1071-5819/$ - se

doi:10.1016/j.ijh

nCorrespond

E-mail addr

Int. J. Human-Computer Studies 69 (2011) 415–427

www.elsevier.com/locate/ijhcs

The influence of intrinsic and extrinsic motivation on individuals’knowledge sharing behavior

Shin-Yuan Hunga, Alexandra Durcikovab,n, Hui-Min Laia,c, Wan-Mei Lina

aDepartment of Information Management, National Chung Cheng University, Taiwan, ROCbDepartment of Management Information Systems, University of Arizona, Tucson, AZ 85721, United States

cDepartment of Information Management, Chienkuo Technology University, Taiwan, ROC

Received 28 August 2009; received in revised form 11 November 2010; accepted 16 February 2011

Communicated by P. Mulholland

Available online 24 February 2011

Abstract

A major challenge in knowledge management involves motivating people to share knowledge with others. The objective of this study

is to deepen our understanding of how to influence an individual’s tendency to engage in knowledge sharing behavior in a team setting.

Specifically, we investigate the effects of intrinsic motivation (altruism) and extrinsic motivation (economic reward, reputation feedback

and reciprocity) on knowledge sharing (number of ideas generated, idea usefulness, idea creativity and meeting satisfaction) in a group

meeting. Results of our experiment show that a knowledge management system with built-in reputation feedback is crucial to support

successful knowledge sharing.

& 2011 Elsevier Ltd. All rights reserved.

Keywords: Knowledge sharing; Intrinsic motivation; Extrinsic motivation; Knowledge management systems; Experimental study

1. Introduction

Knowledge management (KM) issues have increasinglycaptured the interest and attention of researchers and practi-tioners. Organizations implement KM initiatives with theexpectation that they will result in increased competitiveadvantage (Alavi and Leidner, 2001; Bock and Kim, 2002;Jones, 2006; Parent et al., 2000; Tiwana, 2002). Previousresearch in KM has largely focused on understanding howexisting knowledge should be gathered, organized, stored, andshared within an organization (e.g., Markus, 2001). However,creation of new knowledge (Nonaka and Takeuchi, 1995) thatis supported by new cultural and work practices (Holthouse,1998) is an important factor of an organization’s competitive-ness as it is a prerequisite for future strength (Krogh et al.,2000). In order to support knowledge creation and to opti-mally structure its flow so that it is more visible (Holthouse,1998), employees have to engage in knowledge sharing(Nonaka, 1994). Organizations have tried different motivators

e front matter & 2011 Elsevier Ltd. All rights reserved.

cs.2011.02.004

ing author. Tel.: +1 520 621 3927; fax: +1 520 621 2433.

ess: alex@eller.arizona.edu (A. Durcikova).

(Alavi and Leidner, 2001; Muller et al., 2005) that have beenshown to play a critical role in KM success (Alavi andLeidner, 2001; Bock and Kim, 2002; Davenport and Prusak,1998). However, researchers argue that there is a relative lackof attention paid to understanding the link between motiva-tion and knowledge sharing behavior (Kalling and Styhre,2003). Therefore, one of the major challenges in KM involvesmotivating people to share their knowledge with otherswhile also making their contribution visible and concrete(Holthouse, 1998). Our study fills this gap in current literature.Davenport and Prusak (1998) have argued that people’s

time, energy and knowledge are limited such that theyeventually consider whether the value of their knowledgecontribution is rewarded. This reward can be extrinsic orintrinsic in nature (Benabou and Tirole, 2003; Ryan andDeci, 2000). Because of this, organizations have developed awide variety of KM practices that use incentives to build aculture of knowledge sharing. Prior research has highlightedthe importance of motivation on knowledge contributionbehavior (i.e., Kankanhalli et al., 2005; Wasko and Faraj,2005); however, the results have been equivocal. For example,some research (Hall, 2001a, 2001b; Kankanhalli et al., 2005)

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427416

has found a positive relationship between the reward systemand knowledge sharing; others have found a negativerelationship (Bock and Kim, 2002; Bock et al., 2005).Findings are similar when it comes to economic rewards:some suggest that economic rewards have a negativeimpact on creativity (Amabile, 1985) while others findeconomic rewards irrelevant to an individual’s continuedknowledge-contribution behaviors (He and Wei, 2008).Results are also equivocal regarding reciprocity, as somestudies have suggested a positive relationship between reci-procity and knowledge contribution (Kankanhalli et al., 2005;Wasko and Faraj, 2005), but other research has founddifferent results (He and Wei, 2008; Wasko and Faraj, 2005).

Because teams are often the fundamental social units of anorganization’s knowledge creation, it has become importantto study knowledge sharing in team settings (Joshi et al., 2007;Parent et al., 2000). Additionally, knowledge sharing requirescollaboration between the seekers and contributors of knowl-edge (Yang and Chen, 2008). Hence, team-based collaborativework can facilitate knowledge sharing (Tiwana, 2002). Teamscan exchange knowledge synchronously and asynchronously.When knowledge needs to be shared synchronously it oftentakes place in a meeting. The objective of this study is tostrengthen the understanding of how to influence an indivi-dual’s tendency to engage in knowledge sharing behavior in ateam setting. Specifically, we investigate the effects of intrinsicmotivation (altruism) and extrinsic motivation (economicreward, reputation feedback and reciprocity) on knowledgesharing behavior (measured through number of ideas gener-ated, idea usefulness, idea creativity and meeting satisfaction)in a group meeting under experimental conditions within aschool context. To our knowledge, this is the first experimen-tal study investigating these relationships. Because findingsfrom previous studies have been inconclusive, we believe thatconducting a laboratory experiment can more definitivelyclarify these relationships. The results of this study show thatexistence of a built-in reputation feedback mechanism isnecessary to support knowledge sharing.

In Section 2, we briefly review four motivation theories forknowledge sharing. In Section 3, we identify the proposedmotivation and framework for developing our hypotheses.In Section 4, we describe our research method. Section 5discusses the results and their implications for practice andresearch. We close with conclusions.

2. Extrinsic and intrinsic motivation

Knowledge is a critical asset of an organization (Davenportet al., 1998; Krogh et al., 2000; Nonaka, 1994). Frequently,organizations use Information Technology (IT) in orderto ensure that newly created knowledge is stored, transferredand shared. One of the aims of IT is to establish knowledgerepositories and connect communication networks (Alavi,2000), therefore playing a critical role in successful KM.

Information system research has demonstrated the value ofstudying intrinsic and extrinsic motivation (e.g., Venkatesh,1999). When an employee is motivated it means he/she is

moved to do something (Ryan and Deci, 2000). Sincemotivation is therefore a main concern of any manager, ithas been one of the most studied factors in KM (Bock et al.,2005; Kankanhalli et al., 2005; Wasko and Faraj, 2005), and ithas been identified as a key determinant in informationtechnology acceptance behavior (Davis et al., 1992; Leeet al., 2005; Shang et al., 2005). People can be motivatedeither extrinsically or intrinsically. If a person is intrinsicallymotivated, he/she will engage in an action because it isenjoyable and he/she finds it inherently interesting (Deci andRyan, 1980). On the other hand, an extrinsically motivatedindividual’s actions are driven by a goal (Deci and Ryan,1980). Research has shown that these two categories ofmotivation can lead to very different behavior and perfor-mance (Ryan and Deci, 2000).Prior research has shown that KM practices cannot

improve business performance simply by using IT tocapture and share lessons learned (Alavi and Leidner,2001; Cross and Baird, 2000; O’Dell and Grayson,1998). Gold et al. (2001) found that it is an organization’sformal organizational structure and the incentive systemsthat make up its overall KM structure that support opensharing of valuable knowledge (Wood and Gray, 1991).Therefore, we will focus on different theories that incor-porate extrinsic and intrinsic motivation to identify thosefactors that have the highest impact on knowledge sharing.Our literature review suggests that there are four suchtheories:

(1)

Economic exchange theory—In the economic exchangetheory (EET) perspective, each person’s behavior isinfluenced by rational self-interest. When a person feelsthat the obtained rewards are more than the cost, she willshare her knowledge (Constant et al., 1994; Kelley andThibaut, 1978). According to Karlsen and Gottschalk(2004), IT projects often fail because there are noincentives to promote knowledge sharing. These incentiveswill not only influence user behavior but also users’interactions with the system (Ba et al., 2001). This impliesthat people will expect to receive extrinsic benefits such asmonetary rewards, promotions, or educational opportu-nities (Bock and Kim, 2002). In the context of this study,individuals’ knowledge sharing would depend on theexistence of monetary rewards.

(2)

Knowledge market perspective—Davenport and Prusak(1998) used the knowledge market perspective (KMP)to propose knowledge circulation. It consists of the onewho demands knowledge (the buyer), the one whoprovides knowledge (the seller), the broker who acts asthe connecting thread between the buyer and seller,and the price mechanism. Instead of the price mechan-ism that exists in real exchange markets, the pricemechanism here refers to the exchange rewards, whichinclude reputation, reciprocity and altruism. In thecontext of this study, individuals’ knowledge sharingwill depend on existence of reputation feedback andtheir level of reciprocity and altruism.

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427 417

(3)

Social exchange theory—Social exchange theory (SET)proposes that all human behavior involves benefitmaximization and cost minimization. SET posits havingrelatively long-term relationships of interest in contrast toa one-time exchange (Molm, 1997). The differencebetween SET and EET is that there is no clear obligationto receive future benefits (Kankanhalli et al., 2005).During a social exchange, social and individual costsand benefits can influence knowledge contribution. Forexample, cost factors include the loss of knowledgepower and the codification effort, while the benefitfactors include organization reward, knowledge self-efficacy and enjoyment in helping others (Kankanhalliet al., 2005). Therefore, the factors include both intrinsicand extrinsic benefits (Deci and Ryan, 1980; Vallerand,1997). In the context of this study, an extrinsic benefitwould be monetary reward for knowledge sharing (Beerand Nohria, 2000; Hall, 2001b) that can lead to acomparatively better life (Kankanhalli et al., 2005). Otherextrinsic benefits would be reputation feedback that canlead to active participation (Donath, 1999) and recipro-city, the expectation that an individual’s sharing effortswill be reciprocated, thereby ensuring ongoing sharing(Wasko and Faraj, 2005). The one intrinsic motivation isaltruism. Altruism is derived from the intrinsic enjoymentof helping others (Kankanhalli et al., 2005).

(4)

Social capital theory—The social capital theory (SCT)perspective recognizes that social capital can promoteknowledge sharing among partners because they pos-sess common values, enabling them to build mutualtrust. Several prior studies used SCT to understandorganizations’ knowledge creation and sharing process(Nahapiet and Ghoshal, 1998; Tsai and Ghoshal, 1998;Wasko and Faraj, 2005). SCT argues that cooperationand tacit understanding are formed over a long periodof time. This leads to the development of mutual trustand establishment of long-term interpersonal relation-ships of reciprocity within and across groups. In thecontext of our study, individuals will reciprocateothers’ effort to share knowledge by contributing more.

The above literature review suggests that there are fourkey motivators affecting knowledge sharing: one intrinsicmotivator (altruism) and three extrinsic motivators (eco-nomic reward, reputation feedback and reciprocity). Thisfinding is consistent with the work of Davenport andPrusak (1998, p. 31 and pp. 47–48) who argue that amarket price system for knowledge exchange exists withinorganizations. While the medium of exchange for knowl-edge is rarely money, there are some agreed-upon curren-cies that make this exchange happen. These agreed-uponcurrencies are reciprocity, reputation, and altruism. More-over, Davenport and Prusak argue that monetary rewardsare vital element in establishing the culture of knowledgesharing (p. 48). Next, we discuss in detail how these fourmotivators influence knowledge sharing.

3. Research model and hypotheses

3.1. Economic reward

Money is the most obvious way for an organization toreward its employee for suitable behavior. Carrillo et al.(2004) surveyed UK construction organizations and foundthat most reward schemes in organizations were financiallybased. In order to encourage knowledge contributors toshare, the organization can provide different forms ofeconomic rewards such as salary increases, bonuses, jobsecurity, or promotions (Ba et al., 2001; Beer and Nohria,2000; Bock et al., 2005; He and Wei, 2008; Kankanhalliet al., 2005). Results from recent empirical research alsoprovide evidence that economic rewards significantly influenceusage of electronic repositories by knowledge contributors(Davenport and Prusak, 1998; Kankanhalli et al., 2005).Thus, when individuals receive an economic reward fortheir knowledge, they will feel more motivated to shareknowledge, which will lead them to generate more unique,useful, and creative ideas. They will feel that money is afair exchange for their knowledge sharing behavior (Bartoland Srivastava, 2002; Hall, 2001b). Furthermore, as aconsequence of receiving money, an individual will experi-ence a higher level of satisfaction (Calder and Staw, 1975;Osterloh and Frey, 2000). This leads to the followinghypotheses:

Hypothesis 1a. Economic reward will positively influencethe number of ideas generated.

Hypothesis 1b. Economic reward will positively influencethe usefulness of ideas generated.

Hypothesis 1c. Economic reward will positively influencethe creativity of ideas generated.

Hypothesis 1d. Economic reward will increase the perceivedlevel of satisfaction with the meeting.

3.2. Reputation feedback

Reputation can help an individual to obtain and main-tain his or her status within a community (Jones et al.,1997; Marett and Joshi, 2009) and prevent the retention offree riders who do not contribute to the team effort. Inopen-source software projects, a good reputation is thecapital that drives the key contributors to make importantchanges (Stewart, 2005). Several studies suggest thatpeople participate in KM practices because they believethat they can establish and improve their individualreputation (Constant et al., 1996; Donath, 1999; Waskoand Faraj, 2005) or earn peer recognition (Carrillo et al.,2004). As a result, when individuals feel that knowledgesharing can elevate their reputation, they will be moreinclined to share their knowledge (Ba et al., 2001; Daven-port and Prusak, 1998; Wasko and Faraj, 2005). Reputa-tion may also be related to social status: when thecontributor’s status increases, the quality of his or her

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427418

performance also increases (Stewart, 2005). Results fromrecent empirical studies also confirmed that reputationfeedback significantly affects the contributor’s volume andhelpfulness of contribution (Wasko and Faraj, 2005).

It follows from the arguments above that reputationfeedback positively affects an individual’s self esteem.Therefore, we hypothesize that the reputation feedbackmechanism will motivate an individual to perform betterand he/she will then develop more unique ideas, moreuseful ideas, and more creative ideas. The presence ofreputation feedback will also positively influence an indi-vidual’s satisfaction with the team meeting, as her/hiseffort to share knowledge will be publicly acknowledged.This leads to the following hypotheses:

Hypothesis 2a. Reputation feedback will positively influencethe number of ideas generated.

Hypothesis 2b. Reputation feedback will positively influencethe usefulness of ideas generated.

Hypothesis 2c. Reputation feedback will positively influencethe creativity of ideas generated.

Hypothesis 2d. Reputation feedback will increase the per-ceived level of satisfaction with the meeting.

3.3. Reciprocity

In order to contribute knowledge, individuals mustbelieve that their contribution is worth the effort. Accord-ing to Davenport and Prusak (1998), people’s time, energyand knowledge are limited. Therefore, except when profit-able, people are usually unwilling to share these scarceresources with others. Reciprocity is a form of conditionalgain; that is, people expect future benefits from theirpresent actions. This means that a behavior is done inresponse to previous friendly actions (Fehr and Gachter,2000). Many studies have carried out detailed analyses ofreciprocity and found that it can be beneficial to knowl-edge contributors because they anticipate future help fromother people (Connolly and Thorn, 1999; Kollock, 1999).

The norm of reciprocity (Gouldner, 1960) makes twominimal demands: (1) people should help those who havehelped them, and (2) people should not harm those who havehelped them. In a team environment, people who anticipateand are more willing to share their good ideas also expectothers to respond to their ideas and generate new ideas.Fehr and Gachter (2000) pointed out that one of the mostimportant consequences of reciprocity is the power to enhancecollective actions and enforce social norms. Research hasrevealed that reciprocity, a deeply held human behavioral trait(Schultz, 2006, Heineck and Anger, 2010), significantly affectshow much an individual contributes (Bock et al., 2005;Wasko and Faraj, 2005). Thus, people who expect reciprocitywill share more ideas, their ideas will be more useful andcreative, and their satisfaction with the meeting will be higher.This leads to the following hypotheses:

Hypothesis 3a. Reciprocity will positively influence the num-ber of ideas generated.

Hypothesis 3b. Reciprocity will positively influence the useful-ness of ideas generated.

Hypothesis 3c. Reciprocity will positively influence the crea-tivity of ideas generated.

Hypothesis 3d. Reciprocity will increase the perceived levelof satisfaction with the meeting.

3.4. Altruism

Altruism can be seen as a form of unconditional kindnesswithout the expectation of a return (Fehr and Gachter, 2000)where an individual provides help and achieves a sense ofsatisfaction from the action (Kollock, 1999). In many cases,individuals help others whether or not they get anything inreturn (Davenport and Prusak, 1998). Constant et al. (1994)suggested that people who share tangible information maydo so due to pro-social attitudes. Wasko and Faraj (2000,2005) pointed out that these individuals are motivatedintrinsically to contribute knowledge to others because theyenjoy helping others. Results from recent empirical studieshave also confirmed the positive relationship between altru-ism and knowledge contribution. For instance, Kankanhalliet al. (2005) found that altruism significantly affects electronicrepository usage by knowledge contributors and it alsosignificantly increases the helpfulness of the contribution.This is further supported by He and Wei (2008), who suggestthat knowledge workers contribute knowledge to the KMSbecause of their enjoyment in helping others.Therefore, we propose that in a team environment

people with greater altruism will contribute more uniqueideas, propose more useful ideas, generate more creativeideas and also have a higher satisfaction with the meeting.This leads to the following hypotheses:

Hypothesis 4a. Altruism will positively influence the num-ber of ideas generated.

Hypothesis 4b. Altruism will positively influence the use-fulness of ideas generated.

Hypothesis 4c. Altruism will positively influence the crea-tivity of ideas generated.

Hypothesis 4d. Altruism will increase the perceived level ofsatisfaction with the meeting.

The above research model is shown in Fig. 1.

4. Research methodology

4.1. Research design

We conducted a laboratory experiment in order to testthe hypotheses delineated in the previous sections. Experi-mentation as a research method allowed us to manipulateeconomic rewards and reputation feedback in a systematic

Extrinsic Motivation

Economic Reward

Reputation Feedback

Intrinsic Motivation

Reciprocity

Altruism

Outcome of Knowledge Sharing

Number of Ideas

Idea Usefulness

Idea Creativity

Meeting Satisfaction

H1

H2

H3

H4

Fig. 1. Research model.

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427 419

fashion, as well as control extraneous variables moreeffectively. Furthermore, this method makes it possibleto replicate experiments using different subject groups andconditions that will eventually lead to the discovery of anaverage effect of independent variables across people,situations and time (Emory and Cooper, 1991). This type ofexperimentation has been successful in small group interac-tions (Babbie, 2004). Moreover, one extrinsic and one intrinsicfactor which cannot be manipulated, reciprocity and altruism,were measured in the post-experiment session. This studyrepresents a 2� 2 factorial design (with/without economicreward�with/without reputation feedback) that assessed themotivation for knowledge sharing behavior.

Reputation feedback was reported to each participantregarding his/her creative tasks. The facilitator counted thenumber of unique ideas and rank-ordered the teammembers from the most productive to the least productive.This feedback was provided every 7 min. The same facil-itator was used in all experiments.

4.2. Subjects

A total of 140 upper division undergraduate and MBAstudents from a university in Taiwan volunteered to partici-pate in the study. In a pilot study, 20 subjects were recruitedinto four groups to test and fine-tune the questionnaire andexperimental manipulations and procedures. In addition, atotal of 120 subjects volunteered for the actual experiments.After reading and signing a consent form, subjects completeda pre-session survey that gathered some background data.Subjects were then randomly assigned to a five-person group.There were 24 groups, 6 groups in each treatment, and 5participants in each group. Five-person groups were used, asthis size was found to be optimal for brainstorming (Osborn,1953; Slater, 1958; Stewart, 2005). Participants were randomlyassigned to groups, and groups were randomly assignedto different treatments. No significant differences betweensubjects existed across the four experimental treatments interms of their gender or age. Each participant was awarded $3(US) for participation. Moreover, additional incentives wereprovided for the groups receiving an economic reward. Cashbonuses $2 (US) were awarded to those who performed above

average and an additional $7 (US) was awarded to those whoperformed best. Subjects were informed about this economicreward in advance. The level of monetary reward would beattractive to these student subjects according to the results ofprior similar studies (Chen et al., 2007; Hall et al., 2007;Quigley et al., 2007).

4.3. Tasks

McGrath (1984) classified group tasks into four majorcategories: generate (e.g., generate creativity ideas), choose(e.g., choosing solutions), negotiate (e.g., negotiating conflicts)and execute (e.g., performing tasks). The generation task waschosen for this experiment as it is the most suitable forknowledge sharing. Two tasks that had been previously testedand validated were used in the study (Parent et al., 2000).Since the experiment results are likely to be influenced by thedegree of familiarity with certain tasks and systems, oursubjects were first requested to accomplish a practice task. Atthe completion of the practice task, the subjects were asked tocomplete the experimental task. The first task, the practicetask, was to identify which features the new university libraryshould have. The second task, the experimental task, was todescribe how tourism could be improved in the local area.The two tasks are described in Appendix A.

4.4. Experimental system

A web-based group support system using JAVA tech-nology was developed to support a number of functionsthat occur in team meetings: brainstorming, organizinginformation, list building, information gathering, prioritiz-ing, consensus building, and the best choice building.Several tools provided by GroupSystems, a group supportsystem developed originally at the University of Arizona,were used. To support the generation task, the systemprovided: (1) an electronic brainstorming tool; (2) an issueanalyzer; and (3) a ranking/voting tool. The electronicbrainstorming tool is an idea-generation tool that allowsparticipants to input their ideas anonymously and freely.The issue analyzer is then used for the identification andconsolidation of issues generated during electronic

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427420

brainstorming sessions. The ranking/voting tool allowsparticipants to privately rank order their choices and voteon the list of issues generated by the issue analyzer.

The group support system ran in a Microsoft Windowsenvironment on a local area network. Participants wereseated around a U-shaped table in a computer lab andwere supplied with a networked computer and keyboard.

4.5. Variables and measures

4.5.1. Independent variables

The two extrinsic motivators, economic reward and reputa-tion feedback, were manipulated, whereas one extrinsic andone intrinsic motivator, reciprocity and altruism, were mea-sured. The experiment was thus designed to assess thewillingness of individuals to contribute ideas under twoconditions: (1) reciprocity—a deeply held human behavioraltrait (Schultz, 2006; Heineck and Anger, 2010), defined as thebelief that current contributions to group meetings would leadto future requests for knowledge to be met in order to obtainmutual benefit through knowledge sharing (Davenport andPrusak, 1998; Kankanhalli et al., 2005); (2) altruism—definedas the perception of pleasure obtained from helping othersthrough knowledge shared in a group meeting withoutexpecting anything in return (Kankanhalli et al., 2005; Waskoand Faraj, 2000). At the end of the experiment, the subjectsrated themselves on reciprocity and altruism. The reciprocityand altruism constructs are shown in Appendix B.

4.5.2. Dependent variables

Knowledge sharing behavior is defined as the degree towhich an individual conducts knowledge sharing activities in agroup meeting (Davenport and Prusak, 1998). In this study,the dependent variables showed how well the subjectsperformed in the idea generation task and how satisfied they

Table 1

Measures of dependent variables.

Variables Measure

Number of ideas

generated

Each idea was recorded by the group support system an

then independently judged by two tourism experts. The

third judge would join the discussion to reach consensus

any conflict cannot be resolved between two judgesa

Idea usefulness Each of the three tourism experts reported one item on

four-point Likert scale (4—very helpful; 1—not helpful)

and were averaged as a usefulness ratingb

Idea creativity Each of three tourism experts reported two items

(originality and paradigm relatedness) on a seven-point

Likert scale). The scores were averaged as a creativity

ratingc

Perceived meeting

satisfaction

Five self-reported items. Each item used a seven-point

Likert scale. The questionnaire is attached in Appendix

aRaters were unaware of the hypotheses.bThe inter-rater reliability for all three judges was 0.94.cThe inter-rater reliabilities of idea originality and idea paradigm relatedne

were with the sharing process. This was measured by theparticipants’ outcomes of knowledge sharing, which includedknowledge quantity (number of ideas), knowledge quality(idea usefulness and idea creativity) and perceived meetingsatisfaction. Table 1 summarizes the measures of dependentvariables.

4.5.3. Control variables

A number of control variables, such as group size, tasktype, and some contextual factors, were fixed. In this study,the group size was controlled to five people per group.All groups had to solve the same task. All experiments hadthe same facilitator. Since group members were randomlyassigned to treatments, it was assured that several otherfactors (e.g., group history and individual characteristics)known to influence the measures were also controlled for.

4.6. Experimental procedures

The experiment was comprised of seven steps:

�

O

d

if

T

a

a E

t

u

p

E

t

o

T

c

B

P

ss f

Step 1—Subjects were randomly assigned to a five-person group. There were 24 groups. Subjects beganwith a 10 min hands-on introduction to this system.

� Step 2—Each group member was given additional

10 min to perform the practice task and to get to knowthe system.

� Step 3—The experimental task was explained to the

subjects (5 min).

� Step 4—Subjects were then given 20 min to brainstorm ideas.

The system enabled group members to comment on options,ask questions, comment on other members’ comments, watchother members’ options, and so on. Participants typed in theirresponses and the system would immediately make these ideasavailable for other subjects to read on their screens.

perationalization Source

he number of ideas generated by each participant

fter eliminating duplicate and irrelevant ones

Easton et al.

(2003)

ach participant was independently assessed by

hree tourism experts to indicate his/her idea

sefulness. The average usefulness score for each

articipant was calculated thereafter

Wasko and Faraj

(2005)

ach participant was independently assessed by

hree tourism experts to evaluate his/her idea

riginality and idea paradigm relatedness scores.

he average creativity score for each participant was

alculated thereafter

Hender et al.

(2002)

articipant’s satisfaction with the meeting process Green and Taber

(1980)

or all three judges were 0.90 and 0.88, respectively.

Table 3

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427 421

�

Tab

Re

Re

Alt

Sat

Th

var

Results of factor analysis.

Step 5—Subjects were then asked to discuss why theideas were appropriate for the task (5 min).

�

Scale items Reciprocity Altruism Satisfaction

Reciprocity 1 0.614 0.292 0.203

Reciprocity 2 0.866 0.167 0.121

Step 6—Following a two-round voting procedure, sub-jects selected three ideas and assigned weights reflectingthe relative importance of the selected ideas (5 min). Inthis phase, subjects were voting on the best ideas.

Reciprocity 3 0.754 0.244 0.198

� Altruism 1 0.169 0.839 0.267

Altruism 2 0.215 0.863 0.214

Altruism 3 0.311 0.837 0.029

Altruism 4 0.175 0.847 0.138

Satisfaction 1 0.156 0.064 0.758

Satisfaction 2 0.143 0.112 0.742

Satisfaction 3 0.207 0.097 0.735

Satisfaction 4 0.072 0.134 0.782

Satisfaction 5 0.089 0.299 0.799

Step 7—Subjects were asked to complete the post-experiment questionnaires. Data collected during thisphase included the demographic data, manipulationcheck, reciprocity, and altruism, as well as perceivedmeeting satisfaction.

5. Results

5.1. Profiles of the participants

A total of 118 (out of 120) usable responses were used inthe analysis. The sample consisted of 62 males (53%) and56 females (47%) with an average age of 23. Most of thesubjects (82.2%) had taken at least one course on compu-ters. All subjects used computers frequently and most ofthem (98.3%) were very efficient at typing. Most subjects(93.2%) had experience working in teams.

5.2. Data reliability and validity

The constructs were assessed for their reliability andvalidity. Internal consistency for all constructs was inves-tigated using Cronbach’s alpha. The results in Table 2show that the reliability of the three constructs rangedfrom 0.72 to 0.91, which exceeds the recommended valueof 0.70 (Nunnally, 1978).

To ensure content validity, previously validated measure-ments were used. Furthermore, the final questionnaire wasvalidated by three professionals to ensure that no syntax orsemantic biases occurred during the translation from Englishto Chinese. The questionnaire was then translated back toEnglish to ensure that proper translation of all the itemsoccurred. Finally, the pilot study with 20 subjects revealed noproblems with the questionnaire design.

In order to assess construct validity, principal componentwith varimax rotation was performed. The Kaiser–Meyer–Olkin (KMO) index of sampling adequacy was 0.845,confirming the appropriateness of the analysis. Three factorswere extracted that cumulatively explained 69.3% of thevariance; these are shown in Table 3. All the loadings of each

le 2

sults of reliability analysis.

Number

of items

Cronbach’s

alpha

Reciprocity Altruism Satisfaction

ciprocity 3 0.72 0.637

ruism 4 0.91 0.614 0.825

isfaction 5 0.85 0.442 0.429 0.725

e bold numbers in the diagonal row are square roots of the average

iances extracted (AVE).

observed indicator on its latent construct exceed 0.60 and theaverage variances extracted (AVEs) of these three constructsare larger than 0.5, therefore good convergent validity wasdemonstrated (Anderson and Gerbing, 1988). Additionally,in terms of discriminant validity, all the AVE values of thethree constructs exceeded the squared correlation coefficientsbetween the constructs (see Table 2) demonstrating gooddiscriminant validity (Fornell and Larcker, 1981).

5.3. Experimental findings

Prior to testing our model, we performed manipulationchecks. The t-test results indicate that the manipulationwas successful for both factors: specifically, for economicreward the means for the two groups were 4.75 and 1.85(t=8.905, p=0.000) and for reputation feedback themeans were 4.20 and 2.55 (t=3.735, p=0.001).To understand the effects of motivational factors on

knowledge sharing, Multivariate Analysis of Variance(MANOVA) with four categorical independent variables(economic reward, reputation feedback, reciprocity, andaltruism) and four continuous dependent variables (numberof ideas, idea usefulness, idea creativity, and meeting satis-faction) was performed. Subjects were assigned to high andlow levels of reciprocity and altruism based on median split.The results of the MANOVA are summarized in Table 4.

Z-skewness/Z-kurtosis and Levene’s tests were used totest normality and variance homogeneity. Z-skewness/Z-kurtosis test results demonstrate that all the Z-valuesranged between �2.58 and 2.58, implying that the presentdata meet the normality assumption. The F-values of theLevene’s test are 1.239 (p=0.229; for number of ideasgenerated), 0.409 (p=0.747; for idea usefulness), 0.232(p=0.874; for idea creativity), and 1.428 (p=0.239; formeeting satisfaction), respectively, indicating that nostatistically significant differences exist among the variancesof different groups. The correlation of the dependentmeasures was tested by Bartlett’s test of sphericity. Thep-value was below 0.001, satisfying the requirements ofintercorrelation for MANOVA (Hair et al., 1995). Thus,MANOVA was appropriate.

Table 4

Results of the MANOVA analysis.

Source SS DF F-value Significance

Number of ideas generated

Economic reward 2.523 1 0.211 0.647

Reputation feedback 589.539 1 49.222 0.000nnn

Reciprocity 4.399 1 0.367 0.546

Altruism 3.728 1 0.311 0.578

Idea usefulness

Economic reward 0.530 1 1.264 0.263

Reputation feedback 10.471 1 24.976 0.000nnn

Reciprocity 0.034 1 0.081 0.777

Altruism 0.480 1 1.146 0.287

Idea creativity

Economic reward 0.113 1 0.022 0.881

Reputation feedback 95.294 1 18.970 0.000nnn

Reciprocity 0.328 1 0.065 0.799

Altruism 2.300 1 0.458 0.500

Meeting satisfaction

Economic reward 4.408 1 5.526 0.020nn

Reputation feedback 0.235 1 0.294 0.589

Reciprocity 4.078 1 5.113 0.026nn

Altruism 8.022 1 10.057 0.002nn

nnpo0.05.nnnpo0.001.

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427422

Results show that the effect of economic reward on numberof ideas generated, idea usefulness, and idea creativity are notstatistically significant at the 0.05 level (p=0.647, 0.263, and0.881, respectively). Consequently, hypotheses H1a, H1b andH1c are rejected. However, subjects receiving an economicreward are more satisfied with the meeting than subjectswithout it (mean/st.dev were 4.7/0.96 and 4.33/1.00). Conse-quently, hypothesis H1d is supported (po0.05). Economicrewards make the meeting more enjoyable but do not promoteknowledge sharing.

The effect of reputation feedback on number of ideasgenerated, idea usefulness, and idea creativity are statisti-cally significant (p=0.000 for all). Providing reputationfeedback can stimulate subjects to generate more uniqueideas (mean/st.dev were 11.21/3.86 and 6.55/2.95), generatemore useful ideas (mean/st.dev were 2.67/0.60 and 2.08/0.69), and generate more creative ideas (mean/st.dev were6.82/2.10 and 5.03/2.32). Consequently, hypotheses H2a,H2b and H2c are supported. However, the effect ofreputation feedback on meeting satisfaction is not statis-tically significant (p=0.589). Consequently, hypothesisH2d is rejected.

The effect of reciprocity on number of ideas generated,idea usefulness, and idea creativity is not statisticallysignificant (p=0.546, 0.777, and 0.799, respectively). Con-sequently, hypotheses H3a, H3b and H3c are rejected.However, high reciprocity significantly increases the meet-ing satisfaction (mean/st.dev were 4.91/1.09 and 4.27/0.85).Consequently, hypothesis H3d is supported (po0.05).

Finally, the effect of altruism on number of ideas gener-ated, idea usefulness, and idea creativity is not statistically

significant (p=0.578, 0.287, and 0.500, respectively). Hence,hypotheses H4a, H4b and H4c are rejected. Yet altruismsignificantly increases meeting satisfaction (mean/st.dev were4.99/1.04 and 4.20/0.83). Consequently, hypothesis H4d issupported (po0.05).The above results demonstrate an interesting phenom-

enon that economic reward, reciprocity, and altruismaffect meeting satisfaction, but show no significant effectson number of ideas generated, idea usefulness, and ideacreativity. Furthermore, reputation feedback has an effecton the number of ideas generated, idea usefulness, and ideacreativity, but no significant effects on meeting satisfac-tion. The discussion of these findings follows.

6. Discussion

6.1. Implications for research

Perhaps the most interesting finding of this research is thateconomic incentives did not achieve the desired outcome ofincreased knowledge sharing. Instead, reputation feedbackhad the most significant effect on all measures of knowledgesharing. The goal of this research was to understand the rolethat intrinsic and extrinsic motivation play in knowledgesharing behavior, as the results of previous studies conflicted.To address this gap in the literature we conducted anexperiment that investigated the effects of altruism (intrinsicmotivation) and economic reward, reputation feedback andreciprocity (extrinsic motivation) on knowledge quantity,quality (idea usefulness and idea creativity) and satisfactionwith a meeting (because the knowledge was exchangedsynchronously). When discussing the results of this studywe compare our findings with three recently published studieson knowledge sharing (see Table 5).

6.1.1. Economic reward effects

The first extrinsic motivational factor studied waseconomic reward, a concept adopted from EET, KMPand SET. According to these theories, an individual’sdecision to share knowledge is affected by the presenceof an economic reward. In our study, the provision of aneconomic reward significantly affected only satisfactionwith a meeting but had no effect on the quantity andquality of contribution that are of foremost interest in anyKM project. Our result is consistent with Bock et al. (2005)who showed that providing an economic reward did notimprove the user’s attitude toward knowledge sharing.However, Kankanhalli et al. (2005) found that organiza-tional rewards can increase users knowledge contributionthrough Electronic Knowledge Repository (EKR). Theexplanation of this result is attributable to two factors.First, Kankanhalli et al. (2005) suggested that the influenceof organizational reward on knowledge contribution maybe constrained by the contributor’s organizational identi-fication. If the knowledge contributor has more in com-mon with the organization, the contributor will be morelikely to receive other rewards from the organization. The

Table 5

Comparison of this study and three recent studies.

Method KMS Sample Dependent

variable

Economic

reward

Reputation

feedback

Reciprocity Altruism

Kankanhalli

et al. (2005)

Survey Electronic

knowledge

repository

150 respondents

from 10

organizations in

Singapore

EKR usage by

knowledge

contributors

Significant N/A N/A Significant

Wasko and

Faraj (2005)

Survey Electronic

network of

practice

173 responses

from a national

legal professional

association in

U.S.

Helpfulness of

contribution

N/A Significant Insignificant Significant on

helpfulness of

contribution

(po0.1)

Volume of

contribution

Significant Significant Insignificant

Bock et al.

(2005)

Survey No specific

KMS.

(Executives

enrolled in the

CKO program

offer by a

university)

154 responses

from 27

organizations

across 16

industries in

Korea

Intention to

share

knowledge

Extrinsic

reward has a

negative effect

on attitude

toward

knowledge

sharing

(po0.1)

N/A Significant N/A

This study Laboratory

experiment

Group support

system

118 participants

from a university

in Taiwan

Number of

ideas generated

Insignificant Significant Insignificant Insignificant

Idea usefulness Insignificant Significant Insignificant Insignificant

Idea creativity Insignificant Significant Insignificant Insignificant

Meeting

satisfaction

Significant Insignificant Significant Significant

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427 423

experimental groups were temporary groups and thereforegroup identification could not be established within thetimeframe of the experiment. Consequently, economicrewards were not a strong enough factor to be influential inmotivating knowledge sharing. Second, Bock and Kim (2002)noted that the extrinsic reward is only a trigger for the sharingof knowledge; it does not change the contributor’s attitudetowards knowledge sharing. Without creation of personalcommitment, economic reward only supports knowledgesharing for a short period of time. Hence, an economicreward is only a weak reinforcement in the short termthat enhances employees’ compliance with knowledge sharingpractices at the beginning of a project, but may hinder laterknowledge sharing (Benabou and Tirole, 2003). In addition,a previous study by Bartol and Srivastava (2002) alsoindicated that monetary rewards may be less useful incommunities of practice. Therefore the non-existence oforganizational commitment in our experiment supports thefindings of Bock and Kim (2002) and suggests that con-tributors will be satisfied with the participation because thereis a short-term benefit. However, the knowledge sharingoutcome, the focus of KM strategies, will not be helped aseconomic rewards do not increase the quantity or quality ofknowledge contributions.

6.1.2. Reputation feedback effects

The second extrinsic motivational factor in our studywas reputation feedback, a concept adopted from KMP

and SET. Reputation mechanisms, such as eBay’s Feed-back Forum, are widely used in practice. Our resultsindicate that reputation feedback has a significant effecton both the quantity and quality of contributions, but doesnot have a significant effect on perceived meeting satisfac-tion. This is consistent with Wasko and Faraj (2005), whofound that reputation feedback can increase both thehelpfulness of contributions and the number of contribu-tions, and with Marett and Joshi (2009), who pointed outthat reputation improvement and status-building withinthe community are motivation factors for sharing rumors.Helping individuals build expertise and providing recogni-tion may itself encourage knowledge sharing (O’Dell andGrayson, 1998).Appropriate feedback would allow people to understand

that sharing their knowledge helps others. That, in turn,would increase their sense of self-worth (Bock et al., 2005)and peer recognition (Sheehan, 2000) . In addition, Waskoand Faraj (2000) found that when knowledge is owned byindividuals, people participate primarily out of reputation,status and obligation. The reputation mechanism in thisstudy provided the participants information regarding thenumber of unique ideas generated by everyone. A partici-pant may feel honored that he/she created a high numberof ideas. Those who fall behind can be stimulated by thismechanism to get back on track. However, ranking maycause some participants to feel pressured and therefore bedissatisfied with this approach to knowledge sharing.

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427424

6.1.3. Reciprocity effects

The third extrinsic motivational factor in our study wasreciprocity, a concept adopted from KMP, SET and SCT.Reciprocity denotes that when people share their knowl-edge they expect to be compensated with equally valuableknowledge. Our results (see Table 4) indicate that recipro-city does not have a significant effect on the quantity orquality of contributions. However, reciprocity has asignificant positive effect on meeting satisfaction. Previousresearch has found that reciprocity is positively related tointention to share knowledge (Bock et al., 2005) andquantity of contribution (Wasko and Faraj, 2005);however, it had no effect on the quality of contributions(Wasko and Faraj, 2005). Three reasons can explain thisinconsistency. First, the correlation between intention andbehavior is approximately 0.5 (Sheppard et al., 1988) andtherefore reciprocity can positively affect intention and insome cases also quantity of contribution. However, not allcontributions can be of high quality. Second, previous studieson motivational factors discussed reciprocity that is mostlylong-term in influence, such as in an online community(Wasko and Faraj, 2000) or knowledge repository usage(Kankanhalli et al., 2005). Fehr and Gachter (2000) notedthat reciprocity is deeply embedded in social interactions.Due to time and cost limitations, this experiment wasconducted only once. Therefore, future research shouldexamine ‘‘long-term reciprocity’’. Finally, the group meetingsrepresent network-based interactions rather than dyadicinteractions and therefore direct reciprocity is not necessaryto sustain collective action (Wasko and Faraj, 2000).

When a member proposes an idea in the group meetingenvironment, others quickly respond to this idea. This fastexchange of ideas can lead to fast problem solving and inturn is very satisfying for those who care about reciprocity.

6.1.4. Altruism effects

The intrinsic motivational factor in our study was altruism,a concept adopted from KMP and SET. Altruism can be seenas unconditional kindness without the expectation of areward. The reward is usually a good feeling about helpingothers out. The results of this study indicate that altruism hada positive impact on meeting satisfaction but it did not havesignificant effects on either the quantity or quality ofcontributions. This finding is consistent with Taylor’s (2006)viewpoint that altruistic motivation may be insufficient to aidknowledge sharing. This factor remains the most puzzling outof the four investigated as it has been shown in the past toaffect (He and Wei, 2008; Kankanhalli et al., 2005) and notaffect quantity of contribution (Wasko and Faraj, 2005) andpositively affect quality of contribution (Wasko and Faraj,2005). Given that the reward for an altruistic person can comeas a good feeling about his/her action, this might have beencaptured by the sense of satisfaction with the meeting orfulfillment of duty in helping to brainstorm ideas about howto increase tourism in the local area. This is why satisfactionwith the meeting was significantly affected by altruism.

6.1.5. Implications for practitioners

This research has several implications for practitioners,both KMS software developers and managers. The impor-tance of the alignment of information system design withincentives has recently been recognized (Ba et al., 2001). Inthis study, we used economic reward and reputationfeedback as extrinsic incentives that were aligned withthe goal of higher quantity and quality of knowledgesharing. Our study indicates that KMS software devel-opers should incorporate a built-in reputation feedback tothe KMS due to the strong influence of reputation feed-back on both quantity and quality of knowledge shared.First, the quantity of the contributions can be implementedas a system feature and therefore would not require morehuman capital investment. Second, implementation of aranking mechanism for quality of contributions wouldrequire both system and personal changes. An interface inthe KMS would need to be created that allows idea qualityranking. Then, managers need to assign an employee to therole of a moderator who, like the facilitator in ourexperiment, would rate the quality of the contributions.Third, managers should realize that traditional individualperformance-based economic reward has no significantinfluence on knowledge sharing in group settings and itcan sometimes be seen as demeaning (Bock et al., 2008;O’Dell and Grayson, 1998). Thus, for a company managerwho wants to increase knowledge sharing, using solelyindividual based economic rewards may be an ineffectivemethod. However, as previous research indicated, eco-nomic rewards may serve the purpose of stimulatingparticipation at the beginning of a project, however, overthe long term, they may have negative impact (Benabouand Tirole, 2003). This is because individual performance-based economic rewards could create a tournament-likeatmosphere (Taylor, 2006) and trigger competition amongpeople who were expected to closely collaborate (Bocket al., 2008). A viable alternative would be using group-based economic rewards that has been shown to outper-form individual based economic reward in group setting(Taylor, 2006). Finally, while altruism improves meetingsatisfaction, the results show that it does not promoteknowledge sharing. Taylor (2006) suggests that to aidknowledge sharing, high levels of both altruism and knowl-edge of the subject may be necessary. Thus, when knowl-edge sharing is considered an important part of the job, it isvital to hire employees that are both knowledge contentexperts and altruistic.

6.1.6. Suggestions for future research

As with any empirical research, limitations of thepresent study should be recognized. The experimentalcontext of this study, while allowing for precise controlof factors without any extraneous influence and confound-ing factors, may decrease the applicability of the findingsto real-world KMS scenarios. We have attempted tominimize this inherent limitation of an experimental studyby using a web-based group support system that any

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427 425

organization can utilize for idea generation. Additionally,using students as participants may limit the generalizabilityof our results to the rest of the population. However, wewere controlling for this limitation by selecting a task thatdid not require knowledge of a specific subject matter andstudents appeared to participate satisfactorily during theirtask. Finally, reputation feedback was provided by onefacilitator for each group. It is possible that anotherfacilitator would rate reputation differently. However,given that this facilitator provided feedback to all groups,the potential bias was uniform across all groups. Inconclusion, despite the potential limitations this researchmakes some important contributions to both research andpractice. Other studies using different research methods areneeded to gain a thorough understanding of knowledgesharing in group settings.

Several suggestions for future research stem from thisexperiment. First, we examined only one intrinsic motivation– altruism – in this study. However, there are other intrinsicmotivations such as knowledge self-efficacy and collaborationnorms (Bock et al., 2006) that have been shown to influencenot only the behavior of those actively contributing informa-tion but also lurkers (Marett and Joshi, 2009). Several theoriesalso suggest that social influence is crucial in shaping userbehavior. For instance, social capital theory noted that groupmembers tend to contribute due to norms, trust, obligationsand identification (Nahapiet and Ghoshal, 1998). Therefore,running this experiment over a longer period of time couldlead to a better understanding of intrinsic motivation inknowledge sharing. Also, theory of conformity (Bernheim,1994) suggests that groups tend to comply with the groupnorm and this in turn influences knowledge sharing behavior.The effects of these motivations should be examined in futureresearch. Second, although our experiment is conducted ingroups, our reward structure was individually oriented.Future studies should consider team-based rewards that havebeen shown to foster cooperation and encourage knowledgesharing by individuals within teams (Bartol and Srivastava,2002). Third, knowledge can be viewed from differentperspectives, such as object, knowledge embedded in indivi-duals, and knowledge embedded in a community. Themotivators for knowledge exchange of these different typesof knowledge are different (Wasko and Faraj, 2000). In ourstudy, knowledge was viewed as an individual asset; howeverfuture studies should investigate it from the other twoperspectives. Finally, Constant et al. (1996) pointed out thatindividuals with higher expertise were more likely to shareuseful knowledge. Our experiment focused on novices ratherthan experts in the area of tourism. Therefore, future researchshould examine whether higher expertise would lead to moreand better quality contributions.

7. Conclusion

The study aims to explicate the roles of motivators that areeffective in encouraging knowledge sharing in a group meet-ing. The results of our experiment indicate that extrinsic

motivation such as economic reward may not be an adequatemotivator of knowledge sharing. However, economic reward,together with reciprocity and altruism, positively influencemeeting satisfaction. The most important finding of thisstudy is that reputation feedback served as a strong incentivefor both quantity and quality of knowledge shared. Severalother studies have used these motivators previously, howeverthe results were equivocal. This study contributes to theoryand practice in three ways: first, it uses fours theories to selectthree extrinsic motivators and one intrinsic motivator tounderstand their effects on individuals’ knowledge sharingbehavior; second, a controlled experiment is performed totest the derived hypotheses; third, it measures actual knowl-edge sharing (both quantity and quality of knowledge) in ateam setting.

Appendix A. Task description

1.

Practice task: What functions and features should a newuniversity library have?

The university you are attending is planning to builda new library. The planning committee is looking foropinions or ideas from students on what functions andfeatures a new university library should have. Pleaseprovide as many useful ideas as you can.

2.

Experimental task: How could tourism be improved inthe local area?

The Chiayi County Government has been promoting aprogram which can encourage the tourism industry inChiayi County. The County Government is looking foropinions or ideas from student groups on how to improvetourism. Please provide as many useful ideas to improvethe tourism industry in Chiayi County as you can.

Appendix B. Post-experiment questionnaire

Manipulation check for economic reward:

1.

I will receive a financial reward for performing well onthis task. (using a Likert scale; where 1=stronglydisagree, 5=strongly agree)

Manipulation check for reputation feedback:

1.

Others were aware of the quality of my performance onthis task. (using a Likert scale; where 1=strongly disagree,5=strongly agree)

Reciprocity (Source: Kankanhalli et al., 2005) was

measured using a Likert scale; where 1=strongly disagree,7=strongly agree

1.

When I share my knowledge through a group meeting,I believe that I will get an answer when I give an answer.

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427426

2.

1

When I share my knowledge through a group meeting,I expect somebody to respond when I’m in need.

3.

When I contribute knowledge to a group meeting,I expect to get back knowledge when I need it.

4.

When I share my knowledge through a group meeting,I believe that my queries for knowledge will be answeredin the future.1

Altruism (Source: Kankanhalli et al., 2005) was mea-

sured using a Likert scale; where 1=strongly disagree,7=strongly agree

1.

I enjoy sharing my knowledge with others through agroup meeting.

2.

I enjoy helping others by sharing my knowledgethrough a group meeting.

3.

It feels good to help someone else by sharing myknowledge through a group meeting.

4.

Sharing my knowledge with others through a groupmeeting gives me pleasure.

Meeting satisfaction (Source: Green and Taber, 1980)

was measured using a Likert scale (1–7). Subjects were

asked to answer the following question: How would youdescribe your group meeting process?

1.

1=very inefficient, 7=very efficient. 2. 1=very uncoordinated, 7=very coordinated. 3. 1=very unfair, 7=very fair. 4. 1=very confusing, 7=very understandable. 5. 1=very dissatisfying, 7=very satisfying.

References

Alavi, M., 2000. Managing organizational knowledge. In: Zmud, R.W.

(Ed.), Framing the Domains of IT Management Research: Glimpsing

the Future through the Past. Pinnaflex Educational Resources,

Cincinnati, OH.

Alavi, M., Leidner, D.E., 2001. Review: knowledge management and

knowledge management systems: conceptual foundations and research

issues. MIS Quarterly 25 (1), 107–136.

Amabile, T.M., 1985. Motivation and creativity: effects of motivational

orientation on creative writers. Journal of Personality and Social

Psychology, 393–399.

Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in

practice: a review and recommended two-step approach. Psychological

Bulletin 103 (3), 411–423.

Ba, S., Stallaert, J., Whinston, A.B., 2001. Research commentary:

introducing a third dimension in information systems design—the

case for incentive alignment. Information Systems Research 12 (3),

225–239.

Babbie, E.R., 2004. The Practice of Social Research 10th ed. Thomson

Learning Inc., Wadsworth.

Bartol, K.M., Srivastava, A., 2002. Encouraging knowledge sharing: the

role of organizational reward systems. Journal of Leadership &

Organizational Studies 9 (1), 64–76.

Dropped due to poor loading in factor analysis.

Beer, M., Nohria, N., 2000. Cracking the code of change. Harvard

Business Review 78 (3), 133–141.

Benabou, R., Tirole, J., 2003. Intrinsic and extrinsic motivation. The

Review of Economic Studies 70 (3), 489–520.

Bernheim, B.D., 1994. A theory of conformity. The Journal of Political

Economy 102 (5), 841–877.

Bock, G.W., Sabherwal, R., Qian, Z., 2008. The effect of social context on

the success of knowledge repository systems. IEEE Transactions on

Engineering Management 55 (4), 536–551.

Bock, G.W., Kankanhalli, A., Sharma, S., 2006. Are norms enough? The

role of collaborative norms in promoting organizational knowledge

seeking. European Journal of Information Systems 15, 357–367.

Bock, G.W., Kim, Y.G., 2002. Breaking the myths of rewards: an

exploratory study of attitudes about knowledge sharing. Information

Resources Management Journal 15 (2), 14–21.

Bock, G.W., Zmud, R.W., Kim, Y.G., Lee, J.N., 2005. Behavioral

intention formation in knoweldge sharing: examining the roles of

extrinsic motivators, social–psychological forces and organizational

climate. MIS Quarterly 29 (1), 87–111.

Calder, B.J., Staw, B.M., 1975. The self-perception of intrinsic and

extrinsic motivation. Journal of Personality and Social Psychology

31 (4), 599–605.

Carrillo, P., Robinson, H., Al-Ghassani, A., Anumba, C., 2004. Knowl-

edge management in UK construction: strategies, resources and

barriers. Project Management Journal 35 (1), 46–56.

Chen, X.P., Au, W.T., Komorita, S.S., 2007. Sequential choice in a step-

level public goods dilemma: the effects of criticality and uncertainty.

Organizational Behavior and Human Decision Processes 65 (1), 37–47.

Connolly, T., Thorn, B.K., 1999. Discretionary databases: theory, data,

and implications. In: Fulk, J., Steinfield, C. (Eds.), Organizations and

Communication Technology. Sage Publications, Newbury Park, CA.

Constant, D., Kiesler, S., Sproull, L., 1994. What’s mine is ours, or is it?

A study of attitudes about information sharing. Information Systems

Research 5 (4), 400–421.

Constant, D., Sproull, L., Kiesler, S., 1996. The kindness of strangers: the

usefulness of electronic weak ties for technical advice. Organization

Science 7 (2), 119–135.

Cross, R., Baird, L., 2000. Technology is not enough: improving

performance by building organizational memory. Sloan Management

Review 41 (3), 69–78.

Davenport, T.H., DeLong, D.W., Beers, M.C., 1998. Successful knowl-

edge management projects. Sloan Management Review 39 (2), 43–57.

Davenport, T.H., Prusak, L., 1998. Working Knowledge: How Organiza-

tions Manage What They Know. Harvard Business School Press,

Boston.

Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1992. Extrinsic and intrinsic

motivation to use computers in the workplace. Journal of Applied

Social Psychology 22 (14), 1111–1132.

Deci, E.L., Ryan, R.M., 1980. The empirical exploration of intrinsic

motivational processes. Advances in Experimental Social Psychology

13, 39–80.

Donath, J.S., 1999. Identity and deception in the virtual community. In:

Smith, M., Kollock, P. (Eds.), Communities in Cyberspace. Routle-

dge, New York.

Easton, G., Easton, A., Belch, M., 2003. An experimental investigation of

electronic focus groups. Information & Management 40 (8), 717–727.

Emory, C.W., Cooper, D.R., 1991. Business Research Methods. Richard

Irwin, Illinois.

Fehr, E., Gachter, S., 2000. Fairness and retaliation: the economics of

reciprocity. Journal of Economic Perspectives 14 (3), 159–181.

Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models

with unobservable variables and measurement error. Journal of

Marketing Research 18 (1), 39–50.

Gold, A.H., Malhotra, A., Segars, A.H., 2001. Knowledge management:

an organizational capabilities perspective. Journal of Management

Information Systems 18 (1), 185–214.

Gouldner, A.W., 1960. The norm of reciprocity: a preliminary statement.

American Sociological Review 25 (2), 161–178.

S.-Y. Hung et al. / Int. J. Human-Computer Studies 69 (2011) 415–427 427

Green, S.G., Taber, T.D., 1980. The effects of three social decision

schemes on decision group process. Organizational Behavior and

Human Performance 25 (1), 97–106.

Hair, J.F., Tatham, R.L., Anderson, R.E., Black, B., 1995. Multivariate

Data Analysis. Prentice-Hall, Upper Saddle River, NJ.

Hall, C.C., Ariss, L., Todorov, A., 2007. The illusion of knowledge: when

more information reduces accuracy and increases confidence. Organi-

zational Behavior and Human Decision Processes 103 (2), 277–290.

Hall, H., 2001a. Input-friendliness: motivating knowledge sharing across

intranets. Journal of Information Science 27 (3), 139–146.

Hall, H., 2001b. Social exchange for knowledge exchange. In: Proceedings

of the International Conference on Managing Knowledge, University

of Leicester.

He, W., Wei, K.-K., 2008. What drives continued knowledge sharing? An

investigation of knowledge-contribution and -seeking beliefs. Decision

Support Systems 46 (4), 826–838.

Heineck, G., Anger, S., 2010. The returns to cognitive abilities and

personality traits in Germany. Labour Economics 17 (3), 535–546.

Hender, J.M., Dean, D.L., Rodgers, T.L., Nunamaker, J.F., 2002. An

examination of the impact of stimuli type and GSS structure on creativity:

brainstorming versus non-Brainstorming techniques in a GSS environ-

ment. Journal of Management Information Systems 18 (4), 59–85.

Holthouse, D., 1998. Knowledge research issues. California Management

Review 40 (3), 277–280.

Jones, C., Hesterly, W.S., Borgatti, S.P., 1997. A general theory of

network governance: exchange conditions and social mechanisms. The

Academy of Management Review 22 (4), 911–945.

Jones, K., 2006. Knowledge management as a foundation for decision support

systems. Journal of Computer Information Systems 46 (4), 116–124.

Joshi, K.D., Sarker, S., Sarker, S., 2007. Knowledge transfer within

information systems development teams: examining the role of knowl-

edge source attributes. Decision Support Systems 43 (2), 322–335.

Kalling, T., Styhre, A., 2003. Knowledge Sharing in Organizations.

Copenhagen Business School Press, Copenhagen, Denmark.

Kankanhalli, A., Tan, B.C.Y., Kwok-Kee, W., 2005. Contributing

knowledge to electronic knowledge repositories: an empirical investi-

gation. MIS Quarterly 29 (1), 113–143.

Karlsen, J.T., Gottschalk, P., 2004. Factors affecting knowledge transfer

in IT projects. Engineering Management Journal 16 (1), 3–10.

Kelley, H.H., Thibaut, J.W., 1978. Interpersonal Relations: A Theory of

Interdependence. Wiley, New York.

Kollock, P., 1999. The economies of online cooperation: gifts and public

goods in cyberspace. In: Kollock, P., Smith, M. (Eds.), Communities

in Cyberspace. Routledge, New York.

Krogh, G.V., Ichijo, K., Nonaka, I., 2000. Enabling Knowledge Creation:

How to Unlock the Mystery of Tacit Knowledge and Release the

Power of Innovation. Oxford University Press.

Lee, M.K.O., Cheung, C.M.K., Chen, Z., 2005. Acceptance of Internet-

based learning medium: the role of extrinsic and intrinsic motivation.

Information & Management 42 (8), 1095–1104.

Marett, K., Joshi, K.D., 2009. The decision to share information and

rumors: examining the role of motivation in an online discussion

forum. Communications of the Association for Information Systems

24 (1), 47–68.

Markus, M.L., 2001. Toward a theory of knowledge reuse: types of

knowledge reuse situations and factors in reuse success. Journal of

Management Information Systems 18 (1), 57–93.

McGrath, J.E., 1984. Groups: Interaction and Performance. Prentice-Hill,

Englewood Cliffs, NJ.

Molm, L.D., 1997. Coercive Power in Social Exchange. Cambridge

University Press, New York.

Muller, R.M., Spiliopoulou, M., Lenz, H.J., 2005. The influence of

incentives and culture on knowledge sharing. In: Proceedings of the

38th Annual Hawaii International Conference on System Sciences.

Nahapiet, J., Ghoshal, S., 1998. Social capital, intellectual capital, and the

organizational advantage. The Academy of Management Review 23

(2), 242–266.

Nonaka, I., 1994. A dynamic theory of organizational knowledge

creation. Organization Science 5 (1), 14–37.

Nonaka, I., Takeuchi, H., 1995. The Knowledge-Creating Company:

How Japanese Companies Create the Dynamics of Innovation.

Oxford University Press, New York.

Nunnally, J.C., 1978. Psychometric Theory. McGraw-Hill, New York.

O’Dell, C., Grayson, G.J., 1998. If only we knew what we know:

identification and transfer of internal best practices. California

Management Review 40 (3), 154–174.

Osborn, A.F., 1953. Applied Imagination. Charles Scribner’s Sons, New

York.

Osterloh, M., Frey, B.S., 2000. Motivation, knowledge transfer, and

organizational forms. Organization Science 11 (5), 538–550.

Parent, M., Gallupe, R.B., Salisbury, W.D., Handelman, J.M., 2000.

Knowledge creation in focus groups: can group technologies help?

Information & Management 38 (1), 47–58.

Quigley, N.R., Tesluk, P.E., Locke, E.A., Bartol, K.M., 2007. A multi-

level investigation of the motivational mechanisms underlying knowl-

edge sharing and performance. Organization Science 18 (1), 71–88.

Ryan, R.M., Deci, E.L., 2000. Intrinsic and extrinsic motivations: classic

definitions and new directions. Contemporary Educational Psychology

25 (1), 54–67.

Shang, R.-A., Chen, Y.-C., Shen, L., 2005. Extrinsic versus intrinsic

motivations for consumers to shop on-line. Information & Manage-

ment 42 (3), 401–413.

Schultz, M.F., 2006. Fear and norms and Rock & Roll: what jambands

can teach us about persuading people to obey copyright law. Berkeley

Technology Law Journal 21 (2), 653–691.

Sheehan, T., 2000. Building on knowledge practices at Arup: using

knowledge management to help sustain growth. Knowledge Manage-

ment Review 3 (5), 12–15.

Sheppard, B.H., Hartwick, J., Warshaw, P.R., 1988. The theory of

reasoned action: a meta-analysis of past research with recommenda-

tions for modifications and future research. Journal of Consumer

Research 15 (3), 325–343.

Slater, P.E., 1958. Contrasting correlates of group size. Sociometry 21 (2),

129–139.

Stewart, D., 2005. Social status in an open-source community. American

Sociological Review 70 (5), 823–842.

Taylor, E.Z., 2006. The effect of incentives on knowledge sharing in

computer-mediated communication: an experimental investigation.

Journal of Information Systems 20 (1), 103–116.

Tiwana, A., 2002. The Knowledge Management Toolkit: Orchestrating

IT, Strategy, and Knowledge Platforms. Prentice-Hall, Upper Saddle

River, NJ.

Tsai, W., Ghoshal, S., 1998. Social capital and value creation: the role of

intrafirm networks. The Academy of Management Journal 41 (4),

464–476.

Vallerand, R.J., 1997. Toward a hierarchical model of intrinsic and

extrinsic motivation. Advances in Experimental Social Psychology 29,

271–360.

Venkatesh, V., 1999. Creation of favorable user perception: exploring the

role of intrinsic motivation. MIS Quarterly 23 (2), 239–260.

Wasko, M., Faraj, S., 2000. ‘‘It Is What One Does’’: why people

participate and help others in electronic communities of practice.

The Journal of Strategic Information Systems 9 (2–3), 155–173.

Wasko, M., Faraj, S., 2005. Why should I share? Examining social capital

and knowledge contribution in electronic networks of practice. MIS

Quarterly 29 (1), 34–56.

Wood, D.J., Gray, B., 1991. Toward a comprehensive theory of

collaboration. Journal of Applied Behavioral Science 27 (2), 139–162.

Yang, S.J., Chen, I.Y., 2008. A social network-based system for support-

ing interactive collaboration in knowledge sharing over peer-to-peer

network. International Journal of Human–Computer Studies 66 (1),

36–50.