Knowledge sharing behaviourand its predictors

Zahra TohidiniaDepartment of Business Management, Faculty of Management,

University of Tehran, Tehran, Iran, and

Mohammad MosakhaniDepartment of Information Technology Management, Faculty of Management,

University of Tehran, Tehran, Iran

Abstract

Purpose – This paper aims to evaluate the influence of different factors on knowledge donation andcollection. The examined factors are among those determinants which comprise different aspects ofknowledge sharing behaviour in an organisational context.

Design/methodology/approach – Based on the widely accepted theory of planned behaviour(TPB), we tried to develop a comprehensive model. The model covered different individual andorganisational factors. Responses to a total of 502 questionnaires were considered. Structural equationmodelling was then used to test the research model and hypotheses.

Findings – This research evaluated the influence of a series of potential factors on knowledgesharing behaviour. While perceived self-efficacy and anticipated reciprocal relationships had apositive impact on attitude toward knowledge sharing, expected extrinsic rewards did not show asignificant relationship with this variable. Organisational climate had a positive impact on subjectivenorms about knowledge sharing. In addition, the level of information and communication technologyusage reflected a positive effect on knowledge sharing behaviour. Finally, significant relationshipswere found between the TPB elements.

Originality/value – This paper has tried to provide a comprehensive understanding aboutknowledge sharing facilitators in the oil industry. Since there was a lack of such research in an Iraniancontext, this paper can provide theoretical basis for future researches as well as practical implicationsfor managers and practitioners.

Keywords Knowledge sharing, Behaviour, Iran

Paper type Research paper

1. IntroductionToday’s economy has shown the importance of knowledge and intellectual capital toorganisations. Current trends have given birth to new concepts such as “knowledgemap” (Robertson, 2002) and “knowledge broker firms” (Hargadon, 1998). In order to gaina competitive advantage, organisations tend to leverage their knowledge-relatedcompetencies (Kankanhalli et al., 2005). Continuous knowledge management (KM) canpromote organisational innovation and play a key role in the organisation’s success(Nonaka and Takeuchi, 1995). In addition, problems such as maintaining, locating andapplying knowledge have intensified the importance of organisational KM (Alavi andLeidner, 2001). Without their employees, firms are unable to develop knowledge (Lahtiand Beyerlein, 2000). Blair believes that when organisation’s employees “haveknowledge” that is beneficial to their organisation, they own something more than thedata and information stored in the organisation’s information systems (Blair, 2002).

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Received 18 November 2009Revised 16 December 2009

Accepted 29 December 2009

Industrial Management & DataSystems

Vol. 110 No. 4, 2010pp. 611-631

q Emerald Group Publishing Limited0263-5577

DOI 10.1108/02635571011039052

As Tsai suggests, while each unit has its own specialised knowledge it can also learnfrom other organisational units (Tsai, 2001). Thus, one of the main processes that needsto be supported is knowledge sharing (Davenport and Prusak, 1998; Cabrera andCabrera, 2002; Hendriks, 1999; Bock et al., 2005). However, the organisational andindividual enablers of knowledge sharing are not properly clarified (Connelly andKelloway, 2003).

Through the centuries, knowledge sharing has been of benefit to both individuals andgroups (Reid, 2003). Although there are many benefits associated with knowledgesharing (Kautz and Mahnke, 2003), for the most part, its facilitators are unknown(Szulanski, 1996; Wiig, 1997). Moreover, the main studies in knowledge sharing fieldhave been carried out in Western and South-East Asian countries. Clearly, only fewstudies have been conducted in Iranian organisations. Iran has a relatively low Hofstederanking in individuality and the society is collectivist rather than individualist(Hofstede, 1997). Hence, a study on knowledge sharing dimensions can uncover manyimplications for both practitioners and managers. Such a research especially in the oilindustry, which involves a variety of skills and expertise, can help providingappropriate conditions for organisational knowledge sharing.

2. Literature reviewA revision of literature regarding KM shows a variety of definitions and viewpoints toknowledge and KM (Small and Sage, 2005/2006). Nonaka recognizes knowledge as amultidimensional concept (Nonaka, 1994); likewise Davenport and Prusak (1998)recognize knowledge as something deeper and richer than data or information. Bartoland Srivastava (2002) consider knowledge a broad concept which “includes information,ideas and expertise relevant for tasks performed by individuals, teams, work units andthe organisation as a whole”. Despite different wordings, there is a common agreementamong academics and practitioners that knowledge is a vital resource for organisations’success. The late Drucker (1999) states that most part of today’s work is knowledgework. Knowledge sharing practices coordinate organisational knowledge bases withknowledge workers and vice versa (Nonaka and Konno, 1998). Knowledge sharing takesplace when organisational members share organisation-related information, ideas,suggestions and expertise with each other (Bartol and Srivastava, 2002).

If organisations aim to enjoy knowledge sharing advantages, they will have toconsider a number of key factors. Information technology (IT) is considered as one of thedecisive factors in knowledge sharing. A number of reasons count for this such as thegrowing recognition of knowledge work, the ever-increasing complexity of jobs and alsothe speed of changes occurring around us (Huysman and Wulf, 2006). As one of thepotential influences on knowledge sharing, IT has been examined in many researches( Jarvenpaa and Staples, 2000; Huysman and Wulf, 2006). In a study conducted byJarvenpaa and Staples (2000), individuals strongly believed that the use ofcomputer-based information systems and electronic media contributed to providingvaluable information.

Another factor that seems to have a considerable impact on knowledge sharing isorganisational climate. To some practitioners, creating a knowledge sharing culture isone of the main concerns when devising a KM program (Reid, 2003). Without a properatmosphere in organisations, other attempts to share knowledge might be pointless. Ameagre social climate in an organisation might lessen the level of engagement in

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knowledge sharing (Van Den Hooff and Van Weenen, 2004). In addition, the lack of anaspiring culture to communicate and explore new ideas may become a major barrier toknowledge sharing (Sun and Scott, 2005). Concerned about these kinds of setbacks,managers try to provide favourable climate for knowledge sharing. In addition,managers play some other important roles: they grant extrinsic rewards (Saleh andWang, 1993; Kankanhalli et al., 2005; Bock et al., 2005) provide IT facilities (Anantatmulaand Kanungo, 2008) and involve in knowledge auditing (Tsui, 2005). But even whenorganisations provide technological facilities and demand employees to share theirknowledge, in most cases, employees are the ones who finally decide whether to sharetheir knowledge (Constant et al., 1994). Although motivation and expertise mightaccount for individual participation in knowledge sharing (Wang and Lai, 2006) it is notalways easy to predict when and why employees share their knowledge (Duguid, 2005).Thus, individual factors are also among those key elements that need to be consideredwhile studying knowledge sharing behaviour (Nonaka, 1994; Constant et al., 1994;Jarvenpaa and Staples, 2000; Bock et al., 2005; Wasko and Faraj, 2005; Kankanhalli et al.,2005; Kuo and Young, 2008).

3. Research model and hypothesesSince our focus was on knowledge sharing behaviour, we developed our model based onthe theory of planned behaviour (TPB) (Ajzen, 1991). This theory implies that a person’sintention is a key determiner of behaviour (Sheeran and Orbell, 1999). Each of the TPBelements including attitude toward behaviour, subjective norms, perceived behaviouralcontrol and intention is counted as an aspect of actual behaviour (Ajzen, 1991).

Attitude toward a specific behaviour is defined as a person’s evaluation of that behaviourwhen deciding to perform it (Kim et al., 2009). Attitude toward knowledge sharing can bepredicted by evaluating employee’s belief about knowledge sharing (Ajzen and Fishbein,1970), furthermore the perceived consequences of knowledge sharing can influence attitudetoward this behaviour (Chiou, 1998). Behavioural control is the person’s perception of theextent to which s/he has control over a specific behaviour (Ajzen and Fishbein, 2005).People’s knowledge sharing is affected by their confidence in the opportunities andresources that enables them to share their knowledge (Ajzen, 1991). Norms are both explicitand implicit rules of a group that identify the range of acceptable behaviours (Thibault andKelley, 1952). Subjective norms refer to the person’s perception of the expected behaviourwithin a special group and in a certain situation (Ajzen and Fishbein, 1970). Intentions areaffected by the person’s approval of distinctive norms; thereby the person tends to adapt tonorms and is rationally criticisable whenever s/he ignores them (Bratman, 2009). In otherwords, intentions are formed by the motivational factors that affect behaviour; they areindicators of people’s willingness to try hard (Ajzen, 1991).

As Ajzen and Fishbein suggest it’s possible to add some other behaviour-specificvariables to the TPB (Ajzen and Fishbein, 2005); thus, we reinforced the TPB model byadding some other factors that were considered as effective predictors by previousstudies (Armitage and Conner, 1999; Kankanhalli et al., 2005; Bock et al., 2005; Kuo andYoung, 2008).

When organisational members build close relationships with one another, theydevote a notable amount of time to share their mutual problems and concerns (Huang,2009). In some studies it was found that anticipated, open-ended future interaction hasa positive effect on cooperation behaviour (Heide and Miner, 1992). A set of moral

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transformations emerge when people have to interact for a long time (Thibault andKelley, 1952). Infrequent relationships might lead to weak knowledge sharing(Szulanski, 1996). If a person presumes to have enduring relationship with others, s/heis likely to share her/his knowledge with them; thus we propose our H1a as below:

H1a. Anticipated reciprocal relationships will positively influence attitude towardknowledge sharing.

Self-efficacy is defined as the person’s beliefs about his/her capabilities to produce desiredeffects (Bandura, 1994). Perceived self-efficacy leads to the development of those skills thatcan consequently result in related behaviour patterns (Bandura, 1986). Given the goalspeople set, self-efficacy is known as one of the most important encouraging predictors ofpeople’s performance (Heslin and Klehe, 2006). In a knowledge-sharing context,self-efficacy has showed to be one of the main determinants in forming optimistic attitudetoward knowledge sharing (Ye et al., 2006). So we propose the H1b as follows:

H1b. Perceived self-efficacy will positively influence attitude toward knowledgesharing.

Many organisations have established reward systems in order to motivate employeesto share their knowledge (Bartol and Srivastava, 2002). Rewards are likely to affectpeople’s behaviour (Homans, 1974). Absence of clear reward and recognition systemsmay frustrate employees to share their knowledge (Riege, 2005). Introducing a properknowledge sharing incentive system can promote organisational members’ knowledgecontribution (Chua, 2003). Thus, we propose the following hypothesis:

H1c. The level of expected extrinsic rewards will positively influence attitudetoward knowledge sharing.

According to Schneider et al. (1996), the climate of an organisation results from thefeeling of its members; these inferences are in turn built upon policies, practices,procedures and motives that members face in organisations. In this regard, the maintheme of an organisation’s climate is the common discernment of its members’ about theorganisation as a whole (Ashkanasy, 2008). The type of valued behaviour, a congruentnotion with organisational climate, is likely to affect employees’ performance (Hofmannet al., 2003). The results of a research conducted by Hoegl et al. show that organisation’sclimate has a positive effect on knowledge sharing activities (Hoegl et al., 2003); thus, wepropose the following hypothesis:

H2. Organisational climate will positively influence knowledge sharing subjectivenorms.

Attitude toward knowledge sharing has been examined in a number of previous studies(Bock et al., 2005; Lin and Lee, 2004). Attitudes can be a mediator between personalfactors and knowledge sharing intention (De Vries et al., 2006). Attitudes influence aperson’s evaluation of a particular behaviour (Blue et al., 2001). Moreover, attitudes are amajor part of cognitive system and have the potential to influence the intention to shareknowledge (Sun and Scott, 2005). Thus, we propose the following hypothesis:

H3a. Attitude toward knowledge sharing will positively influence intention toshare knowledge.

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Perceived subjective norms are also among the decisive factors that can influenceintention toward a specific behaviour. Subjective norms have shown a significantrelationship with knowledge sharing intention in a number of studies (Ryu et al., 2003;Lin and Lee, 2004). Perceived subjective norms are indicators of people’s willingness tocomply with others (Blue et al., 2001). Since people like to be identified and accepted byother members of their organisation, perceived subjective norms play a key role informing their intention to share knowledge (Sun and Scott, 2005). This results in thefollowing hypothesis:

H3b. Subjective norms regarding knowledge sharing will positively influenceintention to share knowledge.

Perceived behavioural control has also been considered as one of the main variables inprevious studies (Blue et al., 2001; Ryu et al., 2003; Lin and Lee, 2004). In their study onknowledge sharing behaviour, Lin and Lee (2004) found that perceived behaviouralcontrol had a positive effect on intention to share knowledge. The lack of perceivedbehavioural control may negatively affect intention to share knowledge (Ryu et al.,2003). Intention to perform behaviour can be affected by the perception of the personabout the level of his/her control over that behaviour (Blue et al., 2001). Thus, wepropose the following hypothesis:

H3c. Behavioural control over knowledge sharing will positively influenceknowledge sharing intention.

People’s intention to share knowledge is a determiner of desired behaviour (Ryu et al.,2003). As a major element in the TPB framework, intention to share knowledge canhave a significant effect on knowledge sharing behaviour. Findings of previous studiesshow the positive effect of intention on knowledge sharing behaviour (Lin and Lee,2004). Thus, we propose the following hypotheses:

H4a. Intention to share knowledge will positively influence knowledge donation.

H4b. Intention to share knowledge will positively influence knowledge collection.

IT can do a lot more than just storing and retrieving data (Tsui, 2005). By improvingaccess to knowledge and removing temporal and spatial obstacles between knowledgeworkers, information and communication technology (ICT) can enhance knowledgesharing levels (Hendriks, 1999). ICT and its ability to spread knowledge acrossdifferent units of an organisation may allow a better understanding of the complexorganisational environment (Coakes, 2006). Hence we propose the last two hypothesesas follows (Figure 1):

H5a. The Level of ICT use will positively influence knowledge donation.

H5b. The level of ICT use will positively influence knowledge collection.

4. Research methods4.1 Sample and data collectionBased on previous studies (Blue et al., 2001; Bock et al., 2005; So and Bolloju, 2005; Lin,2007), research questionnaire was developed. For selecting the industry we wereprimarily concerned with two criteria: first, the industry to which KM practices were

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important. Second, the industry that had developed appropriate IT infrastructures forknowledge sharing (Kim and Lee, 2006). Oil industry was qualified in terms of both of thecriteria and we chose it as our research’s target industry. In order to find possiblecomposition or content problems in research questionnaire, 20 participants from fiveoil companies were asked to fill the questionnaire and comment on it. Based on theircomments, the questionnaire was revised and then reconsidered using “backtranslation” technique with the guidelines provided by Francis et al. (2004). About tenof the 50 main oil companies listed by Iran’s oil ministry were selected at random.Because of our previous research experience with this industry, we predicted theresponse rate to be between 35 and 45 per cent. Thus, 120 employees were randomlyselected from each of the ten companies and 1,200 questionnaires were distributed. Afterarrangements with companies, nine out of the ten companies preferred the distributorsto deliver the questionnaires personally, however, one of the companies requested thequestionnaires to be sent by mail; accordingly the questionnaires were sent to thatcompany by express mail service post. Likewise for returning the questionnaires, therespondents had the option whether to deliver the questionnaires to the person in chargeof collecting the questionnaires or send it by mail. About 97 per cent of the respondentspreferred to deliver the questionnaires to the person in charge of collecting thequestionnaires and only 3 per cent of the employees sent their questionnaires by mailmainly in less than two weeks. Meanwhile it is noteworthy to say that for all thequestionnaires, a return stamped envelope was enclosed. Of the total of 1,200questionnaires distributed, 502 were usable which gave a response rate of 41.8 per cent.

Figure 1.Research model

H4b

H1a

H3c

H3b

H3a

H2

H1c

H1b

Perceivedbehavioural

control

Anticipatedreciprocal

relationships

Perceivedself-efficacy

Expectedextrinsicrewards

Organizationalclimate

Attitude towardknowledge

sharing

Subjectivenorms regarding

knowledgesharing

Intention toshare

knowledge

Level of ICTusage

Knowledgedonation

Knowledgecollection

H4a

H5a

H5b

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4.2 MeasuresRevised questionnaire included six pages; the first page was the cover page in whichthe research purpose and necessary definitions were stated; the last page was devotedto demographic measures. Most measures were adapted from the TPB questionnairedesigned by Ajzen (2002) together with measures applied in previous studies mainlywith a TPB context (Blue et al., 2001; Bock et al., 2005; So and Bolloju, 2005; Lin, 2007).All measures were devised in a multiple item format. A five item Likert scale (rangingfrom 1 – strongly disagree to 5 – strongly agree) was used for all measures.

A list of items for each of the measures is presented in the Appendix, Table AI.Anticipated reciprocal relationship referred to the person’s perception about formation or

improvements in his/her relationships with other members as a result of knowledge sharing.Anticipated reciprocal relationship was measured by five items all adapted from Bock et al.(2005). Self-efficacy referred to people’s self-judgment regarding their ability to shareknowledge; self-efficacy was measured by five items all adapted from Bock et al. (2005).Expected extrinsic rewards referred to the perception of employees regarding receivingorganisational rewards as a result of their knowledge sharing behaviour (Kankanhalli et al.,2005). Expected extrinsic rewards were measured by four items adapted from Kankanhalliet al. (2005) and Bock et al. (2005). Organisational climate was considered as the employee’spositive or negative feeling regarding organisational environment. Organisational climatewas measured by five items adapted from Saleh and Wang (1993) and Bock et al. (2005).Attitude toward knowledge sharing pointed to the favourable or unfavourable evaluation oforganisational members about knowledge sharing. Attitude toward knowledge sharingwas measured by five items adapted from Ajzen (2002). Knowledge sharing subjectivenorms concerned the member’s perceptions regarding others’ expectations from him/herabout knowledge sharing. Perceived subjective norms were measured by four itemsadapted from Ajzen (2002) and So and Bolloju (2005). Knowledge sharing behaviouralcontrol focused on the perceived presence and power of factors that may help or hinderknowledge sharing. Behavioural control was measured by four items adapted from Ajzen(2002). Intention to share knowledge referred to the consequence of beliefs and attitudesregarding knowledge sharing. Intention to share knowledge was measured by four itemsadapted from Ajzen (2002) and Blue et al. (2001). Level of ICT usage referred to theavailability of IT facilities within the organisation. Level of ICT usage was measured by fouritems adapted from Lin (2007). Finally, knowledge sharing donation and collection wereeach measured by four items, all adapted from De Vries et al. (2006).

5. Results and data analysisTable I lists the respondents’ profile including demographic characteristics. Dataanalysis was carried out by using structural equation modelling; which is able to testcasual relationships between constructs with multiple items ( Joreskog and Sorbom,1996). This approach was applied in a significant number of studies with a theme of theTPB (Kuo and Young, 2008; Bock et al., 2005; Ryu et al., 2003; Blue et al., 2001). We usedLISREL program to test the measurement model and examine the casual relationshipsbetween latent variables.

5.1 Scale validationFirst, we performed a confirmatory factor analysis (CFA) to evaluate the overallmeasurement model. In order to evaluate the validity of measurement model,

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convergent validity and discriminant validity were assessed. Convergent validity isthe degree to which factors that are supposed to measure a single construct, agree witheach other ( John and Benet-Martinez, 2000). We tested convergent validity byassessing factor loadings; which should be significant and exceed 0.5 (Straub, 1989),composite reliabilities which should exceed 0.6 (Bagozzi and Yi, 1988), and the averagevariance extracted (AVE) that should be more than 0.5 for all constructs (Fornell andLarcker, 1981). In our model, all the factor loadings and composite reliabilities fall inthe acceptable ranges and are significant at the 0.01 level. Factor loadings range from0.9 to 0.72 and all composite reliabilities are above 0.6. Moreover, the AVE ranges from0.58 to 0.77 meaning that our model meets the convergent validity criteria. Weevaluated the internal reliability of scales by Cronbach’s alpha; this statistic was higherthan 0.7 for all constructs (Hair et al., 1998). The Appendix, Table AI shows factorloading, composite reliability, Cronbach’s alpha and AVE of constructs in themeasurement model.

Discriminant validity is the degree to which, factors that are supposed to measure aspecific construct do not predict conceptually unrelated criteria ( John andBenet-Martinez, 2000). We used Fornell and Larcker’s approach to assess discriminantvalidity (Fornell and Larcker, 1981). In this approach the AVE for each construct shouldbe higher than the squared correlation between the construct and any of the other

Demographic characteristics Frequency Percentage

GenderMale 395 78.7Female 107 21.3Age21-25 20 4.026-30 119 23.731-35 63 12.536-40 66 13.1Over 40 225 44.8Missing 9 1.8EducationHigh school 74 14.7Collage (2 years) 36 7.2University (4 years) 272 54.2Graduate school 120 23.9Work experience0-3 years 53 10.63-6 years 67 13.36-9 years 64 12.79-12 years 99 19.7Over 12 years 212 42.2Missing 7 1.4PositionDirector 16 3.2Manager 30 6.0Chief employee 194 38.6Employee 228 45.4Other 34 6.8

Table I.Profile of respondents

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constructs (Fornell and Larcker, 1981). Table II indicates that the measurement modelhas satisfactory discriminant validity.

We assessed the measurement model fit by evaluating:. Absolute fit measures including observed normed x 2 (x 2/df), goodness of fit

index (GFI) and root mean square error of approximation (RMSEA).. Incremental fit measures including normed fit index (NFI), adjusted goodness of

fit (AGFI) and comparative fit index (CFI).. Parsimonious fit measures including parsimony goodness-of-fit index (PGFI)

and parsimony normed fit index (PNFI).

Table III shows the overall fit indices of our model.As shown in Table III, CFI (x 2/df), RMSEA, NFI, PGFI and PNFI, all met

satisfactory levels (Ryu et al., 2003; Lin, 2007). Since all fit indices were abovecommonly accepted levels, we can conclude that measurement model fits the data welland thus is able to explain the research hypotheses.

5.2 Hypotheses testingTable IV and Figure 2 show the results of hypothesis testing of the structuralrelationships among the latent variables. By H1a, H1b and H1c, respectively, weexamined the effects of perceived self-efficacy, anticipated relationships and expectedextrinsic rewards on the attitude toward knowledge sharing. The results show thatwhile both perceived self-efficacy and anticipated relationships positively influencedattitude toward knowledge sharing, expected extrinsic rewards had no significantrelationship with attitude toward knowledge sharing. Thus, H1c was not supported.Furthermore, organisational climate was found to be influential on knowledge sharingsubjective norms, supporting H2. H3a, H3b, H3c, H4a and H4b, which considered theTPB framework, were all supported. This means that attitudes, subjective norms aboutknowledge sharing and perceived behavioural control had a positive effect on intentionto share knowledge. Intention to share knowledge, in turn, influenced knowledgecollection and donation. Finally, the level of ICT usage positively influenced bothknowledge collection (H5a) and knowledge donation (H5b).

6. Discussion and conclusionsBased on the TPB, this research aimed to examine knowledge sharing behaviour and itspredictors. In order to uncover the potential influences on knowledge sharing behaviour,a model was devised with a TPB framework. The analysis of research results showed theoverall consistency of findings with the model and also the previous studies (Kuo andYoung, 2008; Kim and Lee, 2006; So and Bolloju, 2005; Blue et al., 2001). As hypothesizedand supported by previous studies (Ye et al., 2006; Bock et al., 2005; Kankanhalli et al.,2005), higher levels of perceived self-efficacy reinforced positive attitudes towardknowledge sharing. In other words, in this study, the employees who felt that by sharingtheir knowledge they will contribute to the achievement of desired outcomes were morelikely to share their knowledge. Research findings also indicate that anticipatedreciprocal relationships have a positive impact on favourable attitudes towardknowledge sharing. This finding was consistent with that of the study conducted byBock et al. (2005) about the effect of anticipated reciprocal relationships on attitudetoward knowledge sharing. This means that employees’ assumptions about future

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Table II.Discriminant analysis

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relationships with other organisational members will improve their attitudes towardknowledge sharing. Cooperating employees tend to exchange ideas and share theirknowledge (Yilmaz and Hunt, 2001). Among the factors considered as influentialon knowledge sharing, organisational rewards did not demonstrate a significantrelationship with attitude toward knowledge sharing; several reasons might count forthis. Since knowledge sharing is a sensitive behaviour, an effective reward systemneeds to be encouraging and goal oriented. The reward systems that employees do notperceive as contingent (Bartol and Srivastava, 2002) or performance based (Kim and Lee,2006) might fail to support knowledge sharing behaviour. The discrepancies betweenwhat managers and employees perceive as an acceptable reward might also be areason for the inefficiency of reward systems in promoting knowledge sharing (Bocket al., 2005). In addition, non-monetary rewards such as social recognition can be moremeaningful to employees than pecuniary rewards (Cabrera and Cabrera, 2002). Researchresults suggest that organisational climate has a positive effect on subjective

Fit index Scores Recommended cut-off value

Absolute fit measuresChi-squares/degree of freedom (x 2/df) 1.895a #2a; #3b; #5b

GFI 0.86b $0.90a; $0.80b

RMSEA 0.042a ,0.08a; ,0.1b

Incremental fit measuresNFI 0.97a $ 0.90a

AGFI 0.84b $0.90a; $0.80b

CFI 0.98a $0.90a

Parsimonious fit measuresPGFI 0.77b The higher, the betterPNFI 0.90a The higher, the better

Notes: Acceptability: aacceptable; bmarginal

Table III.Overall fit indices of the

CFA model

Hypothesis Hypothesized path Path coefficient Remarks

H1a ARR ! ATK 0.60 * SupportedH1b PSE ! ATK 0.55 * SupportedH1c EER ! ATK 20.05 Not supportedH2 OCL ! SUN 0.63 * SupportedH3a ATK ! ISK 0.50 * SupportedH3b SUN ! ISK 0.42 * SupportedH3c PBC ! ISK 0.57 * SupportedH4a ISK ! KDN 0.42 * SupportedH4b ISK ! KCN 0.25 * SupportedH5a ICT ! KDN 0.42 * SupportedH5b ICT ! KCN 0.61 * Supported

Notes: *Significant at the p , 0.01 level; ARR, anticipated reciprocal relationships; PSE, perceivedself-efficacy; EER, expected extrinsic rewards; OCL, organisational climate; ATK, attitude towardknowledge sharing; SUN, subjective norm; PBC, perceived behavioural control; ISK, intention to shareknowledge; KDN, knowledge donation; KCN, knowledge collection; ICT, level of ICT usage

Table IV.Hypothesis-testing

results

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norms toward knowledge sharing. Hence, like previous studies (Lee et al., 2006) we canconclude that organisational climate can support active knowledge sharing andpromote normative commitment (Van Den Hooff and Van Weenen, 2004) to shareknowledge. The results of this study showed compatibility with the TPB suggested byAjzen (1991) and tested by many researchers and practitioners (Blue et al., 2001; Ryuet al., 2003; Kuo and Young, 2008). As proposed by the TPB frame work, attitudes towardknowledge sharing, subjective norms and perceived behavioural control, positivelyinfluence the intention to share knowledge. Research results indicate that, at least in thissample, the higher the intention to share knowledge the higher knowledge donation andcollection. A number of researchers have reported a positive relationship betweenintention and the actual behaviour (Ryu et al., 2003). Finally, the effect of ICT usagewas examined. Research results show that the level of ICT usage has a positive impacton both knowledge collection and donation. ICT supports knowledge sharing (Huysmanand Wulf, 2006) and facilitates storing and disseminating knowledge (Merali, 2000).

In this study, organisational climate, individual perceptions and also IT had apositive impact on knowledge sharing behaviour. Thus, we suggest a systematic effortto improve knowledge sharing behaviour in organisations; an effort in which relevantfactors from different perspectives are considered. Nevertheless, providing appropriateconditions for knowledge contribution needs collective understanding and collaborationfrom all members at different organisational levels. As Saleh and Wang (1993) propose,the role of management in developing a desirable climate is undeniable (Saleh and

Figure 2.Results of structuralmodel

0.25*

0.60*

0.57*

0.42*

0.50*

0.63*

–0.05

0.55*

Perceivedbehavioural

control

Note: *p < 0.01

Anticipatedreciprocal

relationships

Perceivedself-efficacy

Expectedextrinsicrewards

Organizationalclimate

Attitude towardknowledge

sharing

Subjectivenorms regarding

knowledgesharing

Intention toshare

knowledge

Level of ICTusage

Knowledgedonation

Knowledgecollection

0.42*

0.42*

0.61*

IMDS110,4

622

Wang, 2003); hence, managers should plan to support an encouraging atmospherewithin organisations; this can in turn promote knowledge sharing behaviours.Moreover, research results indicate that individual factors had a great impact onknowledge donation and collection. Hence, if managers and practitioners tend to involvetheir organisational members in knowledge sharing activities, they should investigate“individual barriers” (Riege, 2005) and try to remove them. Finally, since ICT cancontribute to intelligent behaviours of employees and their organisations as a whole(Wiig, 1997), the availability and capability (Lee and Choi, 2003) of IT facilities shouldalso be considered.

7. Research limitations and guidance for future researchBefore making firm interpretations on our findings, research limitations should be takeninto account. First, there might have been some other factors that could affect knowledgesharing behaviour. These factors include demographic determinants such as age,gender and natural barriers such as time and space (Bock et al., 2005). In this paper, wedid not study the effects of these potential factors; considering these factors in futureresearches can reveal their influences more clearly. Moreover, we should note that thisstudy was carried out in Iranian organisations with a relative collectivism culture; beforeany generalization is made, the cultural influences must be taken into account. Forfurther validity, this model can be applied to samples in different countries or differentcultures. In addition, the research sample was only consisted of employees in the oilcompanies with appropriate IT infrastructures; generalizations of research findings todifferent industries must be made with cautious. In this regard, further studies in otherindustries can provide more valuable findings about knowledge sharing behaviour andits enablers. Furthermore, future studies can be done to extend and test research’s modeland examine the reasons behind the existing significant and non-significantrelationships. Meanwhile it is noteworthy to mention that our findings are consistentwith those of the studies carried out in countries in which cultures are characterized withhigh levels of collectivism; for example, in terms of TPB constructs and organisationalrewards, the results were similar to those of the research carried out by Bock et al. (2005)in South Korea. In terms of effect of the level of ICT usage on knowledge sharing,findings were similar to those of the research conducted by Kim and Lee (2006) in SouthKorea and also the findings of the study by Lin (2007) in Taiwan. Hence, we suggest thatthe type of dominant culture might also be a factor that influences knowledge sharingbehaviour and can be a topic for future researches. Finally, KM can result in otherorganisational outcomes that can consequently lead to competitive advantage (Nonakaand Takeuchi, 1995). Examining the influence of knowledge sharing on otherorganisational outcomes such as business intelligence and organisational learning areamong interesting themes for further studies.

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About the authorsZahra Tohidinia received her Bachelor of Business Management from Shahid BeheshtiUniversity in Tehran and she is currently studying Master of Business Management inUniversity of Tehran. Her research interests include organizational knowledge management,

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customer knowledge management and customer relationship management. Zahra Tohidinia isthe corresponding author and can be contacted at: z.tohidinia@ut.ac.ir

Mohammad Mosakhani is the Head of Information Technology Management Department inUniversity of Tehran; he is also an Associate Professor of Management Information Systems(MIS) in the Faculty of Management, University of Tehran. Mosakhani received a Bachelor ofArts in Educational Sciences from Tabriz University, a Master of Business Management fromTarbiat Modares University and a Doctor of Philosophy (PhD) in System Management fromUniversity of Tehran. His research interests include KM and MIS. His works have beenpublished in a number of journals including Review of Business Information Systems,International Journal of Electronic Customer Relationship Management and a number ofconference proceedings.

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Table AI.

Knowledgesharing

behaviour

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