In this paper, we describe an empirical study that relates three personality traits (agreeable-

* Corresponding author. Tel.: +43 732 2468 9449; fax: +43 732 2468 9135.E-mail address: kurt.matzler@jku.at (K. Matzler).

Available online at www.sciencedirect.com

Journal of Economic Psychology 29 (2008) 301313

www.elsevier.com/locate/joep0167-4870/$ - see front matter 2007 Elsevier B.V. All rights reserved.ness, conscientiousness and openness) to knowledge sharing. In the existing literature consider-able attention has been paid to managerial inuences on knowledge sharing, technologicalsupport through information and communication systems, or individual characteristics like moti-vation or the perception of conict of interest or vulnerability. Instead we concentrate on therole that personal dispositions play in individuals knowledge sharing behavior. By means ofstructural equation modeling with PLS, we discover signicant correlations between the person-ality traits and knowledge sharing within teams of an engineering company. Our results clearlycontribute to the existing literature, as they oer empirical evidence of the impact of enduringindividual characteristics on knowledge sharing. 2007 Elsevier B.V. All rights reserved.

JEL classication: M54

PsycINFO classication: 3120; 3620

Keywords: Knowledge sharing; Tacit Knowledge; Personality traits; Agreeableness; Conscientiousness; OpennessPersonality traits and knowledge sharing

Kurt Matzler a,*, Birgit Renzl b, Julia Muller b,Stephan Herting c, Todd A. Mooradian d

a Department of International Management, Johannes Kepler University Linz,

Altenbergerstr. 69, 4040 Linz, Austriab Department of Strategic Management, Marketing and Tourism, University of Innsbruck,

Universitaetsstrasse 15, 6020 Innsbruck, Austriac Institute of Management, University of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerlandd Mason School of Business, College of William and Mary, Williamsburg, VA 23187-8795, USA

Received 10 February 2006; received in revised form 11 June 2007; accepted 29 June 2007Available online 7 July 2007

Abstractdoi:10.1016/j.joep.2007.06.004

302 K. Matzler et al. / Journal of Economic Psychology 29 (2008) 3013131. Introduction

As various surveys show, knowledge management still is one of the central topics inmanagement research (e.g., KPMG, 2000; Matzler, Rier, Hinterhuber, Renzl, & Stadler,2005; Serenko & Bontis, 2004). Two factors support this growing interest in knowledgemanagement (Cabrera & Cabrera, 2002). First, knowledge can be seen as an intangibleasset which is most valuable to the rm because of the intensication of globalization,acceleration in the rate of change and expansion in the use of information technology(Badaracco, 1991). Knowledge is a potential source of competitive advantage because itis unique, scarce, path dependent, causally ambiguous, and hard to imitate or substituteby others (Nanda, 1996). The development of the resource-based view of the rm (Wern-erfelt, 1984) and later the knowledge-based view (Boisot, 1998; Grant, 1996, 1997; Spen-der, 1996a) provides the theoretical framework of a whole discipline, namely knowledgemanagement, that emerged in the last years with its own communities, journals and con-ferences. Second, progress in information and communication technologies have renderedpossible to gather and process information from a variety of sources (Ferguson, Mathur,& Sha, 2005) and economically feasible to connect people from dierent departments,units, and geographically dispersed companies to communicate and exchange information(Anand, Manz, & Glick, 1998).

Knowledge sharing is important for companies to be able to develop skills and compe-tences, increase value, and sustain competitive advantages (see for example Grant, 1996;Spender, 1996b) because innovation occurs when people share and combine their personalknowledge with others. According to Nonaka and Takeuchi (1995) knowledge sharing isneeded to convert general ideas and concepts into products and services and thus for inno-vation. Thus the ability of transferring knowledge from one person/unit to another signif-icantly contributes to the organizational performance of rms (Argote, Ingram, Levine, &Moreland, 2000). Firms react to these requirements by introducing interdisciplinary workgroups in order to cope with complex tasks in the workplaces (see Grant, 1996). However,the sharing of knowledge and expertise is delicate because it implies conicts of interestamong the individuals involved (von Krogh, 1998) as the prominent example of opensource projects shows (Gaechter, Haeiger, & von Krogh, 2004). There are also other fac-tors that aect the decision whether to share or conceal knowledge, for example the par-ticular case of a social dilemma (e.g., Cabrera & Cabrera, 2002; von Krogh, 2002). Socialdilemmas are paradoxical situations in which individual rationality, i.e., maximizing thepersonal pay-o, leads to collective irrationality (Kollok, 1998). Increasing individualpay-o may evoke an individuals unwillingness to share knowledge.

Therefore, previous studies focused on individual aspects as predecessors of knowledgesharing, as shown, for example, by Osterloh and Frey (2000) who highlight the importanceof intrinsic and extrinsic motivation for knowledge sharing. Argote, Gruenfeld, andNaquin (2001) give another example and argue that knowledge sharing may evoke aware-ness of conict of interest or vulnerability, which can diminish the individuals motivationto share.

In this paper, we concentrate on another individual aspect that has been mostly ignoredby the knowledge management literature: personal dispositions are expected to aect theprocess of knowledge sharing. More specically, we examine which role three personalitytraits, namely agreeableness, conscientiousness, and openness, play as determinants of the

employees knowledge sharing behavior.

manaknowway o

can be articulated and codied in writing or symbols and can be shared easily (Zander &Kogut, 1995). However, only a small part of our knowledge is explicit. The most knowl-edge to be shared is tacit, or embodied in practice and routines (Nelson & Winter, 1982)and thus non-codiable. This turns knowledge into a sticky element which is dicult toshare (Kogut & Zander, 1992; von Krogh, Roos, & Slocum, 1994). The distinctionbetween these two dimensions of knowledge is essential considering transferability andsharing of knowledge, but it must be noted that knowledge always consists of both, thetacit and the explicit dimension. Knowing is a process, which can be seen as an act of com-bining tacit and explicit knowledge in light of a specic action (see Polanyi, 1966, p. 6; Tso-ukas & Vladimirou, 2001, p. 978). The context of the particular action is crucial or, asOrlikowski, 2002, p. 251, knowing is inseparable from action because it is constitutedthrough such action. Thus, knowledge sharing involves transferring knowledge fromone specic context into another. Previous studies focused mainly on the explicit dimen-sion of knowledge (e.g., Cummings, 2004; Hansen, 2002); in this study, we regard the tacitdimension including the following ve types of knowledge: embrained, embodied, encul-tured, embedded, and encoded knowledge (see Blackler, 1995): embrained knowledge isrelated to conceptual skills and cognitive abilities (or double-loop learning from Argyris& Schon, 1978, e.g., knowledge that from Ryles, 1949). Embodied knowledge is denedas action oriented, acquired by doing, and embedded in particular contexts (e.g., Polanyi,

1983)gement in particular. The dierence between the tacit and explicit dimension ofledge (Baumard, 1999; Nelson & Winter, 1982; Polanyi, 1966) concentrates on thef articulating knowledge. Explicit knowledge consists of facts, rules and policies thatIn the next section, we address the characteristics of knowledge and knowledge shar-ing demonstrating the particularities of the knowledge sharing process. Then we intro-duce personality traits and concentrate on the three traits that are especiallyimportant for knowledge sharing: agreeableness, conscientiousness and openness. Wehypothesize in which way agreeableness, conscientiousness and openness inuenceknowledge sharing. Thereafter we describe our empirical study and report the resultsof our analysis using structural equation modeling with PLS. Finally, we consider impli-cations for practice.

2. Properties of knowledge and knowledge sharing

The ability to share knowledge depends on the properties of knowledge, which inu-ence how easily knowledge can be shared and accumulated, how much and where it isretained and stored, and how easily it ows within and across an organization (Argote,McEvily, & Reagans, 2003, p. 574). Various distinctions of knowledge have been ana-lyzed (e.g., Blackler, 1995; Nonaka & Takeuchi, 1995). The rst distinction in specifyingknowledge properties is between knowledge and information. Knowledge is dierentfrom information:

[I]nformation is a ow of messages, while knowledge is created by that very ow ofinformation, anchored in the beliefs and commitment of its holder. . . . knowledge isessentially related to human action (Nonaka & Takeuchi, 1995, p. 58f).

Furthermore, it can be distinguished between the tacit and the explicit dimension ofknowledge that received great attention in the management literature and in knowledge

K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313 303. Encultured knowledge focuses on the shared understanding, socialization, and

acculturation (e.g., Orr, 1990). Embedded knowledge is located in systemic routines (e.g.,Granovetter, 1985; Nelson & Winter, 1982). Finally, encoded knowledge is codied inbooks, manuals and codes of practice and is made explicit through signs and symbols.

In our study we analyzed knowledge sharing considering these ve types of tacit andexplicit knowledge. Knowledge sharing happens when experience, know-how, expertise,etc. of one unit of an organization has an impact on another unit. Knowledge can beshared explicitly and implicitly: An example for explicit knowledge sharing is when oneperson/team communicates with another person/team about a specic practice whichhas worked out to be successful. Tacit knowledge sharing occurs without being able toarticulate the knowledge acquired, for example, one can gain from increased productivityin the tool without automatically knowing or being able to articulate how the tool hasbeen adapted (see for example Argote et al., 2000). As mentioned above, tacit knowledgeis deeply rooted in the applied context and the people involved. Thus, the individual aspectis crucial. Individuals dier in their behavior due to enduring personality traits. In the fol-lowing we analyze how personality traits aect knowledge sharing behavior.

3. Personality traits

A dramatic upsurge in personality scholarship across the last decade and a half hasgenerated important progress and scientic integration (Funder, 2001, p. 198). An essen-tial part of that revitalization has been the recognition of a universal high-level structuredened by ve broad domains (the Five-Factor Model or Big Five, comprising neu-roticism [vs. emotional stability], extraversion, agreeableness, openness to experience [orintellect], and consciousness). This ve-factor structure serves as a latitude and longi-tude for personality research, organizing, harmonizing, and integrating previously dis-connected taxonomies and ndings (Funder, 2001; Ozer & Reise, 1994, p. 361).Important, related advances include improved and increasingly-specic understandingsof the biophysiological, neurological, and genetic foundations of personality, which clarifythe origins and the content of observable individual dierences (e.g., Zuckerman, 2005).

Neuroticism and extraversion, the two predominantly aective traits within the BigFive, have been the longest and most often recognized and studied (e.g., Eysenck, 1991,1992; Revelle, 1995; Watson, 2000). These two are closely related to temperament (aec-tive, early appearing, extremely stable, constitutionally- and genetically-based, dierencesobserved across species; see Strelau, 1998; Zuckerman, 2005), may be more etiologicallypure (Johnson & Krueger, 2004, p. 467), and have themselves been labeled the BigTwo (Wiggins, 1968): Although ve dimensions are emphasized in descriptive modelsof personality traits, causal theories emphasize E and N (Rogers & Revelle, 1998, p.1592). Less research has focused on, and somewhat more ambiguity lingers regardingthe foundations, content, and measurement of conscientiousness, agreeableness, and open-ness to experience/intellect (Eysenck, 1991, 1992; Revelle, 1995, p. 307).

Robust relationships have been recognized between personality and workplace vari-ables including job satisfaction (Judge, Bono, & Locke, 2000), work attitudes (Judge, Hel-ler, & Mount, 2002), trust (e.g., Mooradian, Renzl, & Matzler, 2006) and job performance(e.g., Barrick & Mount, 1991) as well as dierences in wages (Nyhus & Pons, 2005); nev-ertheless, the precise mechanisms via which personality inuences organizational andwork-place behavior are not well understood (Raja, Johns, & Ntalianis, 2004). Due to

304 K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313the social and cognitive nature of conscientiousness, agreeableness, and openness, due also

lect in lexical solutions; e.g., Goldberg, 1993) is linked to active imagination, aestheticsensitivity, attentiveness to inner feelings, preference for variety, intellectual curiosity,

originality and independence of judgment (Costa & McCrae, 1992). Individuals with highlevels of openness are curious about both inner and outer worlds and are willing to con-sider new ideas and unconventional values, and they experience both positive and negativeemotions more keenly than individuals which score low on openness (Costa & McCrae,1992). Furthermore, highly open people display intellectual curiosity, creativity, exibleto their central role in explaining social behavior in the workplace (Witt, Burke, Barrick, &Mount, 2002), in light of the relative paucity of research on these traits vis-a`-vis extraver-sion and neuroticism (Salgado, 1997), and in the context of constraints on data-collectioninstrument length, we developed our hypotheses and focused this research on these otherthree domains (Bergeman et al., 1993).

4. Hypotheses development

Agreeableness. Agreeableness is the least heritable and most entrained to experience andenvironment among the Big Five traits (Graziano, 1994; Johnson & Krueger, 2004). Peo-ple high on agreeableness are good-natured, forgiving, courteous, helpful, generous, cheer-ful and cooperative (Barrick & Mount, 1991). They are altruistic, sympathetic, andenthusiastic to help others, and they seek cooperation rather than competition (Liao &Chuang, 2004). Agreeableness has been shown to inuence job performance most whencollaboration and cooperation amongst workers is essential (e.g., Mount, Barrick, & Stew-art, 1998; Witt et al., 2002). Agreeableness entails getting along with others in pleasant,satisfying relationships (Organ & Lingl, 1995). Because knowledge sharing is a particularform of workplace helpfulness, cooperation, and collaboration and entails getting alongwith others within interpersonal relationships with colleagues and supervisors all behav-iors directly tied to aspects of Agreeableness as just reviewed we hypothesize that:

H1: Agreeableness is positively related to sharing knowledge with others.

Conscientiousness. Individuals high on conscientiousness are more dutiful, dependable,reliable, responsible, organized, hardworking, and achievement-oriented (Barrick &Mount, 1991). The positive eects of conscientiousness on work performance have wellbeen documented (Barrick & Mount, 1991; Tokar, Fischer, & Mezydlo Subich, 1998).The robust and compelling nature of the conscientiousness-performance relationshipwas emphasized by Barrick, Mount, and Judge: Indeed, it is hard to conceive of a jobwhere it is benecial to be careless, irresponsible, lazy, impulsive, and low in achievementstriving (low conscientiousness) (2001, p. 11). Conscientiousness has been shown toimprove organizational citizenship (i.e., the individual contributions that go beyond rolerequirements and contractually rewarded job accomplishment (Organ & Ryan, 1995)).As a result of these features, people tend to do what is expected of them to carry out work(Liao & Chuang, 2004). Because knowledge sharing is a form of organizational citizenshipthat entails dutiful deference to organizational interests and group norms (especially overself-interest and personal goals), which are also core features of conscientiousness, wehypothesize:

H2: Conscientiousness is positively related to sharing knowledge with others.

Openness to experience. Openness to experience (which has been interpreted as Intel-

K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313 305thinking, and culture (Dingman, 1990) and thus tend to have more positive attitudes

give useful advice; and less likely to contribute when they consider their expertise to be

project manager or constructing engineer. Non-response bias was tested comparing the

responses between early respondents and late respondents (Armstrong & Overton,1977). No signicant dierences were found. Hence, non-response bias seems not to bea problem.

5.2. Measures

Personality traits were assessed by means of the German version of the NEO ve-factorinventory (NEO-FFI), which was originally developed by Costa and McCrae (1992) andtranslated and validated into German language by Borkenau and Ostendorf (1993).The NEO-FFI is a well accepted, widely assessed and extensively used scale to measurethe Big Five personality dimensions (e.g., Mooradian & Olver, 1997; Raja et al., 2004;inadequate (Wasko & Faraj, 2000, 2005). People high in openness are more engaged con-tributing and seeking knowledge. Thus, we hypothesize:

H3: Openness is positively related to sharing knowledge with others

5. Study

5.1. Sample

To examine the hypothesized relationship between agreeableness, conscientiousness,openness and knowledge sharing we gathered data in an internationally operating engi-neering company. The company is positioned among the worlds leading independentengineering consultants, particularly concerning tunnelling, underground constructionand pipeline construction. It employs more than 600 members of sta, mostly civil,mechanical, or electrical engineers. The companys headquarters are in Germany and Aus-tria; however, employees are globally dispersed and include various nationalities. As a pre-survey we conducted ve in-depth interviews with engineers and managers, to get a betterunderstanding of the context, and to adjust the survey instruments. We disseminated thestandardized self-administered questionnaire to 620 members of the company in Germanand English. One hundred and twenty-four fully completed and usable questionnaireswere returned, which is a response rate of 20%. Anonymity and condential treatmentof the responses were assured. Respondents were requested to provide demographic infor-mation as element of the self-report questionnaire, for example, age, job tenure, educationlevel, and professional level. Most of the respondents (79%) are 45 years old or younger,and 41% of them have worked for the company for more than ve years; almost 70% holda university degree, and more than 70% of the respondents are working on the level of atowards learning new things, and are keener to engage in learning experience (Barrick &Mount, 1991).

Cabrera, Collins, and Selgado (2006) discovered that openness is a strong predictor ofknowledge sharing because openness to experience is a reection of a persons curiosityand originality which in turn are predictors of seeking other peoples insights. Therefore,it can be anticipated that open individuals develop more expertise. As Constant, Sproull,and Kiesler (1996) propose, individuals with higher levels of expertise are more likely to

306 K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313Renner, 2002). From the NEO-FFI-questionnaire the subscales concerning agreeableness,

K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313 307conscientiousness, and openness were chosen to asses the items with a ve-point Likertscale (from strong approval to strong disapproval).

In literature, a number of scales have been used to measure knowledge sharing (e.g.,Cummings, 2004; Hansen, 2002, 1999; Szulanski, 1996). For the purpose of this study,knowledge sharing was assessed based on Blackler (1995) knowledge sharing scale whichbest ts the ve types of knowledge outlined above. We developed the following knowl-edge sharing scale using the adapted wording according to the company context: Onaverage, how often did you share each type of knowledge during the project withinthe team: (1) embodied knowledge (e.g., experience-based, learning by doing, etc.), (2)embrained knowledge (e.g., conceptual skills and cognitive abilities), (3) enculturedknowledge (e.g., shared understandings, incidents, etc.), (4) embedded knowledge (e.g.,rm specic routines and procedures, etc.), and (5) encoded knowledge (e.g., manualsand job descriptions, etc.). By means of a ve-point-scale (1 never; 2 on requestand to specic persons; 3 on request to everybody; 4 unrequested to specic persons;5 unrequested to everybody) the regularity of knowledge sharing within the team wasmeasured. A team is a group of people [. . .] who come together to achieve certainresults or performance goals. The members are functionally interdependent and bringtheir individual knowledge and complementary skills to the task so that, individuallyand collectively, they yield the results for which they are held accountable (Gardens-wartz & Rowe, 1994). In the setting of the company under study, teams on average con-sisted of 1015 persons. They worked together as a team for the duration of a project(from several months to several years) in tunnelling, underground construction or pipe-line construction. Depending on the type of project teams consisted of mostly civil,mechanical, or electrical engineers.

5.3. Data analysis and results

The relationships between the constructs were examined with structural equation mod-eling using the partial least squares (PLS) approach. According to Hulland (1999) proce-dure, a PLS model is examined and interpreted in two steps. In the rst step, themeasurement model ought to be examined by performing validity and reliability analyseson each of the measures of the model. This is needed to guarantee that only reliable andvalid measures of the constructs are used before conclusions about the nature of the con-struct relationships are drawn (Hulland, 1999). In the second step, the structural model isexamined by estimating the paths between the constructs in the model, determining theirsignicance as well as the predictive ability of the model.

Reliability and validity were examined observing: (1) individual item reliabilities, (2) theconvergent validity of the measures linked to individual constructs, and (3) discriminantvalidity. The item loadings are displayed in Fig. 1. The reliability examination of thetwo personality traits did not yield exactly the expected results in accordance with the stan-dardized scales (Borkenau & Ostendorf, 1993) and had to be puried by eliminating someof the items with low loadings, making up the scales with remaining ve items on the con-scientiousness-scale, three items on the agreeableness-scale and four items on the open-ness-scale. These results and the required modications are not surprising, as otherresearchers described comparable ndings of the German version of the NEO-FFI scalescomputed in Conrmatory Factor Analyses (e.g., Renner, 2002). The other scales were

showing satisfactory item reliabilities.

Consc 1

Consc 2

Consc 3

Consc 4

Consc 5

Agree 1

Conscien-tiousness

Agreeable-

Share 1

Share 2Knowledge

.71

.73

.73

.61

.73

.77

.81

.24***

.79

308 K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313With the exception of one item (item 3 of the construct openness) all items have load-ings above 0.6 which show high item reliabilities. Convergent validity was assessed usingFornell and Larcker (1981) measure of internal consistency (IC), which is superior toCronbach Alpha because it uses the item loadings obtained within the nomological net-

Agree 3

Agree 2

Open 1

ness

Openness

Share 3

Share 4

Share 5

SharingR=.27.69

.76

.68

.59

.77

.62Open 2

Open 3

Open 4

.87

.81

.27***

.23*** .72

Fig. 1. Structure model and results of partial least squares analysis.work. Finally, average variance extracted (AVE) was calculated with the following values:agreeableness (IC = 0.79, AVE = 0.56), conscientiousness (IC = 0.83, AVE = 0.50), open-ness (CR = 0.76, AVE = 0.45), and knowledge sharing (CR = 0.90, AVE = 0.64). Thesevalues mean that also convergent validity is satisfying.

Discriminant validity means that measures of a given construct dier from the ones ofanother construct (Hulland, 1999). Discriminant validity can be measured from the latentvariable correlations matrix (Table 1), where the square roots of the average varianceextracted values calculated for each of the constructs along the diagonal is shown. The cor-relations between the constructs are shown in the lower left o-diagonal elements in thematrix. Discriminant validity is given, when the diagonal elements (square root AVE)are greater than the o-diagonal elements in the corresponding rows and columns (Fornell

Table 1Latent variable correlation matrix

Knowledge sharing Agreeableness Conscientiousness Openness

Knowledge sharing 0.80Agreeableness 0.36 0.75Conscientiousness 0.33 0.20 0.70Openness 0.39 0.33 0.17 0.67Internal consistency 0.90 0.79 0.83 0.76AVE 0.64 0.56 0.50 0.45

Square root of AVE is on the diagonal.

to submit self-reports concerning personality and personality-like traits (e.g., Barrick,

nizational citizenship behavior (Organ, 1994), i.e., the individuals contributions that go

beyond role requirements and contractually rewarded job achievements (Organ & Ryan,1995). Thus, it could be argued that employees with high levels of conscientiousness aremore willing to engage into the eort to document their knowledge in order to share it withMount, & Judge, 2001). Team members or team leaders, who score high on agreeableness,openness and conscientiousness, are more willing to engage in sharing knowledge. As aconsequence management can try to compose teams according to these personality char-acteristics or assign documentation or sharing roles within the teams accordingly. Thus,critical knowledge is created within certain teams which then can be shared. The theoryand results shown in this study may also support managers to identify potential boundaryspanners (e.g., Wenger, 2000) and, similarly, to identify others reluctant to share knowl-edge, which would inuence knowledge sharing within and across teams.

Another, may be more important implication of the ndings relates to the design ofknowledge management systems and to the assignment of dierent roles within a team.

Empirical studies also found positive relationships between conscientiousness and orga-& Larcker, 1981). As Table 1 shows discriminant validity is satisfactory. Overall, the mea-sures report good reliability and validity.

5.4. Path coecients and predictive ability

Fig. 1 shows the path coecients, their signicance level and the R2 values. PLS uses thebootstrapping method (Efron & Gong, 1983) to calculate the standard errors and therebyassesses the signicance of the structural coecients. Standard errors of parameters werecalculated on the basis of 500 bootstrapping runs. All paths are signicant at p < 0.001level. R2 value of the endogenous construct is 0.27. An explained variance of 27% canbe seen as typical for such studies. It is in the range of explained variance of most otherempirical studies that tested the eect of personality traits on job performance (e.g., Bar-rick & Mount, 1991), organizational citizenship behavior (e.g., Organ & Ryan, 1995), jobsatisfaction (e.g., Judge et al., 2002), and vocational behavior (Tokar et al., 1998).

6. Discussion and conclusion

The results of the study clearly report that stable characteristics of the individuals, i.e.,agreeableness, conscientiousness, and openness inuence knowledge sharing. These resultsare essential as they extend existing literature on knowledge management by taking per-sonal dispositions as inuencing factors of knowledge sharing into account and presentingempirical evidence. Existing literature on knowledge management has been concentratedon environmental, particularly managerial eects on knowledge sharing (e.g., Cabrera &Cabrera, 2002; Nonaka & Takeuchi, 1995) whereas this paper focuses on individual fac-tors. It makes a relevant contribution to the literature on personality psychology and orga-nization studies as well, as these links have not been studied before.

A practical implication of these results is that rms could advance knowledge sharingvia personnel screening. The selection of employees and their retention are central func-tions of management and companies regularly request or require applicants and employees

K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313 309others, to enter their knowledge into knowledge data bases, to use knowledge data bases,

310 K. Matzler et al. / Journal of Economic Psychology 29 (2008) 301313etc. Hence, such individuals could be assigned the role of documenting data, entering thedata in knowledge data bases, and maintaining such data bases.

In previous studies, it has been found that agreeable individuals are altruistic, sympa-thetic, and eager to help others, and that they strive for cooperation rather than compe-tition (Liao & Chuang, 2004). Hence, agreeableness involves getting along with othersin pleasant, satisfying relationships (Organ & Lingl, 1995). As such individuals have stron-ger social ties at the workplace than individuals with lower agreeableness, they could beassigned the role of boundary spanners between teams, assigning them the role to transfernon-codiable knowledge that cannot be stored in data bases.

Finally, openness has been shown to be related with knowledge sharing. Previousstudies have shown that openness also predicts learning and expertise (Cabrera et al.,2006). Hence, the particular role of individuals within a team scoring high on opennesscould be the acquisition of knowledge and the dissemination of the knowledge within theteam.

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Personality traits and knowledge sharingIntroductionProperties of knowledge and knowledge sharingPersonality traitsHypotheses developmentStudySampleMeasuresData analysis and resultsPath coefficients and predictive ability

Discussion and conclusionReferences