RELATIONSHIP BETWEEN KNOWLEDGE MANAGEMENT

AND PERFORMANCE OF COMMERCIAL BANKS IN KENYA

BY

GODFREY MUIGAI KINYUA: BED (EGERTON), MBA (UON)

D86/CTY/PT/25168/2011

A THESIS SUBMITED TO THE SCHOOL OF BUSINESS IN

PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY IN

BUSINESS ADMINISTRATION OF KENYATTA UNIVERSITY

OCTOBER, 2015

ii

DECLARATION

iii

DEDICATION

This thesis is dedicated to my wife Ruth, our sons Eddy and Lee for their love,

understanding and support during the many long hours when I had to juggle between

work, family and study, my siblings for their kind words of encouragement, and my

parents for their love, patience and exemplary guidance.

iv

ACKNOWLEDGEMENT

I am highly indebted to my supervisors, Dr. Muathe SMA (PhD) and Dr. Kilika J.M.

(PhD), for their sustained commitment, expert guidance and mentorship through the

entire process of developing this thesis. I am grateful to the members of staff in the

School of Business of Kenyatta University for their invaluable input, suggestions and

constructive criticisms that contributed immensely in enhancing the quality of this

research work. My appreciation also extends to the members of staff of Kenyatta

University Library for helping me to access requisite information and materials for

developing this thesis. I am equally grateful to all my colleagues in the PhD class for

their invaluable contributions toward the successful completion of this scholarly

pursuit. Indeed, I cannot forget the contribution of Saveliah Printing Enterprise for

facilitating timely printing and binding of this thesis during critical stages of its

development.

v

TABLE OF CONTENTS

DECLARATION ......................................................................................................... ii

DEDICATION ............................................................................................................ iii

ACKNOWLEDGEMENT ......................................................................................... iv

TABLE OF CONTENTS ............................................................................................ v

LIST OF TABLES .................................................................................................... viii

LIST OF FIGURES .................................................................................................... ix

OPERATIONAL DEFINITION OF TERMS .......................................................... x

ABBREVIATIONS AND ACRONYMS ................................................................. xii

ABSTRACT .............................................................................................................. xiii

CHAPTER ONE: INTRODUCTION ....................................................................... 1

1.1 Background of the Study ......................................................................................... 1

1.1.1 Organization Performance ............................................................................ 5

1.1.2 Knowledge Management .............................................................................. 8

1.1.3 Human Capital Repository ........................................................................... 9

1.1.4 Organization Culture .................................................................................. 10

1.1.5 Commercial Banks in Kenya ...................................................................... 12

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

1.3 Objectives of the Study ......................................................................................... 18

1.3.1 General Objective of the Study .................................................................. 18

1.3.2 Specific Objectives of the Study ................................................................ 18

1.4 Research Hypotheses ............................................................................................. 19

1.5 Significance of the Study ...................................................................................... 20

1.6 Scope of the Study ................................................................................................. 20

1.7 Limitations of the Study ........................................................................................ 21

1.8 Organization of the Study ...................................................................................... 22

CHAPTER TWO: LITERATURE REVIEW ........................................................ 23

2.1 Introduction ........................................................................................................... 23

2.2 Theoretical Literature Review ............................................................................... 23

2.2.1 Resource-Based View of the Firm ............................................................. 23

2.2.2 Knowledge-Based View of the Firm .......................................................... 27

2.2.3 Organizational Learning Theory ................................................................ 29

2.3 Empirical Literature Review ................................................................................. 34

vi

2.3.1 Knowledge Conversion and Performance .................................................. 34

2.3.2 Knowledge Transfer and Performance ....................................................... 36

2.3.3 Knowledge Application and Performance ................................................. 38

2.3.4 Human Capital Repository and Performance ............................................. 39

2.3.5 Knowledge Management and Human Capital Repository ......................... 40

2.3.6 Organizational Culture and Performance ................................................... 42

2.4 Summary of Literature Review and Research Gaps ............................................. 46

2.5 Conceptual Framework ......................................................................................... 51

CHAPTER THREE: RESEARCH METHODOLOGY ........................................ 53

3.1 Introduction ........................................................................................................... 53

3.2 Research Philosophy ............................................................................................. 53

3.3 Research Design .................................................................................................... 54

3.4 Empirical Model .................................................................................................... 55

3.5 Target Population .................................................................................................. 60

3.6 Sampling Design and Procedure ........................................................................... 61

3.7 Data Collection Instrument .................................................................................. 62

3.7.1 Test of Validity ........................................................................................... 63

3.7.2 Test of Reliability ....................................................................................... 66

3.8 Data Collection Procedure .................................................................................... 67

3.9 Data Analysis and Presentation ............................................................................ 67

3.10 Ethical Considerations ......................................................................................... 72

CHAPTER FOUR: RESEARCH FINDINGS AND DISCUSSION ..................... 73

4.1 Introduction .......................................................................................................... 73

4.2. Descriptive Analysis ............................................................................................. 73

4.2.1 Analysis of Response Rate ......................................................................... 73

4.2.2 Respondents’ Biographical Information .................................................... 74

4.2.3 Knowledge Conversion .............................................................................. 75

4.2.4 Knowledge Transfer ................................................................................... 79

4.2.5 Knowledge Application .............................................................................. 80

4.2.6 Human Capital Repository ......................................................................... 81

4.2.7 Firm’s Culture ............................................................................................ 83

4.2.8 Performance of Commercial Banks ........................................................... 85

4.3 Regression Analysis ............................................................................................. 86

vii

4.3.1 Diagnostic Tests ................................................................................................. 86

4.3.2 Test of Hypotheses ............................................................................................. 92

4.5 Qualitative Data Analysis ................................................................................... 111

CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS ...... 113

5.1 Introduction ......................................................................................................... 113

5.2 Summary ............................................................................................................. 113

5.3 Contribution of the Study to Knowledge ............................................................ 115

5.4 Conclusion ........................................................................................................... 117

5.5 Recommendations for Policy and Practice .......................................................... 118

5.6 Recommendations for Further Study .................................................................. 120

REFERENCES ........................................................................................................ 121

APPENDICES ......................................................................................................... 146

Appendix I: Letter of Introduction .......................................................................... 146

Appendix II: Questionnaire ...................................................................................... 147

Appendix III: CFA Path ............................................................................................ 152

Appendix IV: CFA Output ........................................................................................ 153

Appendix V: List of Banks ...................................................................................... 157

Appendix VI: Document Review Guide .................................................................. 158

Appendix VII: Research Permit ................................................................................ 159

viii

LIST OF TABLES

Table 2.1 Summary of Literature Review ................................................................... 49

Table 3.1 Decision Criteria for Mediation .................................................................. 58

Table 3.2 Decision Criteria for Moderation .............................................................. 59

Table 3.3 Operationalization of the Research Variables ............................................. 60

Table 3.4 Distribution of Target Population ............................................................... 61

Table 3.5 Distribution of Sample Size ........................................................................ 61

Table 3.6 Confirmatory Factor Analysis ..................................................................... 65

Table 3.7 Results of Reliability Test ........................................................................... 66

Table 3.8 Hypotheses Testing ..................................................................................... 71

Table 4.1 Analysis of Background Information .......................................................... 74

Table 4.2 Descriptive Statistics for Knowledge Conversion ...................................... 76

Table 4.3 Descriptive Statistics for Knowledge Transfer ........................................... 79

Table 4.4 Descriptive Statistics for Knowledge Application ...................................... 80

Table 4.5 Descriptive Statistics for Human Capital Repository ................................. 81

Table 4.6 Descriptive Statistics for Firm’s Culture ..................................................... 83

Table 4.7 Descriptive Statistics for Performance ........................................................ 85

Table 4.8 KMO and Bartlett's Test .............................................................................. 87

Table 4.9 Shapiro-Wilk Statistics ................................................................................ 88

Table 4.10 Collinearity Statistics ................................................................................ 89

Table 4.11 Levene Statistic ......................................................................................... 90

Table 4.12 Analysis of Variance ................................................................................. 91

Table 4.13 Durbin Watson Test .................................................................................. 92

Table 4.14 Regression Results for Direct Relationship .............................................. 93

Table 4.15 Regression Results for Knowledge Management on Performance ......... 100

Table 4.16 Regression Results Human Capital Repository on Performance ............ 101

Table 4.17 Effect of Knowledge Management on Human Capital Repository ......... 102

Table 4.18 Regression Results for Mediation ........................................................... 103

Table 4.19 Decision Criteria for Mediation .............................................................. 105

Table 4.20 Regression Results for Moderation ......................................................... 107

Table 4.21 Decision Criteria for Moderation ............................................................ 109

Table 4.22 Qualitative Data Analysis ........................................................................ 111

ix

LIST OF FIGURES

Figure 1.1 Interactive Drivers of High-Performance Organizations ............................. 2

Figure 2.1 Strategy, Resources, Capabilities and Competences ................................. 26

Figure 2.2 Building an Organization’s Learning Capability ....................................... 33

Figure 2.3 Conceptual Framework .............................................................................. 51

Figure 3.1 Simple Mediation Model ........................................................................... 57

Figure 4.1 Response Rate ............................................................................................ 73

x

OPERATIONAL DEFINITION OF TERMS

Commercial Bank: Commercial Bank is an institution that

undertakes banking businesses including

accepting and making payments on deposits

and current account, making payment on and

accepting cheques, and employing money held

on deposit or on current account, or any part of

the money through lending, investment or in

any other manner for the account and at the risk

of the person so employing the money.

Explicit Knowledge: Explicit knowledge is the knowledge that is

consciously understood and applied. This

knowledge is easy to articulate and can be more

precisely and formally articulated.

Human Capital repository: Human capital repository is the knowledge,

skills, and abilities residing within and utilized

by individuals.

Knowledge Management: Knowledge management is the systematic,

explicit and deliberate building, renewal and

application of knowledge to maximize an

enterprise’s knowledge-related effectiveness

and returns on its knowledge assets.

Knowledge Transfer: Knowledge transfer seeks to organize and

distribute knowledge in order to ensure its

availability for both present and future use.

xi

Learning Organization: A learning organization is an organization that

quickly and deliberately plans and structures

learning into all its processes, such as design,

manufacturing, marketing and accounting.

Furthermore, the value chain of such an

organization includes a domain of integrated

learning. This organization encourages people

to grow and develop, share their knowledge and

learning with others, and to learn from others.

Organizational Performance: Organizational performance is the extent to

which an organization achieves a set of pre-

defined targets that are unique to its mission.

These targets include both objective

(quantitative) and subjective (qualitative)

indicators.

Performance Drivers: Performance drivers are the key dimensions of

an organization’s functioning that are critical to

its capacity to perform.

Tacit Knowledge: Tacit knowledge is the “know-how” kind of

knowledge. Tacit knowledge is automatic,

requires little or no time or thought and helps

determine how organizations make decisions

and influence the collective behaviour of their

members. This knowledge is embedded in

individual’s experiences.

xii

ABBREVIATIONS AND ACRONYMS

AMA American Management Association

ANOVA Analysis of Variance

CBK Central Bank of Kenya

CFA Confirmatory Factor Analysis

ICT Information Communication Technology

KBA Kenya Bankers Association

KBV Knowledge Based View

KM Knowledge Management

KMP Knowledge Management Practices

KMPC Knowledge Management Process Capabilities

KMPI Knowledge Management Performance Index

MDCM Multimedia Development Corporation of Malaysia

MSC Multimedia Super Corridor

NACOSTI National Commission for Science, Technology and Innovation

R&D Research and Development

RBV Resource Based view

SMEs Small and Medium Enterprises

SPSS Statistical Package for Social Sciences

xiii

ABSTRACT

The knowledge-based view has identified innovative knowledge as what companies

require to dominate in an industry. Past studies have dealt with knowledge

management too broadly without considering specific aspects of knowledge

management which has led to a limited level of understanding on the extent to which

the comprehensive nature of knowledge management has influenced firms’

performance. Even though some companies have implemented knowledge

management, there is no conclusive empirical evidence on the influence of

knowledge management on performance. It has been noted that performance of

Commercial Banks suffer because knowledge is hoarded in scattered silos,

fragmented by division, department, region and a host of other organizational factors

such as culture, processes and management style. It is against this background that

this study sought to investigate the relationship between knowledge management and

performance of Commercial Banks in Kenya. The specific objectives of the study

sought to determine the relationship between knowledge conversion and

performance; to establish the relationship between knowledge transfer and

performance; to determine the relationship between knowledge application and

performance; to establish the mediating effect of human capital repository on the

relationship between knowledge management and performance; and to determine the

moderating effect of firm’s culture on the relationship between knowledge

management and performance of Commercial Banks in Kenya. To achieve these

objectives, the study adopted explanatory and cross-sectional survey design. The

target population of this study comprised of all the forty three Commercial Banks in

Kenya. The unit of observation was the functional area in each bank, whereas the

unit of analysis was Commercial Bank. Five functional areas were identified in each

bank comprising human resource, finance, marketing, information communication

technology, and operations. This study used primary and secondary data. Primary

data was collected using a semi-structured questionnaire. The questionnaire was

administered using drop-and-pick later method. Secondary data was collected using

document review and was used to validate information collected through the

questionnaire. The response rate in this study was approximately seventy three

percent which was considered sufficient for making inferences and drawing

conclusions. Descriptive statistics was used to summarise the survey data and

included percentages, frequencies, means, and standard deviations. However,

inferential statistics involved regression analysis and was used for testing hypotheses

and drawing conclusion. Results from quantitative data analysis were presented

using figures and tables. Qualitative data was analysed on the basis of common

themes and presented in narrative form. The findings of the study established that

knowledge management positively influence performance. Moreover, knowledge

conversion, knowledge transfer and knowledge application were found to be

statistically significant. Human capital repository was found to partially mediate the

relationship between knowledge management and performance. Furthermore, the

findings also revealed that firm’s culture moderates the relationship between

knowledge management and performance. Management of Commercial Banks can

use these findings to enhance utilization of organization’s knowledge base and firm's

absorptive capacity. Moreover, management of other knowledge-intensive

organizations can use these findings to formulate knowledge management policies

and promote knowledge management practices.

1

CHAPTER ONE: INTRODUCTION

1.1 Background of the Study

A key ingredient of the theory of the firm is its attempt to explain performance

heterogeneity among firms, an issue that has been in the focus of strategic

management research over the years (Hughes & Morgan, 2007). The resource-based

view (RBV) holds that companies gain sustainable competitive advantages by

deploying valuable resources and capabilities that are inelastic in supply (Grunert &

Hildebrandt, 2004). RBV focuses on characteristics of firm’s resources that contribute

to performance in form of competitive advantage. It assumes resource heterogeneity

between competing firms, and further contends that these resources are not mobile,

which makes long term, sustainable competitive advantage possible based on internal

configuration of strategically relevant resources.

American Management Association (AMA) observes that there are five major drivers

of organizational performance (AMA, 2007). These drivers are shown in Figure 1.1

and include strategic approach, leadership approach, values and beliefs, processes and

structures, and customer approach. Each of these factors interacts with and influences

the others, creating a whole system. A change in one factor creates changes in the

others. Subsequently, the system tends to be in continual flux. High-performance

organizations tend to establish clear visions with clearly articulated philosophies, and

have leaders, managers and employees who behave consistently with the strategic

plan and company’s philosophy.

2

Figure 1.1 Interactive Drivers of High-Performance Organizations

Source: Overholt, Granell, Vicere and Jargon (2006)

These organizations also tend to have clear customers’ approach, and build the

necessary infrastructure and processes to support their customers’ approach.

Moreover, such organizations tend to be clear about what behaviors employees must

exhibit to execute organizational and departmental strategies. Furthermore, these

organizations have processes that reinforce strategy, setting up work flows and tasks

that most effectively enable employees to meet internal and external customers’ needs

within the limits of their strategy. In addition, high-performance organizations

typically have a set of well-established values that are the deep drivers of employee

behavior and are well understood by the vast majority of employees. The values and

beliefs are embedded in the organization and are consistent with the company’s

approach to leadership (AMA, 2007).

Since the early days of strategic management, researchers and managers have tried to

find general rules for developing successful and competitive business strategies. The

Customer

Approach

Leadership

Approach

Strategic

Approach

Values and

Beliefs

Processes

and

Structure

3

resource-based view of strategic management has explored research questions like;

why some firms are more profitable than others or what are the successful strategies

to outperform a competitor (Grunert & Hildebrandt, 2004). Furthermore, Grunert and

Hildebrandt asserted that companies gain sustainable competitive advantage by

deploying valuable resources and capabilities that are inelastic in supply. In particular,

intangible assets such as knowledge, innovation, and intellectual properties have been

identified as value drivers and sources of company’s competitive advantage. The

knowledge-based view (KBV) has identified innovative knowledge as what

companies require to dominate an industry (Malik & Malik, 2008). Companies need

to innovate to create new processes and products in order to sustain competitive

advantage for without innovation a company’s value proposition will eventually be

imitated, eroding its competitive advantage.

Knowledge has increasingly been recognized as the new strategic imperative of

organizations. A fundamental paradigm considers knowledge as power; therefore, one

has to hoard it so as to maintain an advantage (Uriarte, 2008). Multimedia

Development Corporation of Malaysia (MDCM) considered knowledge as an

important resource which has to be effectively and efficiently managed for

organizations to leverage and obtain competitive advantage in a dynamic business

environment (MDCM, 2005). The new, knowledge-based economy places great

importance on creation, use and effective diffusion of knowledge (Metaxiotis,

Ergazakis & Psarras, 2005; Ford & Staples, 2006). Each firm must be able to

accumulate certain intangible knowledge assets that are relevant to its diverse

operations. In addition, Uriate noted that in the new paradigm, knowledge must be

shared in order for it to grow within an organization.

4

Different resources such as technological infrastructure, organizational structure and

organizational culture are linked to a firm’s knowledge infrastructure capability (Lee

& Sukoco, 2007). In addition, knowledge acquisition, knowledge conversion,

knowledge application and knowledge protection are linked to the firm’s knowledge

process capability. Lee and Sukoco also argued that the contribution that each

resource makes to organizational performance is likely to vary across firms. It is this

unique make-up that enables benefits such as competitive advantage and improved

performance to be realized.

An organization in the knowledge age is one that learns, remembers, and acts based

on the best available information and know-how (Dalkir, 2005). In order to be

successful in today’s challenging organizational environment, companies need to

learn from their past errors and not re-invent the wheel again and again.The

effectiveness of building knowledge within firms depend on the ability to monitor and

absorb newly acquired knowledge from many sources and then integrate this

knowledge into the existing knowledge base. It has been noted that firms can acquire

external knowledge from research on previous products, therefore gaining valuable

insights about the product; excel at benchmarking with industry leaders, and rely on

strategic alliances to acquire knowledge resources needed for their business (Danskin,

Englis, Solomon, Goldsmith & Davey, 2005). Firms can also acquire external

knowledge about the market from their customers and distributors.

The creation and diffusion of knowledge have become an increasingly important

factor in competitiveness. More and more, knowledge is being regarded as a valuable

commodity that is embedded in products and in tacit knowledge of highly mobile

employees. Although knowledge is increasingly being viewed as a commodity or an

5

intellectual asset, it possesses some paradoxical characteristics that are radically

different from those of other commodities. Dalkir (2005) observed that application of

knowledge does not result in its consumption neither does transfer of knowledge

result in losing it. Moreover, Dalkir observes that even though knowledge may be

abundant in any given organization, the ability to use it is scarce and that much of

valuable knowledge walks out of the organization at the end of the day.

Knowledge sharing is critical to a firm’s success as it leads to faster knowledge

deployment to portions of the organization that can greatly benefit from it. However,

employees need a strong motivator in order to share knowledge (Syed-Ikhsan &

Rowland, 2004). It is unrealistic to assume that all employees are willing to easily

offer knowledge without considering what may be gained or lost as a result of this

action. It has been argued that organization culture allows the members to create,

acquire, share, and manage knowledge within a context (Jones, Cline and Ryan,

2006). Moreover, organization culture helps in creating competitive advantage by

determining the boundaries, which facilitates individual interaction, and/or by

defining the scope of information processing to relevant levels (Krefting & Frost,

1985; Tseng, 2010). Many leaders are aware that performance comes from

interdependent behavior like cooperation, knowledge sharing, and mutual assistance.

Hence, organizations must foster the underlying culture necessary to support

knowledge conversion, transfer and application.

1.1.1 Organization Performance

Understanding the determinants of firm performance has long been a key goal within

organizational research (Short, McKelvie, Ketchen & Chandler, 2009) because

6

performance is considered the most important criterion in evaluating organizations,

their actions, and environments. In the last decade, the influence of knowledge

management (KM) on performance has been an enduring research theme in

organizational theory (Feng 2004; Gan, Ryan & Gururajan, 2006; Li & Seidel, 2013)

providing empirical evidence that KM significantly affect performance (Choi &Lee

2002; Dröge, Claycomb & Germain, 2003; Sabherwal & Sabherwal, 2005). Extant

researchers (Mohrman, Finegold & Mohrman, 2003; Abdul, Yahya, Beravi & Wah,

2008; Yusoff & Daudi, 2010) identified knowledge conversion, knowledge transfer

and knowledge application as key dimensions of KM whose integration can improve

firm’s performance.

Wilcox King and Zeithaml (2003) observed that KM is intended to increase the

quality and performance of the organizational and help a company to compete

effectively with other companies in the market. In addition, Bogner and Bansal (2007)

distinguished the ability to generate new knowledge as a fundamental mechanism of

KM systems that influence the performance of a company. Zaim, Tatoglu and Zaim,

(2007) noted that effective operation of KM enables companies to perform more

efficiently and survive in the business competitive environment through sustaining

their competitive advantages and developing their knowledge assets. RBV and KBV

consider knowledge and KM as critical resources which substantially influence

organizational success (Beesley & Cooper, 2008).

However, there is a need to extend the empirical literature through the inclusion of

mediating and moderating variables in the relationship between KM and performance

in knowledge-intensive organizations (Lara, Marques & Devece, 2012). The argument

advanced by Chong and Choi (2005) that employees and managers who are well

7

equipped with skills and information are essential success ingredient for any KM

implementation presents a strong case for the need for mediating role of human

capital repository on the effect of KM on performance. In addition, it has been noted

that KM cannot be effectively implemented without significant behavioral and

cultural change in an organization (Akhavan, Jafari & Fathian, 2006; Lai & Ho, 2006;

Rasula,Vukšić & Štemberger, 2012).

Commercial Banks are considered as typical knowledge-intensive organizations

where performance is driven and sustained by information and thus KM is a source of

competitiveness (Shih, Chang & Lin, 2010). As noted by Rono (2011), competition

and most of the work in the banking sector are knowledge-based; therefore, effective

management of knowledge can help Commercial Banks to improve internal

processes, customer service and products. In this study, non financial indicators of

performance such as new products, product improvement, speed of response to

market crises, customer retention and new processes were adopted from Maltz,

Shenhar and Reilly (2003), Raymond and St-Pierre (2005), and Kaplan and Norton

(2007).

According to Jafari, Jalal, Akhavan and Mehdi (2010), non-financial indicators are

suitable for measuring performance because they can be implemented at all levels of

organizations and represent a more precise picture than financial indices whose results

are superficial. Furthermore, Zhang and Li (2009) observed that financial indicators

can only reflect the performance of banks in the past and cannot reflect the bank's

current and future operating conditions. Financial measures of performance which are

based on traditional accounting practices and emphasizes short-term indicators such

8

as profit, turnover, cash flow and share prices, are not fully suitable for measuring

corporate performance (Lee, Lee & Kang, 2005).

1.1.2 Knowledge Management

Knowledge Management (KM) is the new era technological application of knowledge

in critical planning, appraisal, decision making, evaluation and redesign of firm’s

operative systems (Kipchumba, Chepkuto, Nyaoga & Magutu, 2010). It is obvious

that knowledge is slowly becoming the most important factor of production, next to

labor, land and capital (Sher & Lee, 2004). Knowledge-based assets or resources such

as patents provide heterogeneous capabilities that give each company its unique

character and are the essence of competitive advantage (Liu & Wei, 2009). KM

represents a deliberate and systematic approach to ensure full utilization of

organization’s knowledge base, coupled with the potential of individual skills,

competences, thoughts, innovations and ideas to create a more efficient and effective

organization (Dalkir, 2005).

Abdul et al., (2008) considered knowledge management processes to include

knowledge identification, creation, acquisition, transfer, sharing, and exploitation.

Becerra-Fernandez, Gonzales and Sabherwal (2004) noted that KM processes can

help create knowledge, which can then contribute to improved firm’s performance.

Furthermore, firm’s performance is improved when organisations create, transfer, use

and protect knowledge (Mohrman et al., 2003; Marques & Simon, 2006).

Yusoff and Daudi (2010) used KM processes, including knowledge acquisition,

knowledge conversion and knowledge application, to manage and increase social

capital, and enhance firm’s performance. A firm's absorptive capacity could be

9

enhanced through KM processes that allow acquisition, conversion and application of

existing and new knowledge through addition of value to social capital while

remaining competitive in the market. Moreover, Yusoff and Daudi were emphatic that

organisations need to generate knowledge continually, facilitate sharing of knowledge

within the organisation and apply knowledge so that the organisation can generate

new products or services.

1.1.3 Human Capital Repository

The knowledge-based view of the firm considers knowledge as the most strategically

significant resource within an organization. This view considers a firm to be a

"distributed knowledge system" composed of knowledge-holding employees, and

holds that the firm's role is to coordinate the work of those employees so that they

create knowledge and value for the firm (Spender, 1996; Yusoff & Daudi, 2010). It

has been noted that KM can directly cause improvements in people, processes,

products and firm’s performance (Marques & Simon, 2006).

Individuals and their associated human capital repository are crucial for exposing an

organization to technology boundaries that increase its capability to absorb and

deploy knowledge domains (Hill & Rothaermel, 2003). Human capital is the

collective value of the capabilities, knowledge, skills, life experiences, motivation of

workforce and abilities residing within and utilized by individuals (Schultz, 1961;

Kaplan & Norton, 2004). Chong and Choi (2005) observed that employees and

managers who are well equipped with skills and information to fulfill their

responsibilities are essential success ingredient for any KM implementation. The set

of knowledge acquired as employees in organizations progress with age is customized

10

to the firms’ operations (Lesser, 2006). This is what may be construed to depict

human capital repository.

Knowledge as embodied in human beings has always been central to performance of

organizations. KBV acknowledges innovative knowledge as what companies require

to in order to outperform others within an industry (Malik & Malik, 2008). KM

activities can assist the organisation in acquiring, storing and utilising knowledge for

processes such as problem solving, dynamic learning, strategic planning and decision-

making (Takeuchi & Nonaka, 2004). In addition, KM has the ability to protect

intellectual assets from decay and loss (Lang, 2004). Knowledge assets should be

maintained and managed so as to sustain competitive advantage whence conventional

assets are depreciated or replaced. In this context, knowledge management raises

strategic implication for companies (Warner & Witzed, 2004; Stam, 2007; Curado,

2008).

1.1.4 Organization Culture

Daft (2010) contends that in an organization, culture integrates members so that they

know how to relate to one another and helps the organization to adapt to the external

environment. When organizational members (Jones & Hill, 2009) subscribe to the

organization’s cultural norms and values, this bond them to the organization and

increase their commitment to find new ways to help it succeed. A variety of

characteristics describe a healthy culture such as acceptance and appreciation for

diversity, respect for each employee’s contribution, effective communication,

investment in and orientation to innovation, customer service, learning, training, and

employee knowledge (Modaff, DeWine & Butler, 2011).

11

It has been noted that effective KM cannot be implemented without a significant

behavioral and cultural change (Rasula et al., 2012). Linn (2008) considers

organizational culture as the most critical factor that shapes behavior and as such

allows employees to create, acquire, share, and manage knowledge within a context.

Therefore, an appropriate culture should be established to encourage employees to

create and share knowledge amongst themselves (Lee & Choi, 2003). Organizational

performance comes from interdependent behavior such as cooperation, knowledge

sharing, and mutual assistance (Jones et al., 2006). Extant researches (Mathi, 2004;

Wong & Aspinwall, 2005; Wong, 2005; Akhavan et al., 2006) identified

organization’s culture as an enabler of knowledge management. In this case, culture is

used to stimulate knowledge creation, utilization and protection and facilitate

knowledge sharing within an organisation (Lee & Choi, 2003; Yeh, Lai & Ho, 2006).

Pollard (2005) argues that the challenges faced today in getting people to share what

they know and to collaborate effectively are not caused or cured by technologies,

since they are cultural impediments that need culture based solutions. This culture

differs across different sectors. The differences may be accounted by the kind of work

done and the specific type of knowledge that characterizes the industry. Linn (2008)

asserts that there is a need to have a strong culture of trust and transparency in all

areas of the organization.

Banking is a typical knowledge-intensive industry that involves activities of

knowledge exchange (service) rather than exchange of goods (Shih et al., 2010). In

this case, knowledge creation and integration are key elements in value creation and a

source of competitiveness for Commercial Banks. Therefore, managing knowledge is

much more important to Commercial Banks than it is for other kinds of organizations.

12

Indeed, the last open frontier for banks to create competitive advantage may reside in

their ability to leverage knowledge, since banking is not just a business of handling

money but also a business that is driven and sustained by information.

1.1.5 Commercial Banks in Kenya

The banking sector in Kenya comprises of the Central Bank of Kenya (CBK),

Commercial Banks, non-banking financial institutions and foreign exchange bureaus.

According to the CBK, as at 31st December 2014, the sector comprised of forty three

Commercial Banks, one mortgage finance company, nine deposit taking microfinance

institutions, thirteen money remittance providers, eight representative offices of

foreign banks, eighty seven foreign exchange bureaus and two credit reference

bureaus. Thirty five of the banks, most of which are small to medium sized are locally

owned. The industry is dominated by a few large banks most of which are foreign

owned. Six of the major banks are listed on the Nairobi Stock Exchange (CBK, 2014).

The Companies Act, the Banking Act, the Central Bank of Kenya Act and the various

prudential guidelines issued by the Central Bank of Kenya govern the banking

industry in Kenya (Banking Act, Chapter 488 Laws of Kenya; CBK Act, Chapter 491,

Laws of Kenya). The CBK which falls under the supervision of the National Treasury

is responsible for formulating and implementing monetary policy and fostering the

liquidity, solvency and proper functioning of the financial sector. The Central Bank of

Kenya publishes information on Kenya’s Commercial Banks and non-banking

financial institutions, interest rates and other publications and guidelines. Banks in

Kenya have come together under the Kenya Bankers Association (KBA), which

serves as a lobby for the bank’s interests and addresses issues affecting its members.

13

Commercial Banks offer corporate and retail banking services but a small number,

mainly comprising the larger banks, also offer other services including investment

banking.

The CBK Bank Supervision Annual Report of 2013 indicates that the Kenyan banking

sector registered improved performance in 2013 notwithstanding the marginal growth

of the economy. The sector registered a 15.9 percent growth in total net assets from

Ksh. 2.33 trillion in December 2012 to Ksh. 2.70 trillion in December 2013. Equally,

customer deposits grew by 13.5 percent from Ksh. 1.71 trillion in December 2012 to

Ksh. 1.94 trillion in December 2013. Profit before tax for the sector increased by 16.6

percent from Ksh. 107.9 billion in December 2012 to Ksh. 125.8 billion in December

2013. This growth has been mainly underpinned by increased deposit mobilization by

banks as they expanded their outreach and opened new branches to tap new

customers, adoption of agency banking model, increased diversification of income

sources including commissions and earnings from foreign exchange trading, reduction

in interest expenses and adoption of cost effective delivery channels. Competition in

the sector has intensified over the last few years largely driven by increased

innovations and new entrants into the market.

The banking industry is commonly recognised for its contribution to the economic

activity, employment, innovation and wealth creation of a country. Stress tests

conducted by the CBK for the quarter ending on June 30, 2012 showed that the

financial sector grew by 9 percent in 2010 and 7.8 percent in 2011 while the economy

grew by 5.8 percent and 4.4 percent in 2010 and 2011 respectively. It has been

pointed out that Commercial Banks play a significant role in the economic growth of

countries through their intermediation function which facilitates efficient allocation of

14

resources through mobilizing resources for productive activities (Ongore & Kusa,

2013).

The dynamic nature of the global business environment led to liberalization of the

banking sector in 1995 with inherent lifting of exchange controls (CBK, 2012). In

addition, these changes have led banks to rationalize their products and services and

examine the role of KM in improvement of competitiveness. Okira and Ndungu

(2013) identified adoption of Automated Teller Machines, smart cards, internet and

mobile banking as new innovations in the Kenyan banks, which raises a strong case

for a KM approach to management of the banking industry. However, KM is

supported by both structural and cultural systems that should be aligned with strategic

goals leading to sustainable competitive advantage. As noted by Rono (2011), KM is

indispensable in the banking industry because competition and most of the work in

the industry are knowledge-based.

The state of theory on KM may need further integration with management literature to

model the relationship between KM and performance outcomes. As noted by Gray

and Durcikova (2005), banks suffer in their performance because knowledge is

hoarded in scattered silos, fragmented by division, department, region and host of

other organizational factors such as culture, processes and management style among

others. However, CBK (2014) observed that through the use of technology

Commercial Banks have continued to enhance efficiency in offering financial

services. Moreover, in 2013, one employee could serve an average of 642 customers

whereas in 2014 the same employee served 770 customers, a development that raises

implications for KM and resultant performance of Commercial Banks

15

1.2 Statement of the Problem

Performance of Commercial Banks in Kenya has improved tremendously over the last

ten years (Mwega, 2009). Moreover, only two banks have been put under CBK

statutory management in this period compared to 37 bank-failures between 1986 and

1998. However, despite the overall good picture a critical analysis indicates that there

has been heterogeneity in performance of different Commercial Banks. It has been

noted that small and medium sized banks which constitute about 57 percent of

Commercial Banks posted a combined loss before tax, of Ksh 0.09 billion in 2009

compared to a profit before tax of Ksh 49.01 billion posted by the big financial

institutions (CBK, 2009). The huge profitability enjoyed by the large banks vis-a-avis

small and a medium banks suggests that there are some significant factors that

influence the performance of Commercial Banks in Kenya.

As noted by Rono (2011), KM is indispensable in the banking industry because

competition and most of the work in the industry are knowledge-based. The dynamic

nature of the global business environment have led commercial banks to rationalize

their products and processes as well as examine the role of KM in improvement of

performance (CBK, 2012). Commercial Banks have continued to leverage on

knowledge assets in the development of quality services that are efficient and on a

wider scope in the fight for market share and enhanced performance (CBK, 2014).

The knowledge-based view of the firm has identified innovative knowledge as what

organizations require to dominate in an industry (Malik & Malik, 2008). The vast

body of knowledge documented indicates that there are several dimensions of

knowledge that have potential to drive performance (Choi & Lee 2002; Dröge et al.,

16

2003; Sabherwal & Sabherwal, 2005,). Extant researchers (Mohrman et al., 2003;

Abdul et al., 2008; David & Yusoff, 2010) have identified knowledge conversion,

knowledge transfer and knowledge application as key dimensions of knowledge

management whose integration can improve firm’s performance.

Lara et al., (2012) further suggested that there is a need to extend the empirical

literature through inclusion of mediating and moderating variables in assessing the

relationship between KM and performance in knowledge-intensive organizations. The

argument advanced by Chong and Choi (2005) that employees and managers who are

well equipped with skills and information are essential success ingredient for any KM

implementation presents a strong case for possibility of mediating role of human

capital repository on the effect of KM on performance. As noted by a stream of recent

researchers (Akhavan et al., 2006; Lai & Ho, 2006; Rasula et al., 2012), KM cannot

be effectively implemented without a significant behavioral and cultural change in the

organization. There should be a strong culture of trust and transparency in all areas of

the organization.

Furthermore, extant empirical literature (Mathi, 2004; Wong & Aspinwall, 2005;

Wong, 2005) has identified organization’s culture as an enabler of KM. In this case,

culture is used to stimulate knowledge conversion, transfer and application within

organisations (Lee & Choi, 2003; Yeh et al., 2006), and therefore, moderates the

effect of KM on performance. Danish, Munir and Butt (2012) concluded that the

relationship between KM practices and organizational effectiveness is positively

moderated by organizational culture. Although this study utilized regression analysis,

fundamental diagnostics tests were not conducted to establish the appropriateness of

17

the data for making inferences. In addition, the study failed to integrate specific

dimensions of KM.

Stevens (2010) utilizing exploratory research design concluded that companies must

design knowledge transfer strategies conducive to multi-generational workforce

dynamics keeping in mind the generational diversity that exists in the workplace.

Nevertheless, these results could not be generalized owing to the nature of the

research design adopted. Yusoff and Daudi (2010) using correlation analysis and

regression analysis concluded that knowledge application positively influences

performance. However, the conclusion of the study cannot be generalised owing to a

low response rate of thirty eight percent which is below the fifty percent threshold

recommended by Mugenda and Mugenda (2003).

Bourini, Khawaldeh and Al-qudah (2013) concluded that KM activities are positively

correlated to strategy. However, this study was based on exploratory research design

which does not support formulation and testing of research hypotheses. Zaied,

Hussein and Hassan (2012) concluded that knowledge conversion, storing and human

resources affect performance. Nevertheless, this study failed to integrate knowledge

transfer in the KM framework and also concluded that knowledge application and

culture do not affect performance. Mosoti and Masheka (2010) concluded that

knowledge management practices influence efficiency of not-for-profit organizations.

However, this conclusion was based on descriptive statistics and thus lacked the

statistical rigor for making inferences. Ongore and Kusa (2013) utilized such

measures of profitability as return on equity, return on asset and net interest margin as

indicators of performance. Although the study concluded that bank’s specific factors

significantly affect performance, it ignored non-financial indicators which offer a

18

more precise representation of performance on the basis of current and future

operating conditions (Zhang & Li, 2009).

Thus considering these scenarios, KM needs to be modelled in such a way that its

effect on performance can be better explained. In the case of Commercial Banks in

Kenya that have registered mixed performance results in an era characterized by rapid

knowledge development, contribution of knowledge needs to be investigated.

However, extant empirical literature has shown that there are limitations in the

attempt to explain how the comprehensive nature of KM has influenced performance

(Carlucci, Marr & Schiuma, 2004). In addition, the understanding of the influence of

KM on performance is still developing and further research and collation of

knowledge is required to develop this understanding, model new relationships and

formulate universally enduring guidelines for appropriate KM practices. Therefore,

there was a need to investigate the relationship between KM and performance of

Commercial Banks in Kenya while integrating the mediating and moderating role of

human capital repository and firm’s culture respectively.

1.3 Objectives of the Study

1.3.1 General Objective of the Study

The general objective of this study was to investigate the relationship between

knowledge management and performance of Commercial Banks in Kenya.

1.3.2 Specific Objectives of the Study

The specific objectives of this study were;

19

i) To determine the relationship between knowledge conversion and performance of

Commercial Banks in Kenya.

ii) To establish the relationship between knowledge transfer and performance of

Commercial Banks in Kenya.

iii) To determine the relationship between knowledge application and performance of

Commercial Banks in Kenya.

iv) To establish the mediating effect of human capital repository on the relationship

between knowledge management and performance of Commercial Banks in

Kenya.

v) To determine the moderating effect of firm’s culture on the relationship between

knowledge management and performance of Commercial Banks in Kenya.

1.4 Research Hypotheses

The research hypotheses of this study were;

H01: Knowledge conversion has no relationship with performance of Commercial

Banks in Kenya.

H02: Knowledge transfer has no relationship with performance of Commercial

Banks in Kenya.

H03: Knowledge application has no relationship with performance of Commercial

Banks in Kenya.

H04: Human capital repository has no mediating effect on the relationship between

knowledge management and performance of Commercial Banks in Kenya.

H05: Firm’s culture has no moderating effect on the relationship between

knowledge management and performance of Commercial Banks in Kenya.

20

1.5 Significance of the Study

This study provided a basis for establishing the relationship between knowledge

management and performance of Commercial Banks in Kenya. In addition, the study

has provided a basis for understanding the influence of human capital repository and

firm’s culture on the link between knowledge management and performance. The

findings of the study would consequently be relevant for policy formulation in

Commercial Banks. Indeed, this study would ultimately facilitate efficient and

effective utilization of knowledge resources resulting in enhanced performance.

Policy makers in other organizations would equally benefit from the findings of this

research study. The result of the study provides a pool of knowledge on the role and

contribution of knowledge resources in building and sustaining competitive advantage

in an industry. This knowledge if well harnessed would result in above average

performance of a firm in an industry.

Furthermore, scholars would also benefit from the study as the findings add to the

existing body of knowledge in knowledge management and performance. Moreover,

the results of the study would underscore the fundamental role of utilization of

knowledge resources in order to leverage on organization’s performance. In addition,

the study acts as a springing board for future research in KM and performance.

1.6 Scope of the Study

This study was delimited to all Commercial Banks in Kenya. Commercial Banks were

chosen because they are knowledge-intensive (Shih et al., 2010), and as such, they are

at the "cutting edge" of KM applications in Kenya. A knowledge-intensive firm relies

21

heavily on its unique knowledge as an input and produces innovative products. The

variables of the study encompassed knowledge management and performance as the

explanatory and explained variables respectively. Further conceptualization of the

model entailed integration of human capital repository and firm’s culture as mediating

and moderating variables respectively. The unit of observation were the five

functional areas of human resource, finance, marketing, information communication

and operations in each Commercial Bank. The heads of the functional areas that were

identified are part of senior management team that operates at the headquarters of

Commercial Banks. The study was carried out in the period between September and

December 2014.

1.7 Limitations of the Study

This study sought to investigate the relationship between KM and performance of

Commercial Banks in Kenya. It also sought to establish the mediating and moderating

role of human capital repository and firm’s culture on the effect of KM on

performance. In carrying out this study the researcher experienced difficulties in

accessing the target respondents particularly due to policy requirements and the nature

of their positions. This limitation was mitigated through the use of the research permit

from the National Commission for Science, Technology and Innovation (NACOSTI),

seeking consent from Commercial Banks and placing appointments with the

concerned managers.

The researcher also encountered a challenge as a result of the sensitive and strategic

nature of some of the information needed. Nevertheless, this challenge was mitigated

by reassuring the respondents of confidentiality in handling the research data which

22

was upheld through the use of codes in place of identity of individual respondents and

Commercial Banks. In addition, the researcher experienced difficulties in reviewing

empirical literature owing to the fact the area of focus is not adequately researched in

developing countries and more so in the local setting. However, this limitation was

mitigated through the review of similar empirical work in other sectors and developed

countries.

1.8 Organization of the Study

This thesis comprises of the preliminary part and five chapters. The preliminary part

consists of the title page, declaration, dedication, acknowledgement abstract, table of

contents, list of figures, list of tables, abbreviations and acronyms, and definition of

terms. Chapter one presents the background of the study, statement of the problem,

objectives of the study, significance of the study, scope, limitations and organization

of the study. Chapter two comprises of the theoretical review, empirical review,

summary of literature review and research gap and conceptual framework. Chapter

three encompasses the methodology which presents the research philosophy, research

design, empirical model, target population, sampling design and procedure, data

collection instrument, validity of the instrument, reliability of the instrument, data

collection procedure, data analysis and ethical considerations. Chapter four comprises

research findings and discussion which presents the background information,

descriptive statistics, inferential statistics and qualitative data analysis. Chapter five

presents the summary, contribution of the study to knowledge, conclusion,

recommendations for policy and practice, and recommendations for further study.

23

CHAPTER TWO: LITERATURE REVIEW

2.1 Introduction

This chapter focuses on reviewing the available literature on the various aspects of

KM that influence performance of firms. The review delves into various theories and

empirical findings that act as a foundation for this research study. The theories and

findings from past studies unearth the research variables for the study. The chapter

also presents the research gap and a conceptual framework that shows the relationship

between the research variables.

2.2 Theoretical Literature Review

This section presents a critical review of theoretical arguments regarding the linkages

between the research variables.

2.2.1 Resource-Based View of the Firm

According to the resource-based view (RBV), a firm may be perceived as an

aggregation of resources which are translated by management into strengths and

weaknesses of the firm. RBV holds that companies gain sustainable competitive

advantages by deploying valuable resources and capabilities that are inelastic in

supply (Grunert & Hildebrandt, 2004). This perspective contends that a firm’s

competitive advantage is due to endowment of strategic resources that are valuable,

rare, costly to imitate, and costly to substitute. It assumes that organizations must be

successful in obtaining and managing valued resources in order to be effective. In the

resource-based perspective, organizational effectiveness is defined as the ability of the

24

organization in either absolute or relative terms, to obtain scarce and valued resources

and successfully integrate and manage such resources (Dess, Lumkin, Eisner,

Lumpkin & McNamara, 2012).

RBV recognises the strategic importance of social and behavioural interactions in

conceivability of choice and implementation of organization’s strategies.

Furthermore, this approach integrates two perspectives; internal analysis of

phenomena within a company, and external analysis of an industry and its competitive

environment (Dess et al., 2012). In addition, RBV proposes that firm’s resources must

be evaluated on the basis of how valuable, rare, and hard they are for competitors to

duplicate. In the absence of such valuable resources the firm attains only competitive

parity. Makhija (2003) suggests that these valuable resources are frequently found in

organizations in the form of tacit knowledge.

Resources are financial, physical, social or human, technological, and organizational

factors that allow a company to create value for its customers. Company resources are

either tangible or intangible (Jones & Hill, 2009). Intangible resources are non-

physical entities that are creation of managers and other employees, such as brand

names, the reputation of the company, the knowledge that employees have gained

through experience, and intellectual property of the company, including that which is

protected through patents, copyrights, and trademarks. Tangible resources are

physical and include land, buildings, plant, equipment, inventory, and money.

Although physical resources may be the origin of above average returns, intangible

resources developed through a unique historical sequence and having a socially

25

complex dimension, are responsible for creating and sustaining competitive advantage

(Makhija, 2003).

RBV assumes resource heterogeneity between competing firms, and further contends

that these resources are not mobile, which makes long term, sustainable competitive

advantage possible based on internal configuration of strategically relevant resources

(Grunert & Hildebrandt, 2004). In case a resource is firm-specific and difficult to

imitate, a company is likely to have a distinctive competence. Furthermore, a

distinctive competence is a unique firm-specific strength that enables a company to

better differentiate its products and/or achieve substantially lower costs than its rivals

and thus gain competitive advantage. A resource that leads to distinctive competences

is inimitable, valuable, unique, and non-substitutable (Jones & Hill, 2009).

A company may have firm-specific and valuable resources, but unless it has the

capabilities to use those resources effectively, it may not be able to create a distinctive

competence (Jones & Hill, 2009). Capabilities refer to a company’s skills at

coordinating and putting resources to productive use. It has been argued that these

skills reside in an organization’s rules, routines, and procedures-that is, the style or

manner through which a company makes decisions and manages its internal processes

to achieve organizational objectives. A company’s capabilities are a product of its

organization structure, processes, and control systems which are used to specify how

and where decisions are made within a company, the kind of behaviours that should

be rewarded, and the company’s cultural norms and values.

26

Distinctive competencies shape the strategies that are pursued by a company.

Moreover, strategies help in building superior efficiency, quality, innovation, or

customer responsiveness resulting in competitive advantage and superior profitability.

However, it is also important to realize that the strategies that are adopted by a

company can build new resources and capabilities as well as strengthen the existing

resources and capabilities of the company, thereby enhancing distinctive competences

of the enterprise. In this case, the relationship between distinctive competencies and

strategies is not a linear one; rather, it is a reciprocal one in which distinctive

competencies shape strategies, and strategies help to build and create distinctive

competences (Kim & Mauborgne, 2005).

Figure 2.1 Strategy, Resources, Capabilities and Competences

Source: Jones and Hill (2009:59)

Intangible resources can be more difficult to imitate. Furthermore, imitating

company’s capabilities tend to be more difficult than imitating its tangible and

intangible resources because it is hard for competition to discern the way in which

decisions are made and process managed deep within the company. However, on its

own, the invisible nature of capabilities would not be enough to halt imitation;

competitors could still gain insights into how a company operates by hiring people

Resources

Distinctive

Competences

Capabilities

Competitive

Advantage

Superior

Profitability

Strategies

27

away from that company. Nevertheless, a company’s capabilities rarely reside in a

single individual. Rather, they are the product of how numerous individuals interact

within a unique organizational setting. A company’s competitive advantage tends to

be more secure when it is based upon intangible resources and capabilities, as

opposed to tangible resources. Capabilities can be particularly difficult to imitate,

since doing so requires the imitator to change its own internal management processes-

something that is never easy, owing to organizational inertia (Jones & Hill, 2009).

The resource-based view of a firm is suited for studying the effect KM on

performance. It proposes that strategies adopted by an organization such as KM can

be utilized in building and creating new resources and capabilities as well as

strengthen the existing resources and capabilities of the company, thereby enhancing

distinctive competences and performance of the enterprise. It also proposes that

intangible resources such as knowledge asset and capabilities as KM can be used as

source of sustainable competitive advantage. This proposition raises a strong case for

the need to investigate the relationship between KM and performance. If indeed KM

influences performance, Commercial Banks can leverage the resulting competitive

advantage and superior performance since RBV considers KM as rare, unique, firm-

specific and difficult to imitate. Thus, in this study, the postulates of RBV were used

to inform the independent variable.

2.2.2 Knowledge-Based View of the Firm

According to the knowledge-based view (KBV), innovative knowledge is what

companies require to outperform others in an industry (Malik & Malik, 2008). KBV

considers a firm to be a “distributed knowledge system” composed of knowledge-

28

holding employees, and this view holds that the firm's role is to coordinate the work

of those employees so that they can create knowledge and value for the firm. Carlucci

et al., (2004) contends that knowledge assets are as important for competitive

advantage and survival, if not more important, than physical and financial assets.

Knowledge and capabilities-based views in strategy have largely extended resource-

based reasoning by suggesting that knowledge is the primary resource underlying new

value creation, heterogeneity, and competitive advantage (Barney, 2001; Felin &

Hesterly, 2007). Furthermore, Felin and Hesterly contend that research and practice

are replete with empirical and anecdotal evidence of the primacy of individuals as the

locus of knowledge and source of new value. An organizational capability (Tsai, Li,

Tsai & Lin, 2012) is often established by a bundle of related knowledge which

includes knowledge items and the level of such items.

KBV considers knowledge as the most important source for firms’ competitive

advantage (Feng, Chen & Liou, 2005). It has been argued that knowledge is a crucial

resource of firm’s strategies and the origin of competitive advantage as the integration

of a bundle of knowledge rather than individual knowledge (Grant, 1996; Felin &

Hesterly, 2007). Moreover, knowledge aids firms in strategic development of

products and market, and provides an alternative way of achieving differentiation and

competitive advantage.

KBV has facilitated a shift from a competitive advantage that is based on market

position to one that focuses on firm’s capabilities (Felin & Hesterly, 2007). Moreover,

the orientation of firm’s strategies has been also changed from position-based to

capabilities-based. Firms often absorb new knowledge to improve their capabilities

29

from collaborative partners by alliance (Kale & Singh, 2007) or developing effective

models (Capron & Mitchell, 2009). KBV stresses knowledge-based competition and

illustrates that firms can differentiate themselves on the basis of their KM strategies.

While each of the individual knowledge assets is complex to acquire and difficult to

imitate, firms that achieve competitive advantage through KM have also learned to

combine their knowledge assets to effectively create an overall KM capability.

KBV provides a relevant theory for underpinning KM, human capital repository and

performance. This theory considers knowledge assets such as conversion, transfer and

application as primary resources that can be used in strategic development of

products, processes and markets within knowledge intensive organizations. In

addition, this value creation process requires the abilities residing within and utilized

by employees and managers so as to expose an organizations to technology

boundaries that increase its capability to absorb and deploy knowledge assets. This

theoretical proposition raises a conceptual implication on the need for human capital

repository in mediating the effect of KM on performance. In this case, the

propositions of KBV were used to inform the mediating variable in this study.

2.2.3 Organizational Learning Theory

A learning organization is the term given to an organization or a firm that facilitates

the learning of its members and continuously transforms itself. Learning organizations

develop as a result of the pressures facing modern organizations and enables them to

remain competitive in the business environment. A learning organization has five

main features; systems thinking, personal mastery, mental models, shared vision and

30

team learning. The learning organization concept encourages organizations to shift to

a more interconnected way of thinking. Organizations should become more like

communities that employees can feel a commitment to and therefore will work harder

(Serenko, Bontis & Hardie, 2007).

Organizational learning theory argues that, in order to be competitive in a changing

environment, organizations must change their goals and actions to reach those goals

(Janz & Prasarnphanich, 2003). However, for learning to occur, the firm must make a

conscious decision to change actions in response to a change in circumstances,

consciously link action to outcome, and remember the outcome. Organizational

learning has many similarities to psychology and cognitive research because the

initial learning takes place at the individual level: however, it does not become

organizational learning until the information is shared, stored in organizational

memory in such a way that it may be transmitted and accessed, and used for

organizational goals (Cha, Pingry & Thatcher, 2008).

The first part of the learning process involves data acquisition. A firm acquires a

“memory” of valid action-outcome links, the environmental conditions under which

they are valid, the probabilities of the outcomes, and the uncertainty around that

probability. The action-outcome links are acquired through experiential, experimental,

benchmarking, grafting, among others, but they must be a conscious effort to

discover, confirm, or utilize a cause and effect, or they are simply blind actions

relying on chance for success. Notably, a firm’s actions will – and must – change in

response to changes in the environment, as each action-outcome link must be

specified in terms of applicable conditions. Ultimately, successful firms scan their

31

environment to determine when change is necessary: this, of course, presupposes that

they have learned the important indicators to scan and have learned what degree of

change in environmental indicator does or does not require change in actions (Hult,

Tomas, Hurly, Giunipero & Nichols, 2000).

The second part of the process is interpretation. Organizations continually compare

actual to expected results to update or add to their “memory”. Unexpected results

must be assessed for causation, actions adapted or new action-outcome links specified

if necessary, and learning increased. This stage does not imply that any action is

taken. Some theorists insist that there must be action for learning to occur, but others

argue that what matters is expansion of the knowledge base or change in

understanding. Consequently, the third stage is adaptation/action. The firm uses the

interpreted knowledge to select new action-outcome links appropriate to the new

environmental conditions. Once adaptation has occurred, the firm’s knowledge base is

updated to include the new action-outcome link, probabilities, uncertainty, and

applicable conditions and the process continues. This feedback is a continual and

iterative process, and occurs at all stages of the process (Serenko et al., 2007).

Organizations (Debowski, 2006) have experienced many changes in the ways they

operate as a result of the shift to a knowledge economy and the increased streamlining

of work activities because of technological innovations. Furthermore, the shift in

focus from products to services has encouraged greater recognition of the importance

of the knowledge held within an organization. Any organization that desires to attain

and sustain competitive advantage has to learn better and faster from their successes

and failures. In a learning organization, new ideas and information are infused by

32

constantly scanning the external environments, hiring new talent and expertise when

needed, and devoting significant resources to train and develop their employees

(Kinicki & Kreitner, 2009). Moreover, employees’ mistakes should be viewed as

potential sources of new ideas and ways of doing things (Marquardt, 2011).

Organizations seek to use a range of authoritative sources, including knowledge held

by individual and within knowledge systems maintained by the organization. Explicit

knowledge can be documented, categorised, transmitted to others as information, and

illustrated to others through demonstrations, explanations and other forms of sharing.

However, tacit knowledge is difficult to duplicate, replace or interpret, as it is

grounded in a blend of experience, research and induction which may have been

refined over many years (Debowski, 2006). A learning organization proactively

creates, acquires, and transfers knowledge (Kinicki & Kreitner, 2009). New ideas are

a prerequisite for a learning organization; indeed it’s on the basis of new knowledge

and insights that the organization changes its behaviour.

Strategic knowledge management ensures corporate strategic knowledge grows,

learns and matures alongside its individual members. Marquardt (2011) considers the

prime task of management in learning organizations as facilitating employees’

experimentation and learning from experience enhanced by timely feedback and

complete disclosure. Opportunities are created across the entire organization to

develop knowledge, skills, and attitudes. The two major contributors to an

organizations learning are shown in Figure 2.2.

33

Figure 2.2 Building an Organization’s Learning Capability

Source: Klinicki and Kreitner (2009:78)

The facilitating factors are the internal structure and processes that affect how easy or

hard it is for learning to occur and the amount of effective learning that takes place.

These conditions are most likely found in an organization with a supportive learning

environment, concrete learning processes and practices, and leadership behaviour that

provides reinforcement (Garvin, Edmondson & Gino, 2008). Learning modes are the

various ways in which organizations attempt to create and maximise their learning. It

is important to appreciate that a learning organisation does not just promote learning

for the sake of it but to enhance work processes, products and services. In this case, in

an organisation that has a learning culture, individuals move from fearing mistakes to

viewing problems and errors as information to help in decision-making processes and

facilitate success (Kinicki & Kreitner, 2009).

This study uses the theory of learning organization as a framework for integrating and

understanding the role of firm culture in KM and performance. As noted, KM cannot

be effectively implemented without a significant behavioral and cultural change. A

learning organization seek to foster a learning culture which is a fundamental

Facilitating Factors

Learning Mode

Organizations

Learning Capability

Customer

Satisfaction

Organizational

Performance

Sales Growth

Profitability

Internal Structure and

Processes

Culture and Experience

34

ingredient in sustaining innovativeness in processes, products and technologies, and

enhancing corporate performance. Culture can be considered as a facilitating factor

that affects how easy or hard it convert, transfer and apply knowledge within an

organization. In this case an organization with a supportive culture encourages its

members to view mistakes and problems as a source of valuable information for

subsequent decision making processes, and initiating development and improvement

of processes, products and services. Therefore, the postulates and contributions of

organizational learning theory were applied in this study to inform the moderating

variable in the conceptual framework adopted for the study.

2.3 Empirical Literature Review

This section presents a review of extant empirical literature on the basis of the

interaction between the adopted research variables.

2.3.1 Knowledge Conversion and Performance

Knowledge conversion is a social process where individuals with different knowledge

interact and thereby create new knowledge which grows the quality and quantity of

both tacit and explicit knowledge (Sa´nchez & Palacios, 2008). The purpose of

enterprises implementing KM is to improve and enhance corporate performance

(Gottschalk, 2007). A process model of knowledge creation presupposes that

individual and organizations create and enlarge knowledge through conversion of tacit

knowledge into explicit knowledge and vice versa. Through knowledge conversion,

the whole organization can share the explicit knowledge created and convert it into

tacit knowledge for individuals Tseng (2010). Knowledge that is captured from

35

various sources needs to be converted to organizational knowledge for effective

utilization within the business (Lee and Suh, 2003)

Maryam, Rosmini and Wan (2010) revealed that informal training is the main source

of communication for sharing knowledge. Proper integration of business intelligence

and KM helps in managing explicit information and thereby transforming the

information to knowledge which in turn can help bank in making better decisions and

place them in a better position in contemporary business competitive environment

(Rao and Kumar, 2011). Moreover, this integration facilitates the capturing, coding,

retrieval and sharing of knowledge across the bank to gain strategic advantage and

sustain a competitive market. Fattahiyan, Hoveida, Siadat and Talebi (2013) revealed

that organizational structure, knowledge acquisition, knowledge application and

knowledge protection affect organizational performance. Nevertheless, the study

concluded that organizational culture and knowledge conversion have no significant

effect on performance. These results are inconsistent to the extent that not all

knowledge resources are found to contribute to performance.

Tseng (2010) utilizing knowledge externalization, knowledge combination,

knowledge internalization and knowledge socialization to measure knowledge

conversion, revealed that knowledge socialization has no effect on corporate

performance. However, in its composite nature, knowledge conversion positively

influences corporate performance. This study adopted multiple regression analysis for

model specification. Nevertheless, the findings of this study were based on a low

response rate of 20.15 percent with only 135 out 650 filling-in and returning the

36

questionnaire which is not adequate for making generalization and drawing

conclusions as recommended by Mugenda and Mugenda (2003).

2.3.2 Knowledge Transfer and Performance

Syed-Ikhsan and Rowland (2004) observed that very few empirical studies have been

done on KM and knowledge transfer, and even less in the developing countries. Key

cultural factors in the knowledge sharing process are trust, vocabularies, frames of

reference, meeting times and venues, broad ideas of productive work, status and

rewards that do not go to knowledge owners, absorptive capacity, the belief that

knowledge is not the privilege of particular groups, and tolerance for mistakes

(Davenport & Prusak, 1998; Tseng, 2010). The empirical study conducted by Syed-

Ikhsan and Rowland confirmed that there is no significant relationship between

organizational structure and knowledge transfer performance. However, it was noted

that management should consider ensuring that information or knowledge is

accessible and shared in the organization.

Saini (2013) revealed that community involvement programs and training contributed

to the implementation of KM practices as employees could freely exchange their ideas

and contribute to knowledge sharing, transfer and reuse. Moreover, cross-exposure to

different departments was another item that contributed to KM implementation. Saini

focused on KM practices including knowledge capturing, knowledge sharing,

knowledge transfer, knowledge storing and knowledge reuse. Furthermore,

organizational culture was found to be critical in transmitting tacit knowledge among

organizational members and transforming tacit knowledge into explicit knowledge in

37

software SMEs. Syed-Ikhsan and Rowland (2004) asserted that creation and transfer

of knowledge is a critical factor in an organization’s success and competitiveness.

Becheikh, Ziam, Idrissi, Castonguay and Landry (2012) used exploratory research

design to examine knowledge transfer process in education and suggested that linkage

agents are central actors in the knowledge transfer process. The intervention of

linkage agents is critical in helping adapt the knowledge produced by researchers and

make it easier to adopt and use by practitioners. Moreover, the effectiveness of this

process hinges on major factors including determinants related to knowledge

attributes, actors involved in the process and transfer mechanisms. The exploratory

research design used in this study does not support statistical analysis and making

generalization from the findings. Zaied et al., (2012) concluded that knowledge

conversion, storing and human resources affect performance. Nevertheless, this study

failed to integrate knowledge transfer in the KM framework and also concluded that

Knowledge application and culture do not affect performance.

It has been noted that any knowledge transferred between individuals does not only

benefits the organization but also tends to improve competence in both the individuals

that are involved in the process (Syed-Ikhsan & Rowland, 2004). Lin, Seidel,

Shahbazpour and Howell (2013) revealed that technical design knowledge was

predominantly transferred through activities, such as peer-to-peer or group

discussions to solve problems, mentoring, and new product research. Documentation

was generally used for the purposes of administration, product certification and

external communication with manufacturers, suppliers and clients. Furthermore,

transfer of technical design knowledge through documentation often became less

38

useful after a certain period of time, because the content was either out of date or had

been memorized. The company’s explicit knowledge did not cover many in-depth

details regarding technical design.

2.3.3 Knowledge Application and Performance

KM can be identified as the management of knowledge flows between individuals

within an organization through the processes of knowledge identification, use,

creation, sharing and storing (Heisig, 2009). According Momeni, Monavarian,

Shaabani, and Ghasemi (2011), KM process capabilities refers to a higher-order

construct which represents knowledge acquisition, knowledge conversion, knowledge

application and knowledge protection. The empirical results of this study showed that

KMPC positively influence the core competences of the Iranian Automotive Industry.

The study focused on integrative and marketing competencies as the most critical

dimensions of core competences. The argument made by Mohrman et al., (2003),

suggested that organization’s performance is improved when organisations create and

use knowledge.

Knowledge application is the process through which knowledge is directly applied to

task performance or problem solving. Knowledge may be possessed and applied by

individuals or by whole teams (Ajmal & Koskinen, 2008). Companies benefit not

from the existence of knowledge but from its proper application (Alavi & Leidner,

2001; Gasik, 2011). Organizational routines, direct guidelines and instructions, and

self-organizing teams constitute the main mechanisms that guarantee the application

of knowledge (Grant, 1996; Gasik, 2011). Knowledge application may take different

forms such as elaboration (when a different interpretation is required), infusion

39

(finding underlying issues), or thoroughness (when different people or teams develop

different understanding) (King, Chung & Haney, 2008).

Yosuff and Daudi (2010) using a 7-point Likert scale, correlation analysis and

regression analysis concluded that knowledge application positively influences

performance. However, the conclusion of the study cannot be generalised because of

the low response rate of thirty eight percent. McKeen, Zack and Singh (2006) using a

5-point Likert scales, showed that there was a statically significant positive link

between perceptions of high adoption of the KM practices and perceptions of high

organizational performance. KM involves distinct but interdependent processes of

knowledge creation, knowledge storage and retrieval, knowledge transfer, and

knowledge application (Alavi & Leidner 2001; Gunasekaran & Ngai, 2007). Glisby

and Holden (2005) observed that organizations achieve breakthrough by applying KM

concepts to supply chains.

2.3.4 Human Capital Repository and Performance

The most important competitive advantage to any firm is its workforce (Chong and

Choi, 2005). Hence, employees and managers who are well equipped with skills and

information to fulfill their responsibilities are essential success ingredient for any KM

implementation. Human capital is the collective value of the capabilities, knowledge,

skills, life experiences, and motivation of the workforce. Human capital may also be

referred to as intellectual capital to reflect the thinking, knowledge, creativity, and

decision making that people in organizations contribute (Kaplan & Norton, 2004).

Hill and Rothaermel (2003) contend that individuals and their associated human

capital repository are crucial for exposing an organization to technology boundaries

40

that increase its capability to absorb and deploy knowledge domains to create more

efficient and effective organizations.

Knowledge and KM are recognized as valuable corporate resources in the same vein

as land, buildings, financial resources, people, capital equipment, and other tangible

assets (Kipley, Lewis & Helm, 2008). As employees in organizations progress with

age, they acquire a set of knowledge that is customized to the firms’ operations,

structure and culture. More importantly, it is the unique insights and understood

idiosyncrasies about the company that is developed over time which makes learning

difficult to replicate or replace when managing employees transfer out of their

positions (Lesser, 2006). It is this combination of explicit and tacit knowledge that

mature workers possess which has become the most strategically significant resource

of organizations (Calo, 2008).

Yusoff and Daudi (2010) investigated the mediating role of social capital on the

relationship between KM and performance. The conclusion of this study showed that

a firm's absorptive capacity could be enhanced through KM processes that allow

acquisition and conversion of existing and new knowledge which enhance the value

of social capital. However, application of knowledge was not found to have any

statistically significant influence on social capital. In addition, the findings lacked in

academic rigour due to the inherent low response rate of 35% and thus could not be

valid for making generalizations.

2.3.5 Knowledge Management and Human Capital Repository

Human capital embodies the knowledge, talent, judgment and experience of

employees (Souleh, 2014). Organizations can increase their human capital by

41

internally developing the knowledge and skills of their current employees, and by

attracting individuals with high knowledge and skill levels from the external labor

market. An organization cannot create knowledge on its own without individuals

(Choudhury & Mishra, 2010). As individuals learn, they increase their human capital

and create knowledge that potentially forms a foundation for organizational level

learning and knowledge accumulation. Knowledge stocks provide a foundation for

understanding the role of human capital as a potential source of firm’s core

competencies.

Stevens (2010) using exploratory research design showed that companies must design

knowledge transfer strategies conducive to multi-generational workforce dynamics

keeping in mind the generational diversity that exists in the workplace. In this study,

differences in workforce generations and cross-generational methods of passing

knowledge were examined. Nevertheless, these results could not be generalized owing

to nature of the research design adopted. Lin et al., (2013) noted that senior engineers

as opposed to documentation were the primary internal source of valuable knowledge

in product development, particularly in terms of making critical design decisions.

Only those engineers with sufficient experience in their discipline, as well as

collaborative experience with other disciplines, had the holistic understanding to

make decisions.

International Business Machines Corporation and the American Society of Training

and Development revealed that 60% and 50% of respondents utilized mentoring and

documentation respectively for capturing and passing knowledge (Lesser and Rivera,

2006). This study noted that mentoring is most effective form of knowledge transfer

particularly for experiential and tacit knowledge. Furthermore, it can be used to bridge

42

the generational gap and where mentoring relationship cannot be established,

knowledge transfer does not occur. Other forms of knowledge transfer include

classroom training, fostering learning communities, and leveraging multimedia tools

to preserve significant learning from aging employees.

2.3.6 Organizational Culture and Performance

Studies by Gold, Malhotra and Segars (2001), Yang (2007) and Tseng (2010) failed to

indicate that organizational culture is the main barrier to success. As noted by Ravasi

and Schultz (2006), organizational culture is a set of shared mental assumptions that

guide interpretation and action in organizations by defining appropriate behaviour for

various situations. At the same time although a company may have their "own unique

culture", in larger organizations, there is a diverse and sometimes conflicting cultures

that co-exist due to different characteristics of the management team. The

organizational culture may also have negative and positive aspects. Organizational

culture is tightly connected to a certain group of people who have been working

together for a considerable period of time (Linn, 2008). It is the most critical factor

that shapes behavior.

Furthermore, extant empirical literature (Mathi, 2004; Wong & Aspinwall, 2005;

Wong, 2005) has identified organization’s culture as an enabler of KM. In this case,

culture is used to stimulate knowledge conversion, transfer and application within

organisations (Lee & Choi, 2003; Yeh et al., 2006), and therefore, moderates the

effect of KM on performance. Robinson, Carrillo, Anumba and Al-Ghassani (2005)

indicated that learning culture and KM strategies are crucial to enhancing corporate

performance for an enterprise and sustaining innovativeness in its processes, products,

43

and technologies. Jones et al., (2006) considered organizational culture as a

knowledge resource which allows the members to create, acquire, share, and manage

knowledge within a context. The role of organizational culture is strongly associated

with a firm’s competitive performance. Many leaders are aware that performance

comes from interdependent behavior like cooperation, knowledge sharing, and mutual

assistance.

Organizational culture helps create competitive advantage by determining the

boundaries, which facilitates individual interaction, and/or by defining the scope of

information processing to relevant levels (Krefting & Frost, 1985; Tseng, 2010).

Hence, organizations must foster the underlying culture necessary to support

knowledge sharing activities, knowledge workers’ business needs, and collaborative

needs. Organizations should strive for a healthy organizational culture in order to

increase productivity, growth, efficiency and reduce counterproductive behaviour and

turnover of employees (Modaff et al., 2011).

One important kind of behaviour controls that serves the dual function of keeping

organizational members goal-oriented yet open to new opportunities to use their skills

to create value is organizational culture. Daft (2010) asserts that everyone participates

in culture, but culture generally goes unnoticed. It is only when managers try to

implement new strategies or programs that go against basic cultural norms and values

that they come face to face with the power of culture. Organization values are beliefs

and ideas about what kinds of goals members of an organization should pursue and

what kinds of standards of behaviour employees should use to achieve these goals

(Jones & Hill, 2009). Organizational norms are unwritten guidance or expectations

44

that prescribe the kinds of behaviour employees should adopt in particular situations

and regulate the way they behave towards each other.

An appropriate culture should be established within the organisation to encourage

employees to create and then to share knowledge amongst themselves (Lee & Choi,

2003). Daft (2010) contends that in an organization, culture integrates members so

that they know how to relate to one another and helps the organization to adapt to the

external environment. When organizational members (Jones & Hill, 2009) subscribe

to the organization’s cultural norms and values, this bond them to the organization

and increase their commitment to find new ways to help it succeed. Such employees

are more likely to commit themselves to the organizational goals and work actively to

develop new skills and competences to help achieve those goals.

This study sought to establish whether firm’s culture can enhance utilization of

knowledge assets and therefore enhance performance. It has been noted that culture

dictates what kinds of goals members of an organization should be committed to and

what kinds of standards of behaviour employees should use to achieve these goals. In

relation to KM, organisational culture creates an organizational climate that enables

learning and innovative response to challenges, competitive threats, or new

opportunities. KM and organisational culture allow employees to think and behave in

ways that enable an organization to achieve superior performance. Organization

culture affects how employees embrace novel ideas and thus influence the KM

implementation process. Therefore, the relationship between KM and performance

may be conditioned by firm’s culture.

45

Gan et al., (2006) revealed that collaboration, mutual trust, learning, leadership,

incentives and rewards are significant facilitators to knowledge management practice.

Rasula et al., (2012) suggested that organizational change helps an organization to

optimize processes and define process oriented structure as these would help KM to

be adopted correctly within the organization. Furthermore, KM cannot be effectively

implemented without a significant behavioral and cultural change. There should be a

strong culture, trust and transparency in all areas of the organization.

Akhavan et al., (2005) found that resistance to change was a major impediment in the

implementation of KM systems. Wu, Du, Li, and Li (2010) observed that peer

collaboration and open communication are dependent on organizational culture. Chua

and Lam (2005), and Ölçer (2007) found lack of willingness to share knowledge to be

an important failure factor. It was noted that there is a problem when knowledge is

regarded as a source of power or when a corporate culture places value on individual

genius rather than collective work (Dalkir, 2005). Chua and Lam (2005) found that in

some cultures individuals may perceive accessing another member's knowledge as a

sign of inadequacy.

Succeeding in today’s business world requires the ability to innovate, connect across

boundaries and adapt to unparalleled change, with the truly relevant organizations

remaining ahead of their customers, rather than responding to them. It is critical to

understand that a learning organisation is not about promoting learning for the sake of

it but about providing learning to enhance work processes and service. That is why in

an organisation that has a learning culture, individuals move from fearing mistakes to

viewing problems and errors as information to help decision-making processes and

enable success (Kinicki & Kreitner, 2009).

46

Danish et al., (2012) concluded that KM practices have a strong positive association

with organizational effectiveness which positively moderated by the organizational

culture. Although this study utilized regression analysis, fundamental diagnostics tests

were not conducted to establish the appropriateness of the data for making inferences.

In addition, the study did not consider the specific dimensions of KM. Hamzah,

Othman, Hashim, Rashid and Beshir (2013) using hierarchical multiple regression

found that organizational culture moderates the relationship between the leadership

competencies and employees’ job performance. Nevertheless, this study was

characterized by a low response rate of forty three percent which falls below the

recommended threshold of fifty percent (Mugenda & Mugenda, 2003). Mushref

(2014) established that organization culture has a moderating effect on the link

between intellectual capital and business performance. The indicators adopted by

Mushref such as individualism-collectivism, power distance, uncertainty avoidance,

and masculinity and femininity are more biased toward societal culture as opposed to

organizational culture.

2.4 Summary of Literature Review and Research Gaps

There are many empirical studies that have been carried out on KM. However, as

observed by Syed-Ikhsan and Rowland (2004), only a few of these empirical studies

have been carried out in developing countries. The empirical studies reviewed have

convergent results which show that KM influences performance of the studied

organizations (Marques & Simon, 2006; Wu & Lin, 2009; Yusoff & Daudi, 2010).

Carlucci et al., (2004) noted that knowledge assets are as important for competitive

advantage and survival, if not more important, than physical and financial assets. On

47

the basis of RBV, a positive link between knowledge and performance is stressed. It is

expected that a particular category of knowledge, which is valuable, rare, inimitable

and non-substitutable would lead to performance. However, Vera and Crossan (2003)

argued that the conclusion from past empirical studies is not that more knowledge

leads to greater performance, but it may have positive effects on organization

performance. Therefore, the creation of relevant knowledge is an imperative for any

organization that desires to be competitive.

Despite the assumed link (Chakravarthy et al., 2003) it is still possible for KM to

negatively affect organizational performance. This can be understood by considering

important KM processes such as knowledge accumulation, knowledge protection and

knowledge leverage. Whereas each process may be important, tension between

different processes may erode the anticipated competitive advantage. For instance,

aggressive attempts at leveraging knowledge can inhibit knowledge accumulation

because the latter may typically not offer financial returns in the short run whereas the

former often does. Daud and Yosuff (2010) using correlation analysis and regression

analysis concluded that knowledge application positively influences performance.

However, the conclusion of the study cannot be generalised because the low response

rate of thirty eight percent which is below the fifty percent threshold recommended by

Mugenda and Mugenda (2003).

Bourini et al., (2013) concluded that KM activities are positively correlated to

strategy. However, this study was based on an exploratory research design which does

not support formulation and testing of research hypotheses. Maseki (2012) observed

that KM affects performance. Nevertheless, this conclusion was based on descriptive

statistics which limits generalization of its findings. Ongore and Kusa (2013) used

48

measures of profitability such as return on equity, return on asset and net interest

margin as indicators of performance. Although the study concluded that bank’s

specific factors significantly affect performance, it ignored non-financial indicators

which offer a more precise representation of performance on the basis of current and

future operating conditions (Zhang & Li, 2009).

It has also been noted that previous studies have not considered specific aspects of

KM (Firestone & McElroy, 2003; Carlucci et al., 2004; Massa & Testa, 2009), and

thus there is limited understanding of the extent to which KM affect performance,

particularly because this concept is complex in nature. Although commercial banks

are knowledge-intensive organizations with a significant contribution to economic

growth of countries through intermediation function, their performance suffer as a

result of hoarding knowledge in scattered silos, fragmented by division, department,

region and host of other organizational factors such as culture, processes, human

capital repository, management style among others. Despite the extensive scholarly

work on KM and performance, the understanding of the influence of KM on

performance within Commercial Banks and other organizations is still developing.

This presents a strong case for the need for further research and collation of

knowledge in order enhance the understanding, formulate universally enduring policy

guidelines for appropriate KM practices, and enhance the benefits deriving from

utilization of knowledge assets in organizations.

49

Table 2.1 Summary of Literature Review

Author(s) Topic Findings Research Gap

Mushref

(2014)

The moderator role of

organizational culture

between intellectual

capital and business

performance

Organization

culture moderates

the link between

intellectual capital

and performance

Operationalization of some

indicators as individualism-

collectivism, power distance,

uncertainty avoidance, and

masculinity and femininity is

biased toward societal culture

Lin et al.,

(2013)

KM in Small and

Medium-Sized

Enterprises: A New

Zealand focus

KM practices are

immature and

emphasizes

personalization

rather than

codification

strategy

Exploratory research design is

suitable for formulative

research and thus limits

hypotheses testing and

generalization of findings

Fattahiyan

et al.,

(2013)

Relationship between

KM enablers,

processes resources

and organizational

performance in

Universities

Organizational

structure,

knowledge

acquisition,

application and

protection affect

performance

Organizational culture and

knowledge conversion do not

have a significant effect on

performance. Inconsistent

results that not all knowledge

resources affect performance

Okiro and

Ndungu

(2013)

Impact of mobile and

internet banking on

performance of

Kenya’s financial

institutions

Commercial Banks

have the highest

rate of usage of

internet banking

Focused on aspects of

knowledge but not KM. Low

level of statistical rigor limits

generalization of findings

Saini

(2013)

Model development

for key enablers

in the implementation

of KM

Community

involvement

programs and

training affected

KM practices

Findings based on judgmental

and convenience sampling

techniques which are not

suitable for hypotheses testing

and generalization of findings

Rasula et

al., (2012)

Impact of KM on

organizational

performance

Organizational

culture, climate and

collaboration

have positive

impact on practices

of KM

KM maturity model

conceptualized requires

decomposition. Knowledge

conversion is not integrated in

the KM model. Collaboration,

culture and climate are

conceptualized as distinct

organizational elements. Low

response rate of 9.6% and

11.6% in Slovenia and Croatia

respectively does not support

generalization of findings

Yi and

Jayasingam

(2012)

Factors driving

knowledge creation

among private sector

organizations in

Malaysia

Knowledge sharing

culture enhances

knowledge

Creation

Core aspects of culture such as

futuristic orientation and

leaning orientation were

ignored. Sample was selected

using snowball sampling

techniques making the

conclusions not generalizable

50

Zaied et

al., (2012)

The role of KM in

enhancing

organizational

performance

Knowledge

conversion, storing

and human

resources affect

performance

Failed to integrate knowledge

transfer in the KM framework.

Knowledge application and

culture have no effect on

performance Yusoff and

Daudi

(2010)

KM and firm

performance in

SMEs: The role of

social capital as a

mediating variable

Integration of KM

processes and

social capital

enhances firm’s

performance

KM framework used failed to

integrate knowledge transfer.

Low response rate of 35%

does not support

generalization of

findings Mosoti and

Masheka

(2010)

Knowledge

management: The

case for Kenya

KM practices

influence efficiency

Conclusion were based on

descriptive statistics limiting

making of inferences

Tseng

(2010)

Correlation between

organizational culture

and knowledge

conversion on

corporate

performance

Cultural differences

affect knowledge

conversion and

performance

Socialization has no effect on

corporate performance. Low

response rate of 20.15 %

invalidates generalizations

Wu and

Lin (2009)

Strategy-based

process for

implementing KM

KM influences

performance

Low response rate of 16 %

invalidates making

generalizations

Agboola

(2006)

ICT in banking

operations in Nigeria

Technology is the

main driving force

of competition

Findings were based on

descriptive statistics and thus

cannot be generalized

Gan et al.,

(2006)

Effects of culture on

KM practices: A

quantitative case

study of MSC status

companies

Culture affects KM

practices

Integrated leadership as a

dimension of culture. The

study adopted exploratory

research design which is not

suitable for making inferences

Chuang

(2004)

RBV on KM

capability and

competitive

advantage

KM capability

affects competitive

advantage

Low response rate of 32.7%

invalidates making

generalizations

Syed-Ikhsan

and

Rowland

(2004)

KM in public

organizations: A

study on the

relationship between

organizational

elements and the

performance of

knowledge transfer

Organizational

culture affects

performance of

knowledge transfer

Political directives

conceptualized alongside

internal factors as

organizational culture and

technology. Case study

design adopted does not

support inferential statistics

Source: Author and Literature Review (2014)

51

2.5 Conceptual Framework

Based on the preceding theoretical and empirical literature review, the conceptual

framework in Figure 2.3 shows the interaction between the research variables.

Figure 2.3 Conceptual Framework

Source: Author (2014)

Moderating Variable

Dependent

Variable

H01

Performance

New products

Speed of

response to

market crises

Product

improvement

Customer

retention

New processes

Firm’s Culture

Openness

Futuristic orientation

Learning orientation

Knowledge Conversion

Socialization

Externalization

Combination

Internalization

Knowledge Transfer

Information identification

Information evaluation

Avoidance of similar

mistakes

Open discussion

KM training

Information dissemination

Knowledge Application

Problem solving

Elaboration

Efficient processes

IT support

Infusion

Human Capital

Repository

Experience

Education

Innovativeness

Mediating Variable

H04

H05 Independent

Variables

Knowledge Management

H02

H03

52

In this study, KM was hypothesized to influence performance of Commercial Banks

in Kenya. The independent variables of the study are knowledge conversion,

knowledge transfer, and knowledge application. The dependent variable in the study

was performance. Moreover, human capital repository was hypothesized to mediate

the relationship between of KM and performance. Furthermore, firm’s culture was

hypothesized to moderate the relationship between KM and performance.

53

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction

This chapter presents the research methodology adopted for the purpose of

determining the empirical relationship between KM and performance of Commercial

Banks in Kenya. It specifically comprises of the research philosophy, research design,

target population, sampling design and procedure, data collection instrument, validity

and reliability of the research instrument, data collection procedure, data analysis, and

ethical considerations.

3.2 Research Philosophy

The choice of research philosophy helps the researcher to clarify the overall research

strategy to be used, evaluate different methodologies, and be creative and innovative in

either selecting or adapting of methods that have been previously used (Johnson and

Clark, 2006). Furthermore, a research paradigm is a perspective that is based on a set

of shared assumptions, values, concepts and practices. Mcnabb (2008) contends that

there are three research paradigms including positivism, interpretivism and realism

which help a researcher to develop an understanding and knowledge about the topic

of research.

This research study adopted a positivist research philosophy as recommended by

Creswell (2009). Positivism is based on the rationalistic, empiricist philosophy and

reflects a deterministic philosophy in which causes probably determine effects or

outcomes ((Mertens, 2005; Creswell, 2009). Mertens contends that positivism may be

54

applied to the social world on the assumption that the social world can be studied in

the same way as the natural world, utilizing a value free method that provides

explanations of a causal nature. Moreover, Creswell observes that positivist

methodology is directed at explaining relationships as it attempt to identify causes

which influence outcomes and provides a basis for prediction and generalization.

This study sought to offer a rational explanation concerning the relationship between

KM and performance of Commercial Banks in Kenya. In addition, the study utilized

quantitative data as it sought to identify causes that influence performance outcomes

and formulated a set of recommendations. As noted by Creswell, the key assumption

of positivist is that organisations are rational entities, in which rational explanations

offer solutions to rational problems. Positivist research is most commonly aligned

with quantitative methods of data collection and analysis; however, qualitative

methods can still be utilised within this paradigm.

3.3 Research Design

Research can be categorised as exploratory, descriptive and explanatory (Saunders,

Lewis & Thornhill, 2007). An exploratory study seeks to establish what is happening,

and asking questions and assessing phenomena in a new light. In addition,

explanatory study seeks to establish causal relationships between variables.

Nevertheless, a descriptive study seeks to portray an accurate profile of persons,

events or situations.

55

This research study adopted explanatory and cross-sectional survey design as

recommended by Saunders, Lewis and Thornhill (2009). As noted by Saunders et al.,

(2007), explanatory study establishes causal relationships between variables. This

study seeks to establish how KM influences the performance of Commercial Banks in

Kenya. In addition, a cross-sectional study seeks to measure the relationship of

variables at a specified time so as to describe the incidence of a phenomenon and how

the variables are related.

3.4 Empirical Model

Different models can be used in analyzing quantitative data such as regression

analysis, discriminant analysis, logit and probit. According to Field (2009),

discriminant analysis, Logit and probit models are most appropriate for situations

where the dependant variable is binary in nature. However, regression analysis is

suitable for continuous variables. In this study, performance is considered as a

continuous variable and thus regression analysis was adopted as recommended by

Field. Multivariate analysis was used to perform regression on the relationships

between the various variables so as to understand the strength of each predictor

variable. In the first empirical model, knowledge conversion, knowledge transfer and

knowledge application were regressed on performance as shown below.

Bank Performance = β01 + β11 Knowledge Conversion + β12 Knowledge Transfer

+ β13 Knowledge Application + ε ...............................................................3.1

Where; βi= Beta Coefficient

ε= Error Term

56

The researcher adopted causal steps approach that uses different models to determine

mediation (Judd & Kenny 1981; Baron & Kenny, 1986; Muller, Judd & Yzerbyt,

2005; Hayes, 2009). The first model 3.2 was used to estimate the relationship between

the independent variable (knowledge management) and dependent variable

(performance). It sought to establish whether there was an overall effect that could be

mediated.

Bank Performance = β20 + β21 Knowledge Management + ε…….......................3.2

Where; βi= Beta Coefficient

ε= Error Term

The second model 3.3 was used to establish the relationship between the intervening

variable (human capital repository) and dependent variable (performance).

Bank Performance = β30 + β31 Human Capital Repository + ε……....................3.3

Where; βi= Beta Coefficient

ε= Error Term

The third model 3.4 sought to determine the relationship between the intervening

variable (as dependent variable) and knowledge management (as independent

variable).

Bank Human Capital Repository = β40 + β41 Knowledge Management + ε… 3.4

Where; βi= Beta Coefficient

ε= Error Term

57

The schematic display of the model that guided the test for mediation effect is shown

in the Figure 3.1.

Figure 3.1 Simple Mediation Model

Source: Author (2014)

In Figure 3.1, β21 is the total effect of KM (independent variable) on performance

(dependent variable). In addition, β51 is the direct effect of KM on performance after

controlling for human capital repository (mediating variable). Furthermore, β41

represents the effect of the independent variable on the mediator whereas β52 is the

effect of the mediator on the dependent variable after controlling for the independent

variable (Rucker, Preacher, Tormala & Petty, 2011).

Model 3.5 was estimated to determine in case there was total, partial or no mediation

on the relationship between the independent variable (KM) and dependent variable

(performance).

Bank Performance=β50 + β51Knowledge Management + β52Human Capital

Repository + ε……..........................................................................3.5

Where; βi= Beta Coefficient

ε= Error Term

Knowledge

Management

Human Capital

Repository

Performance β51

β 41 β52

Knowledge

Management

Performance β21

58

Table 3.1 Decision Criteria for Mediation

Model 3.2 Model 3.3 Model 3.4 Model 3.5 Test Conclusion

Β21 ;

(p >0.05)

-

-

-

-

No overall

relationship to

mediate

Β21 ;

(p ≤ 0.05)

-

-

-

-

There exist an

overall relationship

to mediate

Β21 ;

(p ≤ 0.05)

Β31 ;

(p ≤ 0.05)

Β41 ;

(p ≤ 0.05)

Β51 and Β52 ;

(p ≤ 0.05)

β21- β51=

β41*β52

Partial mediation

Β21 ;

(p ≤ 0.05)

Β31 ;

(p ≤ 0.05)

Β41 ;

(p ≤ 0.05)

Β51 ; (p >0.05)

Β52 ; (p ≤ 0.05) β21- β51=

β41*β52

Perfect mediation

Source: Rucker, Preacher, Tormala and Petty (2011)

The indirect effect is the product β41*β52. In general, for either kind of mediation it is

expected that the indirect effect β41*β52 would be equivalent to the difference between

the total effect and the direct effect β21- β51. The critical starting point for mediation

analysis is a significant relationship between knowledge management and

performance. Therefore, the statistical significance of β21 coefficient in model 3.2 is a

necessary condition for testing mediation without which the causal steps approach

would collapse given that there is no overall effect to mediate. In the case of partial

mediation, both the direct effect and indirect effect are statistically significant.

However, for perfect mediation the indirect effect is statistically significant but the

direct effect is no longer statistically significant.

Furthermore, the moderating effect of firm’s culture on the zero-order correlation

between knowledge management and performance was tested as guided by the two

models presented below. Whisman and McClelland (2005) contend that in case there

is an overall effect to be moderated, the test for moderation would involve

determining whether the coefficient for the interaction term statistically differ from

zero.

59

Bank Performance = β60 + β61 Knowledge Management+ ε…….........................3.6

Bank Performance = β70 + β71 Knowledge Management + β72Firm Culture +

β73 Knowledge Management * Firm Culture + ε................3.7

Where; βi= Beta Coefficient

ε= Error Term

Table 3.2 Decision Criteria for Moderation

Model 3.6 Model 3.7 Total Effect Conclusion

β61 ; (p>0.05)

-

-

No overall effect to

moderate

β61 ; (p≤0.05) β72 ; (p>0.05)

-

Moderating variable is

an explanatory variable

β61 ; (p≤0.05) β72 ; (p≤ 0.05) β73 Moderating variable has

a moderating effect

Source: Whisman and McClelland (2005)

In case moderation is indicated, the coefficient (β73) of the interaction term

(Knowledge Management * Firm Culture) in model 3.7 would yield the strength and

direction the moderating variable.

60

Table 3.3 Operationalization of the Research Variables

Hypothesis Variable Nature Operational Definition Measurement

Criteria in

Questionnaire

There is no

relationship between

knowledge

conversion and

performance of

Commercial Banks

in Kenya

Knowledge

Conversion

Independent

Activities in the banking

industry involving dialogue,

observation, elicitation,

documentation, integration and

learning by doing

Section B

Questions 4

and 5

There is no

relationship between

knowledge transfer

and performance of

Commercial Banks

in Kenya

Knowledge

Transfer

Independent

Activities undertaken in the

banking industry involving

conveyance of ideas,

experiences and information to

facilitate sharing, collaboration

and networking

Section C

Questions 6

and 7

There is no

relationship between

knowledge

application and

performance of

Commercial Banks

in Kenya

Knowledge

Application

Independent

Activities undertaken in the

banking industry involving

provision of instructions,

directions, and performance of

tasks

Section D

Questions 8

and 9

Human capital

repository has no

mediating effect on

the relationship

between KM and

performance of

Commercial Banks

in Kenya

Human

Capital

Repository

Mediating

occurrences in the banking

industry that results in either

increase, retention or loss of

ideas, experiences and

information held in a bank

through the employees

Section E

Questions 10

and 11

Firm’s culture has

no moderating effect

on the relationship

between KM and

performance of

Commercial Banks

in Kenya

Firm’s

Culture

Moderating

Activities in the banking

industry that manifest values,

core values, beliefs,

assumptions, initiatives,

learning experiences and

expectations

Section F

Questions 12

and 13

Source: Author and Literature Review (2014)

3.5 Target Population

The population of this study comprised of all the 43 Commercial Banks in Kenya.

According to the CBK, Commercial Banks can be stratified into large, medium, and

small on the basis of the size of their market share as indicated in Table 3.1.

61

Table 3.4 Distribution of Target Population

Category Frequency Percentage

Large 6 14.0

Medium 16 37.2

Small 21 48.8

Total 43 100

Source: CBK Bank Supervision Annual Report (2013)

The large banks constitute 14.0%, medium banks 37.2%, and small bank 48.8% of all

Commercial Banks in Kenya

3.6 Sampling Design and Procedure

Census survey of all Commercial Banks in Kenya was used. In choosing census

survey, the practicalities and cost of undertaking a census, representativeness and the

nature of the survey as well as population had been considered. The unit of analysis

was Commercial Bank whereas the unit of observation was functional area in each

bank. Five functional areas were identified in each Commercial Bank comprising

human resource, finance, marketing, information communication technology, and

operations. These functional areas were considered to have the relevant information

relating to KM.

Table 3.5 Distribution of Sample Size

Strata Stratum

Size

Functional

Areas

Sample

Frequency

Percent

Large banks 6 5 30 14.0

Medium banks 16 5 80 37.2

Small banks 21 5 105 48.8

Total 43 215 100

Source: Author (2014)

62

Proportionate stratified sampling of respondents was undertaken on the basis of the

number of banks in each stratum and the five functional areas. The sampling factor

was derived from the identified functional areas in each bank. In this case, the large

banks made a contribution of 30 (14%) as compared to the medium banks at 80

(37.2%) and the small banks at 105 (48.8%) which is proportionate to their strata sizes

of 6, 16 and 21 respectively. Therefore, the resulting sample size of 215 was

considered representative of the three strata comprising large, medium and small

banks.

3.7 Data Collection Instrument

This study used primary and secondary data. Primary data was collected using a

questionnaire. With regard to the effect of KM on performance of Commercial Banks

in Kenya, the study used a semi-structured questionnaire administered to managers of

the five functional areas identified in each bank. The closed-ended questions provided

more structured responses that facilitated quantitative analysis, testing of hypothesis,

and drawing of conclusion. The open-ended questions provided additional

information that may not have been captured by the closed-ended questions.

The questionnaire comprised of eight sections. Section A sought general information

about the respondents and consisted of three questions Q1, Q2 and Q3. Section B

consisted of Q4 and Q5 regarding conversion of knowledge. Section C sought to

gather relevant information on transfer of knowledge and had two questions Q6 and

Q7. Section D consisted of Q8 and Q9 that sought to provide relevant information

relating to application of knowledge. Section E comprised of Q10 and Q11 regarding

63

human capital repository. Section F focused on information regarding firm’s culture

and had two questions including Q12 and Q13. Section G had two questions including

Q14 and Q15 that sought to provide relevant information on performance of

Commercial Banks in Kenya. Secondary data was obtained through document review

of published sources including periodicals from CBK such as CBK Bank Supervision

Annual Report and CBK Monthly Economic Review. This data was useful for

generating additional information and validating data collected through the

questionnaires.

3.7.1 Test of Validity

Validity is concerned with the integrity of the conclusions that are generated from a

piece of research. It is the degree to which an instrument measures what it purports to

measure. It estimates how accurately the data in the study represents a given variable

or construct in the study (Mugenda & Mugenda, 2003). A pilot study was carried out

involving fifteen respondents selected from the target population. The respondents

involved in the pilot test were excluded from the sample selected for the final

research. The purpose of the pilot research was to establish face and content validity

of the questionnaire alongside the opinion sought from professionals and experts in

the field of investigation as recommended by Mugenda and Mugenda.

The dimensions for the independent variable (KM) comprising of knowledge

conversion, transfer and application were verified as appropriate through literature

review and expert suggestions. The choice and development of the dependent

variable, mediating variable and moderating variable was based on the literature

64

review and all dimensions necessary for performance, human capital repository and

firm’s culture were included. Expert suggestions and a careful alignment of the

research instrument on the basis of the reviewed literature facilitated the necessary

revision and modification of the research with the object of enhancing face and

content validity.

Factor analysis was used to establish construct validity for all of the variables

employed in this study (Kerlinger & Lee, 2000). All of the items in the variables were

subjected to factor analysis, and loaded in accordance with prior theoretical

expectations. The results of the data analysis revealed satisfactory outputs for

dependent, independent, mediating and moderating variables. Confirmatory factor

analysis (CFA) was conducted to test the instrument validity. CFA is done to describe

variability among observed variables and correlated variables in terms of lower

number of unobserved/latent variables called factors. According to Hare and

Neumann (2008), factor analysis helps in grouping variables with similar

characteristics together. This helps in reducing a large number of variables for

modelling purposes and to select subset variables from a large set, based on which

original variables had the highest correlations with the factor. Squared factor loading

indicate what percentage of the variance in the original variables is explained by a

factor (Field, 2009).

65

Table 3.6 Confirmatory Factor Analysis

Source: Pilot Data (2014)

Chi-square test output, which is a function of the differences between the observed

co-variances and the co-variances implied by the model, was 637.029 at p < 0.001

(Appendix IV). Goodness-of-fit index (GFI) (0.946) was above the recommended 0.9,

comparative fit index (CFI) (0.970) surpasses the 0.95 standard, and root-mean-square

error of approximation (RMSEA) (.027) is below good (.05) and adequate (0.08)

(Brown, 2006). Thus, the model was good and there was no need of removing any

indicators that have low loadings (below 0.7) or had high standardized co-variances

with other factors.

Model Default Model Saturated Model Independence Model

NPAR 27.000 78.000 12.000

CMIN 637.029 .000 1651.849

DF 51.000 .000 66.000

P .000 .000

CMIN/DF 12.491 25.028

RMR .046 .000 .126

GFI .946 1.000 .308

AGFI .459 .182

PGFI .422 .260

NFI Delta1 .614 1.000 .000

RFI rho1 .501 .000

IFI Delta2 .634 1.000 .000

TLI rho2 .522 .000

CFI .970 1.000 .000

RMSEA .027 .394

LO 90 .254 .377

HI 90 .291 .410

PCLOSE .000 .000

66

3.7.2 Test of Reliability

Reliability was evaluated using Cronbach’s Alpha which measures the internal

consistency and establishes if items within a scale measure the same construct. The

index alpha was computed using SPSS and helped to measure the average of

measurable items and its correlation. Marczyk, DeMatteo and Festinger (2005)

observe that Cronbach Alpha value of 0.7 is the threshold for determining reliability.

Kline (2000) note a scale of 0.7 ≤ α < 0.9 is good and a scale of 0.6 ≤ α < 0.7 is

acceptable. Cronbach’s Alpha was established for every variable (item) which formed

a scale as shown in Table 3.7.

Table 3.7 Results of Reliability Test

Source: Pilot Data (2014)

Table 3.7 shows that human capital repository had the highest reliability (α= 0.903),

followed by firm’s culture (α=0.891), knowledge conversion (α=0.886), knowledge

application (α=0.841), performance (α=0.712) and knowledge transfer (α=0.700).

This illustrates that all the six variables were reliable as their Cronbach’s alpha values

exceeded the prescribed threshold of 0.7 as contended by Marczyk et al., (2005) and

Field (2009). The results of the reliability test also revealed that the six variables had

Variable Cronbach's Alpha Number of Items Comment

Knowledge Conversion 0.886 14 Reliable

Knowledge Transfer 0.700 6 Reliable

Knowledge Application 0.841 6 Reliable

Human Capital Repository 0.903 17 Reliable

Firm’s Culture 0.891 13 Reliable

Performance 0.712 7 Reliable

Overall Reliability Coefficient 0.822 63 Reliable

67

an aggregate alpha value of 0.822 for all the 63 items and as such jointly lie within the

recommended range for reliability.

3.8 Data Collection Procedure

Data collection is an essential element in the production of useful data for analysis

and is subject to empirical research informed by theory (Groves et al., 2009). It is the

collection of information from the selected units of a study. A research permit was

sought from NACOSTI before embarking on data collection. At the bank level,

permission was sought from the bank management to collect data from their

managers. The respondents were requested to indicate their informed consent to

participate in the study.

The questionnaires were delivered by the researcher to all the respondents of the

study. The completed questionnaires were later collected at the time agreed with

individual respondents. Follow-up was made through the office of the respondent so

as to enhance the response rate. The investigator exercised care and control to ensure

all questionnaires issued to the respondents were received and to achieve this, a

register of questionnaires was maintained, which provided a clear account of the

questionnaires that were issued, and those that were received back.

3.9 Data Analysis and Presentation

Before processing the responses, the collected data was prepared for statistical

analysis. Validation and checking was done after the questionnaires were received

from the field. Responses were checked for clarity, legibility, relevance and

68

appropriateness. Moreover, the questionnaires were edited for completeness and

consistency. Coding was done on the basis of the locale of the respondents.

Quantitative data was analysed using descriptive and inferential statistics. Descriptive

statistics was used to summarise the survey data and included percentages,

frequencies, means, and standard deviations. However, inferential statistics involved

regression analysis and was used for testing hypotheses and drawing conclusions.

However, several diagnostics tests such as sampling adequacy, normality, linearity,

multicollinearity and homogeneity were conducted to establish the suitability of the

data for making inferences and drawing conclusions. It has been noted that violations

of assumptions of multiple regression analysis can result in biased estimates of

relationships, over or under-confident estimates of the precision of regression

coefficients and untrustworthy confidence levels and significance tests (Cohen,

Cohen, West & Aiken, 2003; Chatterjee & Hadi, 2012).

Kaiser-Meyer-Olkin measure (KMO) and Bartlett's Test of Sphericity tests were

performed to establish sampling adequacy of the research data. KMO measure varies

between 0 and 1, and values closer to 1 are better with a threshold of 0.5. Williams,

Brown and Onsman (2012) stated that KMO of 0.50 is acceptable degree for sampling

adequacy. Bartlett's Test of Sphericity tests the null hypothesis that the correlation

matrix is an identity matrix; that is, it analyzes if the samples are from populations

with equal variances. Normality was tested using Shapiro-Wilk test which has power

to detect departure from normality due to either skewness or kurtosis or both. Shapiro-

Wilk statistic ranges from zero to one and in case the calculated probability (p-value)

is below 0.05, the data significantly deviate from normal (Razali & Wah, 2011).

69

Therefore, the researcher adopted the p-value of 0.5 as the threshold for testing

normality as recommended by Razali and Wah.

The assumption of linearity was tested using ANOVA test which compares group

means by analyzing comparisons of variance estimates to examine whether or not the

means of several groups are all equal. ANOVA test computes both the linear and non-

linear components of a pair of variables whereby non-linearity is significant if the F

significance value for the non-linear component is below 0.05 (Garson, 2012). In this

a p-value of 0.5 was adopted for testing the assumption of linearity as recommended

by Garson. The researcher utilized Durbin Watson (DW) test to assess if the residuals

of the models were autocorrelated. DW statistic ranges from zero to four with Scores

between 1.5 and 2.5 indicating absence of autocorrelation (Garson, 2012).

Moreover, tolerance and variance inflation factor (VIF) were used to test for

multicollinearity. According to Landau and Everitt (2004), VIFs of at least 10 or

tolerances of at most 0.1 suggest presence of multicollinearity. In this study, VIF ≥10

and tolerance ≤ 0.1 which correspond to R2 ≥ 0.90, were adopted for detecting the

existence of multicollinearity. In addition, Levene’s statistic was used to test for

homogeneity of variance. If the test is not significant (p-value ≥ .05), the two

variances are not significantly different and thus fail to reject the null hypothesis

(Gastwirth, Gel & Miao, 2009). Therefore, the p-value of 0.5 was utilized as the

threshold for testing homogeneity of variance.

Based on the specific objectives, this study made use of multiple regression analysis

which helped to generate a weighted estimation equation that was used to predict

70

values (Cooper & Schindler, 2003) for dependent variable from the values of

independent variables. The study sought to predict performance of commercial banks

on the basis of knowledge conversion, knowledge transfer, and knowledge

application. It also sought to establish the influence of firm’s culture and human

capital repository on the relationship between knowledge management and

performance of Commercial Banks in Kenya.

Inferential analysis examined the relationship between KM and performance of

through the use of multivariate analysis. The research hypotheses were also tested at

95% level of confidence as a statistical basis for making inferences and drawing

conclusions. The responses for each research variable were combined using SPSS to

generate composite scores which were used in the multivariate analysis. Analysis of

variance was used to test whether the overall models were statistically significant by

indicating whether or not R2 could have occurred by chance alone. The F-ratio

generated in the ANOVA table was utilized to measure the probability of chance

departure from a straight line. The p value of the F-ratio generated should be less

than 0.05 for the equation to be statistically significant at 95% confidence level. In

case the p value is greater than 0.05, the model is not statistically significant. For the

individual variables, p values of the coefficients generated in the regression analysis

would have to be less than .05 for their relationship to be concluded significant at

95% confidence level.

71

Table 3.8 Hypotheses Testing

Objective Research

Hypotheses (Ho)

Statistical Approach Thresh-hold

for

Interpretation

Determine the

relationship between

knowledge conversion

and performance of

Commercial Banks in

Kenya

There is no

relationship between

knowledge

conversion and

performance of

Commercial Banks in

Kenya

Multiple regression

analysis

Y = β0 + β1X1 + β2X2 +

β3X3 + ε

R2 Value

F Value

t Value

P ≤ 0.05

Establish the relationship

between knowledge

transfer and performance

of Commercial Banks in

Kenya

There is no

relationship between

knowledge transfer

and performance of

Commercial Banks in

Kenya

Determine the

relationship between

knowledge application

and performance of

Commercial Banks in

Kenya

There is no

relationship between

knowledge

application and

performance of

Commercial Banks in

Kenya

Establish the mediating

effect of human capital

repository on the

relationship between

knowledge management

and performance of

Commercial Banks in

Kenya

Human capital

repository has no

mediating effect on

the relationship

between KM and

performance of

Commercial Banks in

Kenya

Regression analysis Y = β0 + β1X + ε

Me = β0 + β1X + ε

Y = β0 + β1Me + ε

Y = β0+β1X + β2Me + ε

R2 Value

P ≤ 0.05

Determine the moderating

effect of firm’s culture on

the relationship between

knowledge management

and performance of

Commercial Banks in

Kenya.

Firm’s culture has no

moderating effect on

the relationship

between KM and

performance of

Commercial Banks in

Kenya

Regression analysis Y = β0 + β1X + ε

Y = β0 + β1X+β2XZ + ε

Change in R2

Value

Change in F

value

P ≤ 0.05

Change in β1

Source: Author and Literature Review (2014)

Results of quantitative data analysis were presented using figures and tables for easy

understanding and interpretation as recommended by Mash and Ogunbanjo (2014).

Qualitative data from open-ended questions were analysed on the basis of common

themes and presented in a narrative form.

72

3.10 Ethical Considerations

According to Kerridge, Lowe and McPhee (2005), ethics involves making a judgment

about right and wrong behaviour. Ethics as noted by Minja (2009) are the norms

governing human conduct which have a significant impact on human welfare. In this

study, confidentiality was of concern as the information relevant to the study was of

strategic importance. Therefore, the names of the respondents and banks were not

revealed. In addition, responses attributed to specific individuals or banks were

maintained in strict confidence, instead, codes were used to uphold confidentiality of

the information from individuals in the different Commercial Banks. As noted by

Mugenda and Mugenda (2003) the researcher avoided the use of embarrassing and

irrelevant questions, language that would make respondents nervous. Permission was

obtained from the targeted banks and informed consent obtained from the study

participants. These measures enhanced the willingness and objectivity of the

respondents.

73

CHAPTER FOUR: RESEARCH FINDINGS AND DISCUSSION

4.1 Introduction

This chapter presents the descriptive statistics, diagnostic tests, tests of hypotheses

and qualitative data analysis. In addition, it discusses the results on the basis of

theoretical and empirical literature reviewed.

4.2. Descriptive Analysis

The analysis of response rate, characteristics of the respondents who participated in

the study and a summary of responses on the basis of sample mean and sample

standard deviation for the research variables adopted are presented and discussed

below.

4.2.1 Analysis of Response Rate

The researcher administered 215 questionnaires, out of which 156 were filled-in and

returned. The analysis of response rate is presented below.

Figure 4.1 Response Rate

Source: Survey Data (2014)

72.6%

27.4%

74

Figure 4.1 shows that approximately 73% respondents filled-in and returned the

questionnaire. According to Mugenda and Mugenda (2003), a response rate of 50% is

adequate for analysis and reporting; a rate of 60% is good and a response rate of at

least 70% is excellent. This response rate was therefore considered sufficient for

making inferences and drawing conclusions from the research data as recommended

by Mugenda and Mugenda.

4.2.2 Respondents’ Biographical Information

The characteristics of the respondents involved in the study were also analysed on the

basis of sex, tenure of service and designation. The analysis of respondents’

biographical data is presented in Table 4.1.

Table 4.1 Analysis of Background Information

Category Sub-Category Frequency Percent

Sex Male 81 51.9

Female 75 48.1

Total 156 100.0

Tenure At most 3 Years 53 33.9

4 - 7 Years 82 52.6

8 – 11 Years 11 7.1

At least 12 Years 10 6.4

Total 156 100.0

Position Finance Manager 26 16.7

Human Resources Manager 35 22.4

Marketing Manager 43 27.6

ICT Manager 14 9.0

Operations Manager 19 12.2

Others 19 12.2

Total 156 100.0

Source: Survey Data (2014)

75

Table 4.1 shows majority of the respondents were men at 51.9%, whereas the rest

were female at 48.1%, this confirms that there was a fair representation of both

genders in this research. Amongst the respondents, those who had served for a period

of 4 to 7 years comprised the majority at 52.6%. However, the respondents who had

served for a period of at least 12 years constituted the smallest group at 6.4%. The rest

of the respondents at 34% and 7.1% had served for a period of at most 3 years and 8

to 11 years respectively. This indicates that the respondents involved in the study

could be able to provide credible information relating to the research variables.

In addition, majority of the respondents at 27.6% were Marketing Managers with ICT

Managers comprising the smallest group at 9%. The rest of the respondents at 22.4%,

16.7%, 12.2 % and 12.2% were Human Resources Managers, Finance Managers,

Operations Manager and others respectively. The sub-category of others was included

for Commercial Banks that did not have some of the functional areas that were

considered relevant for this study. The analysis confirms that the target functional

areas were fairly represented in the study.

4.2.3 Knowledge Conversion

Knowledge conversion was measured using indicators comprising of socialization,

externalization, combination and internalization. The descriptive statistics for each of

these indicators are presented and discussed below.

76

Table 4.2 Descriptive Statistics for Knowledge Conversion

Source: Survey Data (2014)

Table 4.2 shows that the aggregate mean score for socialization is 3.63. This mean

score approximates to 4.00 (agree) on the 5-point Likert scale adopted for the study.

In addition, there was a low variability of responses from the mean response as

Knowledge Conversion n Min Max Mean

Std.

Dev

Socialization

Interaction with customers is encouraged 156 1.00 5.00 3.88 0.75

Knowledge and experiences are shared through

interaction with employees 156 1.00 5.00 3.37 0.71

Knowledge and experiences are shared through

interaction with suppliers 156 1.00 5.00 3.65 0.70

Aggregate scores for socialization 3.63 0.72

Externalization

Organization members are able to articulate their

ideas or images, in words, metaphors, analogies

into a readily understandable form

156

1.00

5.00

3.72

0.59

Organization members are able to elicit and

translate knowledge of customers into a readily

understandable form

156 1.00 5.00 3.86 1.03

Organization members are able to elicit and

translate knowledge of experts into a readily

understandable form

156 1.00 5.00 3.87 1.03

Aggregate scores for externalization 3.82 0.88

Combination

Knowledge is organized and integrated through

reports 156 1.00 5.00 3.74 0.97

Meetings helps in integrating knowledge 156 1.00 5.00 3.99 0.74

Knowledge is disseminated through briefs 156 1.00 5.00 3.76 0.83

There is use of information technology in editing

or processing information 156 1.00 5.00 3.80 0.85

Exchange of documents helps in integrating

knowledge 156 1.00 5.00 3.67 0.79

Aggregate score for combination 3.80 0.84

Internalization

Bank’s processes enhances understanding and

translating of knowledge (explicit) into

application (tacit knowledge) by organizational

members

156 1.00 5.00 3.19 0.81

There is actualization of concepts and methods

through the actual doing 156 1.00 5.00 3.90 0.75

There is actualization of concepts and methods

through simulations 156 1.00 5.00 3.72 0.84

Aggregate scores 3.60 0.70

Aggregate score for knowledge conversion 3.72 0.81

77

illustrated by the aggregate standard deviation of 0.72. The overall mean score reveals

that there is agreement amongst respondents that activities relating to socialization are

undertaken in Commercial Banks. However, there was uncertainty as to whether

knowledge and experiences are shared through interaction with employees as

indicated by a mean of 3.37 that approximate to 3.00 (moderate) and a narrow

variability of responses indicated by a standard deviation of 0.71. The low overall

standard deviation reveals that the respondents agreed that socialization plays a

critical role in knowledge conversion.

It can also be observed that the aggregate mean score and standard deviation for

externalization are 3.82 and 0.88 respectively. This aggregate mean index tends to

4.00 (agree) on the 5-point Likert scale adopted and thus reveals that the level of

activities relating to externalization in Commercial Banks is high. However, variation

in responses is relatively wider for some items such as organization members are able

to elicit and translate knowledge of customers into a readily understandable form and

organization members are able to elicit and translate knowledge of experts into a

readily understandable form both at 1.03. The aggregate standard deviation for

externalization is small confirming that respondents generally agreed that

externalization is crucial for knowledge conversion and performance.

The aggregate mean score and standard deviation for items on combination are 3.80

and 0.84 respectively. The overall mean score approximates to 4.00 (agree) on the 5-

point Likert scale used in this study indicating that respondents agreed that activities

relating to combination are undertaken in Commercial Banks. Generally, the

responses are clustered closely around the mean responses. Furthermore, the overall

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standard deviation for combination is low revealing agreement amongst respondents

that combination is important for knowledge conversion and performance

Furthermore, the aggregate mean score and standard deviation for internalization are

3.60 and 0.80 respectively. This aggregate mean index approximates to 4.00 (agree)

on the 5-point Likert scale adopted and thus reveals that activities relating to

internalization are practiced in Commercial Banks. However, there was uncertainty as

to whether bank’s processes enhances understanding and translating of knowledge

(explicit) into application (tacit knowledge) by organizational members as indicated

by a mean of 3.19 that approximate to 3.00 (moderate) and a narrow variability of

responses indicated by a standard deviation of 0.81. The aggregate standard deviation

for internalization is small confirming that respondents generally agreed that

internalization is crucial for knowledge conversion and performance.

The aggregate mean score for the three dimensions on knowledge conversion is 3.72

and thus tends to 4.00 (agree) on the 5-point Likert scale utilized in this study. In

addition, the variability of responses from the aggregate mean score is low as

indicated by the aggregate standard deviation of 0.81. This aggregate mean score

reveals that the level of activities relating to conversion of knowledge in Commercial

Banks is high. In addition, the low aggregate standard deviation implies that the

responses are concentrated around the aggregate mean and thus it’s a stable and

reliable estimator of the true mean. In this case, the narrow variation from the overall

mean response confirms that the respondents agreed that knowledge conversion plays

a major role in performance.

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4.2.4 Knowledge Transfer

Knowledge transfer was investigated using activities undertaken in Commercial

Banks involving conveyance of ideas, experiences and information to facilitate

sharing, collaboration and networking. The descriptive statistics from responses on

knowledge transfer are presented in Table 4.3.

Table 4.3 Descriptive Statistics for Knowledge Transfer

Knowledge Transfer n Min Max Mean

Std.

Dev

There is a process of information

identification 156 1.00 5.00 3.90 0.44

There is a process of information

evaluation 156 1.00 5.00 4.20 0.90

Similar mistakes are avoided 156 1.00 5.00 3.95 0.59

Useful information is disseminated 156 1.00 5.00 3.92 0.64

There are open discussions 156 1.00 5.00 3.91 0.57

There is continuous capturing of

information 156 1.00 5.00 3.61 0.67

Aggregate scores for knowledge transfer 3.92 0.64

Source: Survey Data (2014)

Table 4.3 reveals that the aggregate mean score and standard deviation for items on

knowledge transfer are 3.92 and 0.64 respectively. This overall mean score

approximates to 4.00 (agree) on the 5-point Likert scale and therefore reveals that

there is agreement amongst respondents that activities involving transfer of

knowledge are practiced in Commercial Banks. Generally, the responses are clustered

around mean response as illustrated by the low aggregate standard deviation of 0.64.

Moreover, the low variability of responses implies that the aggregate mean score is a

stable and reliable estimator. In this case, the respondents agree that knowledge

transfer plays a key role in performance.

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4.2.5 Knowledge Application

The variable of knowledge application was measured using indicators comprising of

problem solving, elaboration, efficient processes, IT support, and infusion. The

descriptive statistics for knowledge application are presented Table 4.4.

Table 4.4 Descriptive Statistics for Knowledge Application

Knowledge Application n Min Max Mean

Std.

Dev

Bank leadership has pioneered and driven KM

adoption and use 156 1.00 5.00 3.88 0.62

There is a KM training program 156 1.00 5.00 4.27 0.63

There are continuous improvements as a result of

KM application. 156 1.00 5.00 4.03 0.54

There is a KM strategy in the bank 156 1.00 5.00 4.19 0.73

KM has yielded efficient processes 156 1.00 5.00 4.04 0.79

IT used in KM has supported worker’s needs 156 1.00 5.00 4.23 0.83

Aggregate scores 4.12 0.69

Source: Survey Data (2014)

Table 4.4 shows that the aggregate mean score for items on knowledge application is

4.12 and its corresponding standard deviation is 0.69. This overall mean score tends to

4.00 (agree) on the 5-point Likert scale adopted for the study and thus indicates that

respondents generally agreed that activities involving knowledge application are

practiced in Commercial Banks. In addition, the responses are clustered around the

mean response as illustrated by the low aggregate standard deviation. The low

variability of responses reveals that the mean response is a reliable estimator for the

true mean. The narrow variability from the overall mean response confirms that

knowledge application is important for performance.

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4.2.6Human Capital Repository

Human capital repository was investigated using indicators comprising of experience,

education and innovativeness. The descriptive statistics for human capital repository

are presented and discussed below.

Table 4.5 Descriptive Statistics for Human Capital Repository

Human Capital Repository n Min Max Mean

Std.

Dev

Experience

Employee’s experience enhances the task

performance ability 156 1.00 5.00 4.12 0.33

Employees experience facilitates identification and

interpretation of change 156 1.00 5.00 4.12 0.33

Experience enables employees to refine task

performance skills 156 1.00 5.00 3.87 0.60

Experience helps employees to analyze information 156 1.00 5.00 3.73 0.67

Employees experience improves the speed

performing task 156 1.00 5.00 3.61 0.49

Aggregate scores for experience 3.89 0.48

Education

Education confers the employees with skills to

perform organizational tasks 156 1.00 5.00 3.65 0.72

Education is important for identification of

problems 156 1.00 5.00 3.89 0.93

Education helps in distinguishing symptoms from

causes 156 1.00 5.00 3.99 0.88

Education enhances the skills for solving problems 156 1.00 5.00 3.99 0.88

Education is critical for generating alternative

courses of action 156 1.00 5.00 3.51 0.73

Education enables employees to evaluate

alternative courses of action 156 1.00 5.00 3.90 0.61

Education is necessary for matching employees

skills and positions 156 1.00 5.00 3.67 0.66

Aggregate scores for education 3.80 0.77

Innovativeness

The bank has flexible employees 156 1.00 5.00 3.74 0.44

Employees have capacity to generate new ideas 156 1.00 5.00 3.74 0.68

Employees are able absorb new ideas 156 1.00 5.00 3.49 0.51

Employees own initiatives and creativity are

encouraged 156 1.00 5.00 3.83 0.46

Employees are able to transform knowledge and

ideas into new product, processes and systems 156 1.00 5.00 3.90 0.63

Aggregate scores for innovativeness 3.74 0.54

Aggregate scores for human capital repository 3.80 0.62

Source: Survey Data (2014)

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Table 4.5 reveals that the aggregate mean response for items on experience is 3.89

and that of standard deviation is 0.48. Notably, the aggregate mean response

approximates to a value of 4.00 (agree) on the 5-point Likert scale indicating that the

respondents generally agreed that experience is important in performance of tasks

within Commercial Banks. The narrow variability implied by the small aggregate

standard deviation confirms that there is agreement amongst respondents that

experience plays a key role in human capital repository.

In the case of responses to items on education, the aggregate mean score and standard

deviation are 3.80 and 0.77. The aggregate mean index approximates to 4.00 (agree)

on the 5-point scale used in the study confirming that education is a critical

requirement for performance of activities in Commercial Banks. In addition, the low

variability in responses reveals that the aggregate sample mean is a reliable estimator

and that education plays an important role in human capital repository and

performance.

The aggregate mean and standard deviation for items on innovativeness are 3.74 and

0.54 respectively. The overall mean response approximates to 4.00 (agree) on the 5-

point Likert scale revealing that there is agreement amongst respondents that

innovativeness is an integral ingredient to performance of tasks in Commercial Banks.

It can also be noted that the standard deviation is small and thus innovativeness is

considered to play a key role in human capital repository within Commercial Banks.

Furthermore, the overall mean response for all items on human capital repository is

3.80 and the corresponding aggregate standard deviation is 0.62. It can be noted that

the aggregate mean response is tending to 4.00 (agree) on the 5-point scale utilized

implying that there is an agreement amongst respondents that human capital

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repository is crucial for performance of tasks in Commercial Banks. Moreover, there

is a low variability of responses as revealed by the small aggregate standard deviation

confirming that the mean response for human capital repository is a reliable estimator

of the true mean.

4.2.7 Firm’s Culture

Firm’s culture was measured using indicators comprising of openness, futuristic

orientation and learning orientation. The descriptive statistics for firm’s culture are

presented and discussed below.

Table 4.6 Descriptive Statistics for Firm’s Culture

Firm’s Culture n Min Max Mean

Std.

Dev

Openness

Management frequently engage employees in

dialogue 156 1.00 5.00 3.98 0.51

Adequate time is committed to communication,

knowledge exchange and learning 156 1.00 5.00 4.00 0.47

Management welcome and stimulates change 156 1.00 5.00 4.21 0.44

Employees are involved in important business

processes 156 1.00 5.00 4.13 0.34

Aggregate Scores for Openness 4.08 0.44

Futuristic Orientation

Planning is important for developing the future 156 1.00 5.00 3.85 0.60

Current action affects future results 156 1.00 5.00 3.87 0.60

Employees are encouraged to identify and interpret

changes in the environment 156 1.00 5.00 4.11 0.78

Employees are encouraged to adequately respond

to changes in the environment 156 1.00 5.00 4.23 0.68

Aggregate Scores for Futuristic Orientation 4.02 0.67

Learning Orientation

There is a conducive environment for sharing new

information and ideas 156 1.00 5.00 3.78 0.66

There is collaboration in development and use of

new information and ideas 156 1.00 5.00 3.40 0.79

There is commitment to learning 156 1.00 5.00 4.00 0.53

There is open-mindedness in the bank 156 1.00 5.00 3.75 0.69

Adequate resources are committed to training 156 1.00 5.00 4.13 0.79

Aggregate Scores for Learning Orientation 3.81 0.69

Aggregate Scores for Firm’s Culture 3.97 0.61

Source: Survey Data (2014)

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Table 4.6 shows that the aggregate mean score and standard deviation for items on

openness are 4.08 and 0.44 respectively. Notably, the aggregate mean response

approximates to 4.00 (agree) on the 5-point scale used in the questionnaire indicating

that respondents generally agreed that activities relating to openness are undertaken in

Commercial Banks. It can also be observed that the overall standard deviation for

openness is low implying the responses are confined within a small range about the

overall mean response. In this case, there is agreement amongst respondents that

combination plays a key role in firm’s culture.

Furthermore, the aggregate mean response and standard deviation for items on

futuristic orientation are 4.02 and 0.67 respectively. This aggregate mean index tends

to 4.00 (agree) on the 5-point Likert scale confirming that the level of activities on

futuristic orientation is high in Commercial Banks. In addition, the responses are

concentrated around the mean as indicated by a small aggregate standard deviation

confirming that the sample mean is a reliable estimator of the true mean.

The aggregate mean response for items on learning orientation is 3.81, approximately

4.00 (agree) on the 5-point Likert scale adopted in the study. In addition, the

corresponding aggregate standard deviation is 0.69. The overall mean response

reveals that there is agreement amongst respondents that activities relating to learning

orientation are practiced in Commercial Banks. Notably, the standard deviation from

the aggregate mean score is low implying that the responses are clustered closely

together. Therefore, learning orientation is considered to play a key role in firm’s

culture within Commercial Banks.

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The overall mean score and standard deviation for firm’s culture are 3.97 and 0.61.

This aggregate mean response is tending to 4.00 (agree) on the 5-point scale used in

the study indicating that activities relating to firm’s culture are undertaken in

Commercial Banks. Moreover, the aggregate standard deviation is low implying that

individual responses to items on firm’s culture are concentrated around the aggregate

mean response. In this case, firm’s culture plays a major role in performance.

4.2.8 Performance of Commercial Banks

Performance was investigated using indicators comprising of new products, speed of

response to market crises, product improvement, customer retention, and new

processes. The descriptive statistics regarding performance are presented and

discussed in Table 4.7.

Table 4.7 Descriptive Statistics for Performance

Performance n Min Max Mean

Std.

Dev

New products 156 1.00 5.00 4.26 0.61

Increased speed of response to market crises 156 1.00 5.00 4.15 0.48

Improvement of existing product 156 1.00 5.00 4.38 0.70

New processes 156 1.00 5.00 4.58 0.72

Improvement of existing processes 156 1.00 5.00 4.14 0.63

Enhanced customer retention 156 1.00 5.00 4.15 0.73

Aggregate scores 4.28 0.65

Source: Survey Data (2014)

Table 4.7 shows that the overall mean score and standard deviation for items on

performance are 4.28 and 0.65 respectively. The aggregate mean score approximates

to 4.00 (agree) on the 5-point Likert scale used in this research confirming that there

is agreement amongst respondents that the indicators for performance are present in

Commercial Banks. The low aggregate standard deviation reveals a narrow variability

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of responses and thus the aggregate mean responses is a stable and reliable estimator

of the population mean. The overall narrow variability of responses from the

aggregate mean response confirms that performance is important in Commercial

Banks.

4.3 Regression Analysis

Regression analysis was utilized to test the research hypotheses. However, before the

regression analysis was carried out, several diagnostics tests were conducted to

establish the appropriateness of the data for making inferences and drawing

conclusions.

4.3.1 Diagnostic Tests

Testing of assumptions is a critical requirement for researchers utilizing multiple

regression analysis. It has been noted that violations of assumptions of multiple

regression analysis can result in biased estimates of relationships, over or under-

confident estimates of the precision of regression coefficients and untrustworthy

confidence levels and significance tests (Cohen et al., 2003; Chatterjee & Hadi,

2012). The researcher carried out diagnostics tests including sampling adequacy,

normality, linearity, homogeneity and multicollinearity.

4.3.1.1 Tests of Sampling Adequacy

Kaiser-Meyer-Olkin measure (KMO) and Bartlett's Test of Sphericity tests were

performed to establish sampling adequacy of the research data. KMO measure varies

between 0 and 1, and values closer to 1 are better with a threshold of 0.5. Williams,

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Brown and Onsman (2012) stated that KMO of 0.50 is acceptable degree for sampling

adequacy. Bartlett's Test of Sphericity tests the null hypothesis that the correlation

matrix is an identity matrix; that is, it analyzes if the samples are from populations

with equal variances. These results are presented in Table 4.8.

Table 4.8 KMO and Bartlett's Test

Scale Kaiser-Meyer-Oklin

Measure of Sampling

Adequacy

Bartlett’s Test of Sphericity

Approx. Chi-

Square

Df Sig.

Knowledge Conversion .733 928.302 91 .000

Knowledge Transfer .585 74.437 15 .000

Knowledge Application .650 429.893 15 .000

Firm’s Culture .524 3077.221 78 .000

Human Capital

Repository

.731 963.514 83 .000

Performance .702 204.052 15 .000

Source: Survey Data (2014)

Table 4.8 shows that KMO measures of sampling adequacy produced values of

between 0.524 and 0.733 while Bartlett’s test of sphericity had a consistent

significance of calculated probability of 0.000 well below the 0.05 threshold.

Therefore, the research sample was adequate, factorable and further statistical analysis

could be performed as recommended by Williams et al., (2012).

4.3.1.2 Test of Normality

Normality was tested using the Shapiro-Wilk test which has power to detect departure

from normality due to either skewness or kurtosis or both. Its statistic ranges from

zero to one and in case the calculated probability (p-value) is below 0.05, the data

significantly deviate from normal (Razali & Wah, 2011). Shapiro-Wilk test assesses

whether data is normally distributed against null hypothesis (H0) that the sample does

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not follows a normal distribution. These results of Shapiro-Wilk test are presented in

Table 4.9.

Table 4.9 Shapiro-Wilk Statistics

Statistic Df Sig.

Knowledge conversion .934 156 .078

Knowledge transfer .725 156 .092

Knowledge application .874 156 .320

Firm's culture .871 156 .233

Human capital repository .855 156 .419

Performance .811 156 .064

Knowledge management .915 156 .068

Source: Survey Data (2014)

Table 4.9 reveals that the six research variables had values of calculated probability

raging from 0 .064 for performance to 0.419 for human capital repository. In this case,

these calculated probability values were greater than 0.05 and therefore at 95%

confidence level the sample follows a normal distribution as recommended by Razali

and Wah (2011).

4.3.1.3 Test of Multicollinearity

Multicollinearity was tested by computing the variance inflation factors (VIF) and its

reciprocal, the tolerance. VIF quantifies the severity of multicollinearity in an

ordinary least- squares regression analysis. VIF's greater than 10 are a sign of

multicollinearity; the higher the value of VIF's, the more severe the problem. These

results are presented in Table 4.10.

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Table 4.10 Collinearity Statistics

Variables Tolerance VIF Comment

Knowledge conversion .345 2.897 No multicollinearity

Knowledge transfer .735 1.361 No multicollinearity

Knowledge application .193 5.186 No multicollinearity

Firm's Culture .117 8.572 No multicollinearity

Human Capital repository .145 6.884 No multicollinearity

Source: Survey Data (2014)

Table 4.10 reveals that all the research variables had tolerances and VIFs greater than

0.1 and less than 10 respectively. According to Landau and Everitt (2004), VIFs of at

least 10 or tolerances of at most 0.1 suggest presence of multicollinearity. Knowledge

transfer yielded the least VIF at 1.361; however, knowledge transfer generated the

highest VIF at 5.186. This implies that there was no multicollinearity and thus all the

predictor variables were maintained in the regression model as this is consistent with

the threshold recommended by Landau and Everitt.

4.3.1.4 Test of Homogeneity

Homoscedasticity was tested by use of Levene’s test of homogeneity of variances.

This statistic measures whether or not the variance between the dependent and

independent variables is the same. If the test is not significant (calculated probability

≥ .05), the two variances are not significantly different and thus approximately equal

(Gastwirth et al., 2009). These results are presented in Table 4.11.

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Table 4.11 Levene Statistic

Variables Levene Statistic df1 df2 Sig.

Knowledge Conversion 9.843 7 147 .079

Knowledge Transfer 4.532 7 147 .733

Knowledge Application 8.440 7 147 .116

Firm's Culture 6.265 7 147 .194

Human Capital Repository 7.709 7 147 .063

Source: Survey Data (2014)

Table 4.11 shows that the calculated probability is greater than 0.05 for all the

research variables. These values ranged between 0.63 for human capital repository

and 0.733 for knowledge transfer. In this case, the variances were significantly equal

as contended by Gastwirth

4.3.1.5 Test of Linearity

The tests of the assumption of linearity utilized the ANOVA test which compares

group means by analyzing comparisons of variance estimates to test whether or not

the means of several groups are all equal. ANOVA test of linearity computes both the

linear and non-linear components of a pair of variables whereby non-linearity is

significant if the calculated probability value for the non-linear component is below

0.05 (Garson, 2012). Moreover, it helps to establish whether there is a significant

relationship between the dependent and independent variables. ANOVA test is more

superior compared to the two-sample t-test which is susceptible to increased chance of

committing a type I error (error of rejecting a null hypothesis when it is actually true).

These results are presented in Table 4.12.

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Table 4.12 Analysis of Variance

Sum of

Squares

Df Mean

Square

F Sig.

Knowledge

Conversion

Between Groups 5.379 8 .672 2.463 .016

Within Groups 40.136 147 .273

Total 45.516 155

Knowledge

Transfer

Between Groups 3.233 8 .404 2.300 .024

Within Groups 25.822 147 .176

Total 29.054 155

Knowledge

Application

Between Groups 12.883 8 1.610 8.131 .000

Within Groups 29.115 147 .198

Total 41.997 155

Firm's Culture Between Groups 10.013 8 1.252 11.497 .000

Within Groups 16.003 147 .109

Total 26.016 155

Human Capital

Repository

Between Groups 9.878 8 1.235 13.110 .000

Within Groups 13.844 147 .094

Total 23.722 155

Source: Survey Data (2014)

Table 4.12 shows that the calculated probability values for all the research variables

were below the 0.05 threshold. Knowledge transfer had the highest probability value

of 0.24; however, the least calculated probability value of 0.000 was associated with

knowledge application, firm's culture and human capital repository. In this case, the

independent variables were linearly independent as the probability values are within

the threshold recommended by Garson (2012).

4.3.1.6 Tests of Independence

Independence of error terms, which implies that observations are independent, was

assessed through the Durbin-Watson test. Durbin Watson (DW) test check that the

residuals of the models were not autocorrelated since independence of the residuals is

one of the basic assumption of regression analysis. DW statistic ranges from zero to

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four where scores between 1.5 and 2.5 indicate independent observations (Garson,

2012). These results are shown in Table 4.13.

Table 4.13 Durbin Watson Test

Variables Durbin Watson Comment

Knowledge Conversion 1.987 No autocorrelation

Knowledge Transfer 2.084 No autocorrelation

Knowledge Application 2.231 No autocorrelation

Firm's Culture 2.026 No autocorrelation

Human Capital Repository 2.182 No autocorrelation

Source: Survey Data (2014)

Table 4.13 shows that DW statistics ranged between 1.987 for knowledge conversion

and 2.231 for knowledge application. This confirms that all the research variables

yielded DW values that were close to the recommended value of 2.0 (Garson, 2012)

and thus the residuals of the empirical model are not autocorrelated.

4.3.2 Test of Hypotheses

Multivariate analysis was utilized to empirically test the five hypotheses adopted for

this study. The hypotheses were tested at 95% confidence level as a statistical basis

for drawing conclusions. The responses for each research variable were combined to

generate composite scores which were used in the multivariate analysis. The empirical

tests performed systematically investigated the direct relationship, mediated

relationship and moderated relationship as presented and discussed below.

The first three hypotheses were tested by regressing knowledge conversion,

knowledge transfer and knowledge application on performance as shown in Table

4.14.

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Table 4.14 Regression Results for Direct Relationship

Source: Survey Data (2014)

The regression model estimated in Table 4.14 for the direct relationship is presented

below.

Performance = 1.803 + 0.251 Knowledge Conversion +0.071 Knowledge Transfer

+ 0.904 Knowledge Application

The results of regression analysis show that the adjusted coefficient of multiple

determinition = 0.579 which implies that KM explains 57.9 % of the variations in

performance. The proposed regression model fitted the data well as it was statistically

significant at F (3, 152) = 72.081 and calculated probability = 0.000. Moreover,

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Beta Std.

Error

Beta

(Constant) 1.803 .260 1.127 .115

Knowledge Conversion .251 .049 .326 5.109 .000

Knowledge Transfer .071 .054 .074 2.316 .019

Knowledge Application .904 .062 .900 14.488 .000

R R Square Adjusted

R Square

Std. Error of the

Estimate

Durbin-Watson

.766a .587 .579 .27009 2.257

Sum of Squares Df Mean Square F Sig.

Regression 15.774 3 5.258 72.081 .000b

Residual 11.088 152 .073

Total 26.862 155

a. Predictors: (Constant), Knowledge Conversion, Knowledge Transfer, Knowledge

Application

b. Dependent Variable: Performance

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regression analysis revealed that holding KM to constant zero, performance would be

at 1.803. Furthermore, Durbin Watson (DW) test was collaborated to check whether

the residuals of the models were auto-correlated. The resulting DW statistics of 2.257

approximates to the recommended value of 2.0 for residual independence. Therefore,

there was no autocorrelation. Analysis of Variance’s (ANOVA) was used to make

simultaneous comparisons between the means. The test examined if there was a

significant relationship between dependent and independent variables. The ANOVA

statics reveals that the data was suitable for making conclusion on the population’s

parameters as the calculated probability of 0.000 is less than the 5% threshold

adopted.

4.3.2.1Test of Hypothesis One

The first specific objective sought to determine the relationship between knowledge

conversion and performance. The corresponding research null hypothesis proposed

that knowledge conversion has no relationship with performance. The regression

model estimated in Table 4.14 revealed that knowledge conversion is statistically

significant at β=0.251; t = 5.109; p = 0.000, therefore at 95% level of confidence,

knowledge conversion has a positive effect on performance. These results also

illustrates that a unit increase in knowledge conversion is responsible for increasing

performance by 0.251. This study concludes that there is a relationship between

knowledge conversion and performance of Commercial Banks in Kenya.

The conclusion of the study is consistent with the findings of other researchers such as

Tseng (2010) and Zaied et al., (2012) to the effect that knowledge conversion has a

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positive influence on performance. Moreover, the findings of this study also agree

with RBV theoretical propositions that emphasize the strategic importance of social

and behavioural interactions in the conceivability of choice and implementation of the

organization’s strategies. In this regard, knowledge conversion is considered as a

social process that enables individuals with different knowledge to interact and thus

creating new knowledge which grows the quality and quantity of both tacit and

explicit knowledge.

However, as observed from the empirical literature reviewed, Tseng, (2010)

concluded that socialization, a critical element of knowledge conversion has no effect

on corporate performance and Rasula et al., (2012) failed to integrate knowledge

conversion in the model for knowledge management. This study adds to the existing

body of empirical literature by confirming that the four elements including

socialization, externalization, combination and internalization jointly influence

performance. Moreover, the KM model adopted in this study is more comprehensive

as it incorporates the three key dimensions of KM including knowledge conversion,

knowledge transfer and knowledge application.

Despite the effect that knowledge conversion has on performance, the contribution of

the different elements adopted for this variable is relatively different. For instance,

externalization and combination have greater contribution compared to socialization

and internalization. This implies that the emphasis put on the practices associated with

the four elements of knowledge conversion varies considerably in Commercial Banks.

Among the many activities that were adopted for knowledge conversion, there are two

that can be singled out as requiring critical consideration for enhancement. These are

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interaction with customers, and use of bank’s processes to enhance understanding and

translation of knowledge (explicit) into application (tacit knowledge) by

organizational members.

4.3.2.2 Test of Hypothesis Two

The second specific objective sought to establish the relationship between knowledge

transfer and performance. The research null hypothesis formulated proposed that

knowledge transfer has no relationship with performance. The results of regression

analysis in Table 4.14 revealed that knowledge transfer is statistically significant at

β=0.071; t = 2.316; p =0.019, thus at 95% confidence level, knowledge transfer has a

positive effect on performance. In addition, an increase of 0.071 in performance is

attributed to a unit increase in knowledge transfer. This study concludes that there is a

relationship between knowledge transfer and performance of Commercial Banks in

Kenya.

The findings of the study are consistent with the observations made by Syed-Ikhsan

and Rowland (2004) that transfer of knowledge is a critical factor in organizations’

success and competitiveness. However, it has been revealed that knowledge transfer is

the least predictor among the three KM elements. In addition, the conclusion of the

study agrees with the postulates of RBV that intangible resources such as the

knowledge that employees hold and which are developed through a unique historical

sequence with a socially complex dimension are responsible for creating and

sustaining competitive advantage. Knowledge resources that are available must be

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shared between units and individuals within organizations in order to enhance

corporate performance.

As noted from the empirical literature reviewed, researchers such as Daud and Yusoff

(2010) and Zaied et al., (2012) failed to integrate knowledge transfer in their

conceptualized frameworks of knowledge management. Therefore, this study extends

the body of empirical literature by enhancing the conceptual framework of KM

through the inclusion of knowledge transfer. The enhanced framework encompassing

knowledge conversion, knowledge transfer and knowledge application substantially

bridge the gap implied by the suggestions made by Rubenstein-Montano et al., (2001)

and Syed-Ikhsan and Rowland (2004) that KM models and strategies should be more

comprehensive in nature.

Even though knowledge transfer contributes positively to performance, the six

activities considered in this study are not equally practiced in Commercial Banks.

Whereas the practice of information evaluation has the highest mean score of 4.20,

continuous capturing of information is the least practiced with a mean score of 3.61.

Therefore, the practice of continuous capturing of information requires to be enhanced

so that information or knowledge is made more available and accessible for

subsequent sharing within Commercial Banks. This information should also be

allowed to flow unconstrained between individuals and units through the practice of

cross-exposure so as to enhance transmission of tacit knowledge within the banks. It

is necessary to facilitate open discussions and encourage avoidance of similar

mistakes in performance of tasks.

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4.3.2.3 Test of Hypothesis Three

The third specific objective sought to determine the relationship between knowledge

application and performance. The research null hypothesis formulated from this

objective proposed that knowledge application has no relationship with performance.

The results of regression analysis in Table 4.14 confirmed that knowledge application

is statistically significant at β=0.904; t = 14.488; p = 0.000, therefore at 95%

confidence level, knowledge application has a positive effect on performance. In this

case, a unit increase in knowledge application causes an increase of 0.904 in

performance. Therefore, the conclusion of this study is that there is a relationship

between knowledge application and performance of Commercial Banks in Kenya.

These results corroborate empirical findings by other researchers such as Mohrman et

al., (2003), Yusoff and Daudi (2010) and Fattahiyan et al., (2013) to the effect that

application of knowledge positively influence corporate performance. Moreover, the

findings confirm that amongst the three dimensions of KM considered, knowledge

application is the strongest predictor of performance. The results also underscore the

theoretical argument of RBV that considers organizational effectiveness as the ability

of the organization in either absolute or relative terms, to obtain scarce and valued

resources and successfully integrate and manage such resources.

The empirical literature reviewed indicates that there is no conclusive evidence

relating to the influence of knowledge application on organizational performance.

Even though some extant researchers have concluded that knowledge application

affects organizational performance (Mohrman et al., 2003; Daud & Yosuff, 2010;

Fattahiyan et al., 2013), others such as Zaied et al., (2012) have found no significant

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relationship between these two variables. Nevertheless, in some studies such as

Rasula et al., (2012) KM has been conceptualized to include information technology,

organizational elements and knowledge which raise conceptual implications on the

need for decomposition. Therefore, this study makes a contribution to empirical

literature by enhancing the KM framework and revealing that knowledge application

significantly affect organizational performance.

The factors identified for knowledge application are not practiced to the same extent

in Commercial Banks. Notably, KM training program has the highest mean score of

4.27, whereas the level of involvement of bank’s leadership in pioneering and driving

KM adoption and use has the least mean score of 3.88. In this case, KM training

programs substantially enhance knowledge application practices as they facilitate

knowledge acquisition and subsequent utilization in task performance or problem

solving. It is imperative for management in Commercial Banks to pioneer and drive

KM adoption and use so as to enhance elaboration and infusion of knowledge.

Moreover, this can enhance utilization of organization’s knowledge base and firm's

absorptive capacity leading to development of new products and processes as well as

improvement of the existing products and processes.

4.3.2.4 Test of Hypothesis Four

The fourth specific objective sought to establish the mediating effect of human capital

repository on the relationship between knowledge management and performance. The

corresponding research null hypothesis proposed that human capital repository has no

mediating effect on the relationship between KM and performance. This hypothesis

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was tested using causal approach as suggested by Muller et al., (2005), and Hayes

(2009). In the first step, KM was regressed on performance as shown in Table 4.15.

Table 4.15 Regression Results for Knowledge Management on Performance

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

(Constant) 2.626 .346 1.589 .077

Knowledge

Management

.418 .087 .361 4.803 .000

R R Square Adjusted R

Square

Std. Error of

the Estimate

Durbin-

Watson

.661a .437 .425 .38949 1.992

Sum of Squares Df Mean Square F Sig.

Regression 3.500 1 3.500 23.071 .000b

Residual 23.362 154 .152

Total 26.862 155

a. Predictors: (Constant), Knowledge Management

b. Dependent Variable: Performance

Source: Survey Data (2014)

Table 4.15 shows an adjusted coefficient of determination of 0.425. As observed, the

regression model is statistically significant at F (1, 154) = 23.071 and calculated

probability = 0.000. Therefore, the proposed regression model fitted the data well. In

addition, KM explains 42.5% of variation in peformance of Commercial Banks in

Kenya at 95 % level of confidence. The ANOVA statics in the same table reveal a

calculated probability value of 0.000 well below the threshold of 0.05, demonstrating

that the data is ideal for making conclusion on the population’s parameters.

Performance= 2.626+ 0.418 Knowledge Management ……………………Model 1

The regression model estimated established that KM is statistically significant at

β=0.418; t = 4.803; p = 0.000. Notably, the necessary condition for mediation has

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been satisfied given that the relationship between KM and performance is significant

at 95% confidence level. Moreover, the model revealed that holding KM to constant

zero, performance would be at 2.626. In addition, a unit increase in KM leads to an

increase of 0.418 in performance. This model yielded a beta coefficient of 0.418

which comprises the total effect in the test for the nature of mediating effect.

In the second step, human capital repository was regressed on performance as shown

in Table 4.16.

Table 4.16 Regression Results Human Capital Repository on Performance

Source: Survey Data (2014)

Table 4.16 reveals an adjusted coefficient of determination of 0.180. Moreover, the

proposed regression model fitted the data well as it’s statistically significant at F (1,

154) = 35.043 and calculated probability = 0.000. This confirms that human capital

repository explains 18 percent of variation in performance of Commercial Banks in

Kenya. The ANOVA statics suggest that the data is suitable for drawing inferences on

the population’s parameters as the calculated probability of 0.000 is less than the 5%

threshold adopted for this test.

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

(Constant) 1.953 .394 1.254 .090

Human Capital

Repository

.611 .103 .431 5.920 .000

R R Square Adjusted

R Square

Std. Error of the

Estimate

Durbin-Watson

.431a .185 .180 .37696 2.023

Sum of Squares Df Mean Square F Sig.

Regression 4.980 1 4.980 35.043 .000b

Residual 21.883 154 .142

Total 26.862 155

a. Dependent Variable: Performance

b. Predictors: (Constant), Human Capital Repository

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Performance = 1.953 + 0.611 Human Capital Repository ………………Model 2

The results reveals that human capital repository is statistically significant at β=0.611;

t = 5.920; p = 0.000, thus at 95% confidence level human capital repository has a

positive influence on performance.

In the subsequent step, KM was regressed on human capital repository as shown in

Table 4.17.

Table 4.17 Effect of Knowledge Management on Human Capital Repository

Source: Survey Data (2014)

Table 4.17 reveals that an adjusted coefficient of determination (adjusted R square-

value) of 0.381. As observed, the regression model is statistically significant at F(1,

154) = 96.592 and calculated probability = 0.000. Therefore, the proposed regression

model fitted the data well. This confirms that at 95% confindence level, KM accounts

for 38.1 percent variation of human capital repository. In addition, the ANOVA

statics reveals that the data was suitable for making conclusion on the population’s

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

(Constant) 1.803 .205 8.798 .000

Knowledge

Management

.507 .052 .621 9.828 .000

R R Square Adjusted

R Square

Std. Error of the

Estimate

Durbin-

Watson

.621a .385 .381 .23074 1.330

Sum of

Squares

Df Mean Square F Sig.

Regression 5.143 1 5.143 96.592 .000b

Residual 8.199 154 .053

Total 13.342 155

a. Dependent Variable: Human Capital Repository

b. Predictors: (Constant), Knowledge Management

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parameters as the calculated probability of 0.000 is less than the 5% threshold adopted

for this test.

Human Capital Repository = 1.803 + 0.507Knowledge Management… Model 3

The results of regression analysis confirms that KM is statistically significant at

β=0.507; t = 9.828; p = 0.000, therefore at 95% level of confidence, KM has a

positive relationship with human capital repository. This model yields a beta

coefficient of 0.507 that is a critical component of the indirect effect when testing for

mediation.

In the last step, KM and human capital repository were regressed on performance as

shown in Table 4.18.

Table 4.18 Regression Results for Mediation

Unstandardized

Coefficients

Standardized

Coefficients

t

Sig.

B Std. Error Beta

(Constant) 1.766 .408 1.327 .121

Knowledge

Management

.177 .107 .152 2.652 .001

Human Capital

Repository

.477 .131 .336 3.641 .000

R R Square Adjusted R

Square

Std. Error of

the Estimate

Durbin-Watson

.447a .200 .189 .37486 1.051

Sum of

Squares

Df Mean

Square

F Sig.

Regression 5.363 2 2.681 19.083 .000b

Residual 21.499 153 .141

Total 26.862 155

a. Dependent Variable: Performance

b. Predictors: (Constant), Human Capital Repository, Knowledge Management

Source: Survey Data (2014)

Table 4.18 shows that the coefficient of determination is 0.189 and thus human capital

repository and KM are responsible for 18.9% variations in performance. In addition,

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the ANOVA statistics shows that the regression model has a calculated probability =

0.000 which is well below the 0.05 threshold. These results confirm that the model is

statistically significant at 95% level of confidence.

Performance = 1.766 + 0.177Knowledge Management + 0.477Human Capital

Repository....................................................................Model 4

In addition, KM is statistically significant at β=0.177; t = 2.652; p = 0.001, therefore

at 95% confidence level, knowledge management has a positive relationship with

performance. Furthermore, it is evident that human capital repository is statistically

significant at β=0.477; t = 3.641; p = 0.000, thus at 95% level of confidence, human

capital repository has a positive effect on performance. This model yields beta

coefficients of 0.177 and 0.477 which are critical components of the direct and

indirect effects respectively in the test for mediation.

The total effect of KM (independent variable) on performance (dependent variable) is

represented by a beta coefficient (β21) of 0.418. The direct effect of KM on

performance after controlling for human capital repository (mediating variable) is

represented by a beta coefficient (β51) of 0.177. The effect of the independent variable

on the mediating variable is represented by a beta coefficient (β41) of 0.507.

Moreover, the effect of the mediator on the dependent variable after controlling for

the independent variable is represented by a beta coefficient (β52) of 0.477 (Rucker et

al., 2011) as shown in Table 4.19.

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Table 4.19 Decision Criteria for Mediation

Model 1 Model 2 Model 3 Model 4 Test Conclusion

β 21 = 0.418

(p = 0.000)

- - - Necessary condition There is an

overall

relationship to be

mediated

β 21 = 0.418

(p=0.000)

β31=0.507

(p=0.001)

β41=0.611

(p=0.001)

β51=0.177

(p =0.001)

β54=0.477

(p = 0.000)

β21- β51 = 0.418-0.177

= 0.241

Β41*β52 = 0.507*0.477

=0.242 (β21- β51=β41*β52=0.242)

There is partial

mediation

Source: Survey Data (2014)

Table 4.19 confirms that β21 coefficient is statistically significance and thus satisfies

the necessary condition for testing mediation. Moreover, β31, β41, β51 and β52 are

statistically significant at 95% level of confidence. The statistical significance of β51

confirms that there is no possibility for perfect mediation. Conversely, human capital

repository partially mediates the relationship between KM and performance. Notably,

complete mediation would have required that the full effect of the independent

variable on the dependent variable be carried by the mediator (Ryu, West & Sousa,

2009).

Further test reveals that that the indirect effect given by the product β41*β52

(0.507*0.477=0.242) is approximately equivalent to the difference between the total

effect and the direct effect β21- β51 (0.418-0.177= 0.241). The causal steps approach

for mediation confirms that at 95% level of confidence human capital repository

partially mediates the relationship between KM and performance. Therefore, this

study concludes that human capital repository partially mediates the relationship

between knowledge management and performance of Commercial Banks in Kenya.

These findings corroborate the conclusion drawn by Chong and Choi (2005) that

employees and managers who are well equipped with skills and information to fulfill

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their responsibilities are essential success ingredient for implementation of KM.

Moreover, it has been noted that the collective value of the capabilities, knowledge,

skills, life experiences, motivation of the workforce and abilities residing within and

utilized by individuals (Kaplan and Norton, 2004) are crucial for exposing an

organization to technology boundaries that increase its capability to absorb and

deploy knowledge domains (Hill and Rothaermel, 2003). The results are consistent

with the theoretic postulates of KBV which considers a firm to be a “distributed

knowledge system” composed of knowledge-holding employees, and as such the role

of the firm is to coordinate the work of those employees so that they can create

knowledge and value.

The vast body of empirical literature reviewed confirmed that there are only few

studies that have integrated mediating variables in an attempt to enhance the

understanding of the influence of KM on performance. For instance, Yusoff and

Daudi (2010) revealed that social capital partially mediates the relationship between

KM and firm’s performance. Nevertheless, none of the reviewed studies

conceptualized the mediating role of human capital repository. Therefore, this study

extends the empirical literature by integrating human capital repository as a mediating

variable in the link between KM and performance as suggested by Devece (2012).

The three factors adopted as indicators of human capital repository including

experience, education and innovativeness compared favorably on the basis of their

disaggregated contributions. However, it can be inferred that practices that enhance

absorptive capacity of employees for new ideas need to be institutionalized and

operationalized in Commercial Banks. Similarly, the value of education in generating

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alternative courses of action in decision and problem situations needs to evaluated and

enhanced.

4.3.2.5 Test of Hypothesis Five

The fifth specific objective for this study sought to determine the moderating effect of

firm’s culture on the relationship between knowledge management and performance.

The corresponding research null hypothesis proposed that firm’s culture has no

moderating effect on the relationship between knowledge management and

performance. This hypothesis was tested using two regression models as shown in

Table 4.20.

Table 4.20 Regression Results for Moderation

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std.

Error

Beta

1 (Constant) 2.626 .346 1.589 .063

KM .418 .087 .361 4.803 .000

2 (Constant) 3.090 .361 1.572 .068

KM .602 .100 .520 6.043 .000

Firm's Culture -.301 .087 -.296 -3.447 .001

KM * Firm’s Culture .296 .821 .192 2.761 .013

Model Sum of

Squares

Df Mean Square F Sig.

1 Regression 3.500 1 3.500 23.071 .000b

Residual 23.362 154 .152

Total 26.862 155

2 Regression 5.184 3 1.728 12.084 .000c

Residual 21.679 152 .143

Total 26.862 155 R R 2 Adjuste

d R2

Std. Error of

the Estimate

Change Statistics Durbin-

Watson R2

Change

F

Change

df

1

df

2

Sig. F

Change

.661a .437 .425 .38949 .437 21.921 1 154 .000

.839b .703 .681 .3025 .266 11.623 1 153 .001 2.164

a. Dependent Variable: Performance

b. Predictors: (Constant), KM

c. Predictors: (Constant), KM, Firm’s Culture , KM * Firm's Culture

Source: Survey Data (2014)

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In the first model, KM was regressed on performance. However, in the second model,

KM, firm’s culture, and the interaction between KM and firm’s culture were regressed

on performance. Table 4.20 shows that regression model without the interaction term

is statistically significant at F (1, 154) = 23.071 and calculated probability = 0.000. In

addition, the regression model with the interaction term is statistically significant at F

(3, 152) = 12.084 and calculated probability = 0.000. Model 2 with the interaction

between KM and firm’s culture accounts for more variance. Moreover, the change in

coefficient of determination (R-square value) = 0.266, F change = 11.623 and

calculated probability = 0.001 reveals that there is potentially significant moderating

effect of firm’s culture on the relationship between KM and performance.

Performance = 2.626 + 0.418 Knowledge Management ..............................Model 1

In model 1, KM is statistically significant at β=0.418; t = 4.803; p = 0.000, suggesting

that there is a relationship between KM and performance that could be moderated.

Performance = 3.090 + 0.602 Knowledge Management - 0.301Firm Culture +

0.296Knowledge Management * Firm Culture......................Model 2

The regression results for model 2 reveals that at 95% level of confidence, all the

coefficient are statistically significant with KM at β=0.602; t = 6. 043; p =0.000,

firm’s culture at β= -0.301; t = -3.477; p =0.001, and the interaction term at β=0.296;

t = 2.761; p = 0.013.

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Table 4.21 Decision Criteria for Moderation

Model 1 Model 2 Total effect Conclusion

β61=0.418

(p=0.000)

-

-

There is an overall effect

to moderate

β61=0.418

(p=0.000)

β72 =- 0.301 (p=0.001)

-

Moderating variable is

not an explanatory

variable

β61=0.418

(p=0.000)

β72 =- 0.301 (p=0.001) β73= 0.296 Moderating variable has

a moderating effect

Source: Survey Data (2014)

Table 4.21 reveals that firm’s culture moderates the relationship between KM and

performance. As suggested by Whisman and McClelland (2005), the coefficient for

the interaction term β73= 0.296 implying that for each unit increase in firm’s culture

the slope of KM and performance increases by 0.296. Therefore, at 95% level of

confidence, firm’s culture has a moderating effect on the relationship between KM

and performance. Thus, this study concludes that firm’s culture moderates the

relationship between knowledge management and performance of Commercial Banks

in Kenya.

These findings are consistent with the observation made by Linn (2008) that firm’s

culture is a critical factor that shapes behavior. Furthermore, culture allows

organizational members to create, acquire, share, and manage knowledge within a

context. Indeed, past empirical studies have clearly identified organization’s culture as

an enabler of KM (Mathi, 2004; Wong & Aspinwall, 2005; Wong, 2005; Akhavan et

al., 2006). Moreover, Pollard (2005) argues that the challenges faced today in getting

people to share what they know and to collaborate effectively are not caused or cured

by technologies, but are cultural impediments. The results also agree with the

propositions of organization’s learning theory that a learning organization seeks to

110

foster a learning culture which is a fundamental ingredient in sustaining

innovativeness in processes, products and technologies, and enhancing corporate

performance.

Among the empirical studies reviewed only a few have integrated moderating

variables in an attempt to enhance the understanding of the contribution of KM. For

instance, Mushref (2014) concluded that organization culture has a moderating effect

on the link between intellectual capital and business performance whereas Tseng

(2010) concluded that organizational culture moderates the effect of knowledge

conversion on corporate performance. However, Mushref operationalized indicators

such as individualism-collectivism, power distance, uncertainty avoidance, and

masculinity and femininity in a manner that is biased toward societal culture as

opposed to organizational culture. On the other hand, Rasula et al. (2012) considered

collaboration as a distinct variable from culture. Therefore, this study extends the

body of empirical literature by integrating moderating role of firm’s culture in the link

between KM and performance and also through the inclusion of key indicators of

firm’s culture in the conceptualized model.

Even though firm’s culture moderates the relationship between KM and performance,

the contribution of learning orientation ranks lowest relative to openness and futuristic

orientation. In particular, collaboration among organizational members in

development and use of new information and ideas can be singled out as a critical

practice that may require enhancement. Jointly the elements of firm’s culture adopted

complement each other in facilitating conversion, transfer and application of

knowledge within Commercial Banks.

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4.5 Qualitative Data Analysis

Qualitative data from the semi-structured questions were analysed on the basis of

common themes as guided by the research variables. The analysis of qualitative data

is presented and discussed below.

Table 4.22 Qualitative Data Analysis

Themes Description

Knowledge conversion Knowledge conversion is important in Commercial

Banks

Knowledge transfer Commercial Banks have open channels of information

flow

Knowledge application Knowledge application is critical in Commercial Banks

Human capital

repository

Efforts are made to retain employees within Commercial

Banks

Firm’s culture Culture is critical in customer care and banking hall

operations

Performance Knowledge management plays a key role in the

performance of the Commercial Banks

Source: Survey Data (2014)

Table 4.22 shows that commercial banks facilitate open access to communication

resources. Moreover, communication is encouraged to function effectively in both

lateral and vertical directions with managers acting as link agents. Furthermore, the

practice of job rotation provided an opportunity for cross-exposure to different

departments motivating individual employees and affecting their productivity. This

point of view corroborates the observations that management should consider

ensuring that information or knowledge is accessible and shared in the organization

(Syed-Ikhsan & Rowland, 2004), and linkage agents are central actors in the

knowledge transfer process (Becheikh et al., 2012). Notably, the knowledge

transferred between individuals not only benefits the organization but also tends to

improve competence in both the individuals that are involved in the process.

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The respondents considered use of knowledge as a contributing factor for growth in

employees’ experience, learning, quality of decisions, avoidance of repeat mistakes,

and performance. As noted, these findings concur with the suggestion made by

Mohrman et al., (2003), that organization’s performance is improved when

organisations create and use knowledge. Furthermore, Gasik (2011) confirmed that

companies benefit not from the existence of knowledge but its proper application.

Efforts made to retain employees within commercial banks included performance

based bonuses, low interest facilities, training and development programs, and

employee’s welfare programs. In addition, it was noted that organization culture is

most critical in customer care services and banking hall operations. As revealed by

Robinson et al., (2005), the respondents concurred that KM strategies are crucial to

enhancing corporate performance within Commercial Banks.

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CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter presents the summary of findings, conclusion, contribution of the study

to knowledge, recommendations for further study. The purpose of this study was to

investigate the relationship between KM and performance of Commercial Banks in

Kenya. The specific research objectives sought to determine the relationship between

knowledge conversion and performance; to establish the relationship between

knowledge transfer and performance; to determine the relationship between

knowledge application and performance; to establish the mediating effect of human

capital repository on the relationship between knowledge management and

performance; and to determine the moderating effect of firm’s culture on the

relationship between knowledge management and performance of Commercial Banks

in Kenya.

5.2 Summary

The first objective of the study sought to determine the relationship between

knowledge conversion and performance of Commercial Banks in Kenya. The study

revealed that activities relating to socialization, externalization, combination and

internalization are practiced to different levels within Commercial Banks. In addition,

the study illustrates that even though knowledge conversion is crucial, the emphasis

directed to practices associated with the elements of knowledge conversion varies

considerably in Commercial Banks. The expectation of this research was that there is

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a relationship between knowledge conversion and performance. This expectation was

confirmed through further statistical analysis which showed that knowledge

conversion has a positive effect on performance.

The second objective intended to establish the relationship between knowledge

transfer and performance of Commercial Banks in Kenya. Generally, all the activities

regarding transfer of knowledge were found to be substantially practiced although not

to the same extent. Inferential statistics indicated that knowledge transfer has a

positive contribution to performance which confirmed the expectation of this

objective. However, it was evident the knowledge transfer has the least influence on

performance relative to knowledge application and knowledge conversion.

The third objective of this study sought to establish the relationship between

knowledge application and performance of Commercial Banks in Kenya. The focus of

this objective was on activities and practices involving provision of instructions,

directions, and performance of tasks. Toward this end, the study confirmed that all the

activities relating to knowledge application were considerably practiced in

Commercial Banks. Statistical analysis confirmed the expectation of this objective

that knowledge application positively affects performance. In addition, it was evident

that knowledge application has the greatest contribution relative to knowledge

conversion and knowledge transfer.

The fourth objective of this study sought to establish the mediating effect of human

capital repository on the relationship between knowledge management and

performance of Commercial Banks in Kenya. It focused on occurrences that would

lead to increase, retention or loss of ideas, experiences and information held in a bank

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through the employees. The study confirmed that experience, education and

innovativeness bestowed employees with benefits that would enhance exploitation of

knowledge assets within Commercial Banks. On the basis of causal approach utilized

for testing mediation, it was confirmed that human capital repository partially

mediates the relationship between knowledge management and performance.

The fifth objective intended to determine the moderating effect of firm’s culture on

the relationship between knowledge management and performance of Commercial

Banks in Kenya. This objective focused on activities that manifest values, core

values, beliefs, assumptions, initiatives, learning experiences and expectations of

employees. Toward this end, openness, futuristic orientation and learning orientation

were utilized as indicators of firm’s culture. The findings of the study confirmed that

this attributes were substantially fostered so as to enhance the value of knowledge

assets within Commercial Banks. Statistical analysis for moderation confirmed the

expectation of this objective to the effect that firm’s culture moderates the relationship

between knowledge management and performance.

5.3 Contribution of the Study to Knowledge

This study investigated the relationship between knowledge management and

performance of Commercial Banks in Kenya. Despite prior empirical studies

establishing that KM has a significant relationship with corporate performance, it has

been noted that the focus of these past studies had been on organizations and sectors

in developed countries. In addition, these studies had a couple of critical limitations

relating to methodology, context, consistency of results, and conceptualization of

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research variables and models. In the local context, empirical studies conducted

revealed that aspects of knowledge and knowledge management practices (KMP)

influence performance. Nevertheless, these studies considered aspects of knowledge

such as internet banking and KMP encompassing leadership, incentives,

communication, and policies and strategies. This study contributes to empirical

literature by revealing that KM has a positive influence on performance of

Commercial Banks in Kenya.

Furthermore, the study adds to the existing body of empirical literature and

contributes to the debate at the heart of management researchers on factors that

influence corporate performance. The study extends the conceptualization of the

relationship between KM and performance through the integration of a mediating

variable (human capital repository) and moderating variable (firm’s culture). This

integrated research model has fundamental implications to both practitioners and

researchers in knowledge-intensive sectors and organizations. Moreover, the three

critical factors that are utilized in this study comprising of knowledge conversion,

knowledge transfer and knowledge application enhances the conceptualization of KM

framework.

The study also contributes to theoretical literature by providing the basis upon which

the theoretical propositions utilized in the formulation of the research hypotheses can

be empirically tested. The study supports the proposition of RBV that intangible

resources such as the knowledge that employees hold and which are developed

through a unique historical sequence within a socially complex dimension are

responsible for creating and sustaining competitive advantage and enhancing

117

corporate performance. Furthermore, the study supports the theoretical proposition of

KBV that a firm is a “distributed knowledge system” composed of knowledge-

holding employees, and as such the role of the firm is to coordinate the work of those

employees so that they can create knowledge and value. Moreover, the study also

supports the proposition of organization’s learning theory to the effect that fostering

learning culture is a fundamental ingredient in sustaining innovativeness in processes,

products and technologies, and enhancing corporate performance.

5.4 Conclusion

Corporate performance is a key focus of management within organizations. This study

investigated the relationship between knowledge management and performance of

Commercial Banks in Kenya. On the basis of the findings, the researcher inferred

some important conclusions. In regard to the first objective, knowledge conversion is

statistically significant and therefore there is a relationship between knowledge

conversion and performance. Similarly, based on the second objective, knowledge

transfer is statistically significant and thus there is a relationship between knowledge

transfer and performance. In addition, on the basis of the third objective, knowledge

application is statistically significant and hence there is a relationship between

knowledge application and performance.

Furthermore, the study sought to establish the mediating effect of human capital

repository on the relationship between knowledge management and performance of

Commercial Banks in Kenya. Based on this objective, the researcher concludes that

human capital repository partially mediates the relationship between KM and

118

performance. Finally, the study intended to determine the moderating effect of firm’s

culture on the relationship between knowledge management and performance of

Commercial Banks in Kenya. On the basis of this objective, the researcher concludes

that firm’s culture moderates the relationship between KM and performance.

5.5 Recommendations for Policy and Practice

The findings of this study have important implications for policy and practice that can

be drawn for the purpose of enhancing management of knowledge in Commercial

Banks and other organizations in Kenya.

Knowledge conversion was found to positively influence performance of Commercial

Banks in Kenya. Management of Commercials Banks should consider enhancing

practices associated with the different elements of knowledge conversion such as

externalization, combination, socialization and internalization. Particularly,

interaction with customers should be encouraged and bank’s processes should be used

to enhance understanding and translation of knowledge (explicit) into application

(tacit knowledge). Moreover, knowledge transfer was also found to positively

influence performance of Commercial Banks in Kenya. Therefore, management of

Commercial Banks should enhance all activities relating to knowledge transfer.

Information should be made more available and accessible, and its flow should be

enhanced in order to facilitate transmission of tacit knowledge. Furthermore,

knowledge application was found to positively influence performance of Commercial

Banks in Kenya. In this case, management of Commercial Banks should take

119

initiatives to pioneer and drive KM adoption and use as well as commit more financial

resources on KM training programs.

Human capital repository was found to partially mediate the relationship between KM

and performance. In any organization, human capital repository may manifest itself in

the form of the collective value of capabilities, knowledge, skills, experiences,

innovativeness of the workforce and abilities residing within and utilized by

individual employees in the course of performing organizational activities.

Management should make use of human capital repository in order to leverage on

knowledge assets and confer Commercial Banks with competitive advantage. In

addition, management should make initiatives to enhance the absorptive capacity of

employees for new ideas and value of education in generating alternative courses of

action in decision and problem situations. Moreover, managers in other knowledge-

intensive organizations should actively promote and improve KM practices to

enhance efficiency and effectiveness.

Furthermore, firm’s culture was found to moderate the link between KM and

performance. In this case, firm’s culture is an imperative in KM as it facilitates

conversion, transfer and application of knowledge. In Commercial Banks, culture

shapes behavior of employees and enables them to manage knowledge within a

context enhancing corporate performance. Management of Commercial Banks should

enhance collaboration among organizational members in development and use of new

information and ideas as well as promote all practices that foster utilization of

knowledge with respect to bank’s performance

120

5.6 Recommendations for Further Study

This study sought to investigate the relationship between KM and performance of

Commercial Banks in Kenya. It also sought to establish the mediating and moderating

role of human capital repository and firm’s culture on the effect of KM on

performance. In this case, the findings and conclusions are limited to Commercial

Banks in Kenya. The researcher utilized a self-reporting questionnaire which relies on

the honesty and accuracy of participants. In addition, the study ignored the effect of

the specific dimensions of firm’s culture and human capital repository on the

relationship between KM and performance. Furthermore, the study did not consider

other variables such as firm’s size, firm’s environment and firm’s strategy which may

as well have an effect on the relationship between KM and performance.

Future research should focus on validating the findings and conclusion of this study

by undertaking replicative researches in other organizations and sectors in Kenya. In

addition, the limitation of self-reporting questionnaire can be addressed by future

researchers through the use of objective measures of performance. Moreover, further

research should be carried out to investigate the moderating and mediating role of

other variables on the relationship between knowledge management and performance.

121

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APPENDICES

Appendix I: Letter of Introduction

Godfrey Muigai Kinyua

Kenyatta University,

School of Business,

P.O Box 19161 – 00501,

Nairobi.

12th November, 2014

Dear Sir/Madam,

RE: AUTHORITY FOR DATA COLLECTION

I am a PhD student at Kenyatta University in the School of Business undertaking a

Doctoral Thesis on “Relationship between Knowledge Management and

Performance of Commercial Banks in Kenya”

To accomplish this purpose, you have been selected to participate in this scholarly

research. I therefore kindly request you to assist me collect the data by filling in the

research questionnaire. The information that you will provide will be exclusively used

for academic purposes and will be treated with utmost confidence. A copy of the final

report will be availed to you upon request.

Your assistance will be highly appreciated.

Yours sincerely,

Godfrey M. Kinyua

147

Appendix II: Questionnaire

Section A: General Information

Instructions

Kindly tick or write in the spaces provided as appropriate.

1. Kindly indicate your gender.

Male [ ] Female [ ]

2. For how long have you worked in this bank?

3 years and below [ ]

4-7 years [ ]

8-11 years [ ]

12 years and above [ ]

3. What is your position in this bank?

Finance manager [ ]

Human resource manager [ ]

Marketing manager [ ]

ICT manager [ ]

Operations manager [ ]

Other (specify) ………………………

Section B: Knowledge Conversion

4. Please indicate your level of agreement with the statements given below.

Strongly

Disagree

Disagree Moderate Agree Strongly

agree

Socialization

Interaction with customers is

encouraged

Knowledge and experiences are shared

through interaction with employees

Knowledge and experiences are shared

through interaction with suppliers

Externalization

Organization members are able to

articulate their ideas or images, in

words, metaphors, analogies into a

readily understandable form

Organization members are able to

elicit and translate knowledge of

customers into a readily

understandable form

Organization members are able to

148

5. Do you believe knowledge conversion is important? Yes [ ] No [ ]

Kindly explain? …………………………………………………………………………………………………

………………………………………………………………………………………………..

Section C: Knowledge Transfer 6. Please indicate your level of agreement with the statements given below.

Strongly

Disagree

Disagree Moderate Agree Strongly

Agree

There is a process of

information identification

There is a process of

information evaluation

Similar mistakes are avoided

Useful information is

disseminated

There are open discussions

There is continuous capturing

of information

7. Are there open channels of information flow? Yes [ ] No [ ]

Kindly elaborate? ……………………………………………………………

elicit and translate knowledge of

experts into a readily understandable

form

Combination

Knowledge is organized and

integrated through reports

Meetings helps in integrating

knowledge

Knowledge is disseminated through

briefs

There is use of information technology

in editing or processing information

Exchange of documents helps in

integrating knowledge

Internalization

Bank’s processes enhances

understanding and translating of

knowledge (explicit) into application

(tacit knowledge) by organizational

members

There is actualization of concepts and

methods through the actual doing

There is actualization of concepts and

methods through simulations

149

Section D: Knowledge Application 8. Please indicate your level of agreement with the statements given below.

Strongly

Disagree

Disagree Moderate Agree Strongly

Agree

Bank leadership has pioneered

and driven KM adoption and

use

There is a KM training program

There are continuous

improvements as a result of KM

application.

There is a KM strategy in the

bank

KM has yielded efficient

processes

IT used in KM has supported

worker’s needs

9. Do you believe knowledge application is critical in your bank? Yes [ ] No [ ]

Kindly explain? ………………………………………………………………………………

………………………………………………………………………………………………..

Section E: Human Capital Repository 10. Please indicate your level of agreement with the statements given below.

Strongly

Disagree

Disagree Moderate Agree Strongly

Agree

Experience

Employee’s experience enhances

task performance ability

Employees experience facilitates

identification and interpretation of

change

Experience enables employees to

refine task performance skills

Experience helps employees to

analyze information

Employees experience improves

the speed performing task

Education

Education confers the employees

with skills to perform

organizational tasks

Education is important for

identification of problems

Education helps in distinguishing

symptoms from causes

Education enhances the skills for

solving problems

Education is critical for

generating alternative courses of

action

Education enables employees to

evaluate alternative courses of

action

150

Education is necessary for

matching employees skills and

positions

Innovativeness

The bank has flexible employees

Employees have capacity to

generate new ideas

Employees are able absorb new

ideas

Employees own initiatives and

creativity are encouraged

Employees are able to transform

knowledge and ideas into new

product, processes and systems

11. Are there efforts made to retain employees within organization? Yes [ ] No [ ]

Kindly explain?

…………………………………………………………….……………………………………

…………………………………………………………………………………………………..

Section F: Firm’s Culture 12. Please indicate your level of agreement with the statements given below.

Strongly

Disagree

Disagree Moderate Agree Strongly

Agree

Openness

Management frequently engage

employees in dialogue

Adequate time is committed to

communication, knowledge

exchange and learning

Management welcome and

stimulates change

Employees are involved in

important business process

Futuristic orientation

Planning is important for

developing the future

Current action affects future

results

Employees are encouraged to

identify and interpret changes in

the environment

Employees are encouraged to

adequately respond to changes in

the environment

Learning orientation

There is a conducive environment

for sharing new information and

ideas

There is collaboration in

development and use of new

information and ideas

There is commitment to learning

151

There is open-mindedness in the

bank

Adequate resources are

committed to training

13. What are the critical areas in knowledge management process that organizational culture

matters most? …………………………………………………………………………………..

Section G: Performance 14. Please indicate your level of agreement with the statements given below.

Strongly

Disagree

Disagree Moderate Agree Strongly

Agree

KM has resulted in new

products

KM increases the speed of

response to market crises

KM improves existing products

KM generates new processes

KM improves existing

processes

KM enhances customer

retention

KM generates new process

15. In your opinion, do you think knowledge management plays a key role in the performance of

your bank? Yes [ ] No [ ]

Kindly elaborate?

…………………………………………………………………………………………………

………………………………………………………………………………………………..

152

Appendix III: CFA Path

153

Appendix IV: CFA Output

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 78

Number of distinct parameters to be estimated: 27

Degrees of freedom (78 - 27): 51

Result (Default model)

Minimum was achieved

Chi-square = 637.029

Degrees of freedom = 51

Probability level = .000

Group number 1 (Group number 1 - Default model)

Estimates (Group number 1 - Default model)

Scalar Estimates (Group number 1 - Default model)

Maximum Likelihood Estimates

Regression Weights: (Group number 1 - Default model)

Estimate S.E. C.R. P Label

KnowledgeConversion <--- KnowledgeManagement .986 .122 8.076 ***

FirmCulture <--- KnowledgeManagement 1.085 .097 11.180 ***

HumanCapitalRepository <--- KnowledgeManagement .686 .108 6.351 ***

Int <--- KnowledgeConversion 1.000

Com <--- KnowledgeConversion .930 .103 8.990 ***

Soc <--- KnowledgeConversion .599 .069 8.747 ***

Ext <--- KnowledgeConversion 1.349 .124 10.861 ***

Lear <--- FirmCulture 1.000

Fut <--- FirmCulture .827 .061 13.554 ***

Ope <--- FirmCulture .392 .037 10.480 ***

Inno <--- HumanCapitalRepository 1.000

Edu <--- HumanCapitalRepository 1.845 .273 6.753 ***

Expe <--- HumanCapitalRepository .620 .133 4.649 ***

Transfer <--- KnowledgeManagement .132 .093 1.419 .156

Application <--- KnowledgeManagement 1.000

154

Variances: (Group number 1 - Default model)

Estimate S.E. C.R. P

Knowledge Management

.116 .019 6.250 ***

e13

.150 .027 5.555 ***

e14

.135 .018 7.685 ***

e15

-.007 .003 -2.068 .039

e5

.184 .021 8.818 ***

e6

.055 .007 8.042 ***

e1

.133 .022 6.186 ***

e2

.237 .031 7.559 ***

e3

.223 .037 5.958 ***

e4

.108 .014 7.667 ***

e7

-.010 .010 -.990 .322

e8

.126 .016 8.098 ***

e9

.052 .006 8.665 ***

e10

.150 .017 8.718 ***

e11

.006 .006 .961 .337

e12

.070 .008 8.735 ***

Notes for Model (Group number 1 - Default model)

Minimization History (Default model)

Iteration

Negative

eigenvalues Condition

#

Smallest

eigenvalue Diameter F NTries Ratio

0 E 7

-.444 9999.000 1689.215 0 9999.000

1 e* 6

-.349 2.417 1189.849 20 .512

2 e* 2

-.206 .890 961.061 5 .921

3 e* 1

-.986 1.046 817.640 5 .786

4 E 1

-.053 .392 721.624 5 .963

5 E 1

-1.402 .396 682.334 5 .597

6 E 0 779.917

.201 652.971 5 .804

7 E 0 1447.667

.450 641.060 1 .908

8 E 0 6688.471

.161 637.700 1 1.078

9 E 0 5825.096

.238 637.367 1 .495

10 E 0 18180.192

.045 637.045 1 .863

11 E 0 13637.898

.017 637.029 1 1.035

12 E 0 13692.776

.000 637.029 1 1.001

13 E 0 13561.486

.000 637.029 1 1.000

155

Model Fit Summary

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 27 637.029 51 .000 12.491

Saturated model 78 .000 0

Independence model 12 1651.849 66 .000 25.028

RMR, GFI

Model RMR GFI AGFI PGFI

Default model .046 .946 .459 .422

Saturated model .000 1.000

Independence model .126 .308 .182 .260

Baseline Comparisons

Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .614 .501 .634 .522 .970

Saturated model 1.000

1.000

1.000

Independence model .000 .000 .000 .000 .000

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI

Default model .773 .475 .487

Saturated model .000 .000 .000

Independence model 1.000 .000 .000

NCP

Model NCP LO 90 HI 90

Default model 586.029 508.360 671.141

Saturated model .000 .000 .000

Independence model 1585.849 1457.135 1721.943

FMIN

Model FMIN F0 LO 90 HI 90

Default model 4.110 3.781 3.280 4.330

Saturated model .000 .000 .000 .000

Independence model 10.657 10.231 9.401 11.109

156

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model .027 .254 .291 .000

Independence model .394 .377 .410 .000

AIC

Model AIC BCC BIC CAIC

Default model 691.029 695.973 773.375 800.375

Saturated model 156.000 170.282 393.889 471.889

Independence model 1675.849 1678.046 1712.447 1724.447

ECVI

Model ECVI LO 90 HI 90 MECVI

Default model 4.458 3.957 5.007 4.490

Saturated model 1.006 1.006 1.006 1.099

Independence model 10.812 9.982 11.690 10.826

HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 17 19

Independence model 9 9

Execution time summary

Minimization: .004

Miscellaneous: .449

Bootstrap: .000

Total: .453

157

Appendix V: List of Banks

BANKS Peer Group

1. Kenya Commercial Bank Ltd Large

2. Standard Chartered Bank Ltd Large

3. Barclays Bank of Kenya Ltd Large

4. Co-operative Bank of Kenya Ltd Large

5. CFC Stanbic Bank Ltd Large

6. Equity Bank Ltd Large

7. Bank of India Medium

8. Bank of Baroda Ltd Medium

9. Commercial Bank of Africa Ltd Medium

10. Prime Bank Ltd Medium

11. National Bank of Kenya Ltd Medium

12. Citibank N.A. Medium

13. Bank of Africa Kenya Ltd Medium

14. Chase Bank Ltd Medium

15. Imperial Bank Ltd Medium

16. NIC Bank Ltd Medium

17. Ecobank Ltd Medium

18. I & M Bank Ltd Medium

19. Diamond Trust Bank Kenya Ltd Medium

20. Family Bank Ltd Medium

21. Housing Finance Co. of Kenya Ltd Medium

22. Habib Bank Ltd Small

23. Oriental Commercial Bank Ltd Small

24. Habib A.G. Zurich Small

25. Middle East Bank Ltd Small

26. Dubai Bank Ltd Small

27. Consolidated Bank of Kenya Ltd Small

28. Credit Bank Ltd Small

29. Transnational Bank Ltd Small

30. African Banking Corporation Ltd Small

31. Giro Commercial Bank Ltd Small

32. Equatorial Bank Ltd Small

33. Paramount Universal Bank Ltd Small

34. Jamii Bora Bank Ltd Small

35. Fina Bank Ltd Small

36. Victoria Commercial Bank Ltd Small

37. Guardian Bank Ltd Small

38. Development Bank of Kenya Ltd Small

39. Fidelity Commercial Bank Ltd Small

40. UBA Bank Ltd Small

41. K-Rep Bank Ltd Small

42. Gulf African Bank Ltd Small

43. First Community Bank Ltd Small

Source: CBK (2012)

012)

158

Appendix VI: Document Review Guide

1. CBK Bank Supervision Annual Report

2. CBK Monthly Economic Review

3. Personnel Manuals

159

Appendix VII: Research Permit