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DEMOGRAPHIC VARIABLES AND ORGANISATIONAL FACTORS AS

PREDICTORS OF KNOWLEDGE SHARING BEHAVIOUR IN BANKS IN LAGOS,

NIGERIA

BY

TIKOLO, OLUWATIMILEHIN

This project is submitted in partial fulfilment of the requirements for the award of Bachelor of degree of

Loughborough University

Supervisor: Dr Louise Cooke (PhD.)

School of Business and Economics

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ACKNOWLEDGEMENTS My gratitude to God for his grace, guidance and mercies during the course of this study.

I am in indebted my grandmother- Mrs Johnson, my parents- Mr and Dr Mrs Tikolo, my sister- Olayide, my brother- Tobi, my friends, and my supervisor Dr Louise Cooke for their unflinching support and dedication. You were crucial to my success and sanity. This work is as much yours as it is mine. Thank you.

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ABSTRACT This study was intended to investigate the relationships between Knowledge Sharing behaviour and the organisational factors Information Technology, Trust Culture, Organisational Structure and Design, Knowledge Sharing Culture, Knowledge Hoarding Culture, Employee Interaction and demographic variables gender, age, educational qualification and organisational tenure in Nigerian banks.

Ninety-eight employees in five banks within Lagos, Nigeria participated in the study. Statistical tests were then used to analyse the data in order to identify the relationships between the aforementioned variables and knowledge sharing behaviour in Nigerian banks.

No statistically significant relationship was found between the demographic variables and Knowledge Sharing behaviour. However, all the organisational factors were found to positively influence Knowledge Sharing behaviour in Nigerian banks.

Keywords: Knowledge Sharing, Nigerian Banks, Organisational Factors, Demographic Variables.

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Table of Contents

ACKNOWLEDGEMENTS ..................................................................................................... 2

ABSTRACT ........................................................................................................................ 3

1. Introduction ................................................................................................................. 8 1.1 Problem Statement ............................................................................................................ 9 1.2 Research Objectives ........................................................................................................... 9 1.3 Research Hypothesis ........................................................................................................ 10

2. Literature Review ....................................................................................................... 12 2.1 Knowledge Management .................................................................................................. 12 2.2 Tacit and Explicit Knowledge............................................................................................. 12 2.3 Knowledge Sharing ........................................................................................................... 13 2.4 Knowledge Management in Organisations ........................................................................ 14 2.5 Knowledge Management in Banks .................................................................................... 15 2.6 Organisational Factors and Knowledge Sharing ................................................................. 16

2.6.1 Information Technology ...................................................................................................... 16 2.6.2 Trust and Motivation ........................................................................................................... 17 2.6.3 Organisational Structure and Design .................................................................................. 18 2.6.4 Knowledge Sharing Culture ................................................................................................. 19 2.6.5 Knowledge Hoarding ........................................................................................................... 19 2.6.6 Employee Interaction .......................................................................................................... 19

2.7 Demographic factors and Knowledge Sharing .................................................................... 19 2.7.1 Gender ................................................................................................................................. 20 2.7.2 Age ....................................................................................................................................... 20 2.7.3 Level of education/qualification ......................................................................................... 21 2.7.4 Organisational Tenure ......................................................................................................... 21

3. Methodology .............................................................................................................. 22 3.1 Research Design ............................................................................................................... 22 3.2 Population of the study .................................................................................................... 22

3.3 Sample and Sampling Procedure ........................................................................................... 22 3.4 Data Collection ................................................................................................................. 23 3.5 Research Instruments ....................................................................................................... 23 3.6 Validity of instruments ..................................................................................................... 24 3.7 Pilot Study ....................................................................................................................... 24 3.8 Method of Data Analysis .................................................................................................. 24 3.9 Research Limitations ........................................................................................................ 25

4.0 Data Analysis and Findings ........................................................................................ 26 4.1 Section 1 .......................................................................................................................... 26 4.2 Descriptive statistics of the Organisational Factors ............................................................ 28

4.2.1 Descriptive Statistics of Information Technology ............................................................... 28

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4.2.2 Descriptive Statistics of Trust Culture ............................................................................. 29 4.2.3 Descriptive Statistics of Organisational Structure and Design .......................................... 30 4.2.4 Descriptive Statistics of Knowledge Sharing Culture ........................................................ 31 4.2.5 Descriptive Statistics of Knowledge Hoarding culture ..................................................... 32 4.2.6 Descriptive Statistics of Employee Interaction ................................................................ 32 4.3 Means of Organisational factors ....................................................................................... 33 4.4 Cronbach’s Alpha test for Reliability ................................................................................. 34 4.5 Pearson Correlation Coefficient ........................................................................................ 35 4.6 Testing of Research Hypothesis ......................................................................................... 36

4.6.1 Hypothesis 1 ........................................................................................................................ 36 4.6.2 Hypothesis 2 ........................................................................................................................ 36 4.6.3 Hypothesis 3 ........................................................................................................................ 36 4.6.4 Hypothesis 4 ........................................................................................................................ 37 4.6.5 Hypothesis 5 ........................................................................................................................ 37 4.6.6 Hypothesis 6 ........................................................................................................................ 37

4.7 Section 1 Summary ........................................................................................................... 38 4.8 Section 2 .......................................................................................................................... 39

4.8.1 Hypothesis 7 ........................................................................................................................ 39 4.8.2 Hypothesis 8 ........................................................................................................................ 40 4.8.3 Hypothesis 9 ........................................................................................................................ 40 4.8.4 Hypothesis 10 ...................................................................................................................... 41

4.9 Mean Plots ....................................................................................................................... 42 4.9.1 Gender ................................................................................................................................. 42 4.9.2 Age ....................................................................................................................................... 43 4.9.3 Educational Qualification .................................................................................................... 44 4.9.4 Organisational Tenure ......................................................................................................... 45

4.10 Descriptive statistics of the Demographic Variables ......................................................... 45 4.10.1 Gender ........................................................................................................................ 46 4.10.2 Age ............................................................................................................................. 47

4.10.3 Educational Qualification .................................................................................................. 47 4.10.4 Organisational Tenure ....................................................................................................... 48 4.11 Section 2 Summary ............................................................................................................... 49

5. Discussion .................................................................................................................. 50 5.1 Discussion of Organisational Factors and Knowledge Sharing Behaviour ............................ 50

5.1.1 Information Technology ...................................................................................................... 50 5.1.2 Trust Culture ........................................................................................................................ 50 5.1.3 Organisational Structure and Design .................................................................................. 51 5.1.4 Knowledge Sharing Culture ................................................................................................. 51 5.1.5 Knowledge Hoarding culture ............................................................................................... 51 5.1.6 Employee Interaction .......................................................................................................... 51

5.2 Discussion of Demographic Variables and Knowledge Sharing behaviour ........................... 52 5.2.2 Gender ................................................................................................................................. 52 5.2.3 Age ....................................................................................................................................... 52 5.2.4 Educational Qualification .................................................................................................... 53 5.2.5 Organisational Tenure ......................................................................................................... 53

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6. Conclusions and Recommendations ............................................................................ 54 6.1 Implications for managers and practitioners ..................................................................... 54

7. Bibliography ............................................................................................................... 56

8. Appendix A: Questionnaire ......................................................................................... 72

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Table of Figures

Figure 1: Gender ......................................................................................................... 42 Figure 2: Age ............................................................................................................. 43 Figure 3: Educational Qualification .............................................................................. 44 Figure 4: Organisational Tenure ................................................................................... 45

Table of Tables

Table 1 Demographic Characteristics of Respondents ..................................................... 27 Table 2 Information Technology .................................................................................. 28 Table 3 Trust Culture .................................................................................................. 29 Table 4 Organisational Structure and Design ................................................................. 30 Table 5 Knowledge Sharing Culture ............................................................................. 31 Table 6 Knowledge Hoarding culture ............................................................................ 32 Table 7 Employee Interaction ....................................................................................... 32 Table 8 Means of Organisational factors ....................................................................... 33 Table 9 Cronbach’s Alpha ........................................................................................... 34 Table 10 Pearson Correlations ...................................................................................... 35 Table 11 Summary of Hypothesis ................................................................................. 38 Table 12 Gender ......................................................................................................... 39 Table 13 T-Test for Gender .......................................................................................... 39 Table 14 Age .............................................................................................................. 40 Table 15 T-Test for Age .............................................................................................. 40 Table 16 Qualification ................................................................................................. 40 Table 17 T-test for Qualification .................................................................................. 41 Table 18 Organisational Tenure .................................................................................... 42 Table 19 T-test for Organisational Tenure ..................................................................... 42 Table 20 Responses by Gender ..................................................................................... 46 Table 21 Responses by Gender 2 .................................................................................. 46 Table 22 Responses by Age ......................................................................................... 47 Table 23 Responses by Qualification ............................................................................ 47 Table 24 Responses by Organisational Tenure ............................................................... 48 Table 25 Summary of Hypothesis ................................................................................. 49

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1. Introduction This chapter is an overall introduction to the study and provides the reader with an

understanding of the context for the research. Furthermore, it provides information on why

there is need for the research and what the research objectives are.

Economies in the world today now place a high value on knowledge (Mogotsi, Boon &

Fletcher, 2011). Firms that efficiently manage knowledge appear to be more successful than

those that do not (Tiwana, 2000). Knowledge has been found to be an important resource,

which can elevate performance (Grant, 1996). Effective Knowledge Management within

organisations has been found by scholars such as Nonaka & Takeuchi (1991) to be an integral

aspect of successful firms.

The importance of Knowledge Management (KM) is increasingly recognised in the banking

sector. This is largely because financial institutions deal with information on a large scale.

Much of the work of banks is concerned with the elaboration of information and knowledge

on customers, society, markets, businesses, law and the environment. Nigerian banks operate

within a saturated banking sector consisting of twenty commercial banks with just less than

six thousand branches nationwide (Sanusi, 2012). Within this environment, effective

Knowledge Management and share is not only imperative but could also be a means to

sustained competitive advantage (Kalling & Styhre; 2003). Wiig (2006) further suggests that

Knowledge Management could improve service quality and delivery.

Knowledge Sharing is a key facet of Knowledge Management. Knowledge Sharing is an

activity that involves the exchange of knowledge between individuals and facilitates

organisational learning (Wiig, 2006); it enables firms learn from past errors and seize new

opportunities (Mogotsi, Boon & Fletcher, 2011). Knowledge Sharing between employees

however, is a difficult activity (Lee & Al-Hawamdeh, 2002) as employees are generally

reluctant to share knowledge (Chiu, Hsu & Wang, 2006). Firms have realised the importance

of Knowledge Sharing and as such have begun to invest large amounts of time and money

into Knowledge Management systems with the aim of improving Knowledge Sharing

between employees and organisational competitiveness (French, 2010; Cummings 2003; Lin,

2007, Yusof et al. (2012)). However, Knowledge Management systems could fail if the

individual, organisational and cultural factors, which influence Knowledge Sharing are

misunderstood (Carter, 2001)

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This study aims to increase the understanding of the influence demographic variables and

organisational factors have on Knowledge Sharing behaviour within Nigerian banks. In

addition, the study focuses on the Knowledge Sharing behaviour of employees and attempts

to understand the factors, which influence it. Finally, it is hoped that the research findings

will provide information that would be useful in helping Nigerian banks deepen their

understanding of Knowledge Sharing behaviour and improve their processes.

1.1 Problem Statement Whilst the amount of literature on the variables affecting Knowledge Sharing behaviour is on

the rise (e.g. Bock & Young-Gul, 2002; Cummings & Bing-Sheng, 2003), literature that

specifically examines the relationship between demographic variables is still scarce (Mogotsi,

Boon & Fletcher; 2011). Literature that explores the influence of both demographic variables

and organisational factors on Knowledge Sharing is virtually non-existent. In addition, a

majority of the Knowledge Sharing studies tend to focus on developed countries with little

attention being paid to the developing nations (Mogotsi, Boon & Fletcher; 2011). Based on

the review of literature conducted, previous research has shown that there is a mixed

relationship between Knowledge Sharing behaviour and demographic variables and a more

definitive influence of organisational factors on Knowledge Sharing. It is hoped that this

study will identify definitive relationship between demographic variables and organisational

factors on Knowledge Sharing behaviour within Nigerian banks.

1.2 Research Objectives

This study aims to investigate the influence of demographic variables (gender, age, level of

education and organisational tenure) and organisational factors (information technology, trust

culture, organisational culture & design, Knowledge Sharing culture, Knowledge Hoarding

culture and employee interaction) on Knowledge Sharing behaviour in the banking sector in

Nigeria. Specifically, the study aims to achieve the following objectives:

• To identify and investigate the relationship between organisational factors and

Knowledge Sharing behaviour in Nigerian banks

• To examine the relationship between demographic variables and Knowledge Sharing

behaviour in Nigerian banks

• To recommend sustainable enhancements to the current Knowledge Sharing

techniques employed Nigerian banks.

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1.3 Research Hypothesis The following hypotheses would guide this study

Hypothesis 1

H0: Information Technology does not have a significant relationship with Knowledge Sharing

Hypothesis 2

H0: Trust culture does not have a significant relationship with Knowledge Sharing

Hypothesis 3

H0: Organisational structure and design does not have a significant relationship with

Knowledge Sharing

Hypothesis 4

H0: Knowledge Sharing culture does not have a significant relationship with Knowledge

Sharing

Hypothesis 5

H0: Knowledge Hoarding culture does not have a significant relationship with Knowledge

Sharing

Hypothesis 6

H0: Employee interaction does not have a significant relationship with Knowledge Sharing

Hypothesis 7

H0: There is no significant relationship between Gender and Knowledge Sharing behaviour.

Hypothesis 8

H0: Age is not significantly related to Knowledge Sharing behaviour.

Hypothesis 9

H0: Educational Qualification is not significantly related to Knowledge Sharing behaviour.

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Hypothesis 10

H0: There is no significant relationship between Organisational Tenure and Knowledge

Sharing behaviour.

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2. Literature Review This chapter critically examines relevant academic books, journals, articles and websites

concerning the concept of Knowledge Management with a focus on Knowledge Sharing as

well as the influence of demographic variables on Knowledge Sharing behaviour within

organisations.

2.1 Knowledge Management Knowledge is an essential resource that provides a sustainable advantage in a dynamic

economy (French, 2010; Davenport & Prusak, 1998). Knowledge Management is defined by

Davenport (1994) as ‘the process of capturing, distributing, and effectively using knowledge.’

It is also defined by Petrash (1996) as the process of getting the right information in front of

the right people at the right time. Knowledge Management thus involves the capture, creation,

sharing and dissemination of knowledge by organisations with the use of technology,

organizational culture and structure to enhance performance (Argote, 1999; Huber, 1991;

Cummings, 2003; Jashapara, 2011).

Knowledge Management is intended to add value to information that currently exists within a

firm, essentially making it a strategic tool within the organisation (Jayasundara, 2008). As

such, it is important for organisations to take more proactive rather than reactive measures in

order to reap the benefits of Knowledge Management (Gupta, 2013).

2.2 Tacit and Explicit Knowledge The distinction between the 2 facets of knowledge – tacit and explicit (Polanyi, 1966) has

been greatly examined in literature. Tacit Knowledge (‘Know-How’) is knowledge embedded

in the human mind through experience and is communicated personally through scenarios

and dialogue. It can be codified and is specific to an individual (Llopis-corcoles, 2011; Lam,

2000; Awad & Ghaziri; 2004, Davenport & Prusak, 1998; French, 2010). Explicit knowledge

on the other hand, is knowledge which is codified and digitized in books, documents reports

etc., it can be easily retrieved and transmitted more easily than tacit knowledge. In addition, it

is formal and systematic (Llopis-corcoles, 2011; Lam, 2000; Awad & Ghaziri; 2004,

Davenport & Prusak, 1998; Nonaka & Takeuchi; 1995).

Tacit Knowledge and skills obtained through experience are difficult to communicate. In

literature however, there is a greater value placed on this type of knowledge over explicit

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knowledge, even though explicit knowledge is easier to adopt and transfer (Kalling & Styhre,

2003; Coakes, 2003; Lam, 2000; Llopis-corcoles, 2011). Finally, tacit knowledge also

requires a high level of personal contact and trust in order to be effectively shared (French,

2010).

Sharing of codified (explicit) knowledge occurs throughout the workplace. Matthews &

Shulman (2005) argue however, that more insightful Knowledge Sharing takes place via

working relationships. This is a point of view shared by Castenada (2000), as he came to the

conclusion that interpersonal relationships are vital to understanding and exploring the

aspects of Knowledge Sharing. He goes on to suggest that the frequency and nature of

interactions between individuals within an organization would affect an individual’s

willingness to share his expert knowledge. Relationships however, are not absolute

guarantees of Knowledge Sharing and can sometimes be problematic (Matthews & Shulman,

2005).

2.3 Knowledge Sharing Knowledge Sharing may be viewed as the base for Knowledge Management (Mobashar et al.,

2010). In order for an organisation to remain competitive, it needs to share knowledge

effectively between employees (Tobin, 1998). It is therefore imperative for organisations to

understand how to transfer knowledge between employees within the organisation (French,

2010; Davenport & Prusak, 1998). Knowledge Sharing is the exchange of knowledge

amongst individuals and organisations (Jashapara, 2011). It is also the means by which an

organisation obtains access to its own and another organisations’ knowledge Cummings

(2003). The strategic capability of Knowledge Sharing is manifested in various organisational

practices such as meetings, joint work and other activities, which are aimed at sharing an idea,

insight or know-how (Paulin & Suneson, 2010; Huysman & Wit, 2002).

Knowledge is shared with the purpose of plugging the gaps that exist due to the mobility of

employees and to create new ideas. Knowledge Sharing between employees and teams is

crucial as it helps develop the intellectual capital of an organisation (French, 2010). In order

for Knowledge Sharing to be successful, it is important for management and staff to see it is

as a process that is beneficial to all parties involved (Huysman & Wit, 2002; Teece, 2007).

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Davenport and Prusak (1998) identified factors such as sharing-hostile organisational cultures,

insufficient organisational cultures and departmental segregation as hindrances for successful

Knowledge Sharing. In addition, Lin (2008) suggests that the complexity of an organizational

structure is negatively correlated with Knowledge Sharing. In contrast, a flat organisational

structure with less of a hierarchy and one, which encourages a trust culture, has been found to

enable effective Knowledge Sharing within organisations (French, 2010)

2.4 Knowledge Management in Organisations In 1991, Ikujiro Nonaka wrote ‘The Knowledge-Creating Company”. In the article, it was

argued that successful companies in the economy today are those that consistently create new

knowledge, disseminate it, and embody it in new technologies and products (Nonaka; 1991).

For years, multinationals such as Sharp, Canon, Chevron, Xerox, Toyota and Honda

(DeSouza and Paquette; 2011) have been able to respond quickly to customers’ needs,

develop new products and dominate their respective industries. The secret behind their

sustained success according to Nonaka (1991) is their ability to effectively manage

knowledge.

A large amount of attention by scholars has been paid towards highlighting the importance of

knowledge as a resource, which enhances competitive advantage (Llopis-corcoles, 2011;

Jashapara, 2011). As a strategic resource, knowledge is said to be scarce, valuable and

difficult to imitate (Llopis-corcoles, 2011). Davenport & Prusak (1998) share this view. They

write; ‘...by the time competitors match the quality of a market leader’s current product or

service, the knowledge rich leader will have moved unto a new level of creativity, quality or

efficiency’. Other firms may over time be able to replicate a product, but the knowledge

gained in the period other firms would be able to replicate the product; could still keep an

organisation ahead of its competitors (Kalling & Styhre; 2003). The knowledge advantage is

sustainable because it generates increasing returns and continuing advantage (Kalling &

Styhre; 2003). Knowledge is however, likely to have a limited impact on organizational

effectiveness unless individual knowledge is shared with other individuals and the group

(Akbar & Kalam, 2012)

Knowledge Management is intended to add value to information that currently exists within a

firm, essentially making it a strategic tool within the organisation (Jayasundara, 2008).

According to Davenport & Prusak (1998) and Jashapara (2011) knowledge grows through

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transfer and exchange and is a unique organisational asset because it increases with use

unlike material assets.

2.5 Knowledge Management in Banks Banking is a business of information; not just one of money (Lamb, 2001). It is equally as

important to manage knowledge effectively within banks as it is in any other organisation

(Chatzoglou & Vraimaki, 2009). Bankers work in high-pressure environments and usually

have to take risks, which hold large financial implications. Knowledge Management would

be particularly useful within the banking sector because it could facilitate effective decision-

making (Castenada, 2000), and reduce inefficiencies Jain (2013). Effective Knowledge

Management within banks has also been found to improve customer service quality (Collins,

2000), innovation and profits (Sieminiuch & Sinclair, 2004). Gupta & Govindarajan (2000)

additionally suggest that effective Knowledge Management within the banking sector may

enhance dynamic learning and strategic planning. Through knowledge, banks have become

more customer-centric and innovative (Dutt, 2013). They are now able to price products more

competitively as well as attract new customers (Dutt, 2013).

Knowledge Management in banking strives to capture knowledge and experience of both

customers and employees (Mohsen, Ali & Jalal, 2011). In addition, it simplifies the flow of

information through an organisation (Jain, 2013; Bos, 2000). Of recent, banks have come to

recognise the importance of Knowledge Management in their practices (Cabrera & Cabrera,

2005). Some modern banks have personnel whose primary duties are to effectively manage

the stages in the Knowledge Management cycle (Jain, 2013). These banks have also

increased spending on Knowledge Management systems such as data warehouses and

decision support systems (Jayasundara, 2008; Argote, 1999). The knowledge being analysed

may range from the intellectual capital of the bank to information regarding consumer

transactions (Jayasundara, 2008)

Banks attempt to recruit the most adept individuals within the industry (Akbar & Kalam,

2012). However, this is not enough. They implement processes, systems and infrastructure

that are designed to derive maximum output from these individuals (Cabrera & Cabrera,

2005). In addition, banks attempt to efficiently spread knowledge organisation wide (Subudhi,

2013).

Subudhi (2013) outlines 5 measures used by banks for managing knowledge within their

organisation. These measures are listed below:

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• Induction Training for new hires: Banks organise an induction day programme for

new hires informing them of the culture, history and goals of the bank.

• Employee Training: Banks provide both internal and external training for staff to

improve their competency with the banks processes.

• Mentoring: A more experienced employee acts as a guardian to a new recruit for a

specific period.

• Review meetings: Periodic meetings are held to review individual and departmental

progress and achievements in line with goals set

• Knowledge portals and Intranet: Banks implement knowledge portals and intranets to

encourage communication between employees and also archive policy documents and

manuals.

In addition, Jayasundara (2008) identified the key areas for the deployment of knowledge in

banks. These areas include; customer relationship management, risk management and

performance evaluation.

2.6 Organisational Factors and Knowledge Sharing There is a considerable amount of theory in literature that investigates the factors, which

affect Knowledge Sharing (Castenada, 2000). However, according to Klein & Kozlowski

(2000) there is still a lot of potential for empirical research to be undertaken in the field.

Additionally, seeing as Knowledge Sharing is a relatively recent concept, there currently

exists no universal scale to help in measuring it (French, 2010; Coakes, 2003).

Information technology, organisational culture, trust, and organisational structure have been

identified as the major influencers of Knowledge Sharing in organisations (Spender, 1996;

Riege, 2005, French, 2010, Cross & Cummings, 2004).

2.6.1 Information Technology Technology plays an integral role in supporting the sharing of explicit knowledge (Coakes,

2002; Jashapara, 2008; Teece, 2007; Frappaolo, 2002). Information and Technology (IT)

serves to make knowledge accessible as at when needed (Hislop, 2005). It also reduces the

dependence on other traditional forms of sharing knowledge such as face to face and the

production of reports (Jayasundara, 2008) and can enhance Knowledge Sharing by lowering

temporal and spatial barriers between knowledge workers, and improving access to

information about knowledge (Hendriks, 1999; Huysman & Wit, 2002).

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Technology is a facilitator of communication and can enhance Knowledge Sharing (Kalling

& Sthyre, 2003). Information Technology is believed to have a positive impact on

Knowledge Sharing because it raises the performance levels of firms and increases the rate of

Knowledge Sharing (Davenport & Prusak, 1998; Kermally, 2002). Davenport & Prusak

(1998); Choi, Lee & Yoo (2010); Rasula, Vuksic & Stemberger (2012) and Eid & Nuhu

(2011) all found a positive relationship between Information Technology and Knowledge

Sharing.

Although Information Technology can help facilitate the disposition to share, it does not

remove the willingness and effort requirements on the path of the individual to effectively

share knowledge (Awad & Ghaziri, 2004). Information technology should therefore not be

looked at as all-in-one solution to Knowledge Sharing within firms (Awad & Ghaziri, 2004;

Coakes, 2003). Instead, it should merely be seen as a Knowledge Sharing enabler (DeSouza

& Paquette, 2011). The creation of Knowledge Management and sharing initiatives does not

necessarily mean that individuals would participate in the process of Knowledge Sharing

either (Jashapara, 2008). Individuals often find barriers to engage in Knowledge Sharing

initiatives, such as lack of time or lack of trust amongst employees (Llopis-corcoles, 2011;

Huysman & Wit, 2002; Davenport & Prusak, 1998; Awad & Ghaziri, 2004).

Although the Knowledge Sharing is more likely to occur through face-to-face interaction than

technology (Kalling & Sthyre, 2003), technology allows for knowledge to shared amongst

employees who due to reasons such as distance may be unable to take part in socialisation

(Coakes, 2002).

In summary, sharing knowledge is a collective activity rather than an individual one

(Huysman & Wit, 2002). It would only occur in situations where individuals benefit from

sharing it and ICT could only support not replace it (Lam, 2000).

2.6.2 Trust and Motivation Numerous authors have outlined the importance of Trust with regards to Knowledge Sharing

(Jonsson, 2008). Trust plays a significant role in Knowledge Sharing and the willingness to

share knowledge (Davenport & Prusak, 1998; Kermally, 2002; Coates, 2003). Kankanhalli et

al. (2005) propound that the degree of trust has an impact on collaborative efficiency in a

firm (Hung & Chuang, 2009).

An impartial reward structure has been found to motivate employees and increase willingness

to share knowledge (Argote, 1999). It has also been found to reinforce an organisation’s trust

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culture (French, 2010). A large number of authors have approached Knowledge Sharing from

a motivational perspective (Riege, 2005; Cabrera & Cabrera, 2005; Cabrera, Collins &

Salgado, 2006; Constant et al., 1994; Davenport & Prusak, 1998; Cross & Cummings, 2004;

Swift, Balkin & Matusik, 2010; Llopis-corcoles, 2011). Most of the research done in this

field has attempted to analyze the influence of extrinsic and intrinsic motivation on employee

willingness to share knowledge, yielding ambivalent results (Llopis-corcoles, 2011; Hung &

Chuang, 2011).

Financial rewards have been generally found to have only a short-term effect on employee

willingness to share (Frappaolo, 2002; Huysman & Wit, 2002) and hence, are less motivating

than non-financial rewards Vuori and Okkonen (2012). Hendriks (1999) argues that the

motivation of individuals to share their knowledge with other individuals is a major

influencing factor and thus is of critical concern. Once people are unwilling to share their

knowledge with others in a firm, a feeling of distrust is developed and knowledge gaps would

be created (Mobashar et al., 2010).

As the trust level between individuals rise, the willingness to share and the benefits of sharing

knowledge increases (Coakes, 2003). There is a large amount of research that has been

undertaken to explore the relationship between a trusting climate and Knowledge Sharing

(Hooff & Huysman, 2009; Chiu et al 2006; Renzl, Matzler & Mader, 2005; Hung and

Chuang, 2011; Casmiri, Lee & Loon 2012; Holste & Fields 2010; Levin, Cross & Abrams

2002 and Rhodes et al. 2008). In these studies it was found that a culture of trust is critical for

Knowledge Sharing, however it may be difficult to create (Brink & Van Belle, 2003)

2.6.3 Organisational Structure and Design The structure and design of an organisation may influence the Knowledge Sharing behaviour

of its employees (French, 2010). In a dynamic and non-hierarchical environment, employees

are likely to be more willing to share knowledge (Argote, 1999, Cabrera & Cabrera, 2005).

Top-down hierarchical structures stifle creativity and innovation thus preventing Knowledge

Sharing and creation (Bourdreau & Couillard, 1999). Geographic locations of departments

can also influence Knowledge Sharing by either making it more difficult or easier to share

knowledge with interested parties (Brink & Van Belle, 2003). In studies by Bhatt (2001),

Momeni et al. (2013), Rhodes et al. (2008) it was found that a flexible organizational

structure, which revolves around self-managed teams and teamwork, could enhance

Knowledge Sharing behaviour.

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2.6.4 Knowledge Sharing Culture A willingness by employees and an organisation to learn could significantly influence

Knowledge Sharing (Kermally, 2002). Additionally, Organisational Learning, which is the

process by which an organisation attains new knowledge and promotes a culture of openness,

could also impact Knowledge Sharing (Kalling, 2003, Coakes, 2003). It is important for an

organisation to have an open, sharing positive culture, which unifies departments and

prevents them from developing their independent culture (Brink & Van Bell, 2006; Kermally

2002; Huber, 1991). In summary, Knowledge Sharing would not occur in organisations

unless the culture supports it (Awad & Ghaziri, 2004). Therefore, a favourable and open

culture within organisations is imperative in achieving effective Knowledge Sharing (Akbar

& Kalam, 2012).

2.6.5 Knowledge Hoarding Knowledge Sharing is not a natural activity. Indeed, it has been found that people have a

natural tendency to hoard knowledge (Brink & Van Bell, 2006; Davenport & Prusak, 1998).

Employees according to Brink & Van Bell (2006) and Harris & Bair (1998) would rather

retain their knowledge for their own career and personal benefits than to share it with others.

It is thus imperative for organisations to rid their culture and environment of the belief that

knowledge is power in order to promote the sharing of knowledge by employees (Hislop,

2005; Huysman & Wit, 2002).

2.6.6 Employee Interaction Knowledge is shared when individuals interact with one another (Frappaolo, 2002). The more

frequently employees interact with another, the higher the chances of knowledge being

shared are (Coakes, 2003; Castenada, 2000). It is therefore vital for organisations to ensure

the knowledge transferred during these interactions is harnessed and exploited (Castenada,

2000). Chua (2002) and Harzing & Noordhaven (2009) found a high level of employee

interaction to influence Knowledge Sharing.

2.7 Demographic factors and Knowledge Sharing The effect job related factors have on Knowledge Sharing behaviour has been widely

examined in literature. However, the number of studies that analyse the effect of

demographic variables on Knowledge Sharing is still small (Pangil & Nasurdin, 2008;

Rashman & Hartley, 2008). Generally, the results of these studies have been inconclusive

(e.g. Yusof et al., 2012; Pangil & Nasurdin, 2008; Azudin, Ismail & Taherali; 2009; Mogotsi,

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Boon & Fletcher, 2011). The study of the demographic factors and Knowledge Sharing

quality among Malaysian government officers by Ismail & Yusof, (2009) concluded that

there was no significant impact of the demographic factors on Knowledge Sharing quality

among public officers in central agencies in Malaysia. In a similar study by Kathiravelu,

Mansor & Kenny, (2013) that aimed to explore the impact of demographic profiles on

Knowledge Sharing behaviour amongst public sector employees in Malaysia, it was also

concluded that there is no significant impact between employee demographic profiles and

Knowledge Sharing behaviour. However, a study by Teh & Yong (2011) suggests that

demographic factors have an impact on Knowledge Sharing between employees. The

demographic factors that have widely been explored by researchers include age, gender, level

of qualification, and organisational tenure. Kathiravelu, Mansor & Kenny (2013)

2.7.1 Gender Gender appears to have an ambiguous influence over an individual’s Knowledge Sharing

behaviour. Yusof et al. (2012) outline the fact that previous studies (Ojha, 2005; Chowdhury,

2005; Watson & Hewett, 2006) on the relationship between gender and Knowledge Sharing

behaviour have yielded generally inconclusive results. Whilst a study by Lin (2006) suggests

women are more willing to share knowledge because they are more receptive of instrumental

ties and need to overcome occupational challenges (Yusof et al. (2012). In a separate study

by Karakowsky & Miller (2005) male and females appeared to vary in their efforts to share

knowledge. Finally, a study by Mogotsi, Boon & Fletcher (2011) proposes that there is no

significant relationship between Knowledge Sharing behaviour and gender.

2.7.2 Age Individuals seemingly may be more willing to share knowledge with people in their age

group than others that are considerably younger or older (Riege, 2005). Riege (2005)

suggests that age could potentially have an effect on Knowledge Sharing behaviour. However,

he fails to suggest how this may act as a barrier to Knowledge Sharing (Mogotsi, Boon &

Fletcher; 2011). His claims are nonetheless supported by findings in a study by Keyes (2008)

who found a more definitive relationship between age and Knowledge Sharing. A study by

Watson & Hewett (2006) however contradicts this claim by arguing that age does not affect

Knowledge Sharing and these findings are backed up with research by Mogotsi, Boon &

Fletcher (2011) who also reported a similar outcome. From these studies, it can hence be

concluded that age group has an inconclusive impact on Knowledge Sharing.

21

2.7.3 Level of education/qualification Educational level is another demographic variable that has been found to have an ambiguous

influence over Knowledge Sharing behaviour. Yusof et al. (2012) suggest that level of

education is positively correlated with Knowledge Sharing. That is; the lower the education

level, the less likely an individual is to share knowledge and vice versa. Riege (2005) also

claims that the level of education/qualification of an individual could have a fundamental

impact on the individual’s willingness to share knowledge. However, in a separate study by

Abili, Thani & Mokhtarian (2011) educational level is found to not have an effect on

Knowledge Sharing.

2.7.4 Organisational Tenure Organisational tenure is another demographic variable studied in literature alongside gender,

age and level of education. Yusof et al. (2012) argue that research on the relationship

between Knowledge Sharing behaviour and organisational tenure has been inconclusive.

They go further to discuss that although a study by (Ojha, 2005) concluded that

organisational tenure had a negative effect on Knowledge Sharing behaviour, the study by

Watson & Hewett (2006) contends that organisational tenure does in fact have a positive

significant relationship with Knowledge Sharing behaviour. In addition, Pangil & Nasurdin

(2008) argue that organisational tenure has a positive significant impact on Knowledge

Sharing behaviour because an individual would feel more indebted to an organisation the

longer he or she works for the organisation and as such would be more willing to share

knowledge in order to ensure the organisation benefits from the shared knowledge.

In conclusion, the concepts of Knowledge Management in banking and Knowledge Sharing

have been explored via the literature review. The review also identified the benefits of

Knowledge Sharing and the factors, which have been found to influence it. Information

Technology, Trust culture, Organisational structure and design, Knowledge Sharing culture,

Knowledge Hoarding culture and Employee interaction were identified as the organisational

factors which affect Knowledge Sharing behaviour whilst Gender, Age, Educational

Qualification and Organisational Tenure were identified as the demographic variables which

affect Knowledge Sharing behaviour. These factors were then discussed and the importance

for organisations to efficiently manage knowledge was highlighted.

22

3. Methodology

This chapter presents the approach, design and methods used to address the research problem

as outlined.

3.1 Research Design This study was conducted using survey (descriptive) research. Survey research involves the

collection of information from individuals via their responses to questions (Sapsford, 2006).

In addition survey research is useful in obtaining information from a wide group of

individuals (Bryman & Bell, 2011). Finally, survey research is time efficient and encourages

generalizability of results (Sapsford, 2006).

3.2 Population of the study There were twenty commercial banks in operation in Lagos, Nigeria (Central Bank of Nigeria,

2014) as at the time data for the study was collected (December 2013). These twenty banks

could further be split into first-generation and second-generation banks (Central Bank of

Nigeria, 2014). In 2005, the governor of the Central Bank announced that the minimum

capital requirement for banks in Nigeria had been increased to Twenty-Five Billion Naira.

Banks that failed to generate these funds were either ordered to halt operations or merge with

other banks in order to attain the minimum amount required (Articles, 2013). First-generation

banks thus are those, which were in operation prior to the 2005 consolidation of banks whilst

second-generation banks are those that were created as a result of the mandatory merger.

These constituted the population of this study. There are nine first-generation and eleven

second-generation banks in Nigeria.

3.3 Sample and Sampling Procedure There are generally two categories of sampling methods: probability sampling and non-

probability sampling (Bergman, 2008). Probability sampling is centred on the ideology of

random selection. Thus in probability sampling, every element of the population has a

known chance of being selected (Gravetter & Forzano, 2008). In a non-probability sample

however, the chance of an element being included in the survey is unknown (Bergman, 2008).

The stratified random sampling technique was used to select banks from two strata. This form

of sampling involves dividing the population into two or more segments based on variables of

interest and then drawing a sample from each subset (Cochran, 2007). Firstly, a list of the

commercial banks in Lagos, Nigeria was retrieved from the website of the Central Bank

23

(Central Bank of Nigeria, 2014). The banks were then divided into two groups. The

population was stratified by generation, first and second-generation banks. A total of two

first-generation and three second-generation banks constituted the sample, resulting in five

banks in all. The banks were broadly representative of the commercial banks in Lagos State.

With the assistance of senior members of staff, Twenty-five bankers were selected at random

from each bank to complete the Knowledge Sharing Questionnaire. Out of the One hundred

and twenty five questionnaires administered, ninety-eight were returned completed (78.4%

response rate).

3.4 Data Collection After the population was decided upon and the sample drawn, the next step was to collect

data for the study. To this regard, a self-administered questionnaire was adopted for use in

this study. Initially however, online questionnaires were used for the study. These were

initially selected because they allow for a large number of responses to be received without

the issue of distance or time (Ilieva et al., 2002). In addition, data analysis is an automated

process whereby respondent feedback is input themselves and is automatically stored

electronically which makes the process of analysis easier and less time consuming (Bryman

& Bell, 2011). These online questionnaires may nonetheless attract low response rates

because users may either not have Internet access or ignore requests asking them to

participate in the survey (Gravetter & Forzano, 2008). The questionnaire was launched online

for a two-week period; however the response rate (11%) was considerably below what was

desired. To this extent, self-administered questionnaires were then adopted for use because a

large number of respondents could still be reached (Bryman & Bell, 2011) and these

respondents were then required to complete these questionnaires within a limited amount of

time, which greatly increased the response rate. This however, meant that the responses had

to be manually input into a data analysis program. In order to undertake the study, permission

was sought from managers within the banks to approach employees in their respective banks

to participate in the study. Finally, each questionnaire was accompanied by a covering letter

requesting the voluntary participation from employees for the study. Additionally,

participants were told they could withdraw from the study anytime they wished.

3.5 Research Instruments After reviewing the relevant literature and consulting academics, the Knowledge Sharing

Behaviour of Employees within Nigerian Banks Questionnaire (KSBENQ) was developed.

24

Knowledge Sharing Behaviour of Employees within Nigerian Banks Questionnaire

(KSBENQ)

This is a 41-item, two-part, self-administered, closed-ended (Sekaran, 2006) questionnaire.

The first part focuses on the demographic characteristics of the respondents such as gender,

age, educational qualification, and organisational tenure. The second part comprised of likert

styled statements and focuses on the organisational factors and their influence on Knowledge

Sharing. A likert scale is a measure of attitudes and is used in rating disagreement or

agreement to statements by respondents (Monette, Sullivan & DeJong, 2013). The

organisational factors measured were; information technology, trust culture, organisational

culture & design, Knowledge Sharing culture, Knowledge Hoarding culture and employee

interaction. The influence demographic variables and organisational factors have on

Knowledge Sharing was then tested.

3.6 Validity of instruments A review of literature was conducted and the questionnaire was developed to cover the

known content represented in literature, based on previous research. To further strengthen the

content validity, existing questionnaires that focused on relevant content areas were

referenced. Thereafter, the KSBENQ was subjected to criticisms from colleagues and

academics.

3.7 Pilot Study After being tested for its validity, the KSBENQ was then pilot tested. A pilot study is a small

experiment intended to test logistics prior to a larger study being carried out (NC3Rs, 2006).

A major advantage of undertaking a pilot study is that it might give warnings about the

shortfalls of proposed instruments (Bryman and Bell, 2011). Copies of the KSBENQ were

delivered to respondents in the sampled banks. The results obtained were not included in the

main study.

3.8 Method of Data Analysis Descriptive analysis was used to define and describe the demographics of the respondents.

Mean, frequencies and standard deviations were used to understand and analyse the responses

to individual questions and sections. The questionnaire was tested using Cronbach’s Alpha

for reliability in order to ensure it measured what it was intended to. After this, Pearson’s

correlation coefficient and ANOVA were used to explore the influence of the demographic

25

variables and organisational factors on Knowledge Sharing. Finally, mean plots were used to

define the variation in responses between different demographic groups.

3.9 Research Limitations This study focuses on the Knowledge Sharing behaviour of employees within five banks

(large-sized enterprises) in a developing nation. By analysing this population, insights into

the following are not or scarcely taken into account:

• Knowledge Sharing behaviour of employees within Small and Medium-sized

Enterprises (SMEs).

• Knowledge Sharing behaviour of employees in organisations within developed

nations and other cultural settings.

• Banks are investigated therefore findings may not apply to firms within other

industries.

26

4.0 Data Analysis and Findings

This chapter focuses on data analysis and findings of the study. This chapter is divided into

two sections in order to adequately analyse the influence of the demographic and

organizational factors on Knowledge Sharing behaviour of employees in Nigerian banks.

Section 1 examines the influence of the organizational factors on Knowledge Sharing

behaviour, whilst section 2 explores the between the demographic factors and Knowledge

Sharing behaviour.

A total of 5 banks (24% of the total population) were involved in the survey, with 73% of the

bankers in the sampled population (98 out of a possible 135) returning the completed

questionnaire. The main reason accounting for this is the fact that, not all the banks had up to

27 employees available as the survey was carried out at a few days prior to the turn of the

New Year. In such cases, all the bankers present were surveyed. The data for this study was

thus analyzed to answer the research questions.

4.1 Section 1 This section examines the influence of the organizational factors on Knowledge Sharing

behaviour in Nigerian banks

Table 1 gives a general overview of the demographic characteristics of the participants in the

survey. There were considerably more male participants (58.2%) in the survey than female

(41.8%). Further analysis shows that a majority of the bankers (57.1%) were aged between 26

and 34years.

Additionally, the table shows that a majority of the bankers (74.5%) were professionally

qualified, holding a higher national diploma (HND), bachelor’s degree, a postgraduate degree

or doctorate (Ph.D.)

Finally, the bulk of the respondents (48%) had worked between one and five years for their

current employer. Conversely, only (3.1%) of the respondents had worked for between

eleven and fifteen years for the same employer.

27

Table 1 Demographic Characteristics of Respondents

Key:

• Gender

• Age

• EQ: Educational Qualification

• OT: Organisational Tenure

Demographic Characteristics of Respondents

Frequency Percentage

Gender Male 57 58.2%

Female 41 41.8%

Total 98 100%

Frequency Percentage

Age 18-25 24 24.5%

26-34 56 57.1%

35-44 18 18.4%

Total 98 100%

Frequency Percentage

EQ Professional 73 74.5%

Non-Professional 25 25.5%

Total 98 100%

Frequency Percentage

<1 Year 26 26.5%

OT 1-5 Years 47 48%

6-10 Years 22 22.4%

11-15 Years 3 3.1%

Total 98 100%

28

4.2 Descriptive statistics of the Organisational Factors The tables below contain the means and standard deviations for each organisational factor

that affects Knowledge Sharing.

Key:

• N: Number

• SD: Standard Deviation

(Note: Unless stated otherwise, the Likert scale used for questions was split into five points

ranging from Strongly Disagree (1) - Strongly agree (5) and was not reverse coded. Whereby

a question is reverse coded 1 represents Strongly Agree whilst 5 represents Strongly Disagree

4.2.1 Descriptive Statistics of Information Technology Table 2 Information Technology

Information Technology

N Mean SD

There is instant technical support which improves

knowledge and communication flow

98 3.53 1.017

The hardware and software available meets my

requirements

98 3.66 .930

I am reluctant to use IT tools because I am unfamiliar

with them (Reverse Coded)

98 3.81 1.118

There is adequate training provided to help familiarize

me with new IT systems & processes

98 3.35 1.036

The advantages of new systems and processes over

existing ones are clearly made known

98 3.61 .881

The introduction of a bank wide social network would

increase the likelihood of my sharing knowledge

98 3.79 .933

29

From table 2 above, it can be seen that all questions had positive responses. That is, there was

a disposition towards information technology. All the questions experienced means that were

higher than average. The highest scoring question was “The introduction of a bank wide

social network would increase the likelihood of my sharing knowledge” whilst “There is

adequate training provided to help familiarize me with new IT systems & processes” scored

the lowest.

4.2.2 Descriptive Statistics of Trust Culture Table 3 Trust Culture

Trust Culture

N Mean SD

I trust my colleagues not to misuse or take unjust

information for knowledge shared

98 3.24 .942

I trust that knowledge shared by colleagues is accurate

and credible

98 3.52 .828

I would not get the recognition I deserve by sharing

knowledge (Reverse coded)

98 3.46 1.220

The bank is tolerant of employee mistakes 98 2.65

1.202

From table 3 above, it can be seen that a majority questions had positive responses. That is,

there was a disposition towards a trust culture. A majority of these questions experienced

higher than average means. “The bank is tolerant of employee mistakes” was the only item

with a negative response thus making it the lowest scoring question. On the other hand, “I

trust that knowledge shared by colleagues is accurate and credible” was the highest scoring

question.

30

4.2.3 Descriptive Statistics of Organisational Structure and Design Table 4 Organisational Structure and Design

Organizational Structure and Design

N Mean SD

The layout of my department makes it easy to share

knowledge with people who are interested

98 3.65 .851

Resources that enable Knowledge Sharing are in short

supply (Reverse coded)

98

3.19

1.313

The bank recognizes knowledge as part of its asset

base*

98

1.20

.574

There is a sense of competition between various

departments (Reverse coded)

98

2.90

.990

In your opinion, what direction is knowledge commonly

shared within the bank**?

98 2.07

.987

*3- point likert scale: 1- Yes, 2- No, 3- don’t know

**3-point likert scale: 1- Top-Down, Down-Up, All around

From table 4 above, it can be seen that all questions had positive responses. That is, there was

a disposition towards organisational structure and design. All the questions experienced

means that were higher than average. The highest scoring question was “The bank recognizes

knowledge as part of its asset base” whilst “In your opinion, what direction is knowledge is

commonly shared within the bank?” scored the lowest.

31

4.2.4 Descriptive Statistics of Knowledge Sharing Culture Table 5 Knowledge Sharing Culture

*5-point likert scale: 1- never, 3- Daily, and 5- weekly

Knowledge Sharing Culture

N Mean SD

Knowledge Sharing results in increased performance

98 4.45 .775

How often do you socialize with your colleagues*?

98 3.08 .938

It is easier to share knowledge with people of my gender

98

3.54

1.114

It is important to share knowledge with my department

98

4.15

1.230

Easy to share knowledge with people of ethnic group

98 3.19 1.155

My workload leaves me with too little time to share knowledge

amongst peers (Reverse coded)

98 3.32 1.374

Sharing knowledge might reduce my job security (Reverse

coded)

98 3.63 1.255

Sometimes it is difficult to share knowledge because I risk

looking smarter than my boss (Reverse coded)

98 3.17 1.026

Management regularly & clearly communicates the benefits of

sharing knowledge

98 3.91 .774

There are rewards for people who share knowledge 98 3.16 .927

32

From table 5 above, it can be seen that all questions had positive responses. That is, there was

a disposition towards a Knowledge Sharing culture. All the questions experienced means that

were higher than average. By far the highest scoring question in the questionnaire was

“Knowledge Sharing results in increased performance”.

4.2.5 Descriptive Statistics of Knowledge Hoarding culture Table 6 Knowledge Hoarding culture

Knowledge Hoarding culture

N Mean SD

It is important to have more information than others on my team

(Reverse coded)

98 3.09 1.104

It is better to withhold information that makes me appear more

efficient than others (Reverse coded)

98 3.67 1.258

The bank values individuals who withhold/hoard knowledge

(Reverse coded)

98 3.90

1.010

From table 6 above, it can be seen that a majority of the questions had negative responses.

That is, there was a disposition towards a culture low in Knowledge Hoarding. Majority of

the questions experienced means that were lower than average. The highest scoring question

was “It is important to have more information than others on my team” whilst “The bank

values individuals who withhold/hoard knowledge”.

4.2.6 Descriptive Statistics of Employee Interaction Table 7 Employee Interaction

Employee Interaction

N Mean SD

It is difficult to interact/socialize with colleagues in positions of

authority (Reverse coded)

98 2.88 1.008

It is easy to relate with professional members of staff

98 3.57 1.005

33

It is easy to relate with non-professional members of staff

98 3.71 .849

It is easy to relate with all staff members 98 3.73

.892

From table 7 above, it can be seen that all questions had positive responses. That is, there was

a disposition towards positive employee interaction. All the questions experienced means

that were higher than average. The highest scoring question was “It is easy to relate with all

staff members” whilst “It is easy to relate with professional members of staff?” scored the

lowest.

4.3 Means of Organisational factors Table 8 Means of Organisational factors

Factor Number Mean

Information Technology 98 3.625

Trust Culture 98 3.2175

Organizational Structure

and Design

98 2.602

Knowledge Sharing

Culture

98 3.56

Knowledge Hoarding

culture

98 3.55

Employee Interaction 98 3.47

Overall: Knowledge

Sharing

98 3.44

From table 8 above, it can be concluded that ‘Information Technology’ had the most positive

responses closely followed by ‘Knowledge Sharing culture’ and a lack of a Knowledge

Hoarding culture. ‘Trust culture’ received the least positive responses overall. Regardless, all

questions obtained positive responses.

34

4.4 Cronbach’s Alpha test for Reliability Reliability is the degree by which a test measures what it is intended to measure consistently

(Siegle, 2013). Cronbach’s Alpha is commonly used as a test of internal consistency. The

academically agreed minimum reliability for this test is 0.70, which shows a 70% consistency

amongst the results an instrument produces (Siegle, 2013).

Cronbach’s Alpha figures for the 6 organisational factors examined are listed in table 9 below.

Table 9 Cronbach’s Alpha

Factor No of Items Cronbach’s Alpha

Information Technology 6 0.850

Trust Culture 4 0.745

Organizational Structure

and Design

5 0.713

Knowledge Sharing Culture 10 0.836

Knowledge Hoarding

culture

3 0.732

Employee Interaction 4 0.788

Availability of technological

Knowledge Sharing enablers

4 0.861

From the table above, it is clear that the Cronbach Alpha coefficients for all organisational

factors have acceptable reliability with values being higher than 0.70. This indicates that

there is high level of consistency between the responses to questions on the KSBENQ. For

example, if a respondent agreed with the statements ‘It is important to have more information

than others on my team’ and ‘It is better to withhold information that makes me appear more

efficient than others’ it is likely he/she would disagree with the statement ‘It is important to

share knowledge with my department’.

35

4.5 Pearson Correlation Coefficient Pearson’s r measures the strength of the relationship between the organisational factors and

Knowledge Sharing. The maximum value for Pearson’s r is 1 whilst the minimum is -1. The

closer r is to 1, the closer the variables are to having a perfect positive linear relationship. All

six organisational factors have been correlated with Knowledge Sharing behaviour and the

results are listed in table 10 below.

Table 10 Pearson Correlations

Pearson’s Correlations

KSB IT TC

OS.D KSC KHC EI

KSB 1 .655**

.553** .550** .704** .492** .556**

IT .655** 1 .136 .270** .347** .306** .214*

TC .553** .136 1 .138 .254** .304** .255**

OS.D .550** .270**

.138 1 .246** .193* .137

KSC

.704** .347**

.254** .246** 1 .068 .342**

KHC

.492** .306**

.304** .193* .068 1 .072

EI

.556** .214* .255** .137 .342** .072 1

** Correlation is significant at the 0.01 level (1-tailed)

* Correlation is significant at the 0.05 level (1-tailed)

Key:

• KSB: Knowledge Sharing Behaviour

• IT: Information Technology

• TC: Trust Culture

• OS.D: Organisational Structure and Design

• KSC: Knowledge Sharing Culture

• KHC: Knowledge Hoarding culture

36

• EI: Employee Interaction

4.6 Testing of Research Hypothesis The table above highlights the correlation coefficients of the relationships among the

variables of the study. From the analysis above, no negative correlations were identified.

Hence, it can be concluded that most of the observed relationships were strong and positive

(P<0.01).

4.6.1 Hypothesis 1 H0: Information Technology does not have a significant relationship with Knowledge Sharing

From the table above, the null hypothesis is rejected because Information Technology is

positively and significantly correlated (r= .655) with Knowledge Sharing behaviour.

Indicating that there is a positive relationship between Information Technology and

Knowledge Sharing behaviour.

Information Technology was also positively and significantly (P<0.01) correlated with a

flatter organisational structure and design (r= .270), a Knowledge Sharing culture (r= .347),

Knowledge Hoarding culture (r= .306) and employee interaction (r=.214 at P<0.05). This

indicates that when information technology was efficiently used, it improved the Knowledge

Sharing culture, reduced Knowledge Hoarding, improved employee interaction and resulted

in a flatter organisational structure.

4.6.2 Hypothesis 2 H0: Trust culture does not have a significant relationship with Knowledge Sharing.

From the analysis, the null hypothesis is rejected because Trust culture is positively and

significantly correlated (r= .553) with Knowledge Sharing behaviour. Indicating that there is

a positive relationship between Trust culture and Knowledge Sharing behaviour.

Trust culture was also positively and significantly (P<0.01) correlated with Knowledge

Sharing culture (r= .347), Knowledge Hoarding culture (r= .306) and employee interaction

(r= .214). This shows that the more trusting employees were of one another improved their

willingness to share knowledge, reduced their disposition to Knowledge Hoarding and

improved their level of interaction.

4.6.3 Hypothesis 3 H0: Organisational Structure and design does not have a significant relationship with

Knowledge Sharing

37

From the analysis, the null hypothesis is rejected because Trust culture is positively and

significantly correlated (r= .550) with Knowledge Sharing behaviour. Indicating that there is

a positive relationship between Organisational Structure & design and Knowledge Sharing

behaviour.

Information Technology (r= .270), Knowledge Sharing culture (r= .246) at P<0.01 and

Knowledge Hoarding culture (r= .193) at P<0.05 were also all positively and significantly

correlated with organisational structure and design.

4.6.4 Hypothesis 4 H0: Knowledge Sharing culture does not have a significant relationship with Knowledge

Sharing

From the analysis, the null hypothesis is rejected because Knowledge Sharing culture is

positively and significantly correlated (r= .704) with Knowledge Sharing behaviour. This

indicates that there is a positive relationship between Knowledge Sharing culture and

Knowledge Sharing behaviour.

Knowledge Sharing culture also experienced positive and significant (P<0.01) correlations

with information technology (r= .347), trust culture (r= .254), organisational structure and

design (r= .246) and employee interaction (r= .342). This shows that a Knowledge Sharing

oriented culture encouraged the efficient use of information technology, improved trust levels,

flattened the organisational structure and improved employee interaction.

4.6.5 Hypothesis 5 H0: Knowledge Hoarding culture does not have a significant relationship with Knowledge

Sharing

From the analysis, the null hypothesis is rejected because Knowledge Hoarding culture is

positively and significantly correlated (r= .492) with Knowledge Sharing behaviour.

Indicating that there is a positive relationship between having a culture low in Knowledge

Hoarding and Knowledge Sharing.

Knowledge Hoarding culture was also positively and significantly (P<0.01) correlated with

Information Technology (r= .492) and trust culture(r= .304).

4.6.6 Hypothesis 6 H0: Employee interaction does not have a significant relationship with Knowledge Sharing

38

From the analysis, the null hypothesis is rejected because Employee interaction is positively

and significantly correlated (r= .556) with Knowledge Sharing behaviour. Indicating that

there is a positive relationship between Organisational Structure & design and Knowledge

Sharing behaviour.

Employee interaction was also positively correlated with information technology (r= .214,

P<0.05), trust culture (r= .255, P<0.01) and Knowledge Sharing culture (r= .342, P<0.01).

4.7 Section 1 Summary Means and standard deviation were used to provide an overall view of question responses.

The average values for these responses were above the mean proving that questions were

favourably answered. After this, Cronbach’s Alpha was used to measure the reliability of the

items in the KSBENQ questionnaire in measuring Knowledge Sharing behaviour. All

Cronbach’s Alpha values were above the desired 0.7 level. Finally with the use of Pearson’s

correlation coefficient the hypotheses were tested. It was found that all six organisational

factors are positively correlated with Knowledge Sharing, therefore all null hypotheses were

rejected as seen in table 11.

Table 11 Summary of Hypothesis

Organisational

Factor

Pearson R Hypothesis Remark

Information

Technology

.655 Information Technology does not have a

significant relationship with Knowledge

Sharing

Rejected

Trust culture .553 Trust culture does not have a significant

relationship with Knowledge Sharing.

Rejected

Organisational

Structure and

design

.550 Organisational Structure and design does

not have a significant relationship with

Knowledge Sharing

Rejected

Knowledge

Sharing culture

.704 Knowledge Sharing culture does not

have a significant relationship with

Knowledge Sharing

Rejected

Knowledge .492 Knowledge Hoarding culture does not Rejected

39

Hoarding culture have a significant relationship with

Knowledge Sharing

Employee

Interaction

.556 Employee interaction does not have a

significant relationship with Knowledge

Sharing

Rejected

4.8 Section 2 This section examines the relationship between the demographic factors and Knowledge

Sharing behaviour

4.8.1 Hypothesis 7 H0: There is no significant relationship between gender and Knowledge Sharing behaviour.

Table 12 Gender

Gender N Mean SD

Knowledge Sharing

Behaviour

Male 57 3.4019 0.30349

Female 41 3.3843 0.32123

Table 13 T-Test for Gender

Independent Sample t-test for Equality of Means

df Sig.

(2-

tailed)

95% Confidence

Interval of the

Difference

Lower Upper

Knowledge

Sharing

Behaviour

Equal variances

assumed

96 0.783 -0.10884 .14400

Equal variances

not assumed

83.320 0.785 -0.11027 .14544

P<.05

40

From the tables 12 and 13 above, the null hypothesis is accepted because the p value at 0.783

is significantly higher than 0.05 indicating that the null hypothesis is true. Therefore it can be

concluded that within Nigerian banks, gender is not related to Knowledge Sharing behaviour.

4.8.2 Hypothesis 8 H0: Age is not significantly related to Knowledge Sharing behaviour.

Table 14 Age

Age N Mean SD

Knowledge Sharing

Behaviour

18-25 24 3.3813 0.32989

26-34 56 3.3972 0.28700

35-44 18 3.4040 0.36408

Total 98 3.3946 0.30952

Table 15 T-Test for Age

ANOVA

df Sig.

Knowledge

Sharing

Behaviour

Between

Groups

3

0.969

Within Groups 95

P<.05

The calculated p value of 0.969 is considerably higher than the 0.05 level. This proves that

age is not significantly correlated to Knowledge Sharing and thus the null hypothesis is

accepted.

4.8.3 Hypothesis 9 H0: Educational Qualification is not significantly related to Knowledge Sharing behaviour.

Table 16 Qualification

41

Qualification N Mean SD

Knowledge Sharing

Behaviour

Professional 73 3.3910 0.03826

Non-

Professional

25 3.4048 0.05154

Table 17 T-test for Qualification

Independent Sample t-test for Equality of Means

df Sig.

(2-

tailed)

95% Confidence

Interval of the

Difference

Lower Upper

Knowledge

Sharing

Behaviour

Equal variances

assumed

96 0.848 -0.15690 0.12927

Equal variances

not assumed

52.436 0.830 -0.14260 0.11497

P<.05

The P value of 0.848 is higher than the 0.05 level, indicating that the null hypotheses should

be accepted. Therefore, there is no significant relationship between educational qualification

and Knowledge Sharing behaviour.

4.8.4 Hypothesis 10 H0: There is no significant relationship between Organisational Tenure and Knowledge

Sharing Behaviour.

42

Table 18 Organisational Tenure

Organisational

Tenure

N Mean SD

Knowledge

Sharing

Behaviour

< 1 Year 26 3.4079 .25688

1-5 Years 47 3.4036 .31751

6-10 Years 22 3.3664 .36602

11-15 Years 3 3.3434 .28156

Total 98 3.3946 .30952

Table 19 T-test for Organisational Tenure

ANOVA

df

Sig.

Knowledge

Sharing

Behaviour

Between Groups 3

0.952

Within Groups 95

The calculated p value of 0.952 is considerably higher than the 0.05 level. This indicates that

organisational tenure is not significantly correlated to Knowledge Sharing and thus the null

hypothesis is accepted.

4.9 Mean Plots Mean plots are used to determine if there is a variation between different groups of data

(NIST, 2012). Mean plots for the variation in responses on the KSBENQ are listed below.

4.9.1 Gender Figure 1: Gender

43

A study by Karakowsky & Miller (2005) suggests that men and women vary in their efforts

to share and seek knowledge. From the mean plot above, it can be seen that males generally

provided more positive responses (given that a 5-point likert scale was used for questions) to

the KSBENQ indicating that they are more disposed to Knowledge Sharing than females.

4.9.2 Age Figure 2: Age

44

From figure 2 above, it can be seen that the older the respondents got, the more positively

they responded to questions in the KSBENQ. A reason for this could be that respondents

acquire more knowledge as they age and as a result are more disposed to positive Knowledge

Sharing behaviour. The findings also tally with those by Jarvenpaa and Staples (2001) who

proposed that younger employees may have smaller social circles than older employees

which could result in a lack of sharing opportunities or feelings of inadequacy that could in

turn lead to Knowledge Hoarding or distrust.

4.9.3 Educational Qualification Figure 3: Educational Qualification

From figure 3 above, it can be seen that non-professionally qualified employees generally

provided more positive responses (given that a 5-point likert scale was used for questions) to

the KSBENQ than professionally qualified employees. These findings are counter intuitive

and contradict findings by Riege (2005) as it was expected that professionally qualified

employees would be more disposed to positive Knowledge Sharing behaviour because they

would be aware of the benefits of Knowledge Sharing.

45

4.9.4 Organisational Tenure Figure 4: Organisational Tenure

From the mean plot above, it can be seen that the longer respondents worked, the more

negatively they responded to questions in the KSBENQ. A possible explanation for this is

that when an employee is recently hired, he is eager to share knowledge and learn from

colleagues. However, as time passes this initial enthusiasm for Knowledge Sharing is lost as

colleagues may not be reciprocating his actions or he may feel that Knowledge Sharing is not

valued. These findings correlate with Ojha (2005)’s findings that organisational tenure has a

negative impact on Knowledge Sharing.

4.10 Descriptive statistics of the Demographic Variables Unless otherwise stated, questions used a 5-point likert scale from 1 (Strongly Disagree)

through to 5 (Strongly Agree). However, if a question is reverse coded then responses closer

to 1 show strong agreement whilst responses closer to 5 show strong disagreement. 3 is

neutral in both situations.

46

4.10.1 Gender Table 20 Responses by Gender

Gender

I trust my colleagues not to misuse or take unjust

information for knowledge shared

Gender Mean N SD

Male 3.30 57 .801

Female 3.17 41 1.116

Total 3.24 98 .942

The table above shows that females (3.17) generally responded less positively than males

(3.30) to the question.

Table 21 Responses by Gender 2

Gender

I trust that knowledge shared by colleagues is accurate and

credible

Gender Mean N SD

Male 3.54 57 .951

Female 3.51 41 .735

Total 3.52 98 .828

The table above shows that females (3.51) generally responded less positively than males

(3.54) to the question.

From the findings above, it can be seen that females appeared to be less trusting of their

colleagues with regards to Knowledge Sharing. These findings are supported by research

undertaken by Stawiski, Jennifer & Deal (2010), who suggest that women are less trusting of

their co-workers and bosses than men in the workplace because of their experiences. These

findings could be a reason why female employees provided less positive responses to the

KSBENQ.

47

4.10.2 Age Table 22 Responses by Age

Age

It is better to withhold information that makes me appear more efficient than others

Age Mean N SD

18-25 4.00 24 1.063

26-34 3.59 56 1.262

35-44 3.50 18 1.465

Total 3.67 98 1.258

*Reverse coded question

Table 22 above shows that generally, the older the respondents were, the more important they

thought it was to withhold information that made them appear more efficient than others.

These responses to the question are counter-intuitive as it was expected that older and more

experienced employees would less likely to withhold and hoard information from others. The

findings also contrast with those by Jarvenpaa and Staples (2001).

4.10.3 Educational Qualification Table 23 Responses by Qualification

Educational Qualification

Sharing knowledge might reduce my job security

Educational Qualification Mean N SD

Professional 3.64 73 1.284

Non-Professional 3.60 25 1.190

Total 3.63 98 1.255

*Reverse coded question

The table above shows that non-professionally qualified employees (3.60) generally

responded more positively than professionally qualified employees (3.64) to the question.

Indicating that more professionally qualified employees felt that knowledge might have a

negative effect on their job security than non-professionally qualified employees. The

responses are counter intuitive and contradict Keyes (2008) findings that the less qualified

employees are, the less likely they are share to knowledge because they of the fear that

48

Knowledge Sharing would result in a loss of the only asset that makes them valuable to an

organisation.

4.10.4 Organisational Tenure Table 24 Responses by Organisational Tenure

Organizational Tenure

It is important to have more information than others on my team*

Organizational Tenure Mean N SD

<1 Year 3.73 26 .874

1-5 Years 2.83 47 1.148

6-10 Years 2.95 22 .999

11-15 Years 2.67 3 1.155

Total 3.09 98 1.104

*Reverse Coded Question

Table 24 above shows that generally, the longer respondents worked within a bank, the more

important they thought it was to have more information than team members. The responses

contradict the findings by Watson and Hewett (2006) who suggest that organizational tenure

influences Knowledge Sharing behavior positively because the longer an employee works in

an organization, the higher he develops the levels of trust between employees and

commitment to the organization. However, the responses are in line with Schermerhorn’s

(1977) findings that employees with shorter organizational tenure are more likely to share

knowledge. A reason for these findings could be that organizational tenure is major factor in

influencing promotion decisions (Ambrose & Cropanzano, 2003) and thus longer serving

employees may be more likely to hoard knowledge so as to increase their chances of being

promoted. These findings could be a reason why longer serving employees provided less

positive responses to the KSBENQ.

49

4.11 Section 2 Summary The research hypotheses were tested using an independent samples t-test and ANOVA. There

was no statistically significant relationship detected between any of the demographics and

Knowledge Sharing behaviour and all the null hypotheses were accepted. Further to this,

mean plots and descriptive statistics were used to describe the responses to questions by

demographical factors

Table 25 Summary of Hypothesis

Demographic Factor P< 5% significance

level

Null Hypothesis

Gender 0.783 Accepted

Age 0.969 Accepted

Educational Qualification 0.848 Accepted

Organisational Tenure 0.952 Accepted

50

5. Discussion All research objectives for this study have been met. The six organisational factors-

Information Technology, Trust Culture, Organisational Structure & Design, Knowledge

Sharing Culture, Knowledge Hoarding culture and Employee Interaction were found to have

a positive influence on Knowledge Sharing behaviour, whilst no statistically significant

relationship was identified between the four demographic factors- gender, age, educational

qualification, organisational tenure and Knowledge Sharing behaviour in Nigerian banks.

5.1 Discussion of Organisational Factors and Knowledge Sharing Behaviour

5.1.1 Information Technology Information technology was found to have the second strongest correlation (.655) with

Knowledge Sharing behaviour in Nigerian banks. This means that Information Technology

increases the rate of Knowledge Sharing and that in Nigerian banks there is a culture in place,

which encourages the efficient use of Information Technology to enhance Knowledge

Sharing. The null hypothesis was hence rejected. These findings agree with the findings by

Choi, Lee & Yoo (2010) who found information technology and adequate technological

support to positively influence Knowledge Sharing behaviour. The findings also tally with

research by Rasula, Vuksic & Stemberger (2012) who purport that information technology

has a positive influence on Knowledge Sharing as long as it is supported by the

organisational climate, processes and individuals. The findings are further supported by Eid

& Nuhu (2011) and Davenport & Prusak’s (1998) findings that there is a significant positive

relationship between the use of Information Technology and Knowledge Sharing.

5.1.2 Trust Culture Trust culture was found to have a strong correlation (.553) with Knowledge Sharing

behaviour in Nigerian banks. This implies that that there is the presence of a trust culture in

Nigerian banks, which facilitates Knowledge Sharing, and the null hypothesis was rejected.

The analysis further indicates that trust has a positive influence on Knowledge Sharing, as

employees are more likely to share knowledge where there is a high level of trust. These

findings tally with the conclusions drawn by Casimir, Lee & Loon (2012), Holste & Fields

(2010), Levin, Cross & Abrams (2002) and Rhodes et al. (2008) that a trust culture

significantly influences the willingness to share knowledge within firms.

51

5.1.3 Organisational Structure and Design Organisational Structure and Design strongly correlated (.550) with Knowledge Sharing

behaviour in Nigerian banks. This relationship indicates that that the organisational structure

of Nigerian banks is a flat non-hierarchical one that is conducive to Knowledge Sharing thus

the null hypothesis was rejected. Further analysis demonstrates that a less competitive

environment and an organisational design that is laid out to enable knowledge to be shared

with interested parties positively influences Knowledge Sharing as employees are likely to be

more willing to share knowledge in an organization where departments and individuals are

not in competition with one another. These findings correlate with findings by Armstrong

(1995), Momeni et al. (2013) and Rhodes et al. (2008) who found a flexible organizational

structure design to positively influence Knowledge Sharing behaviour.

5.1.4 Knowledge Sharing Culture Knowledge Sharing was found to have the strongest correlation (.704) with Knowledge

Sharing behaviour in Nigerian banks. This implies that that there is the presence of a culture

in Nigerian banks, which facilitates Knowledge Sharing, and the null hypothesis was rejected.

The analysis further indicates rather intuitively that a Knowledge Sharing and learning

culture have a positive influence on Knowledge Sharing as employees are more likely to

share knowledge where there is a culture of openness and willingness to learn (Senge, 1990).

These findings tally with the conclusions drawn by Ahmed (2002), Al-Alawi, Al-Marzooqi &

Mohammed (2007) and Suppiah & Sandhu (2011) which shows that a Knowledge Sharing

oriented culture is positively related to Knowledge Sharing in organisations.

5.1.5 Knowledge Hoarding culture A Knowledge Hoarding culture was found to have a strong correlation (.492) with

Knowledge Sharing behaviour in Nigerian banks. This implies that that there is no presence

of a hoarding culture in Nigerian banks, which could hinder Knowledge Sharing, resulting in

the null hypothesis being rejected. The analysis further indicates that a culture low in

Knowledge Hoarding has a positive influence on Knowledge Sharing, as employees would be

more willing to share knowledge. These findings corroborate with findings by Muller,

Spiliopoulou & Lenz (2005), and Handzic, Lazaro & Toorn (2004), which purport that a

culture, which prevents Knowledge Hoarding positively, affects Knowledge Sharing.

5.1.6 Employee Interaction Employee Interaction strongly correlated (.556) with Knowledge Sharing behaviour in

Nigerian banks. This relationship indicates that that there is frequent and positive employee

52

interaction in Nigerian banks, which is conducive to Knowledge Sharing thus resulting in the

null hypothesis being rejected. Further analysis demonstrates that higher levels of social

interaction have positive influences on Knowledge Sharing. This is likely to be as a result of

the fact that knowledge is shared via interaction and the more frequently this is done, the

more knowledge is shared. These findings tally with findings by Chua (2002) who finds a

positive correlation between the level of social interaction and Knowledge Sharing behaviour.

The findings also tally with findings by Noordhaven & Harzing (2009) who found that the

level of social interaction between managers from different departments positively influenced

intra-organisational Knowledge Sharing. In addition, a positive social interaction culture may

enable female employees to build trust amongst which allows Knowledge Sharing occur

(Connelly & Kelloway, 2003)

5.2 Discussion of Demographic Variables and Knowledge Sharing behaviour

5.2.2 Gender This study found no statistically significant relationship between gender and Knowledge

Sharing behavior which tallies with the findings by Mogotsi, Boon & Fletcher (2011), Ojha

(2005), Chowdhury (2005), Watson & Hewettt (2006) and Yusof et al. (2012). However,

given the influence gender has on communication and communication styles, it is not

farfetched to assume it could affect Knowledge Sharing (Connelly & Kelloway, 2003).

Afterall, the study by Miller and Karakowsky (2005) suggested that men and women varied

in their efforts to share and seek knowledge. In this study as seen in chapter X, males

generally provided more positive responses to the KSBENQ indicating that they are more

disposed to Knowledge Sharing than females.

5.2.3 Age There was also no statistically significant relationship between age and Knowledge Sharing

behavior found in this study. These findings correlate with findings by Watson & Hewett

(2006), Ojha (2005), Mogotsi, Boon & Fletcher (2011). The responses to the questionnaire

proved to be tally with intuition as the older the respondents were, the more disposed they

were to Knowledge Sharing.

53

5.2.4 Educational Qualification This study found no statistically significant relationship between educational qualification

and Knowledge Sharing behavior which tallies with the findings by Mogotsi, Boon &

Fletcher (2011), and Yusof et al. (2012). However, Riege (2005) outlined that there is a

possibility of educational qualification being related to Knowledge Sharing behaviour as the

less qualified an individual is, the less likely the individual is to share knowledge. The

responses to the questionnaire did not tally with this argument as non-professionally qualified

employees generally responded more positively than professionally qualified employees to

the KSBENQ.

5.2.5 Organisational Tenure The mean plots suggest that there is a negative correlation between Organisational Tenure

and disposition to share knowledge. This analysis is supported by Ojha’s (2005) findings.

However, it should be noted that no statistically significant relationship between

organisational tenure and Knowledge Sharing behavior was found. Yusof et al. (2012) and

Keyes (2008) also concluded that no statistically significant relationship existed between

Knowledge Sharing behavior and Organisational Tenure.

54

6. Conclusions and Recommendations This study investigated the relationship between the demographic variables (gender, age,

educational qualification and organisational tenure) and organisational factors (information

technology, trust culture, organisational culture & design, Knowledge Sharing culture,

Knowledge Hoarding culture and employee interaction) on Knowledge Sharing behaviour

within Nigerian banks. All the organisational factors were found to influence Knowledge

Sharing behaviour positively with Knowledge Sharing culture and information technology

having the strongest influence on Knowledge Sharing behaviour. On the other hand, no

statistical relationship was found between the demographic variables and Knowledge Sharing

behaviour and the null hypotheses were accepted. Thus, demographic factors have no

significant relationship with Knowledge Sharing behaviour.

Based on the review of literature undertaken, it was found that demographic variables might

have a relationship with Knowledge Sharing behaviour. This contradicts with the findings of

this study. It is therefore recommended that further research into the relationship between

demographic factors and Knowledge Sharing behaviour amongst Nigerian banks is

undertaken. Further studies should use a larger sample size and should incorporate a larger

number of banks. In addition, other variables, which might affect how the demographic and

organisational factors influence Knowledge Sharing behaviour, should be explored.

6.1 Implications for managers and practitioners Managers should focus the allocation of resources to improving factors which have been

found to be positively related with knowledge sharing.

• Information Technology was strongly and positively related with Knowledge Sharing

behaviour. Managers should therefore seek to exploit the benefits of Information

Technology in improving Knowledge Sharing. It is important however, for managers

to realise that Information Technology only supports Knowledge Sharing and is not a

substitute for human interaction.

• Trust culture was also found to have a positive relationship with Knowledge Sharing

behaviour. However, females appeared to be less trusting of their colleagues

regarding the quality and credibility of knowledge shared. As a result, managers

should attempt to improve the levels of trust within their organisation.

55

• Organisational structure and design was also positively related to Knowledge Sharing.

Managers should therefore seek to implement a flexible, dynamic and non-

hierarchical structure, which should improve information flow.

• A Knowledge Sharing oriented culture and one, which is low in Knowledge Hoarding,

positively influences Knowledge Sharing behaviour. It is thus important for managers

to create a culture whereby the possession of knowledge is not viewed as power and

employees are willing to exchange knowledge to improve the intellectual capital of

the organisation.

• Finally, Employee interaction was found to have a positive relationship with

Knowledge Sharing behaviour. Consequently, it is recommended that managers

endeavour to increase the levels of employee interaction as it was found that higher

levels of social interaction have positive influences on Knowledge Sharing.

(Word Count: 10,991)

56

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8. Appendix A: Questionnaire Knowledge Sharing Behaviour of Employees within Nigerian Banks Questionnaire

Thank you for taking part in this Loughborough University research study.

It is designed to identify the knowledge sharing* techniques, barriers and enablers within Nigerian banks, and

should take approximately 10 minutes to complete.

Please read each question or statement carefully and try to answer all questions honestly and to the best of your

knowledge.

Your identity will be kept anonymous and your responses shall in no way affect your employment status. If at

any point you prefer not to participate, you have the right to refuse to take part or stop completing the form.

Should you have questions about this study and its related research project, please contact Timmy Tikolo

at o.f.tikolo-11@student.lboro.ac.uk.

Knowledge Sharing is an activity through which knowledge is exchanged among people, friends, families,

communities, or organizations. In the context of this questionnaire 'knowledge' may be regarded as

information.

Demographic Data

Gender

Age

What Educational Qualification do you have?

How long have you worked at your bank?

Information Technology

There is instant technical support which improves knowledge and communication flow

The hardware and software available meets my requirements

I am reluctant to use IT tools because I am unfamiliar with them (Reverse Coded)

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There is adequate training provided to help familiarize me with new IT systems & processes

The advantages of new systems and processes over existing ones are clearly made known

The introduction of a bank wide social network would increase the likelihood of my sharing knowledge

Trust Culture

I trust my colleagues not to misuse or take unjust information for knowledge shared

I trust that knowledge shared by colleagues is accurate and credible

I would not get the recognition I deserve by sharing knowledge (Reverse coded)

The bank is tolerant of employee mistakes

Organizational Structure and Design

The layout of my department makes it easy to share knowledge with people who are interested

Resources that enable knowledge sharing are in short supply (Reverse coded)

The bank recognizes knowledge as part of its asset base*

There is a sense of competition between various departments (Reverse coded)

In your opinion, what direction is knowledge is commonly shared within the bank**?

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Knowledge Hoarding Culture

It is important to have more information than others on my team (Reverse coded)

It is better to withhold information that makes me appear more efficient than others

(Reverse coded)

The bank values individuals who withhold/hoard knowledge (Reverse coded)

Employee Interaction

It is difficult to interact/socialize with colleagues in positions of authority (Reverse coded)

Knowledge Sharing Culture

Knowledge sharing results in increased performance

How often do you socialize with your colleagues*?

It is easier to share knowledge with people of my gender

It is important to share knowledge with my department

Easy to share knowledge with people of ethnic group

My workload leaves me with too little time to share knowledge amongst peers (Reverse coded)

Sharing knowledge might reduce my job security (Reverse coded)

Sometimes it is difficult to share knowledge because I risk looking smarter than my boss (Reverse coded)

Management regularly & clearly communicates the benefits of sharing knowledge

There are rewards for people who share knowledge

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It is easy to relate with professional members of staff

It is easy to relate with non-professional members of staff

It is easy to relate with all staff members