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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,
20
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
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