, as well as the creation of specific
relationships between the source and the recipient. In this case, knowledge
transfer may be a source of creation of relational rents and new knowledge.
Participants, thorough their interactions, establish the level of value of the
KMT and new knowledge.
3) The use of knowledge transfer as a means of implementing strategic
objectives. A useful example is Toyota in the automotive industry. By
sharing its production know-how with its suppliers, Toyota benefits from the
effects of knowledge transfer among its suppliers.
In essence, these specific objectives highlight the knowledge base, diffusion,
transfer and utilization of new knowledge.
Although extensive analysis has been carried out of the knowledge transfer
process, there is as yet little research on the topic of knowledge transfer between
vertical relationships such as buyers-suppliers (Squire, Cousins and Brown, 2009) in
3
knowledge intensive industries like Pharmaceuticals, Medical Devices and
Biotechnologies. A quantitative research is proposed to fill this gap in the literature.
1.2. Research Problem, Research Question and Hypotheses
Squire et al. (2009, p. 461) argued "Prior research examining inter-firm
knowledge transfer has focused almost exclusively on horizontal forms of governance
such as strategic alliances and joint ventures, whilst research on vertical forms, such as
buyer–supplier relationships, is limited". Likewise, Martinkenaite (2011) noted that
although research on inter-organizational knowledge transfer is burgeoning, its
antecedents and consequences remain unclear. The research problem identified for this
research is: What are the bases for knowledge transfer in industrial vertical
relationships?
According to Syed-Ikhsan and Rowland (2004), many organizations are
concentrating their efforts on how knowledge can be transferred due to creation and
transfer of knowledge have become critical factors in the organization's success and
competitiveness. Hou and Chien (2010) argued that knowledge has become the major
asset of the organizations and the key to retain their competitiveness. They suggested
that organizations must leverage their existing knowledge and create new knowledge in
order to compete effectively. In a similar vein, Alipour, Idris and Karimi (2011)
suggested that knowledge creation and knowledge transfer are vitals for the success
and competitive advantage of the organizations. These authors acknowledged that
fostering competitive advantage requires an organization's capability to create and
transfer new knowledge.
Considering the importance of knowledge transfer for the organization's success
and competitiveness, this research contributes original research involving the inter-firm
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knowledge transfer by Multinational Corporations (MNCs) to their local suppliers in
Puerto Rico.
Although Puerto Rico has one of the largest life science manufacturing
infrastructures in the world (Montalvo, 2011), limited scholarly work has been conducted
in the knowledge transfer by MNCs to their local suppliers. According to data published
by Puerto Rico Industrial Development Company (PRIDCO, 2012), Puerto Rico is one of
the top jurisdictions with the highest volume in the biotechnological sector, one of the
top 5 jurisdictions with the highest volume in the pharmaceutical sector and one of the
top 10 jurisdictions within highest volume in the medical device sector. Eleven of the
top 20 prescription drugs sold in the United States are manufactured in Puerto Rico.
In this research, MNCs (Pharmaceuticals, Medical Devices and Biotechnologies
companies) are the source of the knowledge and their local suppliers in Puerto Rico are
the recipients of the knowledge. We examine possible constrains in the knowledge
transfer and identify enabling factors that result in a more positive knowledge
management process.
This research is addressed by developing a conceptual framework followed by
conducting a quantitative research to examine the proposed conceptual framework.
For this research, the dependent variable is knowledge transfer and the independent
variables are: trust, dependence, expectation of relationship continuity, type of
knowledge shared (tacit and explicit), quality of information exchanged and absorptive
capacity. Our contention is that the combinations of these variables in this research are
unique.
Search queries were conducted on the EBSCO online database. The numbers
of articles on this database, which satisfy the criterion: "knowledge transfer", "trust",
"dependence", "expectation of relationship continuity", "type of knowledge shared (tacit
5
and explicit)", "quality of information exchanged" and "absorptive capacity" were noted
from 2008 to 2012. This database suggests that researches on "knowledge transfer"
and "trust" have been continuously increasing over the years while variables such as
"dependence", "expectation of relationship continuity", "type of knowledge shared (tacit
and explicit)", "quality of information exchanged" and "absorptive capacity" have been
little investigated.
Based on the proposed conceptual framework, the following objectives and
hypotheses are posed for this research:
Objectives
1) Identify the determinant factors that could facilitate or inhibit knowledge transfer
success between buyer-supplier relationships.
2) Determine the knowledge transfer impact in the outcomes (benefits and usefulness)
of this collaborative relationship.
Hypotheses
Hypothesis 1: Trust will positively impact the knowledge transfer process between
buyers and suppliers.
Hypothesis 2: Dependence will positively impact the knowledge transfer process
between buyers and suppliers.
Hypothesis 3: Expectation of relationship continuity will positively impact the knowledge
transfer process between buyers and suppliers.
Hypothesis 4a: Explicit knowledge will positively impact the knowledge transfer process
between buyers and suppliers.
Hypothesis 4b: Tacit knowledge will positively impact the knowledge transfer process
between buyers and suppliers.
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Hypothesis 5: Quality of information exchanged will positively impact the knowledge
transfer process between buyers and suppliers.
Hypothesis 6: Absorptive capacity will positively impact the knowledge transfer process
between buyers and suppliers.
Hypothesis 7a: Knowledge transfer will positively impact the outcomes (benefits) of the
buyer-supplier collaboration.
Hypothesis 7b: Knowledge transfer will positively impact the outcomes (usefulness) of
the buyer-supplier collaboration.
1.3. Justification for the Research
Although knowledge transfer has received increasing attention from academics
and practitioners (Nonaka, 1994; Simonin, 2004; He, Ghobadian, Gallear and Sohal,
2006; Hau and Evangelista, 2007; Kumar and Ganesh, 2009), there is limited literature
that investigate the knowledge transfer between buyers-suppliers (Inkpen, 2000; Kotabe,
Martin and Domoto, 2003; Squire et al., 2009). Squire et al. (2009, p. 462) reinforced
this point by arguing that "knowledge transfer within vertical modes of governance, such
as strategic buyer–supplier exchange, has received relatively little attention and is thus
deserving of detailed consideration". According to them, the context of the prior
researches has focused almost exclusively on horizontal modes of governance, such as
strategic alliances and joint ventures. In synthesis, it is conceded that there is a gap in
the body of knowledge in this area.
Due to knowledge transfer plays an important role in the buyer-supplier
relationships and that the knowledge transfer may influence the MNCs decisions
(Wadhwa and Saxena, 2005), it seems quite clear that research on this topic should be
expanded in order to contribute to the body of the knowledge transfer.
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1.4. Methodology
According to Squire et al. (2009); Gupta and Govindarajan (2000), the research
of the knowledge transfer can be conducted from at least three different levels of
analysis: nodal which is the research of transfers within the boundaries of the firm;
dyadic which covers the research of transfer between pairs of firms; and systemic which
refers to the research of transfer within inter-organizational networks. This research is
limited to the dyadic level. The unit of analysis for this research is local suppliers of the
multinational corporations (MNCs), representing various industries, company sizes and
ages, located in Puerto Rico.
This research uses quantitative data collection method such as questionnaire in
order to test the hypotheses of the proposed conceptual framework. The advantages of
the questionnaires include rapid data collection, ensure comparability of the data,
increase speed and accuracy of recording and facilitate data processing (Malhotra,
2004; Cooper and Schindler, 2006). Once responses are obtained and non-response
bias are assessed using Armstrong and Overton's (1977) time trends extrapolation
methods, the data is analyzed using the Statistical Package for Social Science (SPSS)
and SmartPLS software. Final findings are compared with the expected outcomes
predicted in the hypotheses. As a result, the hypotheses are either supported or
rejected.
The Chapter 3 aims to build on the research's introduction and ensures that
appropriate procedures are followed. The following statistical methods are used for
summarizing the collection of data:
1) Factor Analysis to describe variability or dimensionality of a set of variables;
2) Cronbach's Alpha to measure the internal consistency or reliability;
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3) Correlation Coefficient to indicate the relationship of two random variables,
including strength and direction;
4) Multiple Regressions for testing and modeling of multiple independent
variables;
5) Structural Equation Modeling for testing and estimating causal relations.
1.5. Outline of the Research
This research is organized in five chapters. The chapters are outlined as follows
(see Figure 1).
Figure 1. Research Structure.
Chapter 1 - Introduction: This chapter introduces the background to the research,
research problem, research question and hypotheses, justification for the research,
methodology, outline of the research, definition of key terms and conclusions.
Chapter 2 - Literature Review: This chapter presents a review of the literature that is
relevant to this research.
Chapter 3 - Methodology: This chapter explains the methodology and justification,
sampling frame, data collection, data analysis, ethical considerations and conclusions.
Chapter 4 - Data Analysis: This chapter presents the findings obtained in this research.
These results are discussed and analyzed in order to address how the data can answer
the hypotheses.
Chapter 5 - Conclusions: This chapter presents a review of the hypotheses and a
summary of the research process. The implications are also outlined.
1 Introduction
2 Literature
Review
5 Conclusions
4 Data Analysis
3 Methodology
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1.6. Definition of Key Terms
Definitions adopted by researches are often not uniform, so key terms are
defined in this section.
1) Knowledge: Information possessed in the mind of individuals (Alavi and Leidner,
2001).
2) Knowledge transfer: Process of exchange of explicit or tacit knowledge between two
agents in which one of them receives and uses the knowledge provided by the other
agent (Kumar and Ganesh, 2009).
3) Trust: Confidence in the partner’s reliability and integrity (Squire et al., 2009).
4) Dependence: Distribution of power between two partners in a relationship (Robbins,
2005).
5) Relationship: Two ways interaction that exists over time and enhances benefits to
both partners (McNeish and Mann, 2010).
6) Explicit knowledge: Codified knowledge that can be articulated and it is easy to
transfer (Polanyi, 1962).
7) Tacit knowledge: Uncodified knowledge that is unarticulated and difficult to transfer
(Polanyi, 1962).
8) Quality of information: Consistently meeting the expectations of the customer through
the flow of information (English, 1996; Nonaka, 1994).
9) Absorptive capacity: Ability to recognize the value of new external information,
assimilate it, and apply it to commercial ends (Cohen and Levinthal, 1990).
1.7. Conclusions
This chapter introduced the research problem, research question and
hypotheses. Then, the research was justified, methodology was briefly described, the
research was outlined and key terms were defined.
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CHAPTER 2
LITERATURE REVIEW
2.1. Introduction
The research problem and hypotheses were introduced in Chapter 1. Chapter 2
presents the relevant literature review related to the research problem and develops the
hypotheses listed previously in section 1.2 of Chapter 1. This chapter is organized in the
following major topics: Knowledge, Knowledge Transfer, Determinant Factors of
Knowledge Transfer and Hypotheses.
2.2. Knowledge
During the past years, the topic of knowledge has captured the attention of some
scholars (e.g., Winter, 1987; Prahalad and Hamel, 1990; Peteraf, 1993; Nonaka, 1994;
Grant, 1996; Argote and Ingram, 2000; Nonaka, et. al., 2001; Ahnn and Chang, 2004;
Wadhwa and Saxena, 2005; Inkpen, 2008; McNeish and Mann, 2010; Zonooz et al.,
2011; Worasinchai and Daneshgar, 2012). According to Grant (1996), knowledge is
considered the most strategically-important resource which firms possess. Similarly,
Wadhwa and Saxena (2005) asserted that knowledge is the key to the success of a
supply chain as it affects decisions.
In the knowledge management field, many definitions of knowledge have been
adopted by researchers. Nevertheless, no consensus exists on how knowledge should
be defined due to this definition has evolved historically from a general phenomenon to
one that is specialized (Drucker, 1993). While defined in many different ways, Drucker
(1993) has argued that knowledge is information effective in action and focused on
results that are seen outside the person. In contrast with Drucker's definition, Nonaka
and Takeuchi (1995) established that knowledge is a dynamic human process of
11
justifying personal belief toward the truth. In their words, both adopted the traditional
definition of knowledge as justified true belief (Nonaka et al., 2001).
Alavi and Leidner (2001, p. 109) defined knowledge as "information possessed in
the mind of individuals". They argued that knowledge is personalized information (which
can or cannot be new, unique, useful, or accurate) related to facts, procedures,
concepts, interpretations, ideas, observations, and judgments. According to these
authors, knowledge may be viewed from several perspectives that lead to different
perceptions of knowledge management: a state of mind (knowing and understanding);
an object (to be stored and manipulated); a process (applying expertise); access to
information (a condition of access to information); and a capability (the potential to
influence action).
Knowledge can be classified into various categories depending on the purpose of
its use (Ernst and Kim, 2002). Polanyi (1967) classified knowledge in two main
categories: Explicit and tacit knowledge. On the one hand, explicit refers to the
knowledge that is codified (e.g. into manuals or procedures) and transferred using
systematic language. On the other hand, tacit knowledge has a personal quality (only
exists in people's minds). As Polanyi (1967) and Nonaka (1994) noted, there is a
distinction between both types and categories of knowledge since explicit is easy to
disseminate by contrast tacit cannot be easily articulated.
Dixon (2000) developed five categories or types of knowledge transfer and made
clear distinctions between them: Serial, Near, Far, Strategic, and Expert. According to
him, Serial transfer applies to a team that does a task and then, the same team repeats
the tasks in a new context (transfer of tacit and explicit knowledge). Near transfer
involves transferring knowledge from a source team to a receiving team that is doing a
similar task in a similar context but in a different location (transfer of explicit knowledge).
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Far transfer involves transferring knowledge from a source team to a receiving team
when the knowledge is about a non-routine task (transfer of tacit knowledge). Strategic
transfer involves transferring very complex knowledge from one team to another in
cases where the teams may be separated by both time and space (transfer of explicit
and tacit knowledge). The final category, Expert transfer, involves transferring
knowledge about a task that may be done infrequently (transfer of explicit knowledge).
Dixon's (2000) key argument is that there exists many and different ways to
transfer knowledge and that knowledge is transferred most effectively when the transfer
process fits the knowledge being transferred.
2.3. Knowledge Transfer
Studies on knowledge transfer have received increasing attention in recent years
(e.g. Joia and Lemos, 2010; Yang, Phelps and Steensma, 2010; Al-Gharibeh, 2011;
Qile, Gallear and Ghobadian, 2011; Antonova, Thomas, Fugate and Koukova, 2011;
Maehler, Márques, Ávila and Pires, 2011; Zonooz et al., 2011; Reiche, 2011; Saari and
Haapasalo, 2012; Worasinchai and Daneshgar, 2012).
Some of them have followed different research designs such as longitudinal,
cross sectional, theoretical and case studies based. Yang et al., 2010 conducted a
longitudinal research using a sample of 87 telecommunications equipment
manufacturers over a ten-year period. They found that an organization’s rate of
innovation and the extent to which this innovation integrate knowledge from the spillover
knowledge pool is greater when it is larger and similar to the organization’s existing
knowledge base. While this research used a longitudinal design, Al-Gharibeh (2011)
used a cross-sectional design to examine the effects of knowledge enablers on
knowledge transfer.
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An example of theoretical research is Qile et al., 2011, who developed a set of
propositions in order to examine the operational characteristics of supply-chain
partnerships and identify the relational attributes that facilitate knowledge transfer in
these types of partnerships. Finally, case studies are more salient in the literature of
knowledge transfer, as for example, Maehler et al., 2011.
The concept of knowledge transfer has been used in different contexts allowing
different meanings. Argote and Ingram (2000, p. 151) defined knowledge transfer as
"the process through which one unit (e.g., group, department, or division) is affected by
the experience of another." Easterby-Smith et al. (2008) argued that knowledge transfer
is an event in which one organization learns from the experience of another
organization. Prevot (2008) proposed that the knowledge transfer is a key activity in the
management of organizations, used for diffusing best practices with the aim of
maximizing productivity, or for transmitting knowledge to facilitate inter-firm relationships.
Kumar and Ganesh (2009) postulated knowledge transfer as a process of exchange of
explicit or tacit knowledge between two agents (e.g. individuals, teams, organizational
units, organization itself or a cluster of organizations), during which one agent
purposefully receives and uses the knowledge provided by another. In essence, these
definitions suggested that the knowledge transfer involves a relationship between a
source (who possesses and delivers knowledge) and a recipient (who acquires and uses
knowledge).
Based on the theory of knowledge creation of Nonaka (1994), knowledge is
created through the interaction between two dimensions of knowledge transfer:
conversion between tacit and explicit knowledge and vice versa; and interaction between
individuals. Nonaka (1994) postulated in his spiral model the following four modes of
knowledge creation (see Figure 2).
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1) Socialization - from tacit to tacit
2) Externalization - from tacit to explicit
3) Combination - from explicit to explicit
4) Internalization - from explicit to tacit
In brief, according to him, new knowledge is created as a result of knowledge
transfer and the knowledge gets transmitted through the interaction between individuals.
Tacit Knowledge To Explicit Knowledge
Tacit Knowledge
From
Socialization
Externalization
Explicit Knowledge
Internalization
Combination
Source: Nonaka (1994, p. 19)
Figure 2. Modes of the Knowledge Creation.
Building on Nonaka's spiral model, Nissen (2002) developed a dimensional
model of knowledge. In this extended model, he postulated four dimensions: life cycle,
time, explicitness (epistemological) and reach (ontological) to explain and visualize the
dynamic of knowledge flows through the firm.
Nissen (2002) asserted that the main objective of the knowledge flow is to enable
the transfer of capability and expertise from where it resides (source) to where it is
needed (recipient) across time, space and firms. Although the modern firm depends
upon timely and effective flows of knowledge for its success, Nissen (2002) argued that
15
the knowledge is unevenly distributed through the firm. Further, the few number of
theoretical models available (e.g. Dixon, 2000; Schultze and Boland, 2000; Swap,
Leonard, Shields and Abrams, 2001) have not been developed to a point in which they
can effectively inform the design of information systems and business processes to
enhance the flows of knowledge through the firm (Nissen, 2002).
Alavi and Leidner (2001) suggested that the knowledge transfer process occurs
at six different levels: between individuals, from individuals to explicit sources, from
individuals to groups, between groups, across groups, and from the group to the
organization. According to them, the knowledge transfer mechanisms can be classified
as informal, formal, personal or impersonal. Informal transfer (e.g. unscheduled
meetings) may be effective for socialization, but inhibit knowledge dissemination.
Formal transfer like training sessions can ensure greater knowledge distribution, but
inhibit creativity. Personal transfer (e.g. personnel transfer) may be most effective for
highly context specific knowledge. In contrast, impersonal transfer such as knowledge
repositories may be most effective for knowledge generalized to other contexts.
2.4. Determinant Factors of Knowledge Transfer and Hypotheses
The importance of determinant factors impacting on knowledge transfer has been
examined several times in literature. Many of the studies have empirically confirmed the
effects of these determinants on knowledge transfer outcomes (Sazali, Haslinda, Jegak
and Raduan, 2010). According to Hamid and Salim (2010), some of the determinant
factors affect the ability to effectively transfer knowledge between firms. They
highlighted that factors such as organizational characteristics, source and recipient
characteristics and type of knowledge have been suggested as important to effective
knowledge transfer.
16
Determinant factors of knowledge transfer were identified based on a review of
existing literature and used for developing the conceptual framework of this research
(see Figure 3). In the discussion of these determinants and the hypotheses, the different
interactions of them will be analyzed.
©Amarilis Delgado, 2012
Figure 3. Conceptual Framework of Knowledge Transfer Between Buyer-Supplier. 2.4.1. Trust
Some definitions of trust have been proposed in the knowledge transfer
literature. McNeish and Mann (2010) argued that although investigated by many
H7(+)
H6(+)
H5(+)
H4(+)
H3(+)
H1(+)
H2(+)
Contextual
Factors Process Outcomes
Trust
Dependence
Expectation of Relationship Continuity
Type of Knowledge
Shared
Quality of Information Exchanged
Absorptive Capacity
Outcomes of the Collaboration
Knowledge Transfer
17
researchers and scholars, the existing literature does not provide a generally acceptable
definition of this concept. These authors agreed that this concept is difficult to define.
A review of literature suggest that trust can be defined as a belief that a partner
will take favorable actions and that there will be no unexpected activities that could
evoke negative consequences (Madlberger, 2009). This author reinforced this definition
by arguing that trust inherits some degree of voluntary vulnerability for the partner.
Spekman, Kamauff and Myhr (1998) defined trust as a belief that the supply
chain partner will act in a consistent manner according to expectations. They
recognized that trust is conveyed thru faith, reliance, belief, or confidence in another
supply partner. Similarly, Squire et al. (2009) argued that trust is the confidence in the
partner’s reliability and integrity. According to them, trust can be measured as the buyer
firm’s confidence in the ability and integrity of the supplier. Consistent with these
definitions and analyzing this concept in much of the literature, other authors (e.g. Cook
and Wall, 1980; Geyskens, Steenkamp, Scheer and Kumar, 1996; Casson, 1997;
Inkpen, 2000; Lane, Salk and Lyles, 2001; Dhanaraj, Lyles, Steensma and Tihanyi,
2004; Wijk, Jansen and Lyles, 2008; Thomas et al., 2011) asserted that trust is the belief
that the partners will act in a consistent manner fulfilling their obligations in the
relationship. In fact, in the International Joint Venture (IJV) literature, the importance of
trust is also defined as the expectation that the partners will act in good faith (Lane et al.,
2001).
Trust is a multidimensional construct that has various dimensions and for many
decades, it has gained interest in different areas of research (Ganesan, 1994; Svensson,
2004; Levin and Cross 2004). For example, in the 1990s and 2000s, some researchers
in the knowledge management discipline supported the relation between trust and
knowledge transfer (e.g. Davenport and Prusak, 1998; Hansen, 1999; Simonin, 1999;
18
Dyer and Nobeoka, 2000; Cummings and Teng, 2003; Levin and Cross, 2004; Chen,
2004). Several authors even considered that a climate of trust enables the transfer of
knowledge. This is critical between partners due to it has an important role in the
evolution of enduring business relationships (Inkpen, 1996; Svensson, 2004; Wijk et al.,
2008).
Trust and absorptive capacity have been examined as two interrelated concepts
(Lane et al., 2001; Ratten, 2004). Lane et al. (2001) highlighted that trust is a critical
part of absorptive capacity because it encourages the “teacher” firm to help the “student”
firm to understand the knowledge it is offering. According to them, trust influences the
extent of the knowledge transferred and the efficiency with which it is transferred.
Ratten (2004) argued that trust and absorptive capacity both influence and affect each
other. The firms will share more information if they have trust in their partners and this
shared information will lead to learning that is important for a firm’s absorptive capacity.
Curral and Judge (1995) also suggested that trust could increase the absorptive capacity
and retention of new knowledge.
Zhang (2010) conducted an empirical research to analyze the effect of absorptive
capacity and inter-organizational trust on knowledge transfer in the Chinese automotive
industry. In his research, he found that inter-organizational trust and absorptive capacity
have a positive effect on the degree of knowledge transfer. Inter-organizational trust
also moderated the relationship between absorptive capacity and knowledge transfer.
One of the most interesting findings of this research is that the positive relationship
between absorptive capacity and knowledge transfer is stronger when trust is higher and
this relationship is weaker when trust is lower.
Dhanaraj et al. (2004) argued that trust facilitates knowledge transfer by creating
a sense of security that the knowledge will not be exploited beyond what is intended.
19
Trust gives partners the confidence that the knowledge will not be misused (McEvily,
Perrone and Zaheer, 2003). According to Squire et al. (2009), trust supports knowledge
transfer by increasing the openness in the relationship. If the source is perceived to be
trustworthy, the recipient will be more open to absorb this knowledge. In their words, if
the levels of trust are high, the recipient is more inclined to accept this knowledge.
Conversely, if the source is not trustworthy, recipients need to verify the veracity
of the knowledge to be transferred (Bhatt, 2000; Squire et al., 2009). This is in line with
Szulanski (1996) point of view, which suggested that if the source is not perceived as
trustworthy or knowledgeable, initiating a knowledge transfer from that source to the
recipient will be difficult. In sum, without trust, the individuals will not be willing to
engage in social exchanges and transfer will not take place (Staples and Webster,
2008).
Given that close buyer-supplier relationships are characterized by trust and
applying the above arguments, it is hypothesized that:
Hypothesis 1: Trust will positively impact the knowledge transfer process
between buyers and suppliers.
2.4.2. Dependence
Based on the literature, the construct of dependence has been subject of study
on different researches, for example: 1) Power-dependence relations (Emerson, 1962);
2) Dependence as a determinant of organization's behavior and strategic decisions
(Pfeffer and Salancik, 1978); 3) Dependence as a determinant of cooperation (Pfeffer
and Salancik, 1978; Heide and John, 1990; Skinner, Gassenheimer and Kelley, 1992);
4) Dependence in a dyadic business relationship (Dwyer, Schurr and Oh, 1987); 5)
Dependence as a determinant of long-term relationship (Ganesan, 1994); 6)
Dependence and trust; Dependence in business relationships (Svensson, 2004);
20
7) Dependence and commitment as determinants of negotiation (Rahmoun and Debabi,
2012). In contrast to previous researches that focus in the dependence of one
organization on its partner, recent researches have also examined both organization's
dependence, that is, organization's dependence on its partner and the partner's
dependence on the organization (e.g. Gundlach and Cadotte, 1994; Kumar, Scheer and
Steemkamp, 1995; Spekman et al., 1998; Ho, 2008; Thomas et al., 2011).
Existing literature provides various definitions of dependence and some of them
have been derived from Emerson's Theory. Robbins (2005) defined dependence as the
distribution of power between two partners in a relationship. It seems to be in line with
Emerson (1962), who proposed that the basis of power is dependency. In his power-
dependence theory, he defined the dependence concept as follows: "Dependence of
actor A upon actor B is 1) directly proportional to A's motivational investment in goals
mediated by B, and 2) inversely proportional to the availability of those goals to A
outside of the A-B relation" (p. 32). Emerson (1962) pointed out that the power of A over
B derives from B's dependence on A. When it occurs, a mutual dependence is
established among the partners.
In their resource dependency theory, Pfeffer and Salancik (1978) used the term
dependence to connote that the dependence relationship between partners is based on
acquiring or exchanging the resources of the other partner. This theory established that
dependence is created by partners who provide important, critical, valuable or strategic
resources. In sum, the dependence between two partners is based on an exchange of
resources (Rahmoun and Debabi, 2012).
Rahmoun and Debabi (2012) argued that dependence summarizes the
relationship in which an organization needs the resources of the other organization to
reach its objective and remain in a competitive environment. Likewise, Frazier (1983)
21
further affirmed that the dependence of a buyer on a supplier refers to buyer's need to
maintain the relationship in order to achieve its goals. The dependence on a supplier
increases as follows: 1) when the outcomes obtained by the buyer from the supplier are
highly valued; 2) when the outcomes obtained by the buyer exceed outcomes available
to the supplier and 3) when the buyer has few alternatives or potential sources of
exchange (Heide and John, 1988). Ganesan (1994) supported this standpoint and
concluded that the lack of alternatives is the primary reason of the dependency.
Alternatively, Svensson (2004) referred to dependence as a link, tie or bond
between one organization in relation to another. Based on Hammarkvist, Hakansson
and Mattsson's (1982) work, he found in his research that dependency relationship has
seven dimensions instead of five: 1) dependence in terms of time; 2) dependence in
terms of knowledge; 3) social dependence (e.g. relationships); 4) economic/juridical
dependence; 5) technical dependence; 6) dependence in terms of market; and 7)
dependence in terms of Information Technology. Svensson's (2004) research also
shows that the interaction process between two companies learning each other may
result in knowledge dependence. As mentioned earlier, frequent interactions increase
the transfer of knowledge (Hansen, 1999). The importance of these interactions has
been examined in previous studies of knowledge transfer (e.g. Hansen, 1999;
Szulanski, 2000; Simonin, 2004). Thus, the following hypothesis is developed:
Hypothesis 2: Dependence will positively impact the knowledge transfer process
between buyers and suppliers.
2.4.3. Expectation of Relationship Continuity
The definition of relationship varies according with the discipline that is being
researched in the literature (e.g. business, strategy, economics or psychology). For
22
example, Gummesson (1997) defined thirty different forms of relationship illustrating the
diversity of this concept.
According to Szwejczewski, Lemke and Goffin (2005), a relationship is
established as soon as two or more partners associate themselves in order to fulfill a
mutual business purpose. The notion of a relationship is that it is a two-way interaction
that exists over time and enhances benefits to both partners (McNeish and Mann, 2010).
One example of a relationship, in which two partners associate, is that between buyer-
supplier (Ellram, 1991).
Cannon and Perreault (1999) argued that during the past decade, scholars have
focused increased attention on the dyadic relationship between buyer-supplier (e.g.
Simonin, 1999, Hansen, 1999; Wijk et al., 2008). Buyer-supplier relationship is defined
as the set of practices and routines that support economic exchanges between two
organizations (Kotabe et al., 2003; Rashed et al., 2010). This is a vertical relationship
that is not in competition (Prevot, 2008) and it is based on collaboration.
The buyer-supplier relationship is closer than it appears. Close relationships
between partners have been examined in the literature by some researchers (e.g.
Hansen, 1999; Simonin, 1999; Lyles and Salk, 2006; Wijk, et al., 2008). This type of
relationship is characterized by trust and commitment. In addition, it is related with some
benefits such as reduced uncertainty, managed dependence, among others (Dwyer et
al., 1987). Essentially, the literature tell us that close relationship means that partners
share the same risks and have willingness to maintain this relationship over long-term
(Cooper and Ellram, 1993). It involves the expectation of relationship continuity
(Anderson and Weitz, 1989; Ganesan 1994; Sosa, Svensson and Mysen, 2011). In
other words, there is a probability of future interaction. According to Kotabe et al.
23
(2003), for a buyer-supplier relationship to endure, each of them must remain satisfied
with the past performance.
In addition, long-term relationships between partners are suggested by the
supply chain management (Spekman et al., 1998). As McNeish and Mann (2010) noted
long-term relationships make the organizations more competitive. The long-term
relationships are usually initiated by the buyer. The benefits have been addressed in
some studies and are mostly associated with the increase in performance, competitive
advantage, lower production costs, among others (Sweeney and Webb, 2002). For
example, Ford Motors uses this type of relationship to increase their competitiveness in
the automotive industry (Rashed, et al., 2010).
Wijk et al. (2008) found that buyer-supplier relationship lead to greater
knowledge transfer. Likewise, Argote (1999) suggested that this type of relationship is
considered one of the factors affecting the knowledge transfer process. The transfer of
knowledge mostly occurs informally, spontaneously, in collaboration level between
buyer-supplier and thru their interactions (Spekman et al., 1998; Inkpen, 2008). In order
to understand the knowledge transfer between buyer-supplier, it is important to consider
the nature of the relationship (Simonin,1999). The nature of the relationship influences
the knowledge transfer success thru the interaction between the source and recipient
(Inkpen and Dinur, 1998). In fact, Nonaka (1994) further affirmed that the knowledge
transfer requires frequent and numerous interactions. This is also broadly consistent
with Hansen (1999) arguments, who emphasized that the frequent interactions increase
the transfer of knowledge.
In sum, the nature of dyadic relationships is considered an important determinant
of successful knowledge transfer. This type of relationship involves consistent
interactions and is characterized by close, long-term and collaborative partnerships. The
24
importance of these interactions is examined in previous studies of knowledge transfer
(e.g. Hansen, 1999; Szulanski, 2000; Simonin, 2004).
As a relationship endures over time, the buyer-supplier relationships develop
interactions that allow them to communicate and facilitate the transfer of new knowledge
(Levinthal and Fichman,1988; Asanuma, 1989; Fichman and Levinthal, 1991). In line
with it, this leads to the third hypothesis.
Hypothesis 3: Expectation of relationship continuity will positively impact the
knowledge transfer process between buyers and suppliers.
2.4.4. Type of Knowledge Shared (Tacit and Explicit)
In order to understand the transfer of knowledge, it is important to consider some
different types of knowledge and its characteristics. In the literature, the types of
knowledge are considered as constructs that influences the knowledge transfer (e.g.
Nonaka, 1994; Simonin, 1999; Hansen, 1999; Lane et al., 2001; Hansen, 2002;
Cummings and Teng, 2003; Chen, 2004; Ganesan, Malter and Rindfleisch, 2005;
Easterby-Smith et al., 2008; Ho, 2008; Lund, 2010).
Lee and Wu (2010) argued that there are many different ways to classify the
knowledge according to previous researches. For example, the following types of
knowledge have been distinguished in the management literature: 1) Tacit versus
Explicit Knowledge (Polanyi, 1958; Nonaka, 1994); 2) Knowledge-That versus
Knowledge-How (Ryle, 1949); 3) Rationalized versus Embedded (Weiss, 1999); 4) Tacit
vs. Explicit, Impossible vs. Possible to Teach, Non-Articulated vs. Articulated, Non-
Observable vs. Observable in Action, Complex vs. Simple and Part of a System vs.
Independent (Winter, 1987); 5) Objective versus Experiential (Penrose, 1959); and 6)
Simple versus Complex, Tangible versus Intangible, Independent versus Systemic
(Garud and Nayyar, 1994; Matusik and Hill, 1998). Although other knowledge
25
classifications exist, the tacit-explicit is widely cited in the literature (Alavi and Leidner,
2001). In fact, the general consensus between researchers is that the knowledge can
be classified as either tacit or explicit (Giannakis, 2008).
Although tacit-explicit terms are of common use in the knowledge management
field, there are clear distinctions between them, as shown in the Table 1. Each
dimension defines ease or difficulty of transfer (Prevot and Spencer, 2005).
Table 1
Tacit and Explicit Knowledge
Tacit knowledge (subjective) Explicit knowledge (objective)
Knowledge of experience (body) Knowledge of rationality (mind)
Simultaneous knowledge (here and now) Sequential knowledge (there and
then)
Analog knowledge (practice) Digital knowledge (theory)
Source: Nonaka et al., 2001.
The distinction between tacit and explicit knowledge was first introduced by the
physical chemist and philosopher Michael Polanyi (1962; 1967) to describe the codified
knowledge (explicit) that can be articulated and it is easy to transfer, in contrast to
uncodified knowledge (tacit) that is unarticulated and difficult to transfer. This distinction
is still used in the research on knowledge transfer (Prevot, 2008).
Polanyi's (1967) tacit and explicit distinction was introduced into the literature on
strategic direction by Nelson and Winter (1982) in their theory of the firm. In the early
1990s, following Polanyi's (1962; 1967) work, Nonaka (1994) introduced and developed
the tacit and explicit knowledge as two dimensions of knowledge creation. According to
Nonaka (1994), new knowledge is created as a result of the interaction between
individuals and the conversion of tacit knowledge to explicit knowledge or vice versa.
26
Nonaka (1994) postulated in his knowledge spiral model the following four
different modes of conversions between tacit and explicit knowledge:
1) Socialization - from tacit to tacit. In this first mode, the knowledge is created through
shared experience, observation, imitation, and practice (for example, on-the-job
training);
2) Externalization - from tacit to explicit. In this second mode, the tacit knowledge is
converted into explicit knowledge through the use of dialogue and metaphor;
3) Combination - from explicit to explicit. In this third mode, the existing information is
reconfigured through sorting, adding, recategorizing and recontextualizing (for
example, the modern computer systems); and
4) Internalization - from explicit to tacit. In this fourth mode, the explicit knowledge is
converted from explicit to tacit through the action. In brief, the interaction between
these two types of knowledge constitutes the key of Nonaka's Theory of
Organizational Knowledge Creation.
Chen (2004) suggested that the efficacy of the knowledge transfer is affected by
the attributes of the knowledge. Hansen (1999) acknowledged that if the transfer is
difficult, then the knowledge transfer will take a long time. Simonin (1999) found that
tacitness is positively related to ambiguity and the ambiguity is negatively related to
knowledge transfer. Zander and Kogut (1995) found that the degree of codification and
how easily capabilities are taught influence the speed of the transfer. Finally, Easterby-
Smith et al. (2008) also agreed that the nature of the knowledge being transferred has
an impact on the knowledge transfer. Applying the above arguments, it is hypothesized
that:
Hypothesis 4a: Explicit knowledge will positively impact the knowledge transfer
process between buyers and suppliers.
27
.
Hypothesis 4b: Tacit knowledge will positively impact the knowledge transfer
process between buyers and suppliers.
2.4.5. Quality of Information Exchanged
Previous studies have addressed the importance of quality of information
exchanged. For example, in the last few decades, this construct has been explored in
the supply chain management, information technology management, knowledge
management, among others fields. In 1979, Neumann and Segev examined several
characteristics of information quality such as: content, accuracy and frequency. During
the 1990s, researchers dealing with this construct continued by carrying through studies
in this field: 1) Miller (1996) examined information quality using ten dimensions:
relevance, accuracy, timeliness, completeness, coherence, format, accessibility,
compatibility, security and validity; 2) Furthermore, the information intensity and quality
were measured by Vijayasarathy and Robey (1997); 3) Four attributes of information
quality: accuracy, frequency, credibility, and availability were measured by McCormack
(1998). In the 2000s, the attention to this construct continued from both practitioners
and researchers. For example, Malhotra, Gosain and El Sawy (2005), examined the
quality of information exchanged in terms of relevancy, completeness, value-added and
timeliness. Zhou and Benton (2007) on the other hand, studied nine aspects of
information quality, such as: accuracy, availability, timeliness, internal connectivity,
external connectivity, completeness, relevance, accessibility, and frequently updated
information.
One of the major aspects of information exchanged is information quality
(Rashed et al., 2010). Most people agree that the information must have quality in order
to be effective (Miller, 1996). The term "information" has been used by researchers in
28
many different ways to refer to different things. In fact, it has been interpreted in multiple
and conflicting ways. In order to define information quality, information must be defined
first and then, consider what quality means for information. It is expected that the
definition of information guides to the definition of information quality.
Based on the literature, the information is perceived useful when it has the
following attributes: high quality, readily accessible, accurate and relevant (Zhou and
Benton, 2007). Tushman and Nadler (1978) argued that information refers to data that
is relevant, accurate, timely and concise. Nonaka (1994) also offered an useful
definition of information. In his words, information is a flow of messages or meanings
which might add to, restructure or change knowledge (p. 15). He argued that
information is necessary for initiating and formalizing knowledge. In sum, information is
a flow of messages.
But, what is quality? According to Total Quality Management (TQM), quality can
be defined as consistently meeting customer’s expectations (English, 1996). Drucker
(1985) argued that quality in a product or service is not what the supplier puts in. It is
what the customer gets out and is willing to pay for. In his words, quality is determined
by the customers and only they can define what quality is based on their expectations.
Another definition that seems to encompass the quality is provided by Crosby (1979),
who referred to quality as conformance to requirements. Based on the definitions
above, information quality can be defined as consistently meeting the expectations of the
customer through the flow of information.
Miller (1996) argued that the meaning of information quality depends on how the
information is perceived and used. According to him, it is the perception of the attributes
of the information that defines this concept. Malhotra et al. (2005); Li and Lin (2006), on
the other hand referred to information quality as the accurate, timeliness, relevance,
29
adequacy, trustworthy, and value of information that is sufficient for the decision-making
and for the information exchanged between an enterprise and the supply chain partner.
Information quality also measures the quality of information shared between suppliers
and buyers (Zhou and Benton, 2007).
The quality of the information that flows in an organization has a direct impact on
the knowledge. This information needs to be timely and relevant to the context of the
organization (Rashed et al., 2010). In line with Rashed's argument, Miller (1996)
proposed ten dimensions of information quality. According to him, the quality
information must meet certain recognized criteria such as: 1) Relevance (If the
information addresses its customer's needs); 2) Accuracy (accurate information reflects
the underlying reality); 3) Timeliness (how quickly new information can be processed
and communicated to its customer); 4) Completeness (how the information that is
complete for one person may be incomplete for another); 5) Coherence (how well the
information is consistent with itself); 6) Format (how the information is presented to the
customer); 7) Accessibility (if the information can be obtained when needed);
8) Compatibility (how the information can be combined with other information and
delivered to a customer); 9) Security (Protecting the information from people and from
natural disasters); 10) Validity (if the information can be verified as true and satisfying
appropriate standards related to other dimensions such as accuracy, timeliness,
completeness and security).
Based on Miller's (1996) research, Malhotra et al. (2005) found that the following
four dimensions: Relevancy, Completeness, Value-Added and Timelines are important
to absorptive capacity and that the quality of information exchanged is beneficial for
partners to exchange information. On this basis, the fifth hypothesis is formed.
30
Hypothesis 5: Quality of information exchanged will positively impact the
knowledge transfer process between buyers and suppliers.
2.4.6. Absorptive Capacity
Absorptive capacity is considered in past studies as one of the most important
determinants of knowledge transfer (Cohen and Levinthal, 1990; Chen and McQueen,
2010; Lee and Wu, 2010). The importance of this construct has been noted across the
fields of organization’s absorptive capacity, organizational learning and knowledge
transfers (e.g. Cohen and Levinthal, 1990; Szulanski, 1996; Lane and Lubatkin, 1998;
Shenkar and Li, 1999; Argote and Ingram, 2000; Gupta and Govindarajan, 2000; Dixon,
2000; Zahra and George, 2002; Minbaeva, Pedersen, Björkman, Fey and Park, 2003;
Inkpen and Tsang, 2005; Lane, Koka and Pathak, 2006; Lyles and Salk, 2006; Easterby-
Smith et al., 2008). This construct has been also measured in investigations of
Research and Development (Cohen and Levinthal, 1994) and strategic alliances (Koza
and Levin, 1998) among others. In sum, these studies agree that the absorptive
capacity is essential in order to facilitate the knowledge transfer.
The term absorptive capacity was first proposed and defined by Wesley M.
Cohen and Daniel A. Levinthal in 1990 (Simonin, 2004; Wijk et al., 2008). Cohen and
Levinthal (1990) defined absorptive capacity as "the ability to recognize the value of new
external information, assimilate it, and apply it to commercial ends" (p. 128). Twenty-two
years later (2012), this is still the most cited definition in the management literature.
Both authors view the absorptive capacity as a firm level construct and suggested that it
involves the previous three components. According to them, the absorptive capacity
tends to develop cumulatively and builds on firm's prior related knowledge.
One decade after Cohen and Levinthal's introduction of absorptive capacity,
Zahra and George (2002) re-conceptualized this construct as "a dynamic capability
31
pertaining to knowledge creation and utilization that enhances a firm's ability to gain and
sustain a competitive advantage" (p. 185). They suggested that the absorptive capacity
exists as two sub-sets of organizational capacities: 1) potential capacities and 2) realized
absorptive capacities. These two capacities form four dimensions that enable the firms
to produce a dynamic organizational capability which can be transformed to competitive
advantage. The first sub-set consists of knowledge acquisition and assimilation while
the second sub-set comprises knowledge transformation and exploitation.
Zahra and George (2002) explained each dimension and suggested that
Acquisition refers to a firm’s capability to identify and acquire externally generated
knowledge that is critical to its operations; Assimilation refers to the firm’s routines and
processes that allow it to analyze, process, interpret and understand the information
obtained from external sources; Transformation, denotes a firm’s capability to develop
and refine the routines that facilitate the combination of existing knowledge with newly
acquired and assimilated knowledge; and Exploitation, denotes the firm’s ability to use
consistently the new knowledge gained for commercial use over an extended period of
time (p. 189 - 190).
In sum, knowledge acquisition depends not only on prior related knowledge, but
also depends on the potential and realized absorptive capacities. Firms must have the
capacity to acquire, assimilate, transform and exploit knowledge to achieve their
competitive advantages (Zahra and George, 2002). These results fall in line with
contributions like Gupta and Govindarajan (2000) and Szulanski (2000), which proposed
that the knowledge recipient must have the levels of absorptive capacities in order to
acquire the knowledge transferred from the knowledge source. In fact, the lack of
absorptive capacity was one of the major barriers to knowledge transfer between source
32
and recipient found by Szulanski (1996) in his research of 122 best practice transfers in
eight companies. Following this line of reasoning, the following hypothesis is posed.
Hypothesis 6: Absorptive capacity will positively impact the knowledge transfer
process between buyers and suppliers.
2.4.7. Knowledge Transfer and Outcomes
Research on the knowledge transfer has been increasing over the last years
(e.g. Kumar and Ganesh, 2009; Joia and Lemos, 2010; Yang et al., 2010; Al-Gharibeh,
2011; Qile et al., 2011; Antonova et al. 2011; Thomas et. al., 2011; Maehler et al., 2011;
Zonooz et al., 2011; Reiche, 2011; Saari, and Haapasalo, 2012; Worasinchai and
Daneshgar, 2012).
Several theories have been used to study this construct, such as: Expectancy
theory, agency theory, equity theory, interdependence theory, Hofstede's cultural
framework, theory of absorptive capacity, social power theory, innovation diffusion
theory, the similarity-attraction paradigm, social cognitive theory, economic exchange
theory, model of the dynamic of trust, job characteristics model, expectation–
confirmation theory, social categorization theory, the Big Five personality theory,
attribution theory, balance theory, social influence theory, mechanistic versus organic
organizational models, theory of planned action, social interdependence theory, socio-
technical perspective, socially-situated view of knowledge and learning, organizational
learning perspective, social categorization theory, resource-based view of the firm and
knowledge-based view of the firm (Wang and Noe, 2010). This last theory recognizes
the knowledge as one of the most important strategic resource of the firms (Nonaka,
1994; Zander and Kogut, 1995).
The transfer and creation of knowledge are considered as critical factors in the
organization's success and competitiveness (Syed-Ikhsan and Rowland, 2004; Alipour et
33
al., 2011). Indeed, the ability to transfer and create knowledge has been recognized in
the knowledge transfer literature as one of the main competitive advantages in the
MNCs (e.g. Gupta and Govindarajan, 1991; Kogut and Zander, 1992; Ghoshal, Korine
and Szulanski, 1994; Gupta and Govindarajan, 2000; Minbaeva et al., 2003; Lee and
Wu, 2010).
Various definitions of knowledge transfer have been covered in the knowledge
management literature. For example, Argote and Ingram (2000) defined knowledge
transfer as the process through which one unit (e.g., group, department or division) is
affected by the experience of another. Kumar and Ganesh (2009) alternatively proposed
that knowledge transfer is a process of exchange of explicit or tacit knowledge between
two agents in which one of them receives and uses the knowledge provided by the other
agent. Szulanski (1996) addressed the knowledge transfer as the dyadic exchanges of
knowledge between a source and a recipient unit in which the identity of the recipient
matters. In light of these definitions, knowledge transfer is seen as a process which
consists of two main activities: transmission and absorption of knowledge between a
source and a recipient (Antonova et al., 2011).
Hau and Evangelista (2007) argued that knowledge transfer is built on several
individual exchanges based on the communication. Inkpen (2008) asserted that
evidence exists indicating that this process is facilitated through the intensive
communication and social interactions between the source and recipient. This
interaction is fundamental in the effective transfer of knowledge due to new knowledge is
created (Nonaka, 1994; Hansen, 1999; Szulanski, 2000; Simonin, 2004; Prevot, 2008).
In his Theory of Organizational Knowledge Creation, Nonaka (1994) proposed that the
interaction between individuals drives to the creation of new knowledge, new ideas and
concepts.
34
Prior researches have postulated that knowledge transfer has an impact on
organizational outcomes (Wijk et al., 2008; Ho, 2008; Ford and Staples, 2006;
Sichinsambwe, 2011). Ford and Staples (2006) argued that the basis of value for
knowledge is its usefulness and benefits. As expressed by them, this implies that the
usefulness and benefits dimensions are factors that determine the value of the
knowledge. In their research, they concluded that the more useful the knowledge was
for the individuals, the more valued the knowledge was and the more benefits received
from the knowledge, the more valued the knowledge was.
Pérez-Nordtvedt, Kedia, Datta and Rasheed (2008, p. 717), refers to the
usefulness of the knowledge transferred as the extent to which such knowledge was
relevant and salient to organizational success. According to these authors if the
knowledge is transferred quickly and it is not considered relevant or easy to comprehend
by the recipient, the transfer will not lead to the desired outcomes.
Knowledge transfer requires the collaboration of a source and a recipient in order
to achieve mutually beneficial outcomes (Nonaka, 1994; Syed-Ikhsan and Rowland,
2004; Thomas et al., 2011). The collaborative relationships (e.g. buyer-supplier) are
considered the primary mechanisms that facilitate this process (Squire et al., 2009).
According to the literature, one of the motivations to establish collaborations between
partners is the acquisition of new specialized knowledge (Hamel, Doz and Prahalad,
1989; Mowery, Oxley and Silverman, 1996; Lane and Lubatkin, 1998). In fact, the
knowledge creation has becoming a priority for the organization due to it provides
sustainable competitive advantage (Inkpen, 1996) in today's business environment.
Based on the above discussion, the following hypotheses are formed:
Hypothesis 7a: Knowledge transfer will positively impact the outcomes
(benefits) of the buyer-supplier collaboration.
35
Hypothesis 7b: Knowledge transfer will positively impact the outcomes
(usefulness) of the buyer-supplier collaboration.
2.5. Conclusions
In brief, Chapter 2 reviews the existing literature showing where the research
problem fits into that body of knowledge and then identifying hypotheses.
36
CHAPTER 3
METHODOLOGY
3.1. Introduction
Chapter 2 presented the relevant literature review related to the research
problem and developed the hypotheses. Chapter 3 aims to build on the introductory
overview of the methodology provided in section 1.4 of Chapter 1. This chapter
describes the methodology used to collect data, which either support or reject the
hypotheses for the present research. This chapter is organized in the following major
topics: Methodology and Justification, Sampling Frame, Data Collection, Data Analysis,
Ethical Considerations and Conclusions.
3.2. Methodology and Justification
Choosing the right methodology for a given research can be an enormous
challenge. The selection of appropriate research tools requires careful consideration
and some factors such as objects of investigation and research problems must be
considered (Masadeh, 2012).
As explained by Masadeh (2012, p. 123), "qualitative research methods entail
reasoning from induction, gathering data and drawing conclusions from a multiplicity of
interpretations and perceptions, beginning with the observation" while "quantitative
approaches are based on the logic of deduction, beginning from accepted theories or
premises and testing them rationally." On the one hand, quantitative research methods
are distinguished by numbers, statistics, and abstracting from data on sample
populations. On the other hand, qualitative research methods tend to focus on quality of
various people’s experiences incorporating anecdotes and comparisons (Masadeh,
2012). Distinctions between quantitative and qualitative researches are summarized in
the Table 2.
37
Table 2
Distinctions between Quantitative and Qualitative Researches
Quantitative style Qualitative style
Measure objective facts Construct social reality, cultural meaning
Focus on variables Focus on interactive processes, events
Reliability is key Authenticity is key
Value free Values are present and explicit
Independent of context Situationally constrained
Many cases, subjects Few cases, subjects
Statistical analysis Thematic analysis
Researcher is detached Researcher is involved
Source: Masadeh (2012, p. 124)
A quantitative research method was deemed most appropriate for this research
taking into account that the objectives that guide this research pretend to measure
statistically:
1) the determinant factors that could facilitate or inhibit knowledge transfer success
between buyer-supplier relationships;
2) the impact of knowledge transfer in the outcomes (benefits and usefulness) of
collaborative buyer-supplier relationships.
According to Cooper and Schindler (2006, p. 198), the "quantitative research
attempts precise measurement of something". As noted by them, it helps in answering
questions related to how much, how often, how many, when, and who.
3.3. Sampling Frame
The sampling frame consisted of 500 local suppliers of the Pharmaceuticals,
Medical Devices and Biotechnologies companies, representing various industries,
company sizes and ages located in Puerto Rico.
38
3.4. Data Collection
A questionnaire was used to collect data to give the answers to the hypotheses
posed for this research. The survey instrument was pre-tested to ascertain content
validity, clarity and the appropriate interpretation for each survey item. Feedback
received resulted in the revision and refinement of the questionnaire.
The questions of the research instrument were developed on the basis of
literature review (see Chapter 2 and Appendix A). Response categories for trust,
dependence, expectation of relationship continuity, type of knowledge shared (tacit and
explicit), quality of information exchanged, absorptive capacity, knowledge transfer and
outcomes (benefits and usefulness) of this collaboration consist of a 5-point Likert scale
ranging from Strongly Disagree to Strongly Agree. Close-ended and open-ended
questions were also included. According to Sudha and Baboo (2011), the Likert scale is
a psychometric scale commonly used in questionnaires, and it is the most widely used
scale in survey research. When responding to a Likert questionnaire item, the
participants specify their level of agreement or disagreement to a statement. The
questionnaire is presented in Appendix B.
The participants of the survey came from local suppliers, members of the Puerto
Rico Manufacturers' Association. They were invited to participate in the research and to
answer the questionnaire. An informational letter with the questionnaire was sent via an
electronic link. A web-based questionnaire powered by QuestionPro was used for this
aim. Follow-ups through e-mail communications were sent one week, after sending the
questionnaire, thanking them for their participation and to remind those who had not yet
responded. Two weeks later, a second reminder was sent to those who had not yet sent
back the completed questionnaire indicating that their responses had not yet been
39
received. Four weeks later, after the questionnaire was first sent, a third reminder was
sent to emphasize the importance of the survey and the value of their responses.
Compared to conventional mail surveys, the advantages of the web-based
surveys are as follows: 1) costs for sending questionnaires and coding data are relatively
low; 2) they have a short turnaround time; 3) web-based surveys reach potential
respondents in geographically remote areas; 4) they offer a means to efficiently survey
larger numbers of individuals; 5) web-based surveys can increase respondents'
motivation to participate by providing a dynamic and interactive process; 6) they may
reduce the errors from transcription and coding (Zhang, 1999).
3.5. Data Analysis
Once responses were obtained and non-response bias were assessed by using
Armstrong and Overton's (1977) time-trend extrapolation test, the data was analyzed
using the Statistical Package for Social Science (SPSS) and SmartPLS software.
Statistical methods included: Factor Analysis (to measure the variability or
dimensionality of a set of variables), Cronbach's Alpha (to measure the internal
consistency or reliability), Correlation Coefficient (to measure the relationship of two
random variables), Multiple Regressions (for testing and modeling of multiple
independent variables) and Structural Equation Modeling (for testing and estimating
causal relations).
Results were compared with the expected outcomes predicted in the hypotheses.
The hypotheses were either supported or rejected based on the sample data collected
(Cooper and Schindler, 2006).
3.6. Ethical Considerations
This research meets the University's requirement of the Institutional Review
Board (IRB) policies and guidelines.
40
The informational letter had information relevant to the purpose of the research
and confidentiality. There were minimal risks that could include fatigue or tiredness.
The identity of the participants was protected and was not disclosed to any unauthorized
persons. All information gathered from the research remained confidential according to
HIPPA.
After analyzing the data, the information will be kept in a locked file by a period of
five years once concluded this research. Then, the data will be destroyed utilizing a
shredder machine according to the ethical procedures of the profession.
3.7. Conclusions
In brief, Chapter 3 described the methodology adopted in more detailed than in
the introductory description of section 1.4. The research was a quantitative research.
Data was collected through a structured questionnaire using a 5-point Likert scale,
close-ended and open-ended questions to answer questions or statements. Then, data
was analyzed using Statistical Package for the Social Sciences (SPSS) and SmartPLS
software. Results determined if the hypotheses posed in this research were either
supported or rejected.
41
CHAPTER 4
DATA ANALYSIS
4.1. Introduction
Chapter 3 described the methods used in this research to collect data which
either support or reject the hypotheses. Chapter 4 presents the results of applying those
methods in this research. This chapter is structured around the following major topics:
Description of the Population, Sample, Data Collection and Data Analysis, Demographic
Information, Factor Analysis, Reliability and Validity, Values of R², Collinearity, Testing
for Hypotheses and Findings, and finally, Data Summary.
4.2. Description of the Population, Sample, Data Collection and Data Analysis
One of the stated objectives of this research was to identify the determinant
factors that could facilitate or inhibit knowledge transfer success between buyer-supplier
relationships. Furthermore, determine the knowledge transfer impact in the outcomes
(benefits and usefulness) of this collaborative relationship.
The unit of analysis was local suppliers of the Pharmaceuticals, Medical Devices
and Biotechnologies companies. The population came from 500 suppliers, members of
the Puerto Rico Manufacturers' Association.
A survey was conducted over a three-month period (July to September 2013).
Data were collected using a web-based questionnaire powered by QuestionPro via an
electronic link.
Of a total of 500 individuals to whom the survey was sent, 110 responded,
resulting in a response rate of 22%. This sample represented various industries,
company sizes and ages located in Puerto Rico (a summary of the Demographic
information is presented in the Appendix C).
42
The Statistical Package for the Social Sciences (SPSS) and SmartPLS software
were used for the analysis and presentation of data. The data were analyzed in two
phases. First, an Exploratory Factor Analysis (EFA) was conducted using the research
model’s original constructs as a baseline factor solution. Next, a Confirmatory Factor
Analysis (CFA) was performed in SmartPLS which generates t-values via bootstrapping
to further test for reliability by extracting and comparing the highest and average
variances shared of the re-analyzed constructs. Taking into account the results of the
EFA and CFA tests and the theoretical framework of this research, a nine-factor solution
was deemed most appropriate. The revised conceptual framework led to the results
presented in the following sections.
4.3. Demographic Information
This section outlines the descriptive statistics calculated on the basis of the
variables included in the demographic section of the questionnaire. The demographic
variables that received attention in this research were: Gender, Age, Education, Years
Employed in the Organization, Role in the Industry, Number of Employees, Industry
Sector, Primary Functional Responsibility, Frequency of Interactions, Type of Interaction,
Company Characteristics and Overall Knowledge (Firm's Perspective and Experience
with the Buyers). Findings are discussed below (a summary of the Demographic
Information is presented in the Appendix C).
Gender
The respondents were 57% male and 43% female. The data collected shows a
higher frequency for male participants compared to female. Figure 4 illustrates the
distribution of the selected sample per gender.
43
Figure 4. Distribution of the selected sample per gender.
Age
It may be deduced that 20% in the sample fall into the age category of 26 - 40. It
can thus be seen that the majority of the individuals in the sample (54%) fall into the age
category of 41 – 55, while only 26% of the respondents indicated that they are older than
56 years. Most respondents were between 41 to 55 years of age. Figure 5 indicates the
distribution of the selected sample per age group.
44
Figure 5. Distribution of the selected sample per age group.
Education
The respondent’s educational qualifications are reflected in this sample. A total
of 45% of the respondents have a Bachelor's Degree, while the 33% of the respondents
have a Master Degree. The 11% of the respondents have Some College, 6% have a
Doctoral Degree and 3% have a Vocational/Technical School. Only 1% of the
respondents have High school or equivalent and another 1% indicated that they have a
Professional degree (MD, JD, etc.) respectively. Majority of the respondents’ state that
the highest level of education has a Bachelor's Degree. Figure 6 displays the
distribution of the selected sample per educational qualification.
45
Figure 6. Distribution of the selected sample per educational qualification.
Years Employed in the Organization
A total of 77% of the respondents have been employed for the organization for
more than five years. Another 16% for between 3 to 5 years, 4% indicated that they
have been employed in the organization for between 1 to 2 years while 3% have served
for less than 1 year in the organization. Figure 7 exhibits the distribution of the years
employed in the organization.
46
Figure 7. Distribution of the years employed in the organization.
Role in the Industry
The respondents' professional levels ranged from Upper management to
Supplier. The 40% of the respondents are from the Upper Management, 23% pertains
to the Middle Management, 15% are suppliers, 12% are part of the administrative staff,
and 6% indicated that they are from Junior Management while 4% is support staff. The
data show that the majority of respondents pertain to the Upper management. Figure 8
indicates the distribution of the roles in the organization.
47
Figure 8. Distribution of the role in the organization.
Number of Employees
A total of 54% of the companies have less than 50 employees, 17% have
between 101 to 500 employees and another 15% indicated that they have between 51 to
100 employees. Only 14% have more than 1000 employees. Figure 9 illustrates the
distribution of the employees in the companies.
48
Figure 9. Distribution of the employees in the companies.
Industry Sector
The 70% of the sample pertains to the Pharmaceutical sector, 6% are in the
Transportation and Logistics sector, while another 6% of the respondents are in the
Construction sector respectively. The 5% of the respondents indicated that pertain to
Consumer Products, 5% are part of the Information communications technology (other
than biomedical), 4% pertains to Medical Devices and 2% are from the Healthcare
sector. Only 1% of respondents are part of the Biotechnology and another 1% pertains
to Public sector. The Pharmaceutical sector was most represented. Figure 10 indicates
the distribution of the industry sector.
49
Figure 10. Distribution of the industry sector.
Primary Functional Responsibility
The primary functional responsibility of 74% of the respondents is in the Supply
Chain/Purchasing, Project Planning, Strategic Planning, General Corporate
Management while 14% is in the Marketing, Product Development/Management,
Product Market Research, New Business Development/Commercialization. The
remaining 12% is in the R&D/Technology, Engineering, Science, Product or Process
Development. The primary functional responsibility is in the Supply Chain/Purchasing,
Project Planning, Strategic Planning, General Corporate Management. Figure 11
displays the distribution of the primary functional responsibility.
50
Figure 11. Distribution of the primary functional responsibility.
Frequency of Interactions
The 43% of the interactions between buyers and suppliers occurs Daily, 20%
Several Times a Week, 14% Occasionally, 13% Weekly and 10% Monthly. The majority
of interactions occur Daily. Figure 12 exhibits the distribution of the frequency of
interactions.
51
Figure 12. Distribution of the frequency of interactions.
Type of Interaction
The 41% of the interactions between buyers and suppliers are Face to Face,
30% by E-mail and 29% by phone. It may be deduced that the majority of interactions
are Face to Face. Figure 13 shows the type of interaction.
52
Figure 13. Distribution of the type of interaction.
Company Characteristics
The 59% of the respondents have worked with the buyers between 1 to 11 years
and another 23% have worked between 12 to 22 years. The 10% of the respondents
indicated that they have been worked with the buyers between 23 to 33 years, 3% of the
respondents have worked between 34 to 44 years while another 3% have worked
between 45 to 55 years. Only 1% has worked with the buyers between 56 to 66 and the
remaining 1% between 67 to 77 years. Figure 14 illustrates the distribution of the years
worked with the buyers.
53
Figure 14. Distribution of the years worked with the buyers.
Overall Knowledge (Firm's Perspective)
Most of the respondents have knowledge about the firm's perspective. The 65%
of the respondents agreed that they have very strong knowledge, 25% of the
respondents have strong knowledge and 8% of the respondents agreed that they have
knowledge. Therefore, 2% of the respondents agreed that they do not have any
knowledge of the firm's perspective. Figure 15 indicates the overall knowledge (Firm's
perspective).
54
Figure 15. Distribution of the overall knowledge (Firm's perspective).
Overall Knowledge (Experience with these Buyers)
The results related to the knowledge of the company experience with the
particular buyer were 58% that agreed that they have very strong knowledge and 25%
agreed that they have strong knowledge. The 12% agreed of having knowledge, 4%
have some knowledge and only 1% of the respondents agreed that they do not have any
knowledge. Figure 16 displays the overall knowledge (Experience with these buyers).
55
Figure 16. Distribution of the overall knowledge (Experience with these buyers).
Respondent Characteristics
This survey had 110 respondents. As shown in Appendix C, the respondents
were 57% male and 43% female. Most respondents (54%) were between 41 to 55 years
of age. Nearly 45% of the respondents stated that the highest level of education was
bachelor's degree. The Pharmaceutical sector (70%) was most represented. Other
industries were Medical Devices (4%) and Biotechnology (1%). Most of the
organizations (54%) had less than 50 employees. Majority of the respondents (77%)
have been employed in the organization for more than 5 years and 40% pertains to the
upper management. The primary functional responsibility is in the Supply
Chain/Purchasing, Project Planning, Strategic Planning, General Corporate
Management (74%). The 59% of the respondents have worked with the buyers between
1 to 11 years. The frequency of the interactions with the buyers occurs Daily (43%) and
Face to Face (41%). The 65% of the respondents stated that have very strong
56
knowledge about the firm's perspective and 58% have very strong knowledge of the
company experience with the particular buyer.
4.4. Factor Analysis
As suggested by Norusis (1994) and Hair et al. (2013), a Factor Analysis was
conducted. The Factor Analysis was used to evaluate if each question in the
questionnaire measures the construct that was supposed and to identify which factors
were significant. Based on the orthogonal approach of Varimax rotation, those factors
that showed Eigenvalues greater than one were identified as significant and included in
the analysis.
The overall Kaiser-Meyer-Oklin (KMO) measure was .679 indicating a sampling
adequacy according to Kaiser (1974) classification of measure values. Bartlett's Test of
Sphericity was statistically significant (p < .05) indicating that the data was likely
factorizable, as shown in the Table 3.
Table 3
KMO and Barlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.679
Bartlett's Test of Sphericity
Approx. Chi-Square 5206.994
Df 1540
Sig. .000
57
The Communalities obtained were ranging from .653 to .923 and are reported in
the Table 4. These communalities show that a substantial portion of the variable’s
variance is accounted for by the factors. All of the communalities were sufficiently high
to proceed with the rotation of the Factor Analysis.
Table 4
Communalities
Initial Extraction
Ta Our buyers keep their promises. 1.000 .851
Tb Our buyers keep our best interests in mind. 1.000 .734
Tc Our company can count on our buyers to be sincere. 1.000 .841
Td Our buyers are reliable and consistent in dealing with us. 1.000 .886
Te Our buyers have a high degree of integrity. 1.000 .849
Da Our buyers are crucial to the company's future performance. 1.000 .821
Db It would be difficult for my company to replace our buyers. 1.000 .661
Dc Our buyers are important to my company. 1.000 .854
Dd Our buyers are essential to our future success in this company. 1.000 .860
De My company is dependent upon our buyers. 1.000 .731
Ca We expects our relationship with our buyers to continue for a long time. 1.000 .679
Cb Renewal of the relationship with our buyers is virtually automatic. 1.000 .661
Cc Our relationship with our buyers is enduring. 1.000 .703
Cd Our relationship with our buyers is a long-term alliance. 1.000 .772
Ce Our relationship with our buyers is an alliance that is going to last. 1.000 .830
KTa is difficult to articulate. 1.000 .688
KTb needs to be explained personally. 1.000 .655
KTc is easily codifiable (in instructions, formulas, etc.). 1.000 .702
KTd is explain in writing (reports, manuals, e-mails, documents, faxes, etc.). 1.000 .834
KRa is difficult to articulate. 1.000 .737
KRb needs to be explained personally. 1.000 .755
KRc is easily codifiable (in instructions, formulas, etc.). 1.000 .820
KRd is explain in writing (reports, manuals, e-mails, documents, faxes, etc.). 1.000 .653
58
Initial Extraction
Qa Relevant to my company needs, compared to information exchanged with
other similar supply chain partners.
1.000 .816
Qb Value-added to my company needs, compared to information exchanged with
other similar supply chain partners.
1.000 .782
Qc Timely to my company needs, compared to information exchanged with other
similar supply chain partners.
1.000 .792
Qd Complete to my company needs, compared to information exchanged with
other similar supply chain partners.
1.000 .837
Qe Accessible to my company needs, compared to information exchanged with
other similar supply chain partners.
1.000 .768
ABa My company has the ability to acquire new external knowledge that is critical
to our operations.
1.000 .777
ABb My company has the ability to assimilate new external knowledge. 1.000 .825
ABc My company has the ability to transform new knowledge in new ideas that lead
to new behavior.
1.000 .824
ABd My company has the ability to apply new external knowledge commercially to
achieve organizational objectives.
1.000 .798
ABe My company has the technical competence to absorb the knowledge
transferred.
1.000 .795
ABf My company has a common language to deal with the knowledge transferred. 1.000 .792
ABg My company has a vision of what it was trying to achieve through the
knowledge transferred.
1.000 .788
ABh My company has the necessary skills to implement the knowledge transferred. 1.000 .836
ABi My company has the managerial competence to absorb the knowledge
transferred.
1.000 .763
KTRa Our buyers and my company have learned greatly from each other. 1.000 .771
KTRb Our buyers and my company have shared significant amount of information
and knowledge with each other.
1.000 .743
KTRc Our buyers and my company have created new skills and knowledge by
working together.
1.000 .794
KTRd Our buyers have exchanged a lot of ideas with our company about how to
improve each other's capabilities (in manufacturing, packaging, logistics, etc.)
1.000 .768
KTRe Our buyers have transferred knowledge to my company. 1.000 .695
Ba Knowledge transferred enhanced our operational efficiency and capacity. 1.000 .900
59
Initial Extraction
Bb Knowledge transferred fostered the innovation.
Bc Knowledge transferred benefited our organizations' economic performance.
1.000
1.000
.838
.891
Bd Knowledge transferred benefited our organizations' perceived market value. 1.000 .882
Be Knowledge transferred made us more valued by our partners. 1.000 .792
Bf Knowledge transferred gave us job security. 1.000 .777
Ua Knowledge transferred proved to be very useful in achieving our organizations'
goals and objectives.
1.000 .823
Ub Knowledge transferred helped us to meet the industry challenges of our
company.
1.000 .845
Uc Knowledge transferred helped our company work more efficiently. 1.000 .854
Ud Knowledge transferred made us better at what we do. 1.000 .839
Ue Knowledge transferred contributed greatly to multiple projects at our company. 1.000 .878
Uf Our company was very satisfied with the knowledge transferred. 1.000 .835
Ug Our company increased the perception about the efficacy of the knowledge
after gaining experience with it.
1.000 .923
Uh Knowledge transferred helped our company in terms of actually improving our
organizational capabilities.
1.000 .817
Extraction Method: Principal Component Analysis.
60
The amount of variance each component accounts is presented in the Table 5.
Factor Analysis revealed nine components that had Eigenvalues greater than one and
accounting for 79.3% of the total variance.
Table 5
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings Rotation Sums of Squared
Loadings
Total % of
Variance Cumulative
% Total
% of Variance
Cumulative %
Total % of
Variance Cumulative
%
1 18.40
8 32.871 32.871
18.408
32.871 32.871 15.412 27.521 27.521
2 7.073 12.630 45.501 7.073 12.630 45.501 6.411 11.448 38.969
3 6.492 11.592 57.093 6.492 11.592 57.093 5.329 9.515 48.485
4 2.863 5.112 62.205 2.863 5.112 62.205 4.698 8.389 56.874
5 2.574 4.597 66.802 2.574 4.597 66.802 3.812 6.808 63.682
6 2.460 4.393 71.196 2.460 4.393 71.196 2.719 4.856 68.538
7 1.956 3.492 74.688 1.956 3.492 74.688 2.561 4.573 73.110
8 1.455 2.597 77.286 1.455 2.597 77.286 1.967 3.513 76.623
9 1.150 2.054 79.339 1.150 2.054 79.339 1.521 2.716 79.339
10 1.062 1.896 81.236
11 .932 1.665 82.900
12 .893 1.595 84.495
13 .796 1.422 85.917
14 .793 1.415 87.333
15 .625 1.116 88.448
16 .546 .976 89.424
17 .505 .901 90.325
18 .461 .824 91.149
19 .423 .756 91.905
20 .384 .685 92.590
21 .356 .635 93.225
22 .332 .592 93.817
23 .297 .531 94.348
24 .291 .520 94.869
25 .267 .476 95.345
26 .245 .438 95.782
27 .222 .397 96.179
28 .211 .376 96.556
29 .193 .344 96.900
30 .189 .337 97.237
61
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings Rotation Sums of Squared
Loadings
Total % of
Variance Cumulative
% Total
% of Variance
Cumulative %
Total % of
Variance Cumulative
%
31 .165 .294 97.531
32 .151 .269 97.800
33 .125 .223 98.024
34 .118 .212 98.235
35 .105 .187 98.423
36 .101 .181 98.603
37 .094 .168 98.771
38 .087 .156 98.927
39 .080 .143 99.070
40 .068 .121 99.192
41 .061 .109 99.301
42 .055 .099 99.400
43 .053 .094 99.494
44 .049 .088 99.582
45 .045 .081 99.663
46 .036 .064 99.727
47 .030 .053 99.780
48 .026 .047 99.826
49 .022 .039 99.865
50 .019 .035 99.900
51 .016 .028 99.927
52 .014 .025 99.953
53 .011 .019 99.972
54 .008 .014 99.986
55 .004 .008 99.994
56 .003 .006 100.000
Extraction Method: Principal Component Analysis.
62
A Varimax orthogonal rotation was used to aid interpretability. The interpretation
of the data was consistent with the factors the questionnaire was designed to measure
with strong loadings of outcomes (benefits and usefulness) and knowledge transfer
items on Component 1, absorption capacity items on Component 2, trust items on
Component 3, dependence items on Component 4, quality of information exchanged
items on Component 5, tacit knowledge items on Component 6, expectation of
relationship continuity items on Component 7, and explicit knowledge items on
Components 8 and 9. Component loadings are presented in the Rotated Component
Matrix. Major loadings for each item are highlighted in the Table 6.
Table 6
Rotated Component Matrix
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Ug Our
company
increased
the
perception
about the
efficacy of
the
knowledge
after
gaining
experience
with it.
.931 .057 .016 .143 .054 -.119 .054 -.033 .100
63
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Ba
Knowledge
transferred
enhanced
our
operational
efficiency
and
capacity.
.905 -.059 .162 .039 .211 -.028 .028 .013 -.059
Ue
Knowledge
transferred
contributed
greatly to
multiple
projects at
our
company.
.904 .022 .044 .118 .118 .020 -.013 .007 .171
Uc
Knowledge
transferred
helped our
company
work more
efficiently.
.903 .027 .031 .130 .098 .032 -.084 -.012 .037
64
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Ub
Knowledge
transferred
helped us to
meet the
industry
challenges
of our
company
Ud
Knowledge
transferred
made us
better at
what we do.
Bd
Knowledge
transferred
benefited
our
organization
s' perceived
market
value.
.895
.890
.888
.009
.058
-.005
.042
.006
.025
.088
.127
.097
.138
.036
.229
-.036
.011
-.108
-.114
-.040
.115
.017
-.066
-.080
.028
.141
-.021
Uf Our
company
was very
satisfied
with the
knowledge
transferred.
.887 .059 .108 .075 .092 -.063 .015 .075 .096
65
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Bc
Knowledge
transferred
benefited
our
organization
s' economic
performance
.885 -.018 .147 -.005 .167 .106 .130 .086 -.150
Uh
Knowledge
transferred
helped our
company in
terms of
actually
improving
our
organization
al
capabilities
.883 .045 .054 .098 .114 -.024 -.032 -.029 .080
Bb
Knowledge
transferred
fostered the
innovation.
.883 .044 .064 .070 .130 -.123 .119 -.031 .011
66
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Ua
Knowledge
transferred
proved to be
very useful
in achieving
our
organization
s' goals and
objectives.
866 -.087 .158 -.009 .149 .101 .012 .090 -.016
Be
Knowledge
transferred
made us
more valued
by our
partners.
.865 -.051 .077 .067 .051 -.078 .143 .015 -.025
KTRc Our
buyers and
my company
have
created new
skills and
knowledge
by working
together.
.820 .166 .215 -.020 .044 .036 .066 .079 -.182
Bf
Knowledge
transferred
gave us job
security.
.785 -.058 .128 -.041 .138 .036 .273 .187 -.101
67
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
KTRa Our
buyers and
my
company
have
learned
greatly
from each
other.
KTRb Our
buyers and
my
company
have
shared
significant
amount of
information
and
knowledge
with each
other.
.745
.738
.215
.248
.119
.095
-.110
-.042
.266
.315
.069
.055
.153
.032
-.181
-.088
.107
.125
KTRe Our
buyers
have
transferred
knowledge
to my
company.
.726 -.089 .134 -.003 .308 .098 .164 .066 -.078
68
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
KTRd Our
buyers have
exchanged
a lot of
ideas with
our
company
about how
to improve
each other's
capabilities
(in
manufacturi
ng,
packaging,
logistics,
etc.)
.670 .200 .220 -.106 .160 .180 .165 .220 -.293
ABh My
company
has the
necessary
skills to
implement
the
knowledge
transferred.
-.011 .879 .093 -.032 .041 .002 -.040 .102 -.201
69
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
ABe My
company
has the
technical
competence
to absorb
the
knowledge
transferred.
.019 .870 .064 -.018 .092 .005 -.070 .125 -.059
ABb My
company
has the
ability to
assimilate
new
external
knowledge.
-.025 .854 -.142 .128 -.032 -.053 .107 .033 .204
ABi My
company
has the
managerial
competence
to absorb
the
knowledge
transferred.
.034 .843 -.026 .025 -.063 .044 -.205 -.035 -.027
70
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
ABc My
company
has the
ability to
transform
new
knowledge
in new
ideas that
lead to new
behavior.
.071 .840 -.104 .016 -.113 -.061 .276 .017 .100
ABd My
company
has the
ability to
apply new
external
knowledge
commerciall
y to achieve
organization
al
objectives.
.053 .837 -.042 -.016 -.074 .001 .280 .085 -.036
71
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
ABa My
company
has the
ability to
acquire new
external
knowledge
that is
critical to our
operations.
-.042 .830 -.117 .110 -.027 -.026 .085 -.027 .225
ABf My
company
has a
common
language to
deal with the
knowledge
transferred.
.268 .654 .233 -.082 -.025
.074 -.273 .032 -.386
ABg My
company
has a vision
of what it
was trying to
achieve
through the
knowledge
transferred.
.261 .647 .235 -.059 -.056 .142 -.294 .030 -.364
72
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Td Our
buyers are
reliable and
consistent in
dealing with
us.
.194 -.010 .874 .180 .049 .066 .189 .089 .038
Ta Our
buyers keep
their
promises.
.096 -.006 .858 .293 .017 .059 .018 .121 .001
Tc Our
company
can count
on our
buyers to be
sincere.
.154 .010 .834 .324 .028 .017 .123 -.016 .028
Te Our
buyers have
a high
degree of
integrity.
.165 .073 .791 .353 -.072 .075 .160 -.091 .145
Tb Our
buyers keep
our best
interests in
mind.
.156 -.177 .758 .118 .055 .145 .193 .040 -.163
De My
company is
dependent
upon our
buyers.
.015 -.020 .042 .835 -.024 .139 .051 .070 -.064
73
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Dd Our
buyers are
essential to
our future
success in
this
company.
.119 .043 .366 .833 .105 .070 .007 .003 -.009
Dc Our
buyers are
important to
my
company.
.158 -.049 .335 .828 .081 .120 .041 -.070 .017
Da Our
buyers are
crucial to the
company's
future
performance.
.128 .028 .333 .817 .119 .005 .039 -.095 -.018
Db It would
be difficult
for my
company to
replace our
buyers.
.020 .038 .081 .714 .000 .300 .163 .155 .044
Ca We
expects our
relationship
with our
buyers to
continue for
a long time.
.143 .150 .422 .586 .065 -.067 .301 -.044 .115
74
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Qa
Relevant to
my
company
needs,
compared to
information
exchanged
with other
similar
supply chain
partners.
.345 -.029 -.147 .138 .798 .030 -.086 -.072 -.069
Qd
Complete
to my
company
needs,
compared to
information
exchanged
with other
similar
supply chain
partners.
.405 .016 .225 -.058 .765 .019 .120 .115 .073
75
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Qb Value-
added to my
company
needs,
compared to
information
exchanged
with other
similar
supply chain
partners.
.424 -.110 .014 .130 .752 .032 .032 .014 -.072
Qe
Accessible
to my
company
needs,
compared to
information
exchanged
with other
similar
supply chain
partners.
.385 -.043 .045 .064 .732 .075 .139 .029 .223
76
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Qc Timely
to my
company
needs,
compared to
information
exchanged
with other
similar
supply chain
partners.
.523 -.058 .019 .111 .702 -.023 .051 -.002 .075
KRb needs
to be
explained
personally.
.033 -.018 .111 .032 .147 .836 .106 .042 .077
KRa is
difficult to
articulate.
.207 -.136 .022 .121 .158 .777 .039 -.158 .071
KTb needs
to be
explained
personally.
-.131 .076 .227 .172 -.179 .712 -.021 -.075 -.067
KTa is
difficult to
articulate.
-.233 .113 -.049 .269 -.067 .701 -.101 -.146 -.136
Ce Our
relationship
with our
buyers is an
alliance that
is going to
last.
.127 .096 .378 .241 .044 -.040 .771 -.025 -.079
77
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
Cc Our
relationship
with our
buyers is
enduring.
.186 .099 .421 .301 .047 .072 .597 -.138 .081
Cd Our
relationship
with our
buyers is a
long-term
alliance.
.283 .101 .408 .385 .076 .015 .587 -.125 -.009
Cb Renewal
of the
relationship
with our
buyers is
virtually
automatic.
.265 -.118 .454 -.078 .147 .148 .562 .075 .002
KRc is
easily
codifiable (in
instructions,
formulas,
etc.).
-.009 .190 .061 .030 -.050 -.134 -.023 .866 -.094
KTc is easily
codifiable (in
instructions,
formulas,
etc.).
.240 -.079 .213 -.184 .384 -.115 -.025 .613 .148
78
Component
Outcomes
and
KT
Absorption
Capac. Trust Dependence
Quality
of Info. Tacit
Exp.
Relation
Continuity
Explicit Explicit
KRd is
explain in
writing
(reports,
manuals, e-
mails,
documents,
faxes, etc.).
-.043 .265 -.131 .195 -.064 -.111 -.082 .611 .360
KTd is
explain in
writing
(reports,
manuals, e-
mails,
documents,
faxes, etc.).
.325 -.082 .249 -.096 .308 .068 -.069 .262 .691
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
79
4.5. Reliability and Validity
A questionnaire was used to measure different and underlying constructs. Eight
of the constructs reported Cronbach's Alpha coefficients above 0.80 (ranging from α =
0.88 to α = 0.97) indicating that the scale had a high level of internal consistency.
Constructs also reported acceptable Composite Reliability ranging from 0.84 to 0.97.
Convergent validity was confirmed in all the constructs ranging from 0.65 to 0.84.
According to Götz, Liehr-Gobbers and Krafft (2009), an Average Variance Extracted
value of at least 0.5 indicates sufficient convergent validity (see Table 7).
Table 7
Overview Quality Criteria
Construct
AVE
Composite
Reliability
R
Square
Cronbach's
Alpha Communality Redundancy
Absorption Capacity
0.652 0.944 0.000 0.938 0.652 0.000
Benefits 0.835 0.968 0.692 0.960 0.835 0.576
Expectation of Relationship Continuity
0.753 0.924 0.000 0.889 0.753 0.000
Dependence 0.795 0.939 0.000 0.914 0.795 0.000
Explicit Knowledge 0.728 0.843 0.000 0.627 0.728 0.000
Knowledge Transfer
0.734 0.932 0.452 0.909 0.734 0.063
Quality of Information
0.735 0.932 0.000 0.910 0.735 0.000
Tacit Knowledge 0.801 0.889 0.000 0.754 0.801 0.000
Trust 0.806 0.954 0.000 0.940 0.806 0.000
Usefulness 0.842 0.977 0.644 0.973 0.842 0.540
The discriminant validity among the constructs was confirmed using the Fornell
and Larcker criterion (see Table 8). The criterion requires that correlations between
constructs are lower than the square root of each construct’s average variance extracted
(Fornell and Larcker, 1981).
80
The AVE of each construct should be higher than the construct’s highest correlation with any other construct in the model
(Hair, Hult, Ringle and Sarstedt, 2013). Overall, crossloadings as the Fornell-Larcker criterion provide evidence for the construct’s
discriminant analysis.
Table 8
Fornell and Larcker Criterion
Absorption
Capacity Benefits
Expectation Relationship Continuity
Dependence Explicit
Knowledge Knowledge
Transfer
Quality of
Info
Tacit Knowledge
Trust Usefulness
Absorption Capacity
0.808
Benefits 0.137 0.914
Expectation Relationship Continuity
0.061 0.395 0.868
Dependence 0.039 0.225 0.439 0.892
Explicit Knowledge
0.019 0.409 0.206 0.016 0.854
Knowledge Transfer
0.292 0.832 0.385 0.206 0.387 0.857
Quality Of Information
0.026 0.533 0.187 0.177 0.420 0.463 0.857
Tacit Knowledge
0.044 -0.273 -0.027 0.161 -0.265 -0.279 -0.114 0.895
Trust 0.105 0.360 0.567 0.530 0.259 0.335 0.198 0.027 0.898
Usefulness 0.211 0.895 0.309 0.253 0.440 0.803 0.489 -0.266 0.314 0.918
81
The correlations between variables are given below in the Table 9. It can be noticed that the variables knowledge transfer,
benefits and usefulness have high correlation values. In contrast, absorption capacity, expectation relationship continuity,
dependence, explicit knowledge, quality of information exchanged, tacit knowledge and trust have low correlation values.
Table 9
Correlation between Variables
Absorption
Capacity Benefits
Expectation Relationship Continuity
Dependence Explicit
Knowledge Knowledge
Transfer Quality of
Information Tacit
Knowledge Trust Usefulness
Absorption Capacity
1.000
Benefits 0.137 1.000
Expectation Relationship Continuity
0.061 0.395 1.000
Dependence 0.039 0.225 0.439 1.000
Explicit Knowledge
0.019 0.409 0.206 0.016 1.000
Knowledge Transfer
0.292 0.832 0.385 0.206 0.387 1.000
Quality Of Information
0.026 0.533 0.187 0.177 0.420 0.463 1.000
Tacit Knowledge
0.044 -0.273 -0.027 0.161 -0.265 -0.279 -0.114 1.000
Trust 0.105 0.360 0.567 0.530 0.259 0.335 0.198 0.027 1.000
Usefulness 0.211 0.895 0.309 0.253 0.440 0.803 0.489 -0.266 0.314 1.000
82
4.6. Values of R²
Squared multiple correlations (R²) for constructs were also assessed as shown in
the Table 7. The R2 values of benefits (0.69) and usefulness (0.64) are considered High
Moderate. In contrast, the R² value of knowledge transfer (0.45) is Moderate (Hair et al.,
2013).
4.7. Collinearity Constructs were tested for Collinearity. According with the results, trust has the
highest VIF value (1.832). Hence, VIF values are uniformly below the threshold value of
.5 as shown in the Table 10. We conclude, therefore, that collinearity does not reach
critical levels in any of the formative constructs and is not an issue for the estimation of
the PLS path model.
83
Table 10
Collinearity
Model Unstandardized Coefficients
Standardized Coefficients t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 6.570E-6 .073 .000 1.000
Absorption Capacity .269 .074 .269 3.645 .000 .986 1.014
Expectation Relationship Continuity
.219 .092 .219 2.394 .018 .641 1.559
Dependence .040 .092 .040 .434 .665 .641 1.560
Explicit Knowledge .126 .087 .126 1.456 .148 .717 1.394
Quality of Information .315 .082 .315 3.822 .000 .792 1.262
Tacit Knowledge -.224 .077 -.224 -2.895 .005 .896 1.116
Trust .072 .099 .072 .728 .468 .546 1.832
a. Dependent Variable: Knowledge Transfer
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition
Index
Variance Proportions
(Constant) Absorption Capacity
Expectation Relationship Continuity
Dependence Explicit
Knowledge Quality of Info
Tacit Knowledge
Trust
1 1 2.264 1.000 .00 .00 .07 .06 .03 .04 .00 .07
2 1.441 1.254 .00 .01 .01 .06 .15 .09 .21 .01
3 1.000 1.505 1.00 .00 .00 .00 .00 .00 .00 .00
4 .990 1.512 .00 .96 .01 .01 .00 .00 .00 .00
5 .844 1.637 .00 .01 .07 .00 .02 .37 .48 .02
6 .602 1.939 .00 .01 .03 .22 .38 .26 .29 .04
7 .487 2.156 .00 .00 .67 .28 .17 .14 .01 .08
8 .371 2.470 .00 .01 .14 .36 .24 .10 .00 .78
a. Dependent Variable: Knowledge Transfer
84
4.8. Testing for Hypotheses and Findings
The hypotheses posed in our research were tested using SmartPLS (refer to
Figures 17 and 18).
Figure 17. SmartPLS Structural Model Measurement with Loadings.
85
Figure 18. SmartPLS Bootstrapping Model with Loadings.
The significance of all paths in each model was tested using a bootstrap
procedure with re-sampling of 500. The structural model calculated the measurement
coefficients as well as the t-values for each coefficient (refer to Table 11). The results of
the SmartPLS analysis are discussed below. Looking at the significance levels, we find
that all formative indicators are significant except trust, dependence and explicit
knowledge.
86
Table 11
Path Coefficients and t-Values
Construct Original Sample
(O)
T Statistics (|O/STERR|)
Trust -> KnowTransf 0.07 0.73
Dependence -> KnowTransf 0.04 0.39
Expectation Relationship Continuity -> KnowTransf
0.21 2.44
Explicit Knowledge -> KnowTransf 0.12 1.31
Tacit Knowledge -> KnowTransf -0.22 2.86
Quality of Info -> KnowTransf 0.31 2.84
Absorption Capacity -> KnowTransf 0.26 2.51
KnowTransf -> Benefits 0.83 21.28
KnowTransf -> Usefulness 0.80 16.20
Hypothesis 1 that trust will positively impact the knowledge transfer process
between buyers and suppliers was not supported (λ=0.73).
Hypothesis 2 that dependence will positively impact the knowledge transfer
process between buyers and suppliers was not supported (λ=0.39).
Hypothesis 3 that expectation of relationship continuity will positively impact the
knowledge transfer process between buyers and suppliers was supported (λ=2.44).
Hypothesis 4a that explicit knowledge will positively impact the knowledge
transfer process between buyers and suppliers was not supported (λ=1.31).
Hypothesis 4b that tacit knowledge will positively impact the knowledge transfer
process between buyers and suppliers was supported (λ=2.86).
Hypothesis 5 that quality of information exchanged will positively impact the
knowledge transfer process between buyers and suppliers was supported (λ=2.84).
87
Hypothesis 6 that absorptive capacity will positively impact the knowledge
transfer process between buyers and suppliers was supported (λ=2.51).
Hypothesis 7a that knowledge transfer will positively impact the outcomes
(benefits) of the buyer-supplier collaboration was supported (λ=21.28). Hypothesis 7b
that knowledge transfer will positively impact the outcomes (usefulness) of the buyer-
supplier collaboration was also supported (λ=16.20). Refer to Appendix D for a
Summary of Hypothesis Testing.
4.9. Data Summary
Empirical data collected suggested that trust, dependence, and explicit
knowledge did not indicate any significance influence in the knowledge transfer process
in our unit of analysis. Therefore, H1, H2, and H4a were not supported.
In contrast, our research findings indicate that the expectation of relationship
continuity, tacit knowledge, quality of information exchanged and absorptive capacity are
positively associated with knowledge transfer. Finally, the benefits and usefulness
dimensions were found to be significantly affected by the knowledge transfer.
Research findings will be discussed within the context of extant literature in
Chapter 5.
88
CHAPTER 5
CONCLUSIONS
5.1. Introduction
Chapter 5 discusses the research findings presented in Chapter 4 and their
possible impact on the current body of knowledge outlined previously in Chapter 2. This
chapter is structured around the following major topics: Discussion of Results,
Conclusions, Contribution to Theory and Practice, Delimitations and Directions for
Future Research.
5.2. Discussion of Results
We set out to expand current understanding of the knowledge transfer process
between multinational corporations and their local suppliers in Puerto Rico. Our findings
point toward the existence of different factors that facilitate or inhibit the knowledge
sharing dynamics in this environment.
According to the empirical data collected from 110 suppliers, members of the
Puerto Rico Manufacturers' Association, the impact of these factors (e.g., trust,
dependence, expectation of relationship continuity, explicit knowledge, tacit knowledge,
quality of information exchanged and absorptive capacity) in the knowledge transfer
process varies in our unit of analysis.
The R² value of the endogenous variable “knowledge transfer” in the inner path
model demonstrated that the four independent variables (expectation of relationship
continuity, tacit knowledge, quality of information exchanged and absorptive capacity)
explain 45% of the variance in the knowledge transfer variable, which signifies a
moderate value of variance explained (Hair et al., 2013). In contrast, the R² values of
benefits (69%) and usefulness (64%) are considered High Moderate (Chin, 1998).
89
5.2.1. Determinant Factors that Facilitate or Inhibit Knowledge Transfer
One of the stated objectives of this research was to identify the determinant
factors that could facilitate or inhibit knowledge transfer success between buyer-supplier
relationships. Furthermore, determine the knowledge transfer impact in the outcomes
(benefits and usefulness) of this collaborative relationship.
Figure 18 exhibits that factors such as expectation of relationship continuity, tacit
knowledge, quality of information exchanged and absorptive capacity facilitate
knowledge transfer. In contrast, trust, dependence and explicit knowledge inhibit
knowledge transfer between buyers-suppliers. In the case of knowledge transfer, it
positively impacts both, the benefits and usefulness of knowledge.
Figure 18. SmartPLS Bootstrapping Model with Loadings.
90
5.2.2. The Effects of Determinant Factors
The main research question was: What are the bases for knowledge transfer in
industrial vertical relationships? The research developed and tested a conceptual
framework based on the literature review which suggested that there are factors that
determine the knowledge transfer in this type of relationship (see Figure 3).
©Amarilis Delgado, 2012
Figure 3. Conceptual Framework of Knowledge Transfer Between Buyer-Supplier. Hypotheses were formulated and statistically tested in Chapter 4. Results of this
research point toward empirical support for the majority of the proposed hypotheses.
H7(+)
H6(+)
H5(+)
H4(+)
H3(+)
H1(+)
H2(+)
Contextual
Factors Process Outcomes
Trust
Dependence
Expectation of Relationship Continuity
Type of Knowledge
Shared
Quality of Information Exchanged
Absorptive Capacity
Outcomes of the Collaboration
Knowledge Transfer
91
Figure 19 displays the revised conceptual framework with those hypotheses that
were supported during the hypothesis testing.
©Amarilis Delgado, 2013
Figure 19. Revised Conceptual Framework of Knowledge Transfer Between Buyer-Supplier. The revised conceptual framework established that the expectation of
relationship continuity, type of knowledge shared (tacit knowledge), quality of information
exchanged and absorptive capacity have an impact in knowledge transfer and that the
knowledge transferred by the MNCs has a direct impact in the outcomes that are
H7a & H7b(+)
H6(+)
H5(+)
H4b(+)
H3(+)
Contextual
Factors Process Outcomes
Expectation of Relationship Continuity
Type of Knowledge
Shared
Quality of Information Exchanged
Absorptive Capacity
Outcomes of the
Collaboration
Knowledge Transfer
Tacit
Benefits Dimension
Usefulness Dimension
92
measured in the benefits and usefulness dimensions. The result of each hypothesis
testing and other findings in the data analysis are presented below.
5.2.2.1. The Effect of Trust on Knowledge Transfer
The literature review supports the relation between trust and knowledge transfer
(Davenport and Prusak, 1998; Hansen, 1999; Simonin, 1999; Dyer and Nobeoka, 2000;
Cummings and Teng, 2003; Levin and Cross, 2004; Chen, 2004). Indeed, several
authors even consider that a climate of trust enables the transfer of knowledge (Lane et
al., 2001; Dhanaraj et al., 2004; Squire et al., 2009). The Hypothesis 1 was formulated
based on this theoretical background. Thus, we could predict that trust will positively
impact knowledge transfer between buyers-suppliers.
The regression analysis results indicated that the beta coefficient (β) was equal
to 0.07 and factor loading (λ) was equal to 0.73. These results show that trust has no
significant impact on knowledge transfer, thereby not supporting Hypothesis 1. This is
one of the most significant and interesting findings to emerge from this research
because it is contrary to extant research that support a relation between these two
variables.
The empirical data collected suggests that the impact of trust in the knowledge
transfer process varies between the buyers-suppliers in Puerto Rico. Considering that
the mean related with this construct was 3.83, one plausible explanation for the No
significant influence of trust in knowledge transfer might be that trust is more important
for buyers than suppliers due to the nature of their needs. In other words, there is a
difference in the perspective of buyers and suppliers with regard to the trust. In this
sense, the source and nature of the dynamics involved in the knowledge transfer and
sharing process could be more significant that trust alone.
The importance of studying the suppliers' trust in the buyers lies in the fact that
there are many studies conducted from the buyers' perspective and few studies in the
93
literature that deal with the perspective of the supplier. In essence, the suppliers' trust is
different from that of buyers. For example, the suppliers' trust in buyers depends on
their commitments so they must be kept. In contrast, the buyers' trust in suppliers is
based on supplier performance. The supplier must consistently perform and deliver in
accordance with the buyers' specifications before earning buyers' trust. Ganesan (1994)
argued that buyer's trust in a supplier have an impact in the long-term relationships
between them in the following ways: (1) less perception of risk associated with
opportunistic behaviors; (2) increase confidence that short-term inequities will be
resolved over a long period; and (3) reduces the transactions costs in an exchange
relationship. It is clear that buyers and suppliers base their trust on different
requirements and procedures with the purpose of selling or buying products,
respectively.
Several studies have also confirmed the existence of possible barriers that can
curtail knowledge flow between organizations (e.g. Spender and Grant, 1996; Grant,
1996; Szulanski, 1996; Davenport and Prusak, 1998; Simonin 2004). Some of them
have been highlighted as more important than others. For example, Davenport and
Prusak (1998) identified some barriers including lack of trust, different cultures, a fear to
taking risk, vocabularies, frames of reference, giving status and rewards to the
knowledge owners, treating knowledge as a prerogative of particular groups, lack of time
and meeting places, narrow idea of productive work among other elements. In line with
other researchers (Nahapiet and Ghoshal, 1998; Goh, 2002; Renzl, 2008), they claimed
that Not trusting the source of knowledge is one of the main barriers to knowledge
transfer. These authors emphasized that without trust, knowledge will not work well.
Similarly, Cummings and Teng (2003) argued that the credibility of the source
with the recipient affect knowledge transfer success since knowledge internalization
requires that a recipient see the value of the knowledge being transferred. They
94
suggested that if the source is seen as less than credible, its knowledge can also lose
value in the eyes of the recipient, therefore affecting the outcomes of knowledge
transfer. The content relevance of the knowledge to be transferred to the recipient’s
context must also be considered because it could be a crucial factor affecting the
credibility of its knowledge and knowledge transfer efforts.
Trust is characterized as the partners' ability to believe that the counterpart's
behavior will remain consistent in the future and it is related to the past experience of the
relationship and common experiences (Spekman et al., 1998; Wijk et al., 2008; Thomas
et al., 2011). That is, the recipient judge for the information that they received in light of
what is already known based on previous behaviors or experiences. As argued by
Davenport and Prusak (1998), knowledge is valued and considered credible to the
extent that the source is trustworthy to the recipient.
Key-supplier and sole-sourcing arrangements between multinational buyers and
local suppliers could also in a number of cases and specialized knowledge industries,
such as life sciences, reduce the level of trust needed between the contracting parties
(Squire et al., 2009).
In sum, contrary to extant research, the results reveal that trust is not a
significant factor in knowledge transfer in our unit of analysis.
5.2.2.2. The Effect of Dependence on Knowledge Transfer
The statement of the Hypothesis 2 is framed in the dependence dimension. The
literature suggested that dependence in a relationship means that an organization needs
the resources of the other organization to reach its objective resulting in frequent
interactions that may increase knowledge transfer (Rahmoun and Debabi, 2012;
Hansen, 1999). Therefore, we could predict that dependence will positively impact the
knowledge transfer process between buyers and suppliers. However, the regression
analysis results (β = 0.04 and λ= 0.39) did not support this hypothesis.
95
Although the mean obtained in this construct was 3.97 indicating that there is a
high concentration on buyers, there is no evidence that dependence impacts knowledge
transfer. Even though, Pfeffer and Salancik (1978) establish in their resource
dependency theory that dependence is created by partners to provide important, critical,
valuable or strategic resources, this research demonstrated that dependence is not a
determinant factor for knowledge transfer.
Previous research has pointed out that dependence is also related to distribution
of power between two partners (Robbins, 2005). Indeed, Emerson (1962) proposed that
the basis of power is dependency. In other words, power and dependence are important
elements to be considered in order to understand knowledge transfer between buyer-
supplier relationships. This is particularly important in relation to transfer of tacit
knowledge.
He, et al. (2006) found that the lack of alternatives is a possible barrier for
knowledge transfer since one of the partners will be more dependent on the other
partner. It means that power will not be balanced and one of the partners will be more
powerful than the other. For example, in the case of the buyer-supplier relationship, the
supplier's dependence on a buyer confers the buyer power over the supplier and
conversely, the buyer's dependence on the supplier gives the supplier power on the
buyer. As explained by Easterby-Smith et al., 2008, the fear of losing power or status,
motivates organizations to protect against unintended transfer of knowledge to their
partners.
In sum, the results (β = 0.04, λ= 0.39, mean = 3.97) show that there is no
significant relationship between dependence and knowledge transfer. In other words,
the assumption that dependence between buyer-supplier will positively impact the
knowledge transfer process is not supported in this research.
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5.2.2.3. The Effect of Expectation of Relationship Continuity on Knowledge
Transfer
The literature review suggested the Hypothesis 3 which establishes that the
expectation of relationship continuity will positively impact the knowledge transfer
process between buyers and suppliers. The statistical analysis (β = 0.21, λ = 2.44,
mean = 3.78) supported this hypothesis indicating that there is a relation between the
two variables.
These results are consistent with findings of Levinthal and Fichman (1988),
Asanuma (1989), Fichman and Levinthal (1991), who suggested that the relationships
that endure over time develop interactions that allow communicating and facilitating the
transfer of new knowledge. Essentially, previous research has indicated that close
relationship means that partners share the same risks and have willingness to maintain
this relationship over long-term (Cooper and Ellram, 1993; McNeish and Mann, 2010). It
involves the expectation of relationship continuity (Anderson and Weitz, 1989; Ganesan
1994; Sosa et al. 2011) or probability of future interaction.
In sum, the results indicate that expectation of relationship continuity plays an
important role in developing long-term close relationships increasing the knowledge
transfer and it is one of the determinant factors that facilitate the knowledge transfer
process between MNCs and their local suppliers in Puerto Rico.
5.2.2.4. The Effect of Explicit Knowledge on Knowledge Transfer
In some studies, explicit knowledge has been considered as a construct that
influence knowledge transfer (e.g. Nonaka, 1994; Simonin, 1999; Hansen, 1999; Lane et
al., 2001; Hansen, 2002; Cummings and Teng, 2003). The explicit knowledge was
defined by Nonaka (1994, p. 16) as "a codified knowledge that is transmittable in formal
and systematic language". This type of knowledge is easy to transfer due to it can be
expressed and communicated in words, symbols, numbers, documents, manuals,
97
policies or procedures. However, to make use of the explicit knowledge, it must be
understood by the recipient. The Hypothesis 4a established that the explicit knowledge
will positively impact the knowledge transfer process between buyers and suppliers.
However, contrary to expectations and literature, the regression analysis results (β =
0.12 and λ = 1.31) did not support this hypothesis.
Taking into account the regression results and that the mean of the construct
was 3.32, it becomes clear that these results suggest that explicit knowledge is not a
significant factor in the knowledge transfer between buyer-supplier. Our research points
toward that the sharing of complex data and knowledge is not facilitated by explicit
knowledge transfer. Results show that in complex industries like Pharmaceutical,
Medical Devices and Biotechnologies, the explicit knowledge (as is easy to disseminate)
does not impact knowledge transfer.
In the literature review some possible barriers have been identified that could
also limit knowledge transfer. For example, the outcomes of knowledge transfer will
depend on the difficulty of knowledge transfer process (Argote, McEvily and Reagans,
2003) and the lack of absorptive capacity or lack of value. As argued by Minbaeva et al.,
2003, the lack of absorptive capacity has been treated as a cognitive or learning barrier
to knowledge transfer.
Although the explicit knowledge can be articulated and it is easy to transfer due
to the nature of its characteristics, the receivers must grasp the meaning of the explicit
knowledge. The full understanding of the information requires absorptive capacity.
The absorptive capacity was defined by Cohen and Levinthal (1990, p. 128) as
"the ability to recognize the value of new external information, assimilate it, and apply it
to commercial ends". They explained that the recipient with limited absorptive capacity
is less like to see the value of new knowledge and internalize it. According to Prevot
(2008), the absorptive capacity is essential to successful knowledge transfer.
98
Knowledge transfer depends on the recipient's absorptive and retentive capacities. High
levels of absorptive capacity increase the probability of knowledge transfer. Low
absorptive capacity and retentive capacities, cultural and communication difficulties will
limit knowledge transfer (Chen, 2004).
Until now, we propose that contrary to previous research, the lack of direct
contacts, communication difficulties, limited levels of trust, and the nature of knowledge
do not indicate any significant impact in this research, but they may be influential in other
business relationships.
5.2.2.5. The Effect of Tacit Knowledge on Knowledge Transfer
In the knowledge transfer literature, tacit knowledge is also considered as a
construct that impact knowledge transfer (e.g. Chen, 2004; Ganesan et al., 2005;
Easterby-Smith et al., 2008; Ho, 2008; Lund, 2010). The tacit knowledge refers to the
uncodified knowledge that is unarticulated and difficult to transfer (Polanyi, 1962).
Based on this argument, the Hypothesis 4b established that tacit knowledge will
positively impact the knowledge transfer process between buyers and suppliers.
The results (β = -0.22, λ = 2.86 and mean = 3.27) provide support for Hypothesis
4b and in addition, suggest that there is a negative or inverse relationship between both
variables. This negative result is an important finding for the sector being analyzed and
it is in line with the research of Kimble (2013). If the knowledge is tacit and difficult to
codify, higher transfer mechanisms are needed to process and transfer the knowledge.
It could be true for complex knowledge in pharmaceutical and life science industries.
It also confirms the limitations of tacit knowledge. Tacit knowledge is usually
described as knowledge that is either (a) impossible to describe in propositional terms,
or (b) implicit, that is, articulable but only with some difficulty (Nonaka, 1991; Kimble,
2013). Tacit knowledge is usually seen as being acquired through direct personal
experience (Cowan, David and Foray, 2000).
99
In the case of the multinationals, they tend to restrict the flow of valuable
knowledge to protect its information and maintain their competitive advantage, and as a
result, knowledge transfer will be limited (He et al., 2006). The tacit and specialized
knowledge are assets highly valued by them due to confers competitive advantage and
are not shared with local partners unless formal channels of communication "pipelines"
are building (Bathelt, Malmberg and Maskell, 2004). In some cases, the value chain
between buyers-suppliers relationship has not been served as a link to develop or
evolve through a learning process, dissemination and knowledge.
Many multinationals tend to be "process driven" and do not stimulate the
innovation (Montalvo, 2011). It is also possible that MNCs agents do no incentive for
creating a knowledge environment and conduits - pipelines - through which suppliers so
they can identify information with economic value and exploit this information to achieve
superior operational and strategic outcomes (Bathelt et al., 2004).
In sum, the results indicate that there is a relationship between tacit knowledge
and knowledge transfer confirming the extant literature.
5.2.2.6. The Effect of Quality of Information Exchanged on Knowledge Transfer
The Hypothesis 5 suggested that the quality of information exchanged will
positively impact the knowledge transfer process between buyers and suppliers. The
results obtained in the regression analysis (β = 0.31, λ = 2.84) and mean (3.86)
supported this hypothesis.
One of the most interesting findings of this research is that the quality of
information is the independent variable that had a stronger impact in knowledge transfer.
As stated by Rashed et al. (2010), one of the major aspects of information exchanged is
the quality of the information. Similarly, Zhou and Benton (2007) argued that the quality
of the information is considered a key determinant when the information has the
following attributes: high quality, readily accessible, accurate and relevant.
100
In this sense, the quality of the information that flows in an organization has a
direct impact on the knowledge. This information needs to be timely and relevant to the
context of the organization (Rashed et al., 2010). In line with Rashed's argument, Miller
(1996) suggested that most people agree that the information must have quality (e.g.,
relevance, accuracy, timeliness, completeness, coherence, format, accessibility,
compatibility, security and validity) in order to be effective.
Based on Miller's (1996) research, Malhotra et al. (2005) also found that the
following four dimensions: Relevancy, Value-Added, Timelines and Completeness are
also important to absorptive capacity and that the quality of information exchanged is
beneficial for partners to exchange information.
In sum, the results indicate that the quality of the information is important for the
decision-making and for the information exchanged between buyers and their suppliers.
5.2.2.7. The Effect of Absorptive Capacity on Knowledge Transfer
Our research confirms the role of absorptive capacity in knowledge transfer
dynamics as the results (β = 0.26, λ = 2.51) show that absorptive capacity has a positive
and significant impact on knowledge transfer process between buyer-suppliers, thereby
supporting Hypothesis 6.
Considering that the mean was equal to 4.18 and the regression analysis, these
results were found to be consistent with previous research on this construct (Cohen and
Levinthal, 1990; Chen and McQueen, 2010; Lee and Wu, 2010). They agreed that the
absorptive capacity is essential in order to facilitate the knowledge transfer and
concluded that absorptive capacity is considered as one of the most important
determinants of knowledge transfer.
Zahra and George (2002) in their study of firm-level absorptive capacity, state
that "firms can acquire and assimilate knowledge but might not have the capability to
transform and exploit it for profit generation" (p. 191). In this sense, knowledge transfer
101
depends on the level of absorptive capacity and the complex of the knowledge to be
transferred (Dhanaraj et al., 2004). Higher levels of absorptive capacity enable
knowledge transfers flows (Minbaeva et al., 2003). In other words, absorptive capacity
is required to facilitate knowledge transfer.
Firm-level absorptive capacity can be also affected by similarities or differences
of both the source and the recipient in terms of knowledge bases and organizational
structures (Lane and Lubatkin, 1998). Two other factors affecting absorptive capacity
through the knowledge assimilation are internal factors (organizational structure, culture,
management practices, level of education, academic degrees of employees, prior
knowledge base, knowledge background or cross-cultural communication) and external
factors (environment and organization's position in knowledge networks) (Lee and Wu,
2010).
In sum, the results indicate that the absorptive capacity is one of the most
important determinants of knowledge transfer in this type of relationship. Buyers and
suppliers share interrelated processes to allow the transfer of tacit knowledge and high-
quality information (relevant, value-added, timely, complete and accessible). However,
the recipient must have the capacity to acquire and assimilate it (absorption capacity) so
the knowledge transfer can occur and be successful. Otherwise, the lack of absorptive
capacity according to the literature could constitute a reason that inhibits knowledge
transfer.
5.2.2.8. The Effect of Knowledge Transfer on the Outcomes
The results of this research clearly show that knowledge transferred has a
positive and significant impact on both, benefits (β = 0.83; λ = 21.28; mean = 3.76) and
usefulness (β = 0.80; λ = 16.20; mean = 3.77) respectively, thereby supporting
Hypotheses 7a and 7b.
102
These results are consistent with prior research that has postulated that
knowledge transferred has an impact on organizational outcomes (Wijk et al., 2008; Ho,
2008; Ford and Staples, 2006; Sichinsambwe, 2011). Ford and Staples (2006) argued
that the basis of value for knowledge is its usefulness and benefits. As expressed by
them, this implies that the usefulness and benefits dimensions are factors that determine
the value of the knowledge. In their research, they concluded that the more useful the
knowledge was for the individuals, the more valued the knowledge was and the more
benefits received from the knowledge, the more valued the knowledge was.
Pérez-Nordtvedt et al. (2008, p. 717), refers to the usefulness of the knowledge
transferred as the extent to which such knowledge was relevant and salient to
organizational success. According to these authors if the knowledge is transferred
quickly and it is not considered relevant or easy to comprehend by the recipient, the
transfer will not lead to the desired outcomes.
Based on the empirical data collected, results of our research confirm the extant
literature. For example, respondents confirmed the benefits from having the knowledge
and considered aspects such as "the knowledge transferred enhanced the operational
efficiency and capacity; fostered the innovation; benefited the organizations' economic
performance and organizations' perceived market value; made them more valued by the
partners and gave them job security".
Within the usefulness dimension, the respondents considered that "knowledge
transferred proved to be very useful in achieving their organizations' goals and
objectives; helped them to meet the industry challenges and work more efficiently; made
them better at what they do; contributed to multiple projects; increased the perception
about the efficacy of the knowledge after gaining experience with it and help in terms of
improving the organizational capabilities".
103
In sum, the results indicate that the usefulness and benefits were derived as
outcomes of the knowledge transferred confirming the literature discussed above.
5.3. Conclusions
Although extant research on knowledge transfer shares the common view that
trust, dependence and explicit knowledge have a positive relationship with knowledge
transfer, the empirical data collected revealed an alternative view of previous research
findings. These three factors did not indicate any significance influence in knowledge
transfer process between buyers-suppliers.
Even though trust, dependence and explicit knowledge are important to maintain
this kind of relationship, they do not ensure that there will be knowledge transfer. The
major implication of this finding in this research is that these factors may be necessary,
but not sufficient for knowledge transfer process from the suppliers' perspective.
In contrast, the research also suggests that the independent variables such as
expectation of relationship continuity, tacit knowledge, quality of information exchanged
and absorptive capacity have a positive impact in knowledge transfer. It confirms the
extant literature that all these factors are essentials to successful knowledge transfer
and are characterized by close, long-term and collaborative relationships. This research
demonstrated that they are important elements that determine the transfer of knowledge
between buyers-suppliers.
The nature of the information exchanged between them reflects a strategic
intention: information is of high quality and the scope is very wide (tacit knowledge).
Although the transfer of a wide range of information is beneficial for this type of
relationship, these four elements are very important in this process. If information flows,
it will have an impact on the transfer of knowledge and at the same time, it will become a
source of competitive advantage.
104
In the case of benefits and usefulness dimensions, the research demonstrated
that they are significantly impacted by the knowledge transfer confirming the literature
review and the value of the knowledge transferred.
In synthesis, the results of this research suggest that increases in expectations of
relationship continuity, successful transfer of tacit knowledge, quality of information
exchanged, and absorptive capacity of local suppliers can enhance vertical supply chain
knowledge transfer.
5.4. Contributions to Theory and Practice
Limited research has been conducted on the determinants factors of knowledge
transfer (Squire et al., 2009). The present research makes a contribution to theory
because it outlines important constructs based on different theoretical approaches,
related to suppliers’ conception of determinants of knowledge transfer, for the benefit of
other researchers and practitioners in the field. Furthermore, it appears to be the first of
its kind in Puerto Rico/Latin American research contexts from the suppliers' perspective.
The results of this research support and contradict previous research evidence.
The significant role of trust in well-functioning or deficient knowledge transfer process is
supported by the majority of previous research (McNeish and Mann, 2010; Squire et al.,
2009; Madlberger, 2009; Staples and Webster, 2008). However, this research did not
identify any significant influence by trust on knowledge transfer. Another contribution
was the creation and validation of an integrated conceptual framework that illustrated the
major determinants of knowledge transfer between MNCs and their local suppliers in
Puerto Rico.
We believe that our research could hold an important contribution to the body of
knowledge as a new possible definition for knowledge transfer has emerged within the
context of the results of this research. Knowledge transfer can be defined as the
process of information exchanged (tacit or explicit) between the source and recipient that
105
involves the quality of information transmitted and the absorptive capacity of the
recipient in an organization.
Our research could be of distinct practical contributions that will be very useful for
academics, practitioners and government policy makers. Results can help to: 1) identify
enabling factors and characteristics in an environment that could facilitate knowledge
spillovers and transfer; 2) expand the knowledge about the impact of the determinant
factors in knowledge transfer process; 3) assist the decision makers to analyze
knowledge transfer between buyers-suppliers before they enter in a relationship with a
supply chain partner; and 4) Know the impact of MNCs in local emerging economies.
5.5. Delimitations
There are some delimitations in this research. First, this research focuses only
on local suppliers of the pharmaceuticals, medical devices and biotechnologies
companies. The sample size may limit the generalizability of our findings to other
companies.
Second, this research is conducted in a specific geographical area (Puerto Rico)
due to time and resources required for accessing a large number of organizations.
Third, constructs are measured by respondents' self-reporting about their firms,
and may be inherently biased. However, potential bias is considered a minimal risk in
this case, for the development of practice-relevant theory, as respondents were not
asked to identify themselves or their organizations (Venkatraman and Ramanujam,
1986).
5.6. Directions for Future Research
This research developed a conceptual framework in which the relationship
amongst several factors impacting knowledge transfer were presented and assessed.
Factors that were not significantly related to knowledge transfer (trust, dependence and
explicit knowledge) provide the basis for future research.
106
It would be valuable to evaluate the tested research model in other industrial
sectors, companies, relationships, countries and cultures. It would be of interest to see if
the same results appear in further studies within other cultures, particularly if there are
similarities or dissimilarities across countries. It provides a better understanding of these
factors and how they impact to knowledge transfer. Furthermore, it could help to reduce
what Alavi and Leidener (2001) called "large gaps in the body of knowledge in this area"
in reference to the little empirical work related with the knowledge, knowledge
management, and knowledge management systems. According to them, research is
now needed that moves beyond the source and state to consider the conditions that
facilitate knowledge creation.
Our research serves as a useful base for researchers to expand further research
into barriers that seem to filter or inhibit knowledge transfer. It can provide a basis for
control variables (e. g. different cultures, geographical proximity, etc.) in future research
models.
Future research is also recommended into the impact of buyer-supplier
interactions and trust in knowledge transfer process. They could constitute moderating
variables between our proposed variables.
Another opportunity for future research is to examine trust and absorptive
capacity as two interrelated factors that influence the extent and effectiveness of
knowledge transfer.
Finally, the possible moderating effect of factors such as trust between buyer-
supplier relationships on knowledge transfer process could also be considered.
107
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APPENDIX A
Constructs and Scale Development Sources
Construct/Variable
Scholarly Sources
Trust
Geyskens, Steenkamp, Scheer and Kumar, 1996 Madlberger, 2009 Spekman, Kamauff and Myhr, 1998 Squire, Cousins and Brown, 2009
Dependence
Ho, 2008 Spekman, Kamauff and Myhr, 1998 Thomas, Fugate and Koukova, 2011
Expectation of Relationship Continuity
Sosa, Svensson and Mysen, 2011
Type of Knowledge Shared (Tacit and Explicit)
Ganesan, Malter and Rindfleisch, 2005 Hansen, 1999 Ho, 2008 Simonin, 1999
Quality of Information Exchanged Malhotra, Gosain and El Sawy, 2005
Absorptive Capacity
Lane and Lubatkin, 1998 Lee and Wu, 2010 Szulanski, 1996 Szulanski, 2000 Zahra and George, 2002
Knowledge Transfer Ho, 2008
Outcomes of the Collaboration (Benefits and Usefulness)
Ford and Staples, 2006 Pérez-Nordtvedt, Kedia, Datta and Rasheed, 2008 Sichinsambwe, 2011
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APPENDIX B
Survey Questionnaire (Strictly Confidential)
Knowledge Transfer in Buyer-Supplier Relationships:
The Case of Multinational Corporations (MNCs)
and their Local suppliers in Puerto Rico
Please answer all questions. Your answers will be held in strictest confidence. You are not asked to identify yourself or your organization.
TRUST 1 (Strongly Disagree) to 5 (Strongly Agree)
1 2 3 4 5 Our buyers keep their promises. ◦ ◦ ◦ ◦ ◦ Our buyers keep our best interests in mind. ◦ ◦ ◦ ◦ ◦ Our company can count on our buyers to be sincere. ◦ ◦ ◦ ◦ ◦ Our buyers are reliable and consistent in dealing with us. ◦ ◦ ◦ ◦ ◦ Our buyers have a high degree of integrity. ◦ ◦ ◦ ◦ ◦
DEPENDENCE
1 (Strongly Disagree) to 5 (Strongly Agree) 1 2 3 4 5 Our buyers are crucial to the company's future performance. ◦ ◦ ◦ ◦ ◦ It would be difficult for my company to replace our buyers. ◦ ◦ ◦ ◦ ◦ Our buyers are important to my company. ◦ ◦ ◦ ◦ ◦ Our buyers are essential to our future success in this company. ◦ ◦ ◦ ◦ ◦ My company is dependent upon our buyers. ◦ ◦ ◦ ◦ ◦
EXPECTATION OF RELATIONSHIP CONTINUITY
1 (Strongly Disagree) to 5 (Strongly Agree) 1 2 3 4 5 We expect our relationship with our buyers to continue for a long time. ◦ ◦ ◦ ◦ ◦ Renewal of the relationship with our buyers is virtually automatic. ◦ ◦ ◦ ◦ ◦ Our relationship with our buyers is enduring. ◦ ◦ ◦ ◦ ◦ Our relationship with our buyers is a long-term alliance. ◦ ◦ ◦ ◦ ◦ Our relationship with our buyers is an alliance that is going to last. ◦ ◦ ◦ ◦ ◦
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TYPE OF KNOWLEDGE SHARED
1 (Strongly Disagree) to 5 (Strongly Agree) 1 2 3 4 5 The knowledge that our buyers has transferred to my company: is difficult to articulate. ◦ ◦ ◦ ◦ ◦ needs to be explained personally. ◦ ◦ ◦ ◦ ◦ is easily codifiable (in instructions, formulas, etc.). ◦ ◦ ◦ ◦ ◦ is explain in writing (reports, manuals, e-mails, documents, faxes, etc.). ◦ ◦ ◦ ◦ ◦ The knowledge that our buyers has received from my company: is difficult to articulate. ◦ ◦ ◦ ◦ ◦ needs to be explained personally. ◦ ◦ ◦ ◦ ◦ is easily codifiable (in instructions, formulas, etc.). ◦ ◦ ◦ ◦ ◦ is explain in writing (reports, manuals, e-mails, documents, faxes, etc.). ◦ ◦ ◦ ◦ ◦
QUALITY OF INFORMATION EXCHANGED 1 (Strongly Disagree) to 5 (Strongly Agree)
1 2 3 4 5 The information exchanged with our buyers is: Relevant to my company needs, compared to information exchanged with other similar supply chain partners.
◦ ◦ ◦ ◦ ◦
Value-added to my company needs, compared to information exchanged with other similar supply chain partners.
◦ ◦ ◦ ◦ ◦
Timely to my company needs, compared to information exchanged with other similar supply chain partners.
◦ ◦ ◦ ◦ ◦
Complete to my company needs, compared to information exchanged with other similar supply chain partners.
◦ ◦ ◦ ◦ ◦
Accessible to my company needs, compared to information exchanged with other similar supply chain partners.
◦ ◦ ◦ ◦ ◦
ABSORPTIVE CAPACITY 1 (Strongly Disagree) to 5 (Strongly Agree)
1 2 3 4 5 My company has the ability to acquire new external knowledge that is critical to our operations.
◦ ◦ ◦ ◦ ◦
My company has the ability to assimilate new external knowledge. ◦ ◦ ◦ ◦ ◦ My company has the ability to transform new knowledge in new ideas that lead to new behavior.
◦ ◦ ◦ ◦ ◦
My company has the ability to apply new external knowledge commercially to achieve organizational objectives.
◦ ◦ ◦ ◦ ◦
My company has the technical competence to absorb the knowledge transferred.
◦ ◦ ◦ ◦ ◦
My company has a common language to deal with the knowledge transferred.
◦ ◦ ◦ ◦ ◦
My company has a vision of what it was trying to achieve through the knowledge transferred.
◦ ◦ ◦ ◦ ◦
My company has the necessary skills to implement the knowledge transferred.
◦ ◦ ◦ ◦ ◦
My company has the managerial competence to absorb the knowledge transferred.
◦ ◦ ◦ ◦ ◦
128
KNOWLEDGE TRANSFER 1 (Strongly Disagree) to 5 (Strongly Agree)
1 2 3 4 5 Our buyers and my company have learned greatly from each other. ◦ ◦ ◦ ◦ ◦ Our buyers and my company have shared significant amount of information and knowledge with each other.
◦ ◦ ◦ ◦ ◦
Our buyers and my company have created new skills and knowledge by working together.
◦ ◦ ◦ ◦ ◦
Our buyers have exchanged a lot of ideas with our company about how to improve each other's capabilities (in manufacturing, packaging, logistics, etc.)
◦ ◦ ◦ ◦ ◦
Our buyers have transferred knowledge to my company. ◦ ◦ ◦ ◦ ◦
OUTCOMES OF THIS COLLABORATION 1 (Strongly Disagree) to 5 (Strongly Agree)
1 2 3 4 5 Benefits Dimension: Knowledge transferred enhanced our operational efficiency and capacity. ◦ ◦ ◦ ◦ ◦ Knowledge transferred fostered the innovation. ◦ ◦ ◦ ◦ ◦ Knowledge transferred benefited our organizations' economic performance. ◦ ◦ ◦ ◦ ◦ Knowledge transferred benefited our organizations' perceived market value. ◦ ◦ ◦ ◦ ◦ Knowledge transferred made us more valued by our partners. ◦ ◦ ◦ ◦ ◦ Knowledge transferred gave us job security. ◦ ◦ ◦ ◦ ◦ Usefulness Dimension: Knowledge transferred proved to be very useful in achieving our organizations' goals and objectives.
◦ ◦ ◦ ◦ ◦
Knowledge transferred helped us to meet the industry challenges of our company.
◦ ◦ ◦ ◦ ◦
Knowledge transferred helped our company work more efficiently. ◦ ◦ ◦ ◦ ◦ Knowledge transferred made us better at what we do. ◦ ◦ ◦ ◦ ◦ Knowledge transferred contributed greatly to multiple projects at our company.
◦ ◦ ◦ ◦ ◦
Our company was very satisfied with the knowledge transferred. ◦ ◦ ◦ ◦ ◦ Our company increased the perception about the efficacy of the knowledge after gaining experience with it.
◦ ◦ ◦ ◦ ◦
Knowledge transferred helped our company in terms of actually improving our organizational capabilities.
◦ ◦ ◦ ◦ ◦
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DEMOGRAPHIC QUESTIONS What is your gender? ○ Female ○ Male What is your age? ○ 25 or under ○ 26 - 40 ○ 41 - 55 ○ 56 or older What is the highest level of education you have completed? ○ Less than High School ○ High school or equivalent ○ Vocational/technical school (2 year) ○ Some college ○ Bachelor's degree ○ Master's degree ○ Doctoral degree ○ Professional degree (MD, JD, etc.) ○ Other (please specify):______________ How long have you been employed in this organization? (Workforce) ○ Less than 1 year ○ 1 - 2 years ○ 3 to 5 years ○ More than 5 years Which of the following best describes your role in industry? ○ Upper management ○ Middle management ○ Junior management ○ Administrative staff ○ Support staff ○ Supplier ○ Other (please specify):______________ How many employees does your company have? ○ Less than 50 ○ Between 51 and 100 ○ Between 101 and 500 ○ More than 1000
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Please indicate the industry sector your company belongs to: ○ Pharmaceutical ○ Medical Devices ○ Biotechnology ○ Consumer Products ○ Biomedical manufacturing (i. e., medical equipment and supplies, diagnostics, and software) ○ Healthcare ○ Transportation and Logistics ○ Construction ○ Financial services ○ Information communications technology (other than biomedical) ○ Legal and accounting services ○ Public sector ○ Other (please specify):______________ Please select from one of the following categories to describe your primary functional responsibility: ○ Supply Chain/Purchasing, Project Planning, Strategic Planning, General Corporate Management ○ R&D/Technology, Engineering, Science, Product or Process Development ○ Marketing, Product Development/Management, Product Market Research, New Business Development/Commercialization ○ Other (please specify):_____________
FREQUENCY OF INTERACTIONS 1 (Occasionally), 2 (Monthly), 3 (Weekly), 4 (Several Times a Week), 5 (Daily)
1 2 3 4 5 How often does your company interact with these buyers and in what way? ◦ ◦ ◦ ◦ ◦ ○ Face-to-Face ○ Phone ○ E-Mail ○ Facsimile
COMPANY CHARACTERISTICS
How many time does your company has worked with these buyers? _______Years
OVERALL KNOWLEDGE 1 (I do not have any knowledge) to 5 (I have a lot of knowledge)
1 2 3 4 5 Please consider how knowledgeable you are concerning your business and your business dealings with these buyers. Indicate the extent that best reflects your knowledge level.
My firm’s perspective. ◦ ◦ ◦ ◦ ◦ Experiences with these buyers. ◦ ◦ ◦ ◦ ◦
131
COMMENTS Do you have comments you want to make about this survey?
Thank you for your kind cooperation!
Questionnaire number: ______
132
APPENDIX C
Summary of Demographic Information
Topic Number of
Respondents %
Gender ○ Female ○ Male
47 63
43% 57%
Age ○ 25 or under ○ 26 - 40 ○ 41 - 55 ○ 56 or older
22 59 29
20% 54% 26%
Education ○ Less than High School ○ High school or equivalent ○ Vocational/technical school (2 years) ○ Some college ○ Bachelor's degree ○ Master's degree ○ Doctoral degree ○ Professional degree (MD, JD, etc.) ○ Other (please specify):______________
1 3
12 50 36 7 1
1% 3% 11% 45% 33% 6% 1%
.
Employed in this organization (Workforce) ○ Less than 1 year ○ 1 - 2 years ○ 3 to 5 years ○ More than 5 years
3 4
18 85
3% 4% 16% 77%
Role in the Industry ○ Upper management ○ Middle management ○ Junior management ○ Administrative staff ○ Support staff ○ Supplier ○ Other (please specify):______________
44 25 7
13 5
16
40% 23% 6% 12% 4% 15%
133
APPENDIX C
Summary of Demographic Information (cont.)
Topic Number of
Respondents %
Number of Employees
○ Less than 50 ○ Between 51 and 100 ○ Between 101 and 500 ○ More than 1000
59 17 19 15
54% 15% 17% 14%
Industry Sector
○ Pharmaceutical ○ Medical Devices ○ Biotechnology ○ Consumer Products ○ Biomedical manufacturing (i. e., medical equipment and supplies, diagnostics, and software) ○ Healthcare ○ Transportation and Logistics ○ Construction ○ Financial services ○ Information communications technology (other than biomedical) ○ Legal and accounting services ○ Public sector ○ Other (please specify):______________
77 4 1 6 2 7 7 5 1
70% 4% 1% 5%
2% 6% 6%
5%
1%
Primary Functional Responsibility
○ Supply Chain/Purchasing, Project Planning, Strategic Planning, General Corporate Management ○ R&D/Technology, Engineering, Science, Product or Process Development ○ Marketing, Product Development/Management, Product Market Research, New Business Development/Commercialization ○ Other (please specify):_____________
81
14
15
74%
12%
14%
Frequency of Interactions ○ Occasionally ○ Monthly ○ Weekly ○ Several Times a Week ○ Daily
15 12 14 22 47
14% 10% 13% 20% 43%
134
APPENDIX C
Summary of Demographic Information (cont.)
Topic Number of
Respondents %
Type of Interaction ○ Face-to-Face ○ Phone ○ E-Mail ○ Facsimile
45 32 33
41% 29% 30%
Company Characteristics How many time does your company has worked with these buyers? (Years)
○ 1 - 11
○ 12 - 22
○ 23 - 33
○ 34 - 44
○ 45 - 55
○ 56 - 66 ○ 67 - 77
65
25
12
3
3
1
1
59%
23%
10%
3%
3%
1%
1%
Overall Knowledge/My firm’s perspective ○ 1 - Do not have any Knowledge ○ 2 - Some Knowledge ○ 3 - Knowledge ○ 4 - Strong Knowledge ○ 5 - Very Strong Knowledge
2 9
27 72
2%
8% 25% 65%
Overall Knowledge/Experiences with these buyers ○ 1 - Do not have any knowledge ○ 2 - Some Knowledge ○ 3 - Knowledge ○ 4 - Strong Knowledge ○ 5 - Very Strong Knowledge
1 4
13 28 64
1% 4% 12% 25% 58%
135
APPENDIX D
Summary of Hypothesis Testing
Hypotheses Findings
Hypothesis 1: Trust will positively impact the knowledge transfer process between buyers and suppliers.
Not Supported
Hypothesis 2: Dependence will positively impact the knowledge transfer process between buyers and suppliers.
Not Supported
Hypothesis 3: Expectation of relationship continuity will positively impact the knowledge transfer process between buyers and suppliers.
Supported
Hypothesis 4a: Explicit knowledge will positively impact the knowledge transfer process between buyers and suppliers. Hypothesis 4b: Tacit knowledge will positively impact the knowledge transfer process between buyers and suppliers.
Not Supported
Supported
Hypothesis 5: Quality of information exchanged will positively impact the knowledge transfer process between buyers and suppliers.
Supported
Hypothesis 6: Absorptive capacity will positively impact the knowledge transfer process between buyers and suppliers.
Supported
Hypothesis 7a: Knowledge transfer will positively impact the outcomes (benefits) of the buyer-supplier collaboration. Hypothesis 7b: Knowledge transfer will positively impact the outcomes (usefulness) of the buyer-supplier collaboration.
Supported
Supported
