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CasePaper5-Knowledge-sharingbehaviourofbankemployeesinGreece.pdf
Knowledge-sharing behaviour of bank employees in Greece
Prodromos D. Chatzoglou and Eftichia Vraimaki Production and Management Engineering Department,
Democritus University of Thrace, Xanthi, Greece
Abstract
Purpose – This paper aims to develop an understanding of the factors that influence knowledge-sharing behaviour within an organisational framework, using widely accepted social psychology theories.
Design/methodology/approach – Knowledge-sharing behaviour of bank employees in Greece is examined using an aggregate model, which is based on the theory of planned behaviour. The suggested research model was tested using structural equation modelling.
Findings – The results indicate that intention to share knowledge is mainly influenced by employees’ attitude toward knowledge sharing, followed by subjective norms.
Research limitations/implications – Knowledge-sharing behaviour was examined solely focusing on salient beliefs. Findings should be confirmed using a larger sample, as well as through cross-sectional studies.
Practical implications – The results highlight the necessity of creating a climate that would help individuals develop a more favourable attitude toward knowledge sharing as well as the important role of the perceived social pressure by organisational members (peers, supervisors, senior management) on the intention of individuals to share knowledge.
Originality/value – The main contributions of this study are the following: examination of the knowledge sharing in the banking sector; testing of a specific well-known research model in the South-Eastern European environment; examination of the actual knowledge-sharing behaviour and not only of the behavioural intention to share knowledge and, finally, examination of the direct effect of the perceived behavioural control on knowledge-sharing behaviour, which, although suggested by theory, has been neglected by previous studies.
Keywords Knowledge management, Knowledge sharing, Employee behaviour, Banks, Greece
Paper type Research paper
1. Introduction Knowledge management (KM) has attracted much attention by the business world since the introduction of the concept by Davenport and Prusak about 12 years ago. Although the essence of managing knowledge is not a newfangled issue (Davenport and Prusak, 2000), the changing contemporary business environment, calls for an active engagement into KM initiatives. Whatever, the KM strategy followed by an organisation is, it targets for the promotion of sharing knowledge, ideas, and experience among individuals and groups (Cabrera and Cabrera, 2002). In this paper, emphasis is placed on knowledge-sharing behaviour, since the success of KM initiatives largely depends on the willingness of organisational members to share their knowledge. And this has been proved as
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-7154.htm
The authors would like to thank Dr Bock Gee-Woo, University of Singapore, and Dr Ryu Seewon, Korean Institute of Health and Social Affairs, for providing us with some valuable suggestions on the research instrument used by this research.
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Business Process Management Journal
Vol. 15 No. 2, 2009 pp. 245-266
q Emerald Group Publishing Limited 1463-7154
DOI 10.1108/14637150910949470
“a promising yet elusive goal for many organizations” (Koulopoulos and Frappaolo, 1999). To support this claim, Davenport and Prusak (2000) explain that organisations do not usually lack knowledge; however, its existence does not guarantee its use.
Having realised the importance of knowledge sharing for successful KM initiatives, this research attempts to examine the factors that influence knowledge-sharing behaviour in an organisational framework. Research has examined several different variables believed to influence sharing behaviour, such as incentive systems and culture, or top management and senior leadership (Bock and Kim, 2002). However, only a few researchers (Bock and Kim, 2002; Ryu et al., 2003; Lin and Lee, 2004; Bock et al., 2005; Kankanhalli et al., 2005) attempted to empirically test such factors in the knowledge-sharing context on a solid theoretical background.
The basic aim of this research is to investigate the knowledge-sharing behaviour of bank employees in Greece. This is important because it is still crucial to accurately explain the knowledge-sharing behaviour of individual professional groups (Ryu et al., 2003). Understanding the factors that influence individuals’ behaviour toward knowledge sharing in the organisational context is essential, in order to implement successful KM initiatives. Nevertheless, very limited number of studies investigating the knowledge-sharing behaviour of bank employees can be found. While most studies in the literature, relating to all aspects of KM, are concerned with the manufacturing industry, the service industry, and banking in particular, has not received much attention (Kubo et al., 2001). The research model for this study is based mainly on the theory of planned behaviour (TPB), an extension of the theory of reasoned action (TRA), a social psychology theory, and other similar research models. Findings of previous studies using similar models suggest that the TPB is superior in explaining intention to share knowledge and the model has a reasonable overall fit (Ryu et al., 2003).
Therefore, the main contributions of this study are the following: . examination of the knowledge sharing in the banking sector; . use of data concerning employees working for the private sector, as opposed to
other studies in the literature (Bock and Kim, 2002; Bock et al., 2005; Kankanhalli et al., 2005);
. testing of a specific well-known research model in the South-Eastern European environment;
. examination of the actual knowledge-sharing behaviour and not only of the behavioural intention to share knowledge (Bock et al., 2005); and
. finally, examination of the direct effect of the perceived behavioural control (PBC) on knowledge-sharing behaviour, which, although suggested by theory, has been neglected by previous studies.
2. Literature review 2.1 Knowledge sharing “Knowledge is one of the few assets that grows – also usually exponentially – when shared” (Quinn, 1996). Knowledge sharing is believed to be one of the most important processes for KM (Bock and Kim, 2002; Lahti and Beyerlein, 2000). Davenport and Prusak (2000) and Chua (2003), however, indicate that knowledge is shared in organisations whether the process is deliberately managed or not. Junnarkar (1997) also supports this
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view of readily established knowledge communities within the organisational framework, even before management makes any kind of effort towards establishing them. Companies are seeking to implement special KM projects, which aim to establish an environment within organisations that will support the effective knowledge creation, transfer and use (Davenport et al., 1998). All “[. . .] knowledge management initiatives try to foster the sharing of knowledge, ideas, and experiences, in whatever form, among individuals or groups”, as Cabrera and Cabrera (2002) point out.
Nevertheless, it is accepted that “willing to share participants” is the key factor for a successful implementation of any KM process (Koulopoulos and Frappaolo, 1999). Since the introduction of the KM discipline, researchers have tried to study many variables that are related to individual’s knowledge-sharing behaviour. Robertson (2002) explains why understanding human behaviour is critical for knowledge-sharing initiatives to work: “[. . .] knowledge sharing is a human activity, and understanding the human who will do it is the first step in successfully supporting that activity”.
2.2 Knowledge sharing in financial institutions “It’s accepted wisdom that banking is a business of information, not just a business of money” (Lamb, 2001). The change in the global business environment has led banks to rationalise their products and services and have also looked into KM in order to improve their competitiveness (Dzinkowski, 2001). Managing knowledge is as important to banking institutions as it is for any other kind of organisation. Hubert Saint-Onge (quoted in Lamb, 2001) points out that: “the last open frontier for banks to create competitive advantage may very well reside in their ability to leverage knowledge”. Supporting this suggestion, Craig Kaylor (quoted in Lamb, 2001) of the Hampden Savings Bank, claims that banks do not sell goods, but rather services and more specifically knowledge.
Ramona Dzinkowski (2001) explains the two basic categories of KM initiatives in financial services companies:
First, knowledge management is seen as an integral part of the overall corporate strategy, and aims to grow, extract and exploit the company’s knowledge to increase shareholder value. The second focuses on improving upon the knowledge necessary to carry out specific business processes and thereby improving efficiency.
Despite the significance of implementing a KM initiative, there are very few banking institutions formally engaged in a fully integrated KM program. Dzinkowski (2001) points out that the most sophisticated strategies in the field, however, can be encountered in the insurance field, as a partial result of the long-term focus of that industry to costumers. Financial success and growth depend heavily on how well managers understand customer needs and subsequently diffuse and exploit that knowledge to the benefit of the organisation. Having this in mind, it is obvious that dismissing KM in an interrelated field, i.e. banking, can lead to perilous results (Lamb, 2001). Practically, it is certain that even bankers without a clear approach to KM are readily engaged in some informal implementation of it. However, Dzinkowski (2001) stresses on the necessity to manage knowledge systematically:
Little quantitative data exist on how managing something intangible as knowledge directly impacts on the bottom line. However, a large number of anecdotal evidence suggests that managing knowledge systematically matters.
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The World Bank however, breaking new ground in the field, launched a knowledge sharing initiative in 1997 (Egan and Kim, 2000). The bank was determined to transform itself into a knowledge bank, while until that time thought itself mainly in traditional banking terms (Cummings, 2003; Cohen and Laporte, 2004). Laporte (2004) reports that by 2000, The World Bank had:
[. . .] a range of knowledge-sharing programmes in place: communities of practices, helpdesk and advisory services, extensive knowledge collections on the web, tacit knowledge debriefings, indigenous knowledge programmes, and a platform to share knowledge with the development community through the Development Gateway web site.
Learning from the benefits these financial institutions have realised from implementing knowledge KM initiatives, financial institutions should recognise the importance of systematic management of knowledge.
3. Theoretical background 3.1 The theory of planned behaviour The basic notion of the TRA and the TPB is that:
[. . .] behavioral intentions, which are immediate antecedents to behavior, are a function of salient information and beliefs about the likelihood that performing a particular behavior will lead to a specific outcome (Ajzen and Fishbein, 1980; Madden et al., 1992).
There are three conceptually independent determinants to intention. The first predictor is the “attitude toward the behaviour” and “[. . .] refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question”, while the second, “subjective norm”, as a social factor, refers to the perceived social pressure to perform or not to perform the behaviour (Ajzen, 1991). Finally, the third predictor of intention is “perceived behavioural control”, which “[. . .] refers to the perceived ease or difficulty of performing the behavior and it is assumed to reflect past experience as well as anticipated impediments or obstacles” (Ajzen, 1991). This additional construct, which was not incorporated in the TRA, poses significant psychological interest, in relation to actual control; Ajzen (1991) explains that PBC is similar to Bandura’s concept of perceived self-efficacy. The TPB proposes that PBC along with behavioural intention can be used directly to predict behavioural achievement.
Ajzen (1991) explains that, as a general rule, “[. . .] the more favourable the attitude and subjective norm, and the greater the perceived behavioral control, the stronger should be an individual’s intention to perform the behavior under consideration”. However, the relative importance of the three predictors of intention is expected to vary across different behaviours and situations. This means, as the author explains, that there are situations where attitudes alone can have a significant impact on intentions, whereas in others attitudes along with PBC are enough to account for intention; there are also situations where all of the three predictors make independent contributions regarding behavioural intention.
Both theories, TRA and TPB, have received a certain amount of criticism, regarding their applicability and predictive power. There have also been suggestions for modifications of the original models. Chang (1998) modified the TPB, using a causal path linking subjective norm to attitude, and he reported a significant improvement on model fit. The findings of Ryu et al. (2003) research seem to validate Chang’s findings. In addition, Sheeran and Orbell (1999), claim that, findings of past research suggest that
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many people with positive intentions do not succeed in establishing a new behaviour. Millar and Shevlin (2003) integrated the TPB model by adding the “past behaviour” variable, and they found that future behaviour is best explained by past behaviour.
To conclude, despite the criticism, the TPB “[. . .] provides a useful conceptual framework for dealing with the complexities of human social behavior” (Ajzen, 1991). The model’s applicability, in quite diverse behaviours, has been indicated by past research (Chang, 1998; Sheeran and Orbell, 1999; Millar and Shevlin, 2003; Ryu et al., 2003; Lin and Lee, 2004). According to Ajzen (1991) the TPB incorporates some of the central concepts in the social and behaviour sciences; the three predictors of intention are defined in a way that permits the highly accurate prediction and understanding of particular behaviours in specific contexts. In the knowledge sharing area, Bock and Kim (2002) and Bock et al. (2005) conducted a research using the TRA. Ryu et al. (2003), on the other hand, investigated knowledge-sharing behaviour of physicians in hospitals and Lin and Lee (2004) investigated the perceptions of senior managers regarding knowledge-sharing behaviour, both using the TPB.
4. Research model and hypotheses The purpose of this study is to investigate the behaviour of bank employees in Greece toward knowledge sharing, applying the TPB model. Past studies, have indicated that there is a strong causal link between intention and targeted behaviour (Ryu et al., 2003), as the theory also suggests. According to the TPB, the stronger the intention to practice a behaviour, the higher the likelihood that the individual will actually end up in engaging in that behaviour (Ajzen, 1991). Intention, on the other hand, is determined by three conceptually independent factors: attitude toward behaviour, subjective norms about the behaviour, and PBC over the behaviour.
Based on the theoretical framework of the TPB and past research models, the following research model and hypotheses are proposed (Figure 1). The following research model is an aggregate model incorporating the basic TPB model (Ajzen, 1991), including also another variable (level of information technology usage (ITU))
Figure 1. The research model and
hypotheses
Perceived Behavioural Control to knowledge sharing
Attitudes toward knowledge sharing
Subjective Norms about knowledge
sharing
Intention to share knowledge
Knowledge sharing Behaviour
Level of IT Usage
H1(+)
H2(+)
H3(+)
H6(+)
H4(+)
H5(+)
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suggested by Bock and Kim (2002). Table I lists the model constructs and their operational definitions, as well as the related literature.
Since attitudes, subjective norms, and PBC are proposed to determine intention to engage in a behaviour, the following three hypotheses are formulated:
H1. Individual’s attitude toward knowledge sharing has a positive effect on the intention to share knowledge.
H2. Individual’s subjective norms, related to knowledge sharing, have a positive affect on the intention to share knowledge.
H3. Individual’s PBC over sharing knowledge has a positive affect on the intention to share knowledge.
H1 examines the relationship between attitude and intention regarding knowledge sharing in the organisational context. H2 and H3 investigate the relationship between subjective norms and intention toward knowledge sharing, as well as between PBC and intention, respectively. For H1, H2, and H3, intention is the dependent variable.
The next hypothesis (H4) examines the relationship between intention to engage in a knowledge sharing act and the actual behaviour of sharing knowledge. For this hypothesis, knowledge-sharing behaviour is the dependent variable. Therefore, H4 proposes that:
H4. Individuals intention to share knowledge has a positive affect on the individual’s knowledge-sharing behaviour.
H5 refers to the individual’s level of information technology usage (ITU). Information technology (IT) is believed to be an important enabler in KM (Bock and Kim, 2002);
Constructs Operational definition Items References
Knowledge-sharing behaviour
The degree to which an employee actually shares knowledge with other organisational members
5 Ajzen (1991, 2002), Bock and Kim (2000, 2002), Bock and Pan (n.d.), Lee (2001) and Lin and Lee (2004)
Level of ITU The degree of one’s frequency of using IT
6 Bock and Kim (2000, 2002), Bock and Pan (n.d.)
Intention to share knowledge
The degree to which one believes that he or she will engage in a knowledge-sharing act
5 Ajzen (1991, 2002), Bock and Kim (2000, 2002), Bock and Pan (n.d.), Ryu et al. (2003) and Lin and Lee (2004)
Attitude toward knowledge sharing
The degree of one’s positive feelings regarding sharing his or her knowledge
5 Ajzen (1991, 2002), Bock and Kim (2000, 2002), Bock and Pan (n.d.), Ryu et al. (2003) and Lin and Lee (2004)
Subjective norms about knowledge sharing
Perceived social pressure in order to perform or not the knowledge-sharing behaviour
4 Ajzen (1991, 2002), Bock and Pan (n.d.), Ryu et al. (2003), Lin and Lee (2004)
PBC of knowledge sharing Perception of the ease or difficulty related to performing the knowledge-sharing behaviour
4 Ajzen (1991, 2002), Ryu et al. (2003) and Lin and Lee (2004)Table I.
The model constructs and operational definitions
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therefore, the effect of the level of ITU on knowledge-sharing behaviour is also examined. The addition of this variable is based on the TRA. For H5 knowledge-sharing behaviour is also the dependent variable:
H5. The level of ITU of the individual will have a positive affect on the individual’s knowledge-sharing behaviour.
As it was previously mentioned, Ajzen’s (1991) theory proposes that PBC, in conjunction with behavioural intention, can be used directly for the prediction of behavioural achievement. Ajzen (1991) presented two rationales for establishing the direct link from PBC to actual behavioural performance. First, “holding intention constant, the effort expended to bring a course of behavior to a successful conclusion is likely to increase with perceived behavioral control” and second, “[. . .] perceived behavioural control can often be used as a measure of actual control” (Ajzen, 1991). As it was previously explained, “actual control” over a behaviour summarises all non-motivational factors as availability of the requisite opportunities and resources. Based on this proposal, the direct link between PBC to knowledge sharing and sharing behaviour is examined through the following H6:
H6. Individual’s PBC over sharing knowledge has a positive affect on the individual’s knowledge-sharing behaviour.
For H6, knowledge-sharing behaviour is the dependent variable. The direct link between PBC and behaviour has not been examined by any relative research on knowledge-sharing behaviour.
5. Research methodology 5.1 Sampling and data collection The population for this study consisted of bank employees in Greece, including both state-owned as well as private bank branches. The choice of branches included in the survey was random. Contact information, when necessary, was retrieved from the banks’ official web sites.
A total of 600 questionnaires were administered using mainly the “in-person drop-off” method (Zikmund, 2003), whereas, for geographically dispersed branches, questionnaires were sent either through e-mail or post, depending on the participants’ preferences. For questionnaires sent by post, an envelope with pre-paid postal expenses was included. For questionnaires that were personally delivered, a pick-up date was arranged, usually after one or two weeks. There were cases, however, where respondents preferred to answer the questionnaires immediately. In other cases, the questionnaires had not been completed within the agreed upon period, so they had to be collected at a later time, which in some occasions exceeded the period of one month. The survey questionnaires were distributed and collected between October 2004 and June 2005.
The questionnaire was seven pages long, including a cover letter on the first page, and necessary general (demographic) questions on the last. The cover letter briefly explained the aim and purpose of the study and assured confidentiality. It also included a short introductory note, describing the meaning of “knowledge” and “knowledge sharing”, so that participants could more easily understand the nature of the questions that followed. The main section of the questionnaire was constructed
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using “structured questionnaire format” (Armitage and Conner, 1999). This section was divided into five parts, in order to reflect the thematic components of the model used. Each individual part also included an epigrammatic introduction, preceding each set of questions, which elucidated the nature of the questions in the specific part. Main questionnaire items can be found in the Appendix. Questionnaire layout was based on Bock and Pan (n.d.)
A total of 1,276 usable questionnaires were collected, for a response rate of 46 per cent. The demographic characteristics of the sample are presented in Table II. Tests have shown that there is no significant difference in the characteristics of the respondents who answered the questionnaire in time or late, who come from private or public banks, and finally, between people who received the questionnaire through the post (or e-mail) and those who received them in person.
5.2 Measurement development The measures developed in order to operationalise the constructs of the research model were adopted mainly from past studies on knowledge-sharing behaviour (Bock and Kim, 2002; Lee, 2001; Ryu et al., 2003; Lin and Lee, 2004). These measures were based on the guidelines for structuring a TPB questionnaire offered by Ajzen (2002).
Content validity was established through questionnaire pre-testing (Zikmund, 2003). Initially, an internal testing process was carried out with the participation of five academics, who mainly commended on questionnaire wording and ease of understanding. Some wording modifications were made in order to ensure that the original text was clearly interpreted in the target language, i.e. Greek. Then, the translated questionnaires were validated using the “back-translation” method, i.e. “translating back into the original language to establish equivalence with the
Measure Items Frequency Percentage
Ownership State-owned 108 39.1 Private 168 60.9
Gender Male 130 47.1 Female 146 52.9
Age 22-29 years 140 50.7 30-39 years 84 30.5 40-60 years 52 18.8
Education High school or below 64 23.2 Undergraduate studies 182 65.9 Post graduate studies 30 10.9
Designation Manager 26 9.4 Deputy Manager 14 5.1 Head of Department 26 9.4 Deputy Head of Department 16 5.8 Employee/Clerk 186 67.4 Other 8 2.9
Work experience 0.5-4.5 years 122 44.2 5.0-9.5 years 66 23.9 10.00 ! years 88 31.9
Notes: Less experienced: 0.5 years; most experienced: 35 years Table II. Profile of respondents
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original version” (Francis et al., 2004), with the original version being that of Ryu et al. (2003) and Bock and Pan (n.d.). The wording of some of the questions was slightly modified three times before the final format was established, based on remarks and suggestions offered by the pre-testing participants. Questionnaire items appeared in Appendix, reflect, to the extent this is possible, the actual Greek wording used in the final version of the questionnaire.
The study measured six constructs:
(1) knowledge-sharing behaviour (B);
(2) intentions to share knowledge (IN);
(3) attitudes toward knowledge sharing (AT);
(4) subjective norms about knowledge sharing (SN);
(5) PBC to knowledge sharing; and
(6) level of ITU.
All constructs were measured using multiple items and all items were measured using five-point Likert-type scale (Lin and Lee, 2004), as suggested by previous studies (Table I). At this point, it should be noted that actual knowledge-sharing behaviour was also measured using self-report scales. This method was selected in an attempt to capture, to the extend this is possible, all kinds of knowledge sharing, including every-day, informal and non-IT mediated exchanges, for which to the best of the authors’ knowledge, on objective measures have been yet developed.
5.3 Statistical analysis The descriptive characteristics of the sample (Table I) were assessed using SPSS 11.0 statistical package, based on the guidelines provided by Dimitriadis (2003). The research model (Figure 1) was tested using structural equation modelling (SEM) with latent variables (LISREL 8.7), as suggested by other researchers who studied the TRA or TPB models (Chang, 1998; Blue et al., 2001; Millar and Shevlin, 2003; Ryu et al., 2003; Lin and Lee, 2004; Bock et al., 2005). As it has been suggested, the structural equation approach has several advantages over traditional analyses (Bagozzi and Yi, 1989). Data were analysed using the two-step approach suggested by Anderson and Gerbing (1998) and applied to prior research in the field (Chang, 1998; Blue et al., 2001; Millar and Shevlin, 2003; Ryu et al., 2003; Lin and Lee, 2004). In the first step, a confirmatory factor analysis (CFA) was performed, which helps assess the adequacy of the measurement model (Chang, 1998), or in other words, “[. . .] the measurement models (or confirmatory factor models) specify how hypothetical constructs are measured in terms of the observed variables” (Lin and Lee, 2004). In the second step of the data analysis, the structural model is tested using SEM; structural equation models specify causal relationships among latent variables (Lin and Lee, 2004).
6. Data analysis and results 6.1 Measurement model 6.1.1 Content validity. An instrument can be valid on the grounds of the content of the measurement items (Straub, 1989). As Bock and Kim (2002) explain, content validity is related to how representative and comprehensive are the items that are used to create a scale. For this research, content validity was established through rigorous pre-testing.
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The definition of the constructs was based on the TPB and on past research using similar models.
6.1.2 Construct validity. Construct validity is actually an operational issue: “[. . .] it asks whether the measures chosen are true constructs describing the event or merely artifacts of the methodology itself” (Straub, 1989). There is a large number of aspects regarding construct validity offered by the psychometric theory (Bagozzi et al., 1991). Construct validity of an instrument can be tested in terms of convergent and discriminant validity (Straub, 1989). In this study, construct validity is assessed through confirmatory factor analysis (CFA), following a similar approach as other past studies (Bock and Kim, 2002; Ryu et al., 2003; Lin and Lee, 2004).
Convergent validity was tested by examining the factor loading of each construct (item), as well as composite reliability and variance extracted of the latent constructs, using CFA. The results of the measurement model fit are summarised in the following (Table III). In more detail, factor loadings ranged from 0.59 (SN4) to 0.94 (SN2), all of them exceeding the recommended cut-off value of 0.5, suggested by Straub (1989), for a sample of 276 observations at a 0.05 level of significance ( p , 0.05). It should also be noted that, there are suggestions in the literature of accepting a threshold of 0.35 for factor loadings (Hair et al., 1998, cited in Ryu et al., 2003).
Latent construct Item Factor loading
Composite reliability
Variance extracted
Attitude toward knowledge sharing (AT) AT1 0.80 0.8599 0.5518 AT2 0.76 AT3 0.71 AT4 0.70 AT5 0.74
Subjective norms about knowledge sharing (SN) SN1 0.75 0.8462 0.5857 SN2 0.94 SN3 0.72 SN4 0.59
PBC of knowledge sharing (PBC) PBC1 0.75 0.7901 0.4869 PBC2 0.76 PBC3 0.66 PBC4 0.61
Intention to share knowledge (IN) IN1 0.85 0.8961 0.6360 IN2 0.88 IN3 0.79 IN4 0.83 IN5 0.61
Knowledge-sharing behaviour (B) B1 0.71 0.8315 0.4978 B2 0.76 B3 0.75 B4 0.65 B5 0.65
Note: Factor loadings are from CFA Table III. Measurement model fit
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Composite reliability helps assess the internal consistency of the measurement model:
[. . .] the resultant coefficient is similar to that of Cronbach’s alpha, except that it also takes into account the actual factor loadings rather than assuming that each item is equally weighted in the composite load determination (Lin and Lee, 2004).
There are many propositions in the relative literature regarding the reliability measures. Chin (1998, cited in Bock et al., 2005) suggests that 0.7 should be the recommended value for a reliable construct, whereas Ryu et al. (2003) suggest a 0.8 cut-off value, while Bagozzi and Yi (1988) recommend the benchmark of 0.6. In this study, the composite reliability of the latent constructs exceeds even the highest of the above recommended cut-off values (0.8), apart from PBC that is only marginally below (0.7901).
Finally, variance extracted measures ranged from 0.6360 for intention to share knowledge (IN) to 0.4869 for PBC. Recommended threshold value for variance measures is 0.5 (Fornell and Larcker, 1981; Straub, 1989; Ryu et al., 2003). In this study, three constructs (AT, SN and IN) are above the recommended value of 0.5, whereas PBC and knowledge-sharing behaviour (B) are marginally below 0.5, that is 0.4869 and 0.4978, respectively.
In the research model shown in Figure 1, an additional construct the level of ITU, measured by six items was originally included. However, statistical analysis did not indicate good applicability of this construct and was excluded from the final investigated model.
6.1.3 Overall, model fit. The overall model fit was assessed using four common fit measures from two perspectives: absolute fit and comparative fit (Ryu et al., 2003). In more detail, the absolute fit measures used in the evaluation of the CFA model are: x
2/df), root mean square error of approximation, and goodness-of-fit index. Comparative fit index was used to measure comparative fit. The following (Table IV) summarises the overall fit indices of the CFA model. The CFA indicated that the measurement model fitted the data to a very satisfactory level, as all fit indices are above commonly accepted levels.
6.2 Structural model The first stage of analysis of the proposed research model (Figure 1), performed using CFA, indicated the adequacy of the measurement model (Tables III and IV). After dropping a latent construct (level of ITU), originally included in the research model (Figure 1), the theoretical model was tested using SEM, in order to examine the causal relationships among the remaining latent variables. The Figure 2 shows the structural model, as produced by LISREL 8.7, along with path coefficients and factor loadings.
Model-fit index Scores Recommended value
x 2/df 1.022 * * 1 , x 2/df , 2
Root mean square error of approximation (RMSEA) 0.013 * * ,0.1 Goodness-of-fit index (GFI) 0.87 * .0.9 (accepted if $ 0.8,
as suggested by Ryu et al. (2003)) Comparative fit index (CFI) 1.00 * * .0.9
Notes: *Marginal; * *accepted
Table IV. Overall fit of the CFA
model
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Moreover, in order to examine the validity of the hypothesised paths, the statistical significance of each structural parameter estimate was examined. The following Table (V) summarises the structural parameter estimates, significance levels and hypotheses tests results.
6.3 Hypotheses testing results H1 and H2 proposed a positive influence of attitude and subjective norms on intention to share knowledge, respectively. The resultant coefficients indicate that attitude toward knowledge sharing has the strongest direct affect on the intention to share knowledge (path coefficient: 0.57), followed by subjective norms (0.29). Both path coefficients were significant at the p , 0.05 level. These results seem to support Ajzen’s TPB that there is a positive effect of attitude and subjective norms on behavioural intention.
The results are also consistent with prior research results on knowledge-sharing behaviour, using the TRA and TPB. In more detail, Ryu et al. (2003) have found a positive influence of attitudes and subjective norms in behavioural intentions, with attitude having the strongest direct effect, followed by subjective norms. Lin and Lee (2004) and Bock et al. (2005) research findings also indicate a positive relationships among these variables. However, the direct effect on subjective norms is stronger than the effect of attitude on behavioural intention. These variances do not contradict the
Figure 2. The structural model
0.36
0.24
0.49
0.51
0.45
0.43
0.11
0.48
0.65
0.43
0.42
0.56
0.62 PBC4
PBC3
PBC2
PBC1
SN4
SN3
SN2
SN1
AT5
AT4
AT3
AT2
AT1
0.80 0.76 0.71 0.70 0.74
0.75 0.94 0.72 0.59
0.75 0.76 0.66 0.61
PBC
SN
AT 0.57
0.29
0.14
0.01
0.16
IN
0.85
IN1
IN2
IN3
IN4
IN5
B1
B2
B3
B4
B5 0.57
0.57
0.43
0.42
0.49
0.62
0.31
0.37
0.22
0.27
0.88 0.79 0.83 0.61
0.71 0.76 0.75 0.65 0.65
B
Hypothesis Path Path coefficient Remarks
H1 Attitude ! intention 0.57 * Positive supported H2 Subjective norms ! intention 0.29 * Positive supported H3 Perceived behavioural control ! intention 0.14 Positive but insignificant H4 Intention ! behaviour 0.16 Positive but insignificant H5 Level of ITU ! behaviour – Dropped H6 PBC ! behaviour 0.01 Positive but insignificant
Note: *Significant at the p , 0.05 level
Table V. Hypotheses testing results
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theory’s propositions regarding the predictors of behavioural intention. As Ajzen (1991) explains, the relative importance of the three predictors of intention is expected to vary across situations and behaviour. In other words, there may be situations where attitude alone can have a significant impact on intention, others where the former along with PBC are sufficient to account for intentions, or even some where all three predictors make independent contributions. Finally, Bock and Kim (2002) research findings also indicate a positive relationship of attitudes on intention to share knowledge; their study, however, does not examine the effect of subjective norms.
H3 examined the effect of PBC on behavioural intention. The estimated coefficient (0.14) indicated a small positive direct effect on intention to share knowledge. The path coefficient, however, was not significant at the p , 0.05 level, indicating that the hypotheses is only “partially” supported. This means that, although the relationship between subjective norms and intention was found to be positive, as the TPB (Ajzen, 1991) and past research (Ryu et al., 2003; Lin and Lee, 2004) suggest, the results for the PBC could only be characterised as inconclusive. These results can possibly be attributed to the number of the observations included in the statistical analysis. Hence, it could be suggested that, if the number of observations was higher, the path coefficient could become significant, and therefore the hypothesis fully supported.
Past research findings do not indicate a uniform effect of PBC on intention. In more detail, Lin and Lee (2004) research findings indicate that subjective norms pose the strongest effect on intention, in relation to attitudes and subjective norms. Ryu et al. (2003), on the other hand, found the subjective norms have the smallest direct effect on intentions in the TPB model. Note that the path coefficient from PBC to intention in Ryu et al. (2003) research was 0.140 ( p , 0.1), as also found in this study (Table V).
H4 examined the relationship between intention to engage in a knowledge-sharing activity and the actual behaviour of sharing knowledge. This hypothesis proposed that individuals’ intention to share knowledge has a positive effect on knowledge-sharing behaviour. The path coefficient (0.16) indicated a positive but weak relationship between these variable. The results, however, were not significant at the p , 0.05 level, as in the case of H3 discussed earlier. The indicated positive relationship does not contradict the suggestions of Ajzen’s (1991) theory; Lin and Lee (2004) also confirm the positive relationship. However, many people with positive intentions do not succeed in establishing a new behaviour, as Sheeran and Orbell (1999) point out. Moreover, Millar and Shevlin (2003) found that future behaviour was best explained by past behaviour, as past behaviour was found to have a stronger effect than intention on behaviour, when integrated in the TPB. These findings can, to a certain extent, elucidate the weak direct effect of intention to share knowledge on knowledge-sharing behaviour found in this study.
H6, on the other hand, proposed a positive effect of PBC on knowledge-sharing behaviour. The resultant coefficient indicated an extremely weak direct effect of PBC on B (0.01), which was also insignificant at the p , 0.05 level. The TPB suggests that, PBC, in conjunction with behavioural intention, can be used directly for the prediction of behavioural achievement (Ajzen, 1991). However, the weak direct effect of PBC on behaviour does not contradict theory propositions. Although both predictors make significant contribution to the prediction of behaviour, at any given situation, one may be more important than the other, or only one of the two may be significant (Ajzen, 1991). At this point it should be noted that the causal relationship between PBC and behaviour has not been examined in prior research investigating knowledge-sharing behaviour.
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Finally, H5, suggested a positive relationship between an individual’s level of ITU and knowledge-sharing behaviour (Figure 1), based on the Ajzen and Fishbein’s (1980) proposition that several external variables could have an affect when an intention is realised to perform a behaviour. However, the latent construct of level of ITU was dropped during the process of statistical analysis, as explained earlier. It should be noted, however, that Bock and Kim’s (2002) findings suggest that “[. . .] the individual’s level of ITU does not show a significant moderating effect on the knowledge sharing behaviour”.
In conclusion, the findings of this study (Table V) indicate that attitudes and subjective norms have a positive influence on the intention to share knowledge in the investigated sample of bank employees in Greece, and can be used, consequently, to predict behavioural intention to engage in knowledge sharing. In general, these results point toward:
. the necessity of creating a climate that would help individuals develop a more favourable attitude toward knowledge sharing; and
. the important role of the perceived social pressure by organisational member, such as peers, supervisors and senior management, on the intentions of individuals to share knowledge.
The effect of PBC on behavioural intention, as well as on knowledge-sharing behaviour, and the effect of intention on behaviour, although positive, were not significant for p , 0.05, and should be regarded as inconclusive. As it has been explained, this can be largely attributed to the sample size of this study. Moreover, it should be taken into account that the possible moderating effect of gender, educational level and work experience, as suggested by prior studies (Constant et al., 1994; Connelly and Kelloway, 2003; Miller and Karakowski, 2005). However, the findings overall seem to coincide with the propositions of the TPB, where this research model was based on. Moreover, the results are, to a large extent, consistent with prior research investigating knowledge-sharing behaviour (Bock and Kim, 2002; Bock et al., 2005), professional knowledge-sharing behaviour (Ryu et al., 2003; Lin and Lee, 2004), prediction of unethical behaviour (Chang, 1998), career information-seeking behaviour (Millar and Shevlin, 2003), and exercise among blue-collar workers (Blue et al., 2001), all of which applied the TPB and/or its predecessor, the TRA.
7. Conclusions 7.1 General conclusions KM has been gaining ground in the management agenda, since organisational knowledge has been realised as a key source of competitive advantage. However, the establishment of successful knowledge sharing initiatives is very hard to accomplish. This can be largely attributed to the fact that organisations are merely preoccupied with technology infrastructure, failing to focus on the individuals who are engaged in knowledge sharing.
In the light of knowledge sharing being in essence a human activity (Robertson, 2002), the main purpose of this study was to develop an understanding of the factors that influence individuals’ knowledge-sharing behaviour in the organisational context. In more detail, a research model based on Ajzen’s (1991) TPB was developed in order to examine the knowledge-sharing behaviour of bank employees in Greece, as relative
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literature advocates the need to focus our attention on the sharing behaviour of individual professional groups (Ryu et al., 2003). The choice of the banking industry was based on the belief that KM is very important for financial institutions, as various sources indicate. The change in the global business environment has led banks to rationalise their products and services and have also looked into KM in order to improve their competitiveness (Dzinkowski, 2001). It should also be noted that the banking industry in Greece is growing fast, offering a wide range of new products and services.
One of the main contributions of this study is that it is probably one of the very few that attempted to explore knowledge-sharing behaviour of the specific professional group, i.e. bank employees, using widely accepted social psychology theories. Moreover, the current study includes a large sample of private organisations while it is one of the few that examines knowledge sharing in the European context, whereas, past studies in the literature are mainly concerned with the public sector in Asian countries (Bock and Kim, 2002; Bock et al., 2005; Kankanhalli et al., 2005). This is probably important as research results may be influenced by the sample’s national culture characteristics. This research is also among a limited number of studies to examine individuals’ actual knowledge-sharing behaviour, as opposed to others in the literature, which are focused on the investigation of knowledge sharing intentions (Bock et al., 2005). Finally, the research model has tested the direct effect of PBC on knowledge-sharing behaviour, which, although suggested by theory, was not examined in other research models.
The research model used, identified the cognitive predictors of knowledge-sharing behaviour of bank employees. The research results indicated that the model fitted the data well and that findings are consistent with the propositions of the TPB, whose good applicability indicated by the statistical analysis, designate its use in organisational research. In more detail, attitudes and subjective norms were found to positively influence individuals’ intention to share knowledge. The research findings indicate that an individual’s attitude toward knowledge sharing is the primary factor influencing intention to share knowledge, meaning that whether a person actually shares knowledge with others primarily depends on his/her personal, favourable or unfavourable, appraisal or evaluation of the behaviour in question (Ajzen, 1991). Second, intention to share knowledge was found to be influenced by subjective norms, that is by the perceived social pressure to perform or not knowledge sharing (Ajzen, 1991).
Finally, the direct effect of PBC on intention and on behaviour, respectively, as well as the effect of intention on knowledge-sharing behaviour, although positive they are basically regarded as inconclusive. The inconclusiveness of the results regarding the above causal paths is possibly attributed to the small sample included in the research (276 observations), as well as to the potential effect of gender, educational level and work experience, as past research indicates. The TPB suggests that the stronger the intention to engage in a behaviour the more likely should be its performance. The estimated path coefficient (0.16) points toward a positive relationship, which however is not very strong. Past research has indicated that people with strong intention to choose a specific behaviour pattern do not always succeed in establishing that new behaviour (Sheeran and Orbell, 1999). To summarise, the research findings are consistent with both the TPB and past research on knowledge-sharing behaviour, and, on the whole, are believed to offer a significant behavioural perspective on knowledge sharing.
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7.2 Managerial implications Based on the research findings the following suggestion could be considered by organisations wishing to implement KM initiatives and promote organisational knowledge sharing. First, since attitude and subjective norms were found to influence individual’s intention to share knowledge, knowledge sharing initiatives should be targeted towards creating an environment that can positively influence those factors. As Ryu et al. (2003) have also suggested, the establishment of such a positive climate will require the promotion of several cultural factors, including professional autonomy, cohesiveness and communication structure. Bock et al. (2005) advocate that organisational efforts should emphasise the creation of a work context characterised by high levels of organisational citizenship. This way, mutual social relationships can be cultivated, which are considered as highly influencing knowledge sharing intentions.
Second, from the technological viewpoint, the establishment of a knowledge sharing system should promote the workplace communication (Ryu et al., 2003). Although IT cannot, at any degree, substitute for personal communication, the establishment of an efficient KM system could enhance knowledge sharing, especially in geographically dispersed organisations. Moreover, organisations should focus on the creation of communities of practice within the workplace, as human networks are the best way to achieve knowledge sharing (McDermott and O’Dell, 2001). Bock et al. (2005) suggest that managers should provide appropriate feedback to all employees about the achievements of “referent communities”, regardless of whether they are engaged in knowledge sharing. These actions, the authors explain, are closely related to exercised pressure of one’s referent group and enhancement of individual’s sense of self-worth. The importance of social pressure in order to engage in knowledge-sharing behaviour is consistent with the research findings indicating that subjective norms positively influences intention to share knowledge.
Bock and Kim (2002) explain that a positive attitude toward knowledge sharing is formed by the expectation that knowledge sharing is mutual and by the belief that one can contribute to improvements of organisational performance. However, special attention should be paid on turning positive attitude and intention into knowledge-sharing behaviour. This means that knowledge-sharing behaviour should be promoted through “enhancing the positive mood state for social associations which precedes knowledge sharing behaviours”, rather than incorporating sophisticated incentive and evaluation systems into KM initiatives (Bock and Kim, 2002; Bock et al., 2005). As Gurteen (1999) argues, if people understand that sharing what they know helps them: “[. . .] do their job more effectively; [. . .] retain their jobs; helps them with personal development and career progression; [. . .] and brings more personal recognition, then knowledge sharing will become a reality”. As a final point, it should be stressed that there is an increased need for organisations to include their knowledge sharing strategy into corporate strategy (Lin and Lee, 2004).
7.3 Limitations Some research limitations that do not allow for the generalisation of the findings are the following. First, since this research only included 276 observations, findings should be confirmed through a larger sample in order to increase generalisability. Second, the present study is solely concerned with a particular professional group,
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i.e. bank employees. For that reason, since this is a longitudinal and not a cross-sectional study, the results could not be generalised in other sectors. Third, the data collection was restricted to bank employees in Greece; consequently, due to the cultural factors that characterise the sample under investigation, the results may not be confirmed, when examining the same sector in other counties with different national cultures. As a general rule, in order to verify and generalise the research results, the research should be expanded geographically, as well as in other industries (Bock et al., 2005).
Fourth, the present study investigated possible motivators related to knowledge-sharing behaviour based on the TPB. This means that knowledge-sharing behaviour was examined solely focusing on salient beliefs, as the theory suggests, considering knowledge sharing “[. . .] as a very individualistic behaviour” (Bock and Kim, 2002). The TPB, however, does not take into account other factors that may inhibit knowledge-sharing behaviour, such as culture and social factors. As Bock et al. (2005) have also pointed out, the model used may have failed to notice other factors, indicated by other researchers, that impede knowledge sharing. These factors include: natural barriers such as time and space availability, cognitive barriers that inhibit personal communication, and structural barriers, such as authority and status hierarchies as well as functional boundaries, that restrain the development of personal relationships and the free information flow (Bock et al., 2005). Additionally, the present model does not take into account neither the possible moderating effect of education and work experience (Constant et al., 1994) nor gender influences (Connelly and Kelloway, 2003; Miller and Karakowski, 2005) on knowledge sharing, that has been suggested by previous research.
Finally, the variables of the TPB model were measured using self-report scales (Bock and Kim, 2002; Millar and Shevlin, 2003), meaning that the results of the study may be influenced by common method bias. Moreover, it should be considered that the answers could have been influenced by what is known as “social desirability”, which could greatly affect the quality of the responses. As previous studies using similar models suggest, more direct and objective measures should be developed (Bock and Kim, 2002), in order to examine the accuracy and validity of the conceptual model.
7.4 Suggestions for further research Considering the limitations of the study, it is necessary to examine knowledge-sharing behaviour in other industries and also include a larger sample, so as to confirm research findings. It would also be interesting to investigate further the potential differences of the knowledge-sharing behaviour between employees in the private and public banking sectors, especially in the higher hierarchical levels of the organisation. Moreover, in order to increase the exploratory power of the research model, additional factors should be considered, such as culture, leadership style, task structure (Ryu et al., 2003) and HRM practices (Kubo et al., 2001) to accurately reflect the effects of organizational framework, as well as the role of social networks. At the same time, research should be expanded to include relationships with costumers, partners and suppliers. The role of motivation, extrinsic in particular, should be further explored, as past research has provided with contradictory results as to their effect on knowledge-sharing behaviour. Finally, the direct effect of PBC on intention and on behaviour, respectively, and most importantly the effect of intention on
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knowledge-sharing behaviour, should be investigated further. There is also a need to develop more direct and objective measures for the independent variables (Bock and Kim, 2000), increasing the accuracy and validity of the conceptual model could be, as common method bias could be avoided and the effects various phenomena, such as “social desirability” can be diminished.
As far as the banking sector in particular is concerned, research should investigate further the role of regulatory surveillance, which has been found to lead to low-inter-firm knowledge sharing (Kubo et al., 2001). Finally, the effect of de-nationalisation, as well as that of mergers and acquisitions by foreign or domestic institutions, which have radically changed the banking environment, especially in Greece, on the KM efforts should be also explored.
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Appendix
Behaviour (B) How frequently do you use the following knowledge with your organisational members?
1 – Very rarely to 5 – very frequently (for all five items) Explicit knowledge Implicit knowledge B1 Reports, official documents B4 Experience, know-how B2 Manuals, methodologies, models B5 Expertise from education and training B3 Know-where, know-whom Level of ITU How frequently do you use the following IT to share your knowledge?
1 – Very rarely to 5 – very frequently (for all six items) ITU1 Bulletin board system ITU4 Chat-room ITU2 E-mail ITU5 Electronic document management system ITU3 Webpage ITU6 Knowledge repository, databases Intention to share knowledge (IN)
1 – Extremely unlikely to 5 – extremely likely (for all five items) IN1 I will always share my knowledge with my colleagues IN2 I will try to share my knowledge with my colleagues
more frequently in the future IN3 I will try to share my knowledge with my colleagues
in a more effective way IN4 I try to share my knowledge with my colleagues, if it
will be helpful to the organisation IN5 I intend to share my knowledge with my colleagues, if they ask Attitude toward knowledge sharing (AT) AT1 My knowledge sharing with other organisational members is good
1 – Very good to 5 – very bad AT2 My knowledge sharing with other organisational
members is very harmful 1 – Very harmful to 5 – very beneficial
AT3 My knowledge sharing with other organisational members is an enjoyable experience
1 – Very pleasant to 5 – very unpleasant AT3 My knowledge sharing with other organisational
members is valuable to me 1 – Very valuable to 5 – very worthless
AT4 My knowledge sharing with other organisational members is a wise move
1 – Very wise to 5 – very unwise Subjective norms about knowledge sharing (SN) SN1 It is expected of me that I share my knowledge
1 – Extremely unlikely to 5 – extremely likely SN2 Most colleagues that are important to me, believe that I should share my knowledge with others
1 – I should always to 5 – I should never SN3 Most colleagues that are important to me, share their knowledge with others
1 – Definitely true to 5 – definitely not true SN4 Colleagues, whose opinion I value, would approve of my knowledge-sharing with others
1 – Strongly approve to 5 – strongly disapprove PBC to knowledge sharing PBC1 For me to share my knowledge is always possible PBC2 If I wanted to, I could always share my knowledge
1 – Definitely true to 5 – definitely not true (for both items) PBC3 It is mostly up to me whether or not I share knowledge with others
1 – Strongly agree to 5 – strongly disagree PBC4 To what degree is it up to you to share your knowledge with others
1 – Absolute control to 5 – absolutely no control
Table AI. Questionnaire items
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About the authors Prodromos D. Chatzoglou is an Associate Professor of MIS in the Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece, and a visiting fellow at Greenwich University, London, UK. He received a Bachelor of Arts (BA) in Economics from the Graduate Industrial School of Thessaloniki, Greece, a Master of Science (MSc) in Management Sciences, and a Doctor of Philosophy (PhD) in Information Engineering both from UMIST, Manchester, UK. His research interests include information systems (IS) project management, knowledge management, e-business, IS economics, strategic management and business performance. His work was published in the Information Systems Journal, Automated Software Engineering Journal, European Journal of Information Systems, International Journal of Project Management and Information and Software Technology Journal, among others. He serves as a reviewer, and member of the scientific committee of several international journals. Finally, he has participated in many EU funded research projects as a coordinator or project leader. Prodromos D. Chatzoglou is the corresponding author and can be contacted at: [email protected]
Eftichia Vraimaki is a PhD candidate in the Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece. She holds a Bachelor of Arts (BA) in Librarianship and Information Science from the Technological Educational Institute of Athens, Greece, and a Master of Science (MSc) in Finance and Financial Information Systems from the University of Greenwich, London, UK. She has presented some of her research work in scientific conferences. She is also involved in a couple of EU funded research projects. Her research interests include knowledge management, human resources management, psychology and library information systems.
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