attitude toward knowledge sharing in construction teams
TRANSCRIPT
Attitude toward knowledgesharing in construction teams
Peihua Zhang and Fung Fai NgDepartment of Real Estate and Construction, The University of Hong Kong,
Hong Kong, China
Abstract
Purpose – The purpose of this paper is to employ the theory of reasoned action (TRA) as atheoretical framework to investigate factors affecting individuals’ attitudes toward knowledge sharingin construction teams in Hong Kong. Specifically, the factors are analyzed from a cost and benefitperspective grounded in social exchange theory.
Design/methodology/approach – An exploratory study using semi-structured interviews isconducted first to explore context-specific cost and benefit factors. Based on the exploratory studyresults and TRA, a research model and hypotheses are developed. A questionnaire survey is thenconducted among professionals working in contractors in Hong Kong. The quantitative data areanalyzed using structural equation modelling.
Findings – The research results indicate that individuals’ attitudes toward knowledge sharing arepositively affected by knowledge self-efficacy and knowledge feedback, while negatively affected bylosing face. Further, it is revealed that attitude toward knowledge sharing significantly determinesintention to share knowledge, which then determines knowledge sharing behavior.
Originality/value – The paper is one of the first to employ existing theories in social psychology toexamine knowledge sharing behavior in the construction sector. The research results provideimportant implications for construction companies to promote knowledge sharing in project teams.
Keywords Hong Kong, Construction industry, Knowledge management, Information dissemination,Project management, Knowledge sharing, Theory of reasoned action, Social exchange theory,Construction teams
Paper type Research paper
IntroductionIn modern economic society, organizations’ competitiveness relies heavily on theirability to leverage and manage knowledge (Davenport et al., 1998). However,organizations are not able to create knowledge themselves. They depend on theiremployees to create, share and apply knowledge in work process (Ipe, 2003). Davenportand Prusak (1998, p. 5) define knowledge as:
[. . .] a fluid mix of framed experience, values, contextual information, and expertise insightthat provides a framework for evaluating, and incorporating new experiences andinformation. It originates and is applied in the minds of knowers.
Since knowledge is created by individuals, leveraging knowledge is only possible whenindividuals share their knowledge with others. A challenge for contemporaryorganizations is thus to successfully encourage their employees to share theirknowledge with others within teams and even cross organizational units (Choi et al.,2008; Hansen and Avital, 2005).
The construction industry is described as a knowledge-intensive industry, whereoutput products (e.g. buildings, bridges) need substantial input of professionalknowledge and problem-solving know how (Egbu and Robinson, 2005).
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Received 15 April 2012Revised 25 May 2012Accepted 4 June 2012
Industrial Management & DataSystemsVol. 112 No. 9, 2012pp. 1326-1347q Emerald Group Publishing Limited0263-5577DOI 10.1108/02635571211278956
Organizations in the construction industry heavily rely on their employees to applyknowledge to provide service products and create value. Knowledge sharing amongindividuals could bring benefits of innovative ideas, enhanced personal capability,improved work efficiency, better decision making, etc. (Fong and Chu, 2006). To meet therequirements of “innovation, improved business performance and client satisfaction”(Kamara et al., 2002, p. 56), organizations in the construction industry should encourageknowledge sharing (Dainty et al., 2005). There are however few empirical studies thatexplore how to encourage knowledge sharing practice in the construction industry.
Researchers maintain that organizations can successfully promote knowledgesharing not only by directly addressing knowledge sharing in their business strategy,but also by changing employees’ attitude and behavior so that employees share theirknowledge willingly and consistently ( Jones et al., 2006; Alavi and Leidner, 2001).Consequently, organizations should take measures to remove employees’ fears insharing knowledge and enable employees to develop a positive attitude towardknowledge sharing (Lin, 2007a; Robinson et al., 2005). In this study, Ajzen andFishbein’s (1980) theory of reasoned action (TRA) is employed to investigate factorsaffecting individuals’ attitude toward knowledge sharing, and how attitude eventuallyaffects knowledge sharing behavior. The research focus is professionals working inconstruction teams, which are the project teams internally organized by contractors tomanage construction work. A construction team usually consists of professionals fromdifferent disciplines including quantity surveyors, buildings engineers, structureengineers, etc. It is crucial for the professionals to share their knowledge to achievecooperation, establish mutual understanding, jointly seek effective solutions, andimprove project delivery efficiency. Furthermore, a construction team usuallydissolves for other projects once the current project is completed. Important knowledgelearned by team members through knowledge sharing can be transferred and appliedin other projects, thus avoiding “reinventing the wheel” and reducing repetition ofprevious mistakes (Ma et al., 2008; Tan et al., 2006).
Theoretical backgroundTheory of reasoned action (TRA)TRA is widely accepted and has been employed to predict and explain various behaviorsin social psychology (Lin, 2007a). As shown in Figure 1, TRA suggests that an individual’sbehavior is predicted by his/her behavioral intention, which in turn is determined by theindividual’s attitude toward and subjective norm regarding the behavior. Each attitudeand subjective norm is affected by a set of salient beliefs. An individual may have a largenumber of beliefs about a given behavior, but he/she can only attend to a relatively smallnumber of beliefs at a specific moment (Ajzen and Fishbein, 1980). The attendedbeliefs are salient beliefs, which are uppermost in the individual’s mind.
Figure 1.Theory of reasoned action
BehaviorIntention
Attitude
Subjectivenorm
Behavioralbeliefs
Normativebeliefs
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Specifically, attitude is determined by “behavioral beliefs” concerning the likelyconsequences of performing the behavior. Subjective norm is determined by “normativebeliefs”, which are about the likelihood that important referents encourage or discouragethe behavior. TRA assumes that human beings are rational and they make systematic useof information available to form beliefs (Ajzen and Fishbein, 1980).
TRA has been adopted by many researchers to study knowledge sharing behavior.Ding and Ng (2009) empirically test the TRA model in predicting architects’ knowledgesharing behavior in project design teams. They found that attitude is more importantthan subjective norms in determining architects’ knowledge sharing intention. Bockand Kim (2002) were the first to study salient beliefs associated with knowledgesharing attitude. They identify expected association and expected contribution as twosignificant determinants of individuals’ attitude toward knowledge sharing. Later,Bock et al. (2005) employ TRA to develop a comprehensive research model examiningfactors supporting or inhibiting individual knowledge sharing intention. The resultsindicate that anticipated reciprocal relationships positively affect attitude towardknowledge sharing, while sense of self-worth influences subjective norm of knowledgesharing. In addition, a positive organizational climate significantly impacts bothsubjective norm and intention to share knowledge.
This study posits that knowledge is created by individuals and knowledge sharing isa kind of individualistic behavior. Besides, the argumentation that knowledge sharingcould be promoted by changing individuals’ attitude toward knowledge sharinghas been developed. Thus, this study only focuses on individual motivation towardknowledge sharing rather than normative motivation. Therefore, only thebelief-attitude-intention-behavior relationship suggested by TRA is investigated inthis study.
Social exchange theory (SET)TRA is a general model, which does not specify salient beliefs concerning a particularbehavior. Researchers need to consider salient beliefs for a specific behavior in a givencontext when adopting TRA to explain social behaviors. Davenport and Prusak’s(1998) definition of knowledge implies that individual knowledge can be regarded as aprivate good owned by individuals. People could share knowledge through anexchange mechanism with the purpose of receiving commensurate benefits (Wasko andFaraj, 2000). So knowledge sharing can be examined as a form of social exchange(Bock et al., 2005). Therefore, SET is applicable to analyze behavioral beliefs affectingattitude toward knowledge sharing.
SET suggests that actors perform a behavior expecting rewards that bring benefits,and they are likely to choose alternative behaviors that maximize benefits and minimizecosts (Blau, 1964; Cook and Rice, 2006). In other words, people would evaluate thepotential benefits and costs before performing a behavior. SET is entailed by unspecifiedobligations, which means the nature of the rewards is not specified in advance and thetime when the rewards are delivered is not clear (Blau, 1964). If a person does not receiveany reward after providing a favor to other people, he/she may terminate the favor.However, if other people reciprocate with a return, more rounds of exchange may beinitiated. Therefore, SET is characterized by reciprocal interdependence, i.e. one party’saction is contingent on the other party’s behavior (Blau, 1964; Cropanzano andMitchell, 2005).
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Resources involved in social exchange include tangible economic outcomesconcerning financial needs or intangible socio-emotional outcomes relating to socialand esteem needs (Cropanzano and Mitchell, 2005). In the exchange process, resourcesgiven away and any negative outcomes of exchange are considered as costs; resourcesreceived and any positive outcomes of exchange are seen as benefits (Kankanhalli et al.,2005). Depending on whether a benefit is detachable from the source that supplies it,benefits can be divided into extrinsic benefits (e.g. advice and service) and intrinsicbenefits (e.g. personal attraction) (Blau, 1964).
The hybrid of TRA and SET has been employed by researchers to study knowledgesharing behavior. Huang et al. (2008) identify loss of knowledge power as a cost factornegatively influencing employees’ attitude toward knowledge sharing, while image,sense of self-worth and anticipated extrinsic reward are benefit factors that positivelyaffect employees’ knowledge sharing attitude. Lin (2007a) adopts TRA and SET toinvestigate various motivational factors to explain individuals’ knowledge sharingattitude and intention. The ideintified instrisic motivation includes knowledgeself-efficacy and enjoyment in helping others, and the extrinsic motivation includesreciprocal benefits. Bock and Kim (2002) also use SET to explain the relationshipbetween expected association and attitude toward knowledge sharing.
Research model and hypothesesIn existing literature, the common cost factors include loss of knowledge power andcodification effort, while the common benefit factors include economic reward,reciprocal relationships, knowledge self-efficacy, enhanced reputation, enjoyment inhelping others, etc. Given the specific characteristics of construction teams(e.g. professionals of multi-discipline and emphasis on teamwork), some of the factorsmay not be applicable or have very limited impact on individuals’ attitude towardknowledge sharing in construction teams. Besides, there may be factors that areimportant in construction team context but have not been captured in existing literature.Therefore, an exploratory study was conducted to evaluate the existing factors andexplore other context-specific factors.
Seven semi-structured interviews were conducted with professionals from a largeconstruction company in Hong Kong. All the interviewees were working in constructionteams. The transcripts of interviews were content-analyzed by the technique of coding,which divided relevant content of transcripts into categories of different themes(Krippendorff, 2004). Two independent coders were invited to code the transcriptsfollowing Mile’s (1994) and Gillham’s (2000) methods of qualitative data presentation.The main purpose of coding was to identify categories of high frequency. The frequencywas the number of interviewees who had reported a specific category. As there were onlyseven interviewees, a cut-off line of one interviewee was employed. Categories reportedby only one interviewee were discarded to avoid occasional incidence. Through theexploratory study, one cost factor (i.e. losing face) and five benefit factors were identified(i.e. economic reward, reduced workload, knowledge feedback, enhanced personalrelationship and knowledge self-efficacy). The factors reported by interviewees aremuch different from factors frequently examined in existing literature. For example,interviewees do not consider loss of knowledge power as a cost. It is possible that peoplefear to lose knowledge power when individual performance is assessed in appraisalstrategy. However, collective project performance is emphasized in construction teams.
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Professionals need to work collaboratively to deliver projects successfully. Therefore,interviewees consider that sharing knowledge with others would reduce their workloadinstead of losing knowledge power. The identified factors were incorporated into thetheoretical framework to formulate the research model as shown in Figure 2.
Intention to share knowledge indicates how hard people are willing to try and howmuch effort people are planning to exert to perform knowledge sharing behavior(Ajzen, 1991). Based on TRA, the higher the intention a person has toward knowledgesharing, the more likely the person will engage in knowledge sharing. Thus:
H1. Individuals’ intention to share knowledge has a positive effect on theirknowledge sharing behavior in construction teams.
Attitude toward knowledge sharing is the amount of positive preference one has forknowledge sharing (Fishbein and Ajzen, 1975). The positive relationship betweenattitude and intention suggested by TRA has been found in many knowledge sharingstudies (Ryu et al., 2003; Bock et al., 2005; Huang et al., 2008). An individual will have ahigher tendency to share knowledge if he/she evaluates knowledge sharing positively.Thus:
H2. Individuals’ attitude toward knowledge sharing has a positive effect on theirintention to share knowledge in construction teams.
Losing face refers to that people may feel shame, embarrassed or dishonored bysharing failure experiences or sharing knowledge that others consider as useless. Leeand Green (1991) argue that people in Confucian cultures have high concern with otherpeople’s perceptions of themselves and with maintenance of good status. Therefore,face is an important issue in Eastern countries influenced by Confucian cultures.Huang et al. (2008) empirically observe that people have a low intention to shareknowledge if they are afraid of sharing wrong or incorrect knowledge in order to “saveface” in Chinese context. It is considered that professionals in Hong Kong constructionteams would take “face” into account when evaluating the likely outcomes ofknowledge sharing. Thus:
H3. Perceived losing face has a negative effect on individuals’ attitude towardknowledge sharing in construction teams.
Researchers recommend that economic rewards provided by organizations couldovercome costs entailed to knowledge contributors (Bartol and Srivastava, 2002;Cabrera and Cabrera, 2005; Hall, 2001). Economic rewards include monetary form
Figure 2.Research model
Attitude towardknowledge
sharing
Perceived costLosing face (–)
Perceived benefitsEconomic reward (+)Reduced workload (+)Knowledge feedback (+)Enhanced personalrelationship (+)Knowledge self-efficacy (+)
H3–H8 Intention toshare
knowledge
Knowledgesharing
behavior
H2 H1
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(e.g. higher salary and bonus) to non-monetary form (e.g. promotion, opportunities, jobsecurity and better assignments) (Bock et al., 2005; Kankanhalli et al., 2005; Lin, 2007a).Bartol and Locke (2000) maintain that organizational rewards are useful in motivatingemployees to perform target behaviors, given that the rewards systems are fair, goalsare set to achieve the rewards and employees have sufficient self-efficacy to perform thetasks. Huang et al. (2008) find that anticipated organizational rewards positively affectindividuals’ attitude toward knowledge sharing in Chinese organizations. Therefore, itis conjectured that:
H4. Perceived economic reward has a positive effect on individuals’ attitudetoward knowledge sharing in construction teams.
Reduced workload is a benefit factor identified from the exploratory study but seldomexamined in previous research. After people teaching their teammates the skills forcertain tasks, they do not need to perform all the tasks by themselves. As suggested byan interviewee in the exploratory study: “When more people know how to do the work,more people share the workload”. In a project-based working environment, teamworkis important as no one can handle all the tasks. Team members should collaborate andactively share knowledge. Thus:
H5. Perceived reduced workload has a positive effect on individuals’ attitudetoward knowledge sharing in construction teams.
The exploratory study indicates that when individuals share knowledge withteammates, they may obtain knowledge feedback including comments, suggestions,mistakes pointed out by teammates, etc. In Wasko and Faraj (2000), respondents reportthat feedback obtained helps them to refine their thinking and develop new insights.Thus:
H6. Perceived knowledge feedback has a positive effect on individuals’ attitudetoward knowledge sharing in construction teams.
Previous studies suggest that people share knowledge with others to strengthen theirsocial ties and to expand their scope of association (Bock and Kim, 2002; Bock et al., 2005;Lin, 2007a). Enhanced interpersonal relationships are identified as a benefit factor fromthe exploratory study. Social exchange is entailed with long-term interdependenttransactions, which would establish high-quality interpersonal relationships (Blau, 1964;Cropanzano and Mitchell, 2005). Constant et al. (1994) also assert that when individualsare influenced by their social and organizational context, social relationships become animportant determinant for knowledge sharing. Thus:
H7. Perceived enhanced personal relationships has a positive effect onindividuals’ attitude toward knowledge sharing in construction teams.
Self-efficacy is defined as “people’s judgment of their capabilities to organize andexecute courses of action required to attain designated types of performance” (Bandura,1986, p. 391). Perceived self-efficacy plays a crucial role in affecting individuals’motivations and behaviors (Bandura, 1982). Knowledge self-efficacy is typicallymanifested in people believing that their knowledge is useful to colleagues, could help tosolve job-related problems and improve work efficacy (Kankanhalli et al., 2005;Lin, 2007a). After individuals have shared knowledge with others, they may obtain
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feedback would enables them to understand how their shared knowledge has improvedthe work of others and/or organizational performance (Bock et al., 2005; Cabrera et al.,2006). The understanding may increase individuals’ knowledge self-efficacy, andenables them to develop a more favorable attitude toward knowledge sharing. Thus:
H8. Perceived knowledge self-efficacy has a positive effect on individuals’ attitudetoward knowledge sharing in construction teams.
Research methodsDevelopment of measuresItems to measure attitude, intention and behavior regarding knowledge sharing aredeveloped according to Ajzen’s (2002) procedures. Following Bock et al. (2005), the typesof knowledge shared are specified based on the exploratory study. References are madeto Bock et al. (2005) and Taylor and Todd (1995) in selecting the wording for itemsto measure intention to share knowledge and attitude toward knowledge sharing. Items tomeasure economic rewards, enhanced personal relationships and knowledge self-efficacyare adopted from exsting measures (Kankanhalli et al., 2005; Bock et al., 2005; Kalman,1999). Items to measure losing face are developed based on Hwang et al.’s (2003) measureof “Fear Mianziloss” and statements from the exploratory study. However, no existingmeasures are found for reduced workload and knowledge feedback. Statements from theinterviews are converted into items to measure reduced workload. Besides, statementsfrom the interviews and Wasko and Faraj’s (2000) qualitative study are used fordeveloping items to measure knowledge feedback. Regarding the format of measurescales, items in the attitude scale are measured with a semantic differential scalerecommended in Ajzen and Fishbein (1980). Items in other scales are measured byseven-point bi-polar scale following Hanson’s (1997) recommendation. The items and theircorresponding references are listed in Appendix. All the items are compiled into aquestionnaire for data collection.
Data collectionThe target research population includes all individuals working in construction teams inHong Kong. Approaching the individuals directly is not feasible due to lack of contactdetails. However, the information of their companies is accessible. Therefore, thecompanies were identified as the first step. Following Nueman’s (2003) recommendation,a sampling frame was identified by searching various sources, including The HKSARGovernment List of Approved Contractors for Public Works, the list of registered generalbuilding contractors from Hong Kong SAR Buildings Department, and The Hong KongBuilders Directory. TheHKIE Year Book 2009 published by The Hong Kong Institutionof Engineers was used to identify the research sample. The HKIE Year Book listed allmembers’ basic information (e.g. names, education qualification, and membershiphistory) and some members’ additional information (i.e. working companies, officetelephone numbers, and e-mails). Thus, members with additional information wereselected. Their employer companies were checked against the sampling frame. Finally, alist of 430 individuals from 172 organizations was compiled.
The sample size of 430 individuals was small. As a result, the “key contact person”method was employed. The 430 individuals were invited to fill in the questionnaire andalso requested to find another three individuals in their teams to fill in the questionnaire.The main survey was carried out from March to June 2010. A total of 430 packages
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were sent to the key contact persons by mail. There was one invitation letter, fourquestionnaires and four freepost envelopes in each package. However, 88 packages wereretuned due to various reasons, including person resignations, unwillingness to help,companies moved, etc. So the effective sample size of key contact persons is 342 afterexcluding the eligible sample of 88 persons. When the survey was closed, a total of238 questionnaires were collected from 97 key contact persons, producing a response rateof 28.4 percent concerning key contact persons, and 17.4 percent concerning total sample.Among the 238 returned questionnaires, seven questionnaires were ineligible andexcluded from data analysis. Table I summarizes the demographic information of therespondents. It shows that engineers and project managers constitute a high proportion ofthe respondents. One possible reason is the method of identifying key contact persons.However, only theHKIEYearBook is found to provide construction professionals’ contactinformation. So the sampling method employed is the best feasible solution available.
Data analysisReliability of measurementScale reliability is indicated by internal consistency measured by Cronbach’s a anditem-total coefficient. Table II shows that all the a values are higher than the thresholdof 0.7 suggested by Nunnally (1978) and all the item-total correlations exceed thecriteria of 0.4 recommended by Spector (1992).
Variable Categories Number of cases Frequency (%)
Gender Female 26 11.3Male 203 87.9Missing 2 0.9
Education High school graduate 5 2.2Certificate or associate degree 33 14.3Bachelor degree 142 61.5Postgraduate 49 21.2Missing 2 0.9
Job position Project manager 60 30.0Site agent 17 7.4Engineer 67 29.0Quantity surveyor 28 12.1Safety manager 4 1.7Other 51 22.1Missing 4 1.7
Working experience incurrent company (years)
,5 93 40.35-10 60 26.010-15 29 12.615-20 18 7.8.20 27 11.7Missing 4 1.7
Working experience inconstruction industry(years)
,5 33 14.3
5-10 44 19.010-15 46 20.015-20 27 11.7.20 79 34.2Missing 2 0.9
Table I.Demographic information
of respondents
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Structural equation modelingStructural equation modeling (SEM) is used for analyzing the data. SEM is able toshow the relationship between each indicator and a corresponding latent variable, andis also efficient to estimate a series of multiple regression equations. Kline’s (2005)
Reliability Validity
Construct ItemCronbach
a
Item-totalcorrelation
Factorloading
Compositereliability AVE
Knowledge sharing behaviour (KSB) KSB1 0.737 0.601 0.706 0.746 0.428KSB2 0.599 0.739KSB3 0.440 0.509KSB4 0.492 0.638KSB1 0.730 0.596 0.698 0.732 0.477KSB2 0.587 0.732KSB4 0.493 0.649
Intention to share knowledge (INT) INT1 0.867 0.742 0.847 0.873 0.636INT2 0.750 0.850INT3 0.597 0.632INT4 0.799 0.839INT1 0.880 0.783 0.862 0.882 0.715INT2 0.791 0.862INT4 0.739 0.811
Attitude toward knowledge sharing(ATT)
ATT1 0.892 0.787 0.855 0.896 0.685
ATT2 0.829 0.912ATT3 0.803 0.856ATT4 0.639 0.668
Losing face (LF) LF1 0.830 0.630 0.687 0.843 0.645LF2 0.763 0.913LF3 0.694 0.794
Economic reward (ER) ER1 0.928 0.737 0.747 0.931 0.773ER2 0.893 0.957ER3 0.896 0.963ER4 0.809 0.831
Reduced workload (RW) RW1 0.831 0.678 0.772 0.834 0.627RW2 0.683 0.770RW3 0.719 0.832
Knowledge feedback (KF) KF1 0.842 0.564 0.569 0.858 0.609KF2 0.746 0.811KF3 0.764 0.919KF4 0.690 0.780
Enhanced personal relationship (EPR) EPR1 0.874 0.663 0.729 0.875 0.584EPR2 0.720 0.774EPR3 0.713 0.785EPR4 0.744 0.795EPR5 0.673 0.735
Knowledge self-efficacy (KSE) KSE1 0.870 0.692 0.816 0.873 0.635KSE2 0.788 0.890KSE3 0.781 0.803KSE4 0.641 0.662
Table II.Scale reliabilityand validity
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two-step modeling method is followed, i.e. testing measurement model withconfirmatory factor analysis (CFA) first and then testing structural model with pathanalysis. AMOS 18.0 is the software used to process the SEM analysis.
Confirmatory factor analysis. CFA aims to validate indicators underlying each latentconstruct and test the measurement model fit (Kline, 2005; Schumacker, 2010). Hair et al.(1998) and Ryu et al. (2003) suggest that construct validity should be assessed byexamining factor loadings of indicators, composite reliability and average varianceextracted (AVE). Table III shows that the factor loadings are from 0.509 (KSB3) to 0.963(ER3), higher than the minimum acceptable level of 0.5 (Hair et al., 2010). All thecomposite reliabilities exceed the cut-off level of 0.7 suggested by Hair et al. (1998).Concerning AVE, a threshold of 0.5 is recommended by researchers (Fornell andLarcker, 1981; Hair et al., 1998; Ryu et al., 2003). Therefore, AVE for the construct ofknowledge sharing behavior (i.e. 0.428) is not acceptable. Since KSB3 also has the lowestfactor loading, it is removed from the construct. Thereafter the AVE value increases to0.477 which is marginally acceptable. Because the measures for intention to shareknowledge and knowledge sharing behavior are designed uniformly in terms of thetypes of knowledge (Appendix), INT3 is also eliminated to maintain the uniformity.
The overall model fit is assessed by absolute fit measures (x2/df, RMSEA, SRMR),incremental fit measures (NNFI, CFI) and parsimonious fit measures (AIC) asrecommended by Hair et al. (1998) and Schermelleh-Engel et al. (2003). Table III showsthat all the goodness-of-fit indices achieve desired levels of values, indicating that themeasurement model fits the data well.
Path analysis. To test the hypotheses in the research model, a structural model isdeveloped as shown in Figure 3. However, SEM results suggest that the model shouldbe rejected because several goodness-of-fit indices fail to achieve the desired values.Accordingly, an alternative structural model should be developed. Modification indices(MI) in AMOS output are referred to modify the structural model.
The revised structural model with standardized path coefficients is shown in Figure 4.Compared to the original model, the revised model incorporates correlated relationshipsbetween constructs and between error items. Table IV indicates that the model issupported with all goodness-of-fit indices higher than acceptable levels. Table V listsregression weights before standardization and significance levels. The results indicatethat knowledge sharing behavior is significantly determined by intention to shareknowledge (path coefficient is 0.59), which in turn is significantly predicted by attitudetoward knowledge sharing (path coefficient is 0.78). Both path coefficients are significant
Index Calculation of measures Acceptable level Acceptability
Absolute fit measuresx2/df 1.686 ,3 AcceptedRMSEA 0.055 ,0.10 AcceptedSRMR 0.059 ,0.10 AcceptedIncremental fit measuresNNFI 0.919 .0.90 AcceptedCFI 0.929 .0.90 AcceptedParsimonious fit measuresAIC 1,095.383 , 1,260.000 Smaller than AIC for comparison model Accepted
1,095.383 , 5,692.652
Table III.Goodness-of-fit indices
for measurement model
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at 0.001 level. Among the factors supposed to affect attitude toward knowledge sharing,knowledge feedback and knowledge self-efficacy significantly affect attitude at 0.001level (path coefficients are 0.24 and 0.38, respectively). Besides, the path coefficientbetween losing face and attitude is negatively significant at 0.01 level with path coefficientof 20.24. However, economic reward, reduced workload and enhanced personalrelationship are found to be insignificant antecedents to attitude. The percentages ofvariance explained for knowledge sharing behavior, intention and attitude are 34, 61 and42 percent, respectively. Table VI summarizes the results of hypotheses testing.
Figure 3.Proposed structural model
Notes: LF, losing face; ER, economic reward; RW, reduced workload; KF, knowledgefeedback; EPR, enhanced personal relationship; KSE, knowledge self-efficacy; ATT,attitude; INT, intention; KSB, knowledge sharing behavior; e1-e33, errors associatedwith observed variables; res1-res3, residual error
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Figure 4.Revised structural model
with standardized pathcoefficients
Notes: LF, losing face; ER, economic reward; RW, reduced workload; KF, knowledgefeedback; EPR, enhanced personal relationship; KSE, knowledge self-efficacy; ATT, attitude;INT, intention; KSB, knowledge sharing behavior; e1-e33, errors associated with observedvariables; res1-res3, residual error
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Discussion of research findingsThe research findings provide evidence for positive causal relationships betweenattitude, intention, and behavior regarding knowledge sharing, which is consistentwith Bock and Kim (2002) and Tohidinia and Mosakhani (2010). The results imply thatchanging individuals’ attitude toward knowledge sharing is important for promotingknowledge sharing in project teams.
The results also show that perceptions of losing face negatively affect individuals’attitude toward knowledge sharing. Researchers point out that people have high concernfor their face in collectivist cultures such as Chinese culture (Chow et al., 2000; Li et al.,2007). It is argued that “though collectivists are expected to place the collective interestsabove their own, they still may feel some deterrence from sharing knowledge that candamage their face or social standing” (Chow et al., 2000, p. 68). Since Hong Kong is rootedin traditional Chinese culture, people in construction teams also take face into account inwhen they sharing their knowledge. Similar result is found in other studies where “facesaving” negatively affect Chinese employees’ tendency to share knowledge inorganizations (Huang et al., 2008) and in electronic ShareNet (Voelpel and Han, 2005).
Knowledge feedback as a significant benefit from knowledge sharing issupported by Quinn et al. (1996, p. 8), who suggest that knowledge receivers “feedback questions, amplifications, and modifications that add further value for theoriginal sender, creating exponential total growth”. The result indicates that people inHong Kong construction teams keep an open mind on learning and self-improvement.Knowledge self-efficacy is found to be most critical to individuals’ attitudetoward knowledge sharing. This finding is consistent with many previous studies,where knowledge self-efficacy is reported to be an important motivation for individualsto share knowledge (Constant et al., 1994; Kankanhalli et al., 2005; Lin, 2007a, b). Theresult implies that people in Hong Kong construction teams have high tendency toestablish work-related confidence and competency.
An unexpected finding is that perceived economic reward has no significant effecton individuals’ attitude toward knowledge sharing. Several plausible explanations aresuggested by researchers. Kohn (1993, p. 55) argues that:
[. . .] rewards succeed at securing one thing only: temporary compliance. When it comes toproducing lasting change in attitudes and behavior, however, rewards, like punishment, arestrikingly ineffective. Once the rewards run out, people revert to their old behaviors.
Index Calculation of measures Acceptable level Acceptability
Absolute fit measuresx2/df 1.701 ,3 AcceptedRMSEA 0.055 ,0.10 AcceptedSRMR 0.097 ,0.10 AcceptedIncremental fit measuresNNFI 0.923 .0.90 AcceptedCFI 0.930 .0.90 AcceptedParsimonious fit measuresAIC 978.555 , 1,260.000 Smaller Accepted
978.555 , 5,407.767
Table IV.Goodness-of-fit indices ofrevised structural model
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Since economic reward is found to be ineffective in encouraging people to shareknowledge in many studies, why do researchers still suggest using economic incentivesto reward knowledge sharing? Based on Kohn’s (1993) argument, Bock and Kim (2002)suggest that incentives would only be effective at the initiation stage of knowledgemanagement, i.e. rewards only act as a trigger for knowledge sharing rather than as asustainable force to form a person’s attitude. Another possible explanation is that
Estimate SE CR P Label
ATT ˆ KF 0.214 0.066 3.250 0.001 par_25ATT ˆ ER 20.004 0.033 20.137 0.891 par_26ATT ˆ LF 20.174 0.057 23.060 0.002 par_29ATT ˆ RW 0.048 0.042 1.153 0.249 par_31ATT ˆ EPR 0.103 0.091 1.136 0.256 par_34ATT ˆ KSE 0.366 0.080 4.577 * par_37INT ˆ ATT 0.844 0.071 11.818 * par_27KSB ˆ INT 0.495 0.072 6.857 * par_28LF3 ˆ LF 1.000LF2 ˆ LF 1.166 0.087 13.422 * par_1LF1 ˆ LF 1.043 0.097 10.710 * par_2ER3 ˆ ER 1.000ER2 ˆ ER 1.001 0.031 32.325 * par_3ER1 ˆ ER 0.823 0.053 15.650 * par_4RW3 ˆ RW 1.000RW2 ˆ RW 0.913 0.082 11.140 * par_5RW1 ˆ RW 1.074 0.096 11.217 * par_6KF3 ˆ KF 1.000KF2 ˆ KF 0.938 0.061 15.276 * par_7KF1 ˆ KF 0.842 0.089 9.420 * par_8EPR3 ˆ EPR 1.000EPR2 ˆ EPR 0.963 0.085 11.389 * par_9EPR1 ˆ EPR 0.899 0.084 10.712 * par_10KSE3 ˆ KSE 1.000KSE2 ˆ KSE 1.127 0.080 14.094 * par_11KSE1 ˆ KSE 0.990 0.077 12.897 * par_12ER4 ˆ ER 0.923 0.046 19.978 * par_13KF4 ˆ KF 0.839 0.058 14.424 * par_14EPR4 ˆ EPR 0.913 0.076 11.955 * par_15EPR5 ˆ EPR 0.879 0.081 10.800 * par_16KSE4 ˆ KSE 0.793 0.068 11.608 * par_17ATT1 ˆ ATT 1.000ATT2 ˆ ATT 0.994 0.058 17.103 * par_18ATT3 ˆ ATT 1.025 0.065 15.646 * par_19ATT4 ˆ ATT 0.743 0.067 11.131 * par_20INT1 ˆ INT 1.000INT2 ˆ INT 1.084 0.068 15.865 * par_21INT4 ˆ INT 0.896 0.063 14.234 * par_22KSB1 ˆ KSB 1.000KSB2 ˆ KSB 1.115 0.139 8.019 * par_23KSB4 ˆ KSB 0.773 0.102 7.583 * par_24
Note: Significant at: *p , 0.01
Table V.Regression weights of
revised structural model
Knowledgesharing
1339
knowledge sharing behavior is seldom considered in individual performance evaluationsystem in construction companies. Osterloh and Frey (2000) argue that sharing tacitknowledge within teams cannot be observed and measured directly, and the output canhardly be attributed to a particular person. In a construction team, if the teamperformance is improved through team members’ active sharing of ideas andcooperation, rewards would be awarded to the whole team. As a result, economicrewards may be an indirect outcome of knowledge sharing but not the main concernwhen members share knowledge in construction teams.
The research results also indicate that perception of reduced workload does notcontribute to individuals’ positive attitude toward knowledge sharing. Individuals inconstruction teams usually face the challenge of heavy workloads and tight schedules(Fong and Chu, 2006). They may consider their burden not substantially reduced even iftheir colleagues share a certain proportion of their workload. Moreover, a constructionteam constitutes professionals of multiple disciplines. Knowledge sharing among themwould improve cooperation and develop effective solutions. In this sense, knowledgesharing improves individuals’ work efficiency, but not necessarily reduces their workload.
The result that perception of enhanced personal relationships does not affectindividuals’ attitude toward knowledge sharing contradicts other researchers’findings, e.g. Bock and Kim (2002), Bock et al. (2005) and Tohidinia and Mosakhani(2010). A possible reason is that people in Hong Kong construction teams considerwork and personal relationships as independent aspects in their working environment(Huang et al., 2008). Therefore, they participate in knowledge sharing mainly for moreeffective work rather than relationship maintenance (Huang et al., 2008). Similar to theeconomic rewards, enhanced personal relationships may be a byproduct ofknowledge sharing, but not the main concern.
Conclusion, implications and limitationsThis study is one of the first to employ existing theories in social psychology toexamine knowledge sharing behavior in construction teams. Based on the TRAand SET, important factors affecting employees’ attitude toward knowledge sharing,which then significantly contribute to knowledge sharing intention and knowledgesharing behavior, are identified.
The results suggest some managerial implications for managers to promoteknowledge sharing in construction teams. To enhance individuals’ knowledgeself-efficacy, managers could promote the climate of appreciation in construction
Hypotheses Path Path coefficient Result
H1 Intention ! behavior 0.56 * * SupportedH2 Attitude ! behavior 0.79 * * SupportedH3 Losing face ! attitude 20.24 * SupportedH4 Economic reward ! attitude 20.01 RefutedH5 Reduced workload ! attitude 0.07 RefutedH6 Knowledge feedback ! attitude 0.24 * * SupportedH7 Enhanced personal relationship ! attitude 0.09 RefutedH8 Knowledge self-efficacy ! attitude 0.38 * * Supported
Note: Significant at: *p , 0.01 and * *p # 0.001
Table VI.Summary of hypothesestesting results
IMDS112,9
1340
teams, i.e. individuals should learn to appreciate teammates’ knowledge contribution.Teammates’ appreciation and recognition are beneficial to individual’s formation ofself-worth (Bock et al., 2005). Managers could also provide useful feedback to improveteam members’ knowledge self-efficacy (Husted and Michailova, 2002; Lin, 2007a),e.g. notifying knowledge contributors that their knowledge contribution makes asignificant difference to project implementation. Managers should encourage people tokeep an open mind in knowledge sharing and provide constructive knowledgefeedback to knowledge contributors. They should make team members be aware thatthe interactive process can add value to the original shared knowledge, benefiting boththe knowledge contributor and receiver. Further, managers should remove the barrierof individuals’ fear of losing face. They should create a climate of mistake tolerance inconstruction teams. Mistakes can be powerful sources of learning opportunities(Husted and Michailova, 2002). Construction team members should be encouraged tolearn from mistakes, engage in innovation and undertake reasonable risk. Besides,neutralizing trust and close relationships among team members may also help toremove fear of losing face. When individuals have prior satisfactory interactionexperience with their teammates, they may have a reasonable assurance thatteammates would not ridicule them in public or depreciate the knowledge they shared(Ardichvili et al., 2003).
There are some limitations in this study, which provide directions for future study.Several new scales are developed with an acceptable level of reliability and validity.However, they have not been examined in other contexts. It is recommended thatresearchers could validate and refine these scales in future studies to make themapplicable universally. Only some potential influential factors of attitude towardknowledge sharing are examined in this study. Future studies could elaborate the researchmodel with additional factors. This study only focuses on attitude toward knowledgesharing. According to TRA, intention and behavior are also affected by subjective norm.Future study could incorporate the research model with subjective norm and normativebeliefs to increase the explanatory power of the model. Further, only individuals inHong Kong construction teams are considered in this study. Therefore, the results may notbe applicable to other regions due to different construction practice and culturalcharacteristics. Researchers could conduct similar research in other regions. Due to limitedresources, a cross-sectional research design is employed, which limits the extent ofcausality inferred from results. Also, the cross-sectional design contains common methodbias such as consistency bias in answering questionnaires, which may artificially inflaterelationships between beliefs, attitude, intention and behavior (Podsakoff et al., 2003).In future, longitudinal data could be collected to eliminate the common method bias and toinvestigate the causal relationships between constructs of beliefs, attitude, intention andbehavior of knowledge sharing.
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(The Appendix follows overleaf.)
Corresponding authorPeihua Zhang can be contacted at: [email protected]
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Appendix
Con
stru
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Mea
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tit
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(continued
)
Table AI.Measurement itemsdeveloped
IMDS112,9
1346
Con
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Mea
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Sou
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nom
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Th
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Kal
man
(199
9)
Table AI.
Knowledgesharing
1347