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Why people share knowledge invirtual communities?
The use of Yahoo! Kimo Knowledgeþas an example
Fu-ren Lin and Hui-yi HuangInstitute of Service Science, National Tsing Hua University,
Hsinchu City, Taiwan
Abstract
Purpose – The purpose of this paper is to answer the question: why Google Answers and Yahoo!Kimo Knowledgeþ , both virtual communities built on users asking and answering questions withdifferent rewarding mechanisms, have different outcomes.Design/methodology/approach – Based on the theory of reasoned action (TRA), the authorsdeveloped the constructs, including self-efficacy, altruism, reward, and the sense of virtual community,that influence the intention of sharing knowledge in terms of answering questions on Knowledgeþ .Findings – The results show that users showing higher levels of contribution tended to bemotivated by virtual rewards, such as advanced ranks, and the need for self-fulfillment. Additionally,for these knowledge providers, altruism is also an important factor. Therefore, these users sharenot because of a reward but because of altruism and fulfillment. The findings can answer why GoogleAnswers failed with its monetary rewards but Knowledgeþ remains with its virtual rewardingmechanism.Research limitations/implications – This study extends the literature on understanding theantecedents of sharing knowledge in terms of answering others’ questions in virtual communities.Especially, it identifies different factors affecting the intention of users in different levels ofengagement with the community to share knowledge.Practical implications – The various effective factors influencing users’ knowledge sharingbehavior identified in this study can guide the incentive mechanism design for virtual communities.Originality/value – Besides proposing research models to identify the constructs affecting the users’intention to answer questions in a virtual community, such as Knowledgeþ , this study compares themodels explaining the intention to share knowledge in different user groups with different levels ofknowledge contribution. This research design is unique from the prior literatures; Moreover, theresults shed light on designing incentive mechanisms for knowledge sharing in virtual communities.
Keywords Knowledge sharing, Virtual community, Theory of reasoned action, Virtual worlds,Knowledge management
Paper type Research paper
1. IntroductionKnowledge sharing has been a very popular issue in the literature of informationsystems (e.g. Bock et al., 2005; Taylor and Todd, 2001; Wasko and Faraj, 2005; Yu andChu, 2007). However, most of the previous studies have been focussed on knowledgesharing within an organization. In an organizational context, although prior studieshave focussed on the sharing behaviors between employees (Kankanhalli et al., 2005;Yu and Chu, 2007), some studies have examined knowledge exchange behavior amongemployees with weak ties in geographically dispersed organizations via the internet
The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1066-2243.htm
Received 10 September 2011Revised 20 January 2012
15 March 201217 May 2012
Accepted 19 May 2012
Internet ResearchVol. 23 No. 2, 2013
pp. 133-159r Emerald Group Publishing Limited
1066-2243DOI 10.1108/10662241311313295
The authors would like to express gratitude for the sponsorship from the National ScienceCouncil, Taiwan in project #NSC 98-2752-H-007-002-PAE.
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(Constant et al., 1996). However, the knowledge sharing practices of individuals acrossorganizational boundaries, which have been prevailing on the internet, have notreceived sufficient study, especially in the contexts of answering questions andproblem solving.
There have been many types of question-answer (Q&A) web sites on the internet,e.g. Answer.com, WikiAnswers, InnoCentive, Google Answers, and Yahoo! Answer.Among them, “Yahoo! Kimo Knowledgeþ ” ( )[1], launched in November2004 and owned by “Yahoo! Kimo” (Kimo was a company in Taiwan and was mergedwith Yahoo!), has been the most popular service for knowledge sharing in the form ofanswering user-asked questions in Chinese. On Yahoo! Kimo Knowledgeþ (hereafter,denoted as Knowledgeþ ), users can ask questions, and other users of Knowledgeþcan answer them and be compensated by non-monetary rewards, unlike the monetaryreward offered by Google Answers. In Knowledgeþ , those who answer questionsreceive reward points by answering questions posted by others. As users accrue morepoints, they reach higher levels. The highest level on Knowledgeþ that users canreach is called “knowledgist” ( ).
The world’s largest search engine, Google, also owned a Q&A web site called“Google Answers.” Unlike Knowledgeþ , Google Answers was more like amarketplace, where people can buy knowledge[2]. On Google Answers, people needto pay for answers and can decide the fee they would pay for the answer before theyask questions. Additionally, people who answered questions were all qualified by andcontracted with Google. These knowledge providers are called “researchers.” If ananswer seeker is not satisfied with the answer, s/he only pays 50 cents and is refundedthe original fee. However, the Google Answers service was terminated in December2006, having been operational for less than five years (launched in April 2002)(Helft, 2006). Why has Knowledgeþ survived, when Google Answers failed? Does thedifference in features between these two web sites, such as reward systems and expertidentification, explain this outcome? Some people may argue that the questions onKnowledgeþ are information rather than knowledge. However, individual learningand new knowledge creation occur when people combine and exchange their personalknowledge with others. Therefore, information sharing is an essential activity forknowledge creation and sharing.
In this study, we identified factors related to the intention of knowledge owners toshare their knowledge. According to previous studies, there is a negative relationshipbetween the intention to share and the anticipated extrinsic rewards (Wasko and Faraj,2005). Eisenberger and Cameron (1996) have also proposed that task-contingentrewards may negatively impact intrinsic motivations. The way Google Answersrewarded their researchers may not be a good choice in a sharing environment.However, what kind of a reward system is suitable? Or is no reward better? Are peopleanswering questions triggered by their altruism?
On Google Answers, people need to be certified as an expert (i.e. “researchers”);however, on Knowledgeþ , people answering questions believe that they have thecapability to respond to questions. Some researchers discovered that the differentways of evaluating experts may influence user relationships and sharing behavior(Bock et al., 2005). The definition of an expert is different between these two web sites,which could be the reason that Knowledgeþ remains in operation.
The objective of this study is to deepen our understanding of the factors thatmotivate people to answer questions in a virtual community. We use Knowledgeþ asan example to examine factors, such as self-efficacy, altruism, reward, and the sense of
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virtual community (SOVC), and to integrate the theory of reasoned action (TRA) (Ajzenand Fishbein, 1980).
2. Virtual communityHeller (1989) defined community as a group that is mainly characterized by rationalinteractions or social ties that draw people together. There are two types ofcommunities: one is the traditional territorial or geographic community, such as aneighborhood, a town, or a region and the other is a relational community, concerninghuman relationships without reference to location. For example, there are communitiesof interest, such as hobby clubs or fan clubs (Gusfield, 1975). These two types ofcommunities are not necessary mutually exclusive; they can be located throughexploration on the internet, and most of them are on the internet now. The communitiessprouting on the internet are called virtual communities because their members arenot geographically or physically bounded (Wellman and Gulia, 1999).
A virtual community forges social relations on the internet through repeatedcontacts within a specified boundary, which is characterized by the following features:aggregations of people, rational members, interaction in cyberspace without physicalcollocation, social exchange processes, and shared objectives, properties, identities,or interests between members (Balasubramanian and Mahajan, 2001; Fernback andThompson, 1995). A virtual community comprises people, shared purposes, policies,and computer systems (Preece, 2000). A virtual community’s cyber-place was alsotermed a virtual settlement ( Jones, 1997). According to previous studies, we defineKnowledgeþ as a virtual community residing on Yahoo! Kimo, a virtual settlementfor various virtual communities besides Knowledgeþ , e.g. news, auction, blogs, etc.Knowledgeþ combines questioners and answerers ( people) to ask or to answerquestions (shared purposes), the rules set by Knowledgeþ (policies), and the web site(a virtual settlement).
2.1 Knowledge sharing in virtual communityThe establishment of mutual understanding to comprehend conversations andknowledge contributions on the internet are inevitably more difficult than face-to-facecommunications in a small group (Ma and Agarwal, 2007). Previous studies havediscussed this issue (Table I). Hsu et al. (2007) adopted social cognitive theory (SCT) asa basic model and used the environment, individuals, and behavior as constructs toprove that the trust in the environment affects both the behavior and the self-efficacyof knowledge sharing; additionally, the self-efficacy of knowledge sharing influencesknowledge sharing behavior. Other researchers have studied factors influencingpeople’s involvement in online software development communities (Wellman andGulia, 1999). In taking the research results from Wasko and Faraj (2005) that usedsocial capital as a basic model to identify that reputation, centrality, and tenure in thefield affect the volume of knowledge sharing, we chose the high contributiongroups in Knowledgeþ , such users possessing reputation with the rank abovepostgraduate (which is a title used in Yahoo! Knowledgeþ , and not an educationaldegree) as the sample for study. In turn, they resided in the virtual communityrelatively longer time than those with the rank below postgraduate. However, in thisstudy, we did not evaluate network centrality since it is mainly measured for networkeffects, which was not the major issue we concerned in this study. Instead ofusing these factors as constructs as antecedents of knowledge sharing intension,we used these factors to identify the categories of samples, i.e. those users with high
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commitment and good reputation as the potential knowledge contributors inKnowledgeþ .
Many researchers have raised questions about why people share knowledge invirtual communities. For example, Nardi et al. (2004) explained why people blog in fivemotivations: documenting one’s life, providing commentary and opinions, expressingdeeply felt emotions, articulating ideas through writing, and forming and maintainingcommunity forums. Nov (2007) identified eight motivational categories: fun, ideology,values, understanding, enhancement, protective, career, and social. Among them, fun,understanding, enhancement, and protective are four major motivational categorieshighly correlated with contribution level, which are more in intrinsic motivationcontrast to extrinsic motivations, such as value, career, ideology, and social. In thisstudy, before we distributed the questionnaires, we spent two months observing tenknowledgists (shown in Table II) and then interviewed them. Through observations
Subject Methodology Results Literature
Members of a nationallegal professionalassociation in the USA
ObservationQuestionnaire
People contribute their knowledgewhen they perceive that itenhances their professionalreputations, when they have theexperience to share, and when theyare structurally embedded in thenetwork
Toro et al. (1987)
Blue-shop Expert groupQuestionnaire
Community-related outcomeexpectations play an importantrole underlying knowledge sharingin terms of both quantity andquality. Social interaction ties,reciprocity, and identificationincreased individuals’ quantity ofknowledge sharing
Chiu et al. (2006)
Discussion forum ofYahoo! Groups andprofessionalassociations
Questionnaire Trust of the environment affectsboth behavior and knowledgesharing self-efficacy besideknowledge sharing self-efficacyinfluence knowledge sharingbehavior
Hsu et al. (2007)
Six online messageboards: Yahoo! Kimoblog, Wretch blog, Sinablog, Yam blog, Xuiteblog, and PChome blog
Questionnaire The result shows that the ease ofuse and enjoyment, altruism, andreputation were positively relatedto attitude toward blogging.Community identification andattitude toward bloggingsignificantly influenced the users’intension to continue to use blogs
Hsu et al. (2007)
Three professionalvirtual communities(PVC): Pure C,Programmer-club,Blue-shop
Expert groupQuestionnaire
The results show that trustsignificantly influences knowledgesharing self-efficacy, perceivedrelative advantage, and perceivedcompatibility, which in turnpositively affect knowledgesharing behavior
Lin (2007a, b)
Table I.Related literatures ofknowledge sharing invirtual community
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and interviews, we learned more about the people who spend their time and effort onsharing their knowledge, through which we formulated the research model not onlyfrom literature but also from observations and interviews. These interviews beganwith asking participants for the domain of their expertise. Some follow-up questionsare listed as follows: “Why did you answer questions?”; “How do you feel aboutKnowledgeþ ?”; “Please use a sentence to describe Knowledgeþ ”; “Did you enjoyanswering questions and helping others?” We then employed empirical studies fromliterature and key issues expressed by the interviewed knowledgists to identify factorsthat influence knowledge sharing behaviors on Knowledgeþ .
3. A conceptual model of sharing knowledge in virtual communitiesIn this study, we identified factors that influence individuals while sharing knowledgeby adopting the TRA (Ajzen and Fishbein, 1980) as an initial theoretical frame. Ajzenand Fishbein (1980) developed this model in 1968 to predict the correlation betweenattitude and intention, and added the subjective norm to this model in 1980 throughseveral tests and verifications. Ajzen and Fishbein (1980) mentioned that an individualbehavioral intention is directly influenced by the intention of the behavior, in additionto the subjective norm and the attitude toward the behavior (Figure 1). TRA also hasbeen found to be useful in predicting a wide range of behaviors and is widely used toforecast and interpret behavioral intentions and actual behavior in social psychology(Chang, 1998; Njite and Parsa, 2005; Slocombe, 1999). Recently, TRA has been used toexplain individuals’ use of web site usage behavior (Lu and Lin, 2003). In this study, wediscovered the factors that explain the reasons why individuals are willing to sharetheir knowledge.
Knowledgists Domain expertise
Tony Notebooks, desktop computersButterfly The dance, performancesTai Maps, the South of TaiwanBean Health care, diseasesSummer snow The law, the taxNOP Computer graphics, programmingLoVe MakeupJay Digital camera, the mobile phone of NokiaAdam Maps, roadsJerry Loans, credit card
Table II.The list of observed
knowledgists
Beliefs andevaluations
Attitudetoward
behavior (A)
Behavioralintention
(BI)
Actualbehavior
Normative beliefsand motivation to
comply
Subjectivenorm (SN)
Source: Ajzen and Fishbein (1980)
Figure 1.Theory of reasoned action
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TRA is a general model, and does not specify beliefs about a particular behavior.Therefore, because researchers have adopted TRA to explain social behaviors, salientbeliefs were considered to a certain extent (Hsu and Lin, 2008). Bock et al. (2005) usedTRA as a basic model to measure the factors driving the intention to share knowledge,but their research is in an organizational context. In this study, the users of research arepeople in a virtual community, so we developed different factors and questions. Fromthe interviews and observed behaviors, we verified the concepts in TRA and alsobrought in the sense of community (SOC) construct. From interviews, we discoveredthat individuals shared because they perceived themselves as having the ability toshare, which verified the construct, self-efficacy, in TRA. We also identified theimportance of individuals’ altruism influencing their sharing attitude, which verifiedthe construct, altruism, in TRA. From the interview question of their feeling aboutKnowledgeþ , we expected to measure their attitude toward subjective norm. Weidentified a set of significant attributes which belong to the concept of the SOVC fromliterature review. Moreover, we intended to compare Google Answers andKnowledgeþ in different reward incentives in this study. Thus, attitude towardreward becomes an important construct, which is also verified by TRA. These inputsfrom observation and interviews enriched the research model to explain why users inKnowledgeþ share their knowledge.
3.1 The attitude toward knowledge sharingAlthough there are studies identifying that reward is not a significant factorwhen people refer to knowledge sharing (Bock et al., 2005; Deci et al., 1999), thisstudy aims to verify this in a knowledge sharing community such as a Q&A web site,which has a rewarding mechanism incentivizing the sharing of knowledge orexperiences. Additionally, some researchers have said that a verbal reward mayinfluence more than tangible rewards while people share knowledge (Constant et al.,1996; Preece, 2000).
In research on information systems, self-efficacy has already been considered to bean important factor for predicting and improving computer training performance,computer usage, and internet behaviors (Chiu et al., 2006). Following the result ofBandura’s (1982) study, if people have no confidence in their abilities, they would notperform knowledge sharing behavior. Nevertheless, would the personality of anindividual influence knowledge sharing behavior? Would an individual with thepersonality of helping others have a high motivation to share knowledge? Therefore,we add the factor altruism to this research model.
3.2 A subjective norm to knowledge sharingA SOC is believed to benefit work organizations; for example, SOC was found toincrease job satisfaction and organizational citizenship (Burroughs and Eby, 1998).According to McMillan and Chavis (1986), SOC has a framework with four dimensions:
(1) feelings of membership, where people feel the sense of belonging to andidentity with the community;
(2) feelings of influence, where people feel the sense of having an influence on orbeing influenced by the community;
(3) integration and fulfillment of needs, where people feel the sense of beingsupported by others in the community while also supporting them; and
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(4) shared emotional connection, where people feel a sense of relationships, sharedhistory, and a “spirit” of community.
With the growth of Web 2.0, more people began to interact through the internet; someof them formed online groups, which are typically called virtual communities.However, these groups are only virtual settlements unless the members of these groupsdevelop affective bonds, thus forming virtual communities (Jones, 1997). In otherwords, if the group uses computers acting as connected devices and there is no strongrelationship between the members, this group can only be called a virtual settlement.However, if this online group does have affective bonds and becomes a virtualcommunity, does it have a SOC? To examine this issue, Blanchard and Markus (2002)studied a successful, established virtual community called Multiple Sports Newsgroup(MSN) using participated observations and member interviews over seven months.Their finding shows that MSN’s dimensions of SOVC include the recognition ofmembers, the exchange of support, attachment and obligation, identity (self) andidentification (of others), and relationships with specific members. The first dimensionis similar to the dimensions of SOC proposed by McMillan and Chavis (1986): “feelingsof membership” and “feelings of influence,” the second is similar to the “integrationand fulfillment of needs,” the third is similar to “shared emotional connection,” and thelast two are new findings of Blanchard and Markus’ (2002) research.
4. Research model and hypothesesThe purpose of this study is to understand the motives of knowledge sharing interms of answering questions in Q&A knowledge services. In this study, we adoptedTRA as the basic model and examined constructs found in the extant literatureand from observation and interviews. We used three constructs for attitude towardknowledge and one construct for subjective norms. From interviews, we discoveredthat individuals shared because they perceived themselves as having the abilityto share. Finally, both the extant literature and interviews present the importanceof individuals’ altruism influencing their sharing attitude. Additionally, we introducedthe SOVC to examine an individual’s subjective norm toward their knowledgesharing intention.
4.1 The constructs of attitude toward knowledge sharing4.1.1 Reward. Social exchange theory (Blau, 1986) describes exchange behaviorbetween human beings. According to the theory, people exchange with others becausethey believe the giving and the reward to be worthwhile. In other words, whensomeone is giving, s/he also expects returns.
Deci et al. (1999) conducted a meta-analysis of 128 laboratory studies on the effectsof rewards on intrinsic motivation, and they found that verbal rewards (e.g. positivefeedback) had a positive influence on intrinsic motivation. Ryan et al. (1983) foundthat positive feedback was superior to tangible rewards. From the previous studies,tangible rewards usually have a negative relation with the willing to share;nevertheless, on Knowledgeþ , there still are some rewards in the form of rewardpoints. From the interview, one of the knowledgists said, “There should be anotherlevel higher than knowledgist because there are already too many at this rank.”Another knowledgist mentioned, “When my rank is upgraded, I feel fulfilled.” We caninfer that the reward still has some influence on knowledge sharing. We summarize theprevious studies in Table III. In this study, we define a reward as something of any
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form that people will perceive themselves as earning after they share their knowledgeand derive hypothesis as follows:
H1. The more rewards a sharer receives, the more favorable attitude towardknowledge sharing s/he will have.
4.1.2 Self-efficacy. Self-efficacy is a form of self-evaluation that influences a decisionregarding what actions to take. People who have high self-efficacy are morelikely to perform a related action than those with low self-efficacy (Hsu and Chiu,2004). Self-efficacy has been used by many information system (IS) researchersto form a variety of research streams. One of these research streams concentrates onthe effect of computer self-efficacy on computer training performance (e.g. Compeauand Higgins, 1995; Compeau et al., 1999) and on IT usage (e.g. Toro et al., 1987).Another research stream focusses on the construct of internet self-efficacy (e.g.Howellsi et al., 2009).
Recently, this concept of self-efficacy has been applied to knowledge management tovalidate the effect of personal efficacy beliefs on knowledge sharing (Hsu et al., 2007).Lin et al. (2009) identified self-efficacy as an important concept in social psychologyderived from SCT. In the context of knowledge sharing, Kankanhalli definedself-efficacy as knowledge sharing self-efficacy, referring to the confidence inone’s ability to provide knowledge that is valuable to others (shown in Table IV)(Kankanhalli et al., 2005). Consequently, the current study defines self-efficacy
Context Result Literature
Ten organizations used ERKs asknowledge contributors
Knowledge had significant positiverelationships with ERK usage
Kankanhalli et al. (2005)
Discussion forum of Yahoo!Groups and professionalassociations
People have high self-efficacy willbe more likely to do the relatedaction
Hsu et al. (2007)
Three professional virtualcommunities: Programmer-club,Blue-shop, and Pure C
Knowledge sharing self-efficacy,perceived relative advantage, andperceived compatibilitysignificantly and positivelyinfluence knowledge sharingbehavior
Lin et al. (2009)
Table III.Related literatureof self-efficacy
Context Result Literature
Members of a national legalprofessional association inthe USA
Individuals who enjoy helpingothers will contribute moreresponses to electronic networksof practice
Wasko and Faraj (2005)
Placed survey message on overten online message boards
Altruism will positively affectusers’ attitudes towardparticipating on a blog
Hsu and Chiu (2004)Table IV.Related literatureof altruism
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as individuals’ confidence regarding the knowledge they shared and deriveshypothesis as follows:
H2. The greater the sense of self-efficacy through knowledge sharing a sharerobtains, the more favorable attitude toward knowledge sharing s/he will have.
4.1.3 Altruism. The definition of altruism according to Wikipedia is the unselfishconcern for the welfare of others. Hsu and Lin (2008) defined altruism as a conditionin which a person is willing to assist other people without expecting returns.Altruism exists when people derive intrinsic joy from helping others withoutexpecting anything in return (Kollock, 1999; Smith, 1981). Kollock (1999) found thatindividuals may contribute knowledge in an electronic network of practicebecause they feel that helping others with challenging problems is interesting andbeneficial. Individuals are motivated intrinsically to contribute knowledge to othersbecause they enjoy engaging in intellectual pursuits and helping others (Waskoand Faraj, 2005). Hsu and Lin (2008) referred to the degree to which a person waswilling to increase other people’s welfare without expecting returns (shown in Table V).In this study, we define altruism as a condition in which individuals will help othersno matter whether the behavior of sharing will earn feedback or not and derivehypothesis as follows:
H3. The more altruism a sharer possesses, the more favorable attitude towardknowledge sharing s/he will have.
4.2 The construct of subjective normStudies have discussed the relationship between a sense of belonging and the intentionto use a virtual community (Hagerty, 1996; Lin, 2007a, b). Roberts also found thatparticipants with a more pronounced sense of belonging will put in more time andeffort into their virtual community (Roberts, 1998). Blanchard and Markus (2002)identified the relationship between maintaining the experience of belonging and SOVC.A stronger social infrastructure enhances knowledge creation and knowledge sharingcapabilities (Hall, 2004). However, as we mentioned before, not every virtual settlementhas the SOVC. When, there is a SOVC, it arises from a set of interacting social processesthat also serve to maintain the SOVC (shown in Table VI). If it occurs, an individual will
Context Result Literature
Semi-structured interview was usedand interviewed ten people who arefrom three different levels: leaders,participants, and lurkers
The members in MSN reallyexperience the sense of virtualcommunity. Besides the resultssuggest a process model of senseof virtual community creationand maintenance that is simplerand powerful
Blanchard andMarkus (2002)
Use questionnaire and 425 sampleparticipants from nine onlinecommunities in Taiwan, includingYahoo! Kimo, CPB, Sony music, etc.
The sense of community has a positivemoderating effect on the impact ofhelping behaviors
Chu (2009)Table V.
Related literature of senseof virtual community
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be influenced by the community, which would affect the individual’s norm. Thefollowing hypothesis describes this observation:
H4. The greater the extent of the SOVC a sharer has, the greater the subjectivenorm to share knowledge s/he will perceive.
4.3 The construct of intention to share knowledgeFrom the studies of Bock and other researchers, attitudes toward knowledge sharingsignificantly influence the intention to share knowledge (Bock et al., 2005), as describedin hypothesis as follows:
H5. The more favorable attitude toward knowledge sharing a sharer has, thegreater intention to share knowledge s/he will have.
Ajzen and Fishbein (1980) used TRA to conclude that the subjective norm willinfluence the behavior intention. Taylor and Todd examined the subjective norm withinformation technology usage and found that subjective norm and the behaviorintention have a significant and positive relationship (Toro et al., 1987). According toprevious studies, we derive hypothesis as follows:
H6. The greater the subjective norm to share knowledge a sharer has, the greaterthe intention to share knowledge s/he will have.
In a previous study (Wasko and Faraj, 2005), researchers also examined therelationship between the subjective norm to share knowledge and the attitude towardknowledge sharing. In this study, we derive hypothesis as follows:
H7. The greater the subjective norm to share knowledge a sharer has, the morefavorable attitude toward knowledge sharing s/he will have.
The basic model to explain the intention of sharing knowledge in the Q&A type ofknowledge service is illustrated in Figure 2.
5. Conduct of researchTo measure the proposed model, we developed a questionnaire. In this questionnaire,we edited the constructs based on previous studies summarized in Table VII. Theconstructs for rewards, self-efficacy, and altruism referenced studies on knowledge
Constructs Literature
Reward Lin et al. (2009); Kankanhalli et al. (2005)Self-efficacy Hsu et al. (2007)Altruism Bock et al. (2005); Hsu et al. (2007)Attitude toward knowledge sharing Blanchard and Markus (2002)Sense of virtual community Blanchard and Markus (2002)Subjective norm Lin et al. (2009)Intension to knowledge sharing Lin (2007a, b); Kaiser (1974)Knowledge sharing behavior Hsu et al. (2007)
Table VI.The cited literaturesof constructs
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sharing in different environments, such as on an organization’s intranet or an onlineexpert forum. The items for the SOVC adopted the result of Blanchard and Markus’s(2002) research, which developed 18 items to evaluate the SOVC. The items of altitudetoward knowledge sharing, subjective norms, and the intention to share knowledgereferred to the studies that were developed based on the TRA (Ajzen and Fishbein, 1980).
RewardAttitudetoward
knowledgesharing
Intention toshare
knowledge
Subjectivenorm
Sense ofvirtual
community
Altruism
Self-efficacy
H1
H2
H3
H4
H5
H6
H7
Figure 2.Research modeland hypotheses
Symbol Title Answers Acceptance rate (%)
Knowledgista 2,000 70
Master 600-1,000 60
Expert 150-250 50
Postgraduate 0-125 40
More than 2,501 reward pointsBeginner 0-2,500 reward points 0-30
Note: aNovember 18, 2004-November 18, 2009 had 189 knowledgists
Table VII.The rank of Yahoo!Kimo Knowledgeþ
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In conducting the survey, we translated the questions from English to Chinese. Theinitial version of the questionnaire was reviewed by a professor with PhD degree andtwo PhD candidates both major in knowledge management. We used a five-pointLikert scale as a measurement scale, where 1 denotes strongly disagree, and 5 denotesstrongly agree.
5.1 The users of this studyKnowledgeþ is a Q&A web site on which people can freely ask and answer questions.More than one person can answer the same question, and after answering the question,the answer that best satisfies the answer seeker is chosen. The provider of the bestanswer can get the reward points (the rewards points are given by the answer seeker);if the provider earns enough reward points, s/he can achieve a higher level (Table VIII).After a knowledge provider accumulates more than 2,501 reward points, to achieve ahigher level, s/he needs to improve both the quality and quantity of answers. Thehighest level on Knowledgeþ is “knowledgist.” According to the calculation of Tai(one of the knowledgists), there were 189 knowledgists as of December 18, 2009.However, for people to achieve this level, they must answer at least 2,000 questions.If one question takes five minutes to answer, 2,000 questions take 10,000 minutes(about seven days), which represents the minimum time necessary to be promoted to aknowledgist. Why do these people want to spend time on answering a question askedby a person they do not know? In this study, we focus on the people who share more byonly choosing the first four levels that have more than 2,501 reward points, includingthe levels of knowledgist, master, expert, and postgraduate. In addition to examiningthe entire sample of this research, we also examine the different motivations forthese four levels.
5.2 PretestA pilot study was conducted with 29 respondents who had experience with sharingknowledge on the web. All respondents except two were students. More than a half ofthe respondents’ ages were between 21 and 30 years. The instrument consistency anddiscriminated validity of the survey were assessed. Cronbach’s a value is 0.942, theKMO values range from 0.5 (self-efficacy) to 0.82, and factor loadings range from 49.55to 86.01 percent. Due to low item-to-total correlation (o0.6), one item from self-efficacy,one from altruism, one from attitude toward knowledge sharing, and two from theSOVC were dropped. Cronbach’s a was slightly decreased to 0.932 after dropping theseitems; nevertheless, the lowest factor loading increased to 63.96 percent. According toa previous study (Kaiser, 1974), the values of factor loading need to be above 0.5;therefore, the items will be accepted.
Context Result Literature
In total, 96 introductory psychologystudents participated to fulfill a courserequirement
Positive feedback was superiorto tangible rewards
Ryan et al. (1983)
A meta-analysis of 128 laboratorystudies on the effects of rewardson intrinsic motivation
Verbal rewards have a positiveinfluence on intrinsicmotivation
Deci et al. (1999)Table VIII.Related literatureof reward
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5.3 Sample and data collectionThe refined instrument, in the form of a questionnaire, was then used to collect datafrom participates who were highly participative from the levels of knowledgist, expert,master, and postgraduate on Knowledgeþ . We developed a web site to collect data. InKnowledgeþ , we can send e-mails to members of the web site. We used this approachto provide the highly participative members of the web site, who were willingto receive e-mails from other members, with questionnaires. We collected 900 members’e-mail addresses, sent out 800 e-mails (some of the members did not accept e-mailsfrom other members), and received 167 replies (an approximately 22.88 percent returnrate). Among them, 34 responses were returned from knowledgists, 40 from experts, 50from masters, and 43 from postgraduates. Table IX denotes the demographic statics ofrespondents. We calculated the demographic data of these four groups of users insample size, age, education level, and seniority in the virtual community as shownin Table X. Through ANOVA test, there is no significant difference among groups.
5.4 Data analysis methodThis study used the partial least squares (PLS) method to apply the collected data totest the hypotheses. PLS can be used for testing the latent constructs of a model, and itdemands a minimal sample size to validate the model. This study used VisualPLSversion 1.04b1[3]. The model was validated by three types of validity: content validity,convergent validity, and discriminant validity. Content validity is used to ensureconsistency between measurements and the previous studies. To examine convergent
Measure Items Frequency % Measure Items Frequency %
Gender Male 121 72.46 Gender Female 46 27.54
Age Under 20 23 13.77Use time(in year) Under 1 20 11.98
21-30 53 31.14 1-2 31 18.5631-40 36 38.2 2-3 38 22.7541-50 37 21.56 3-4 32 19.1650-60 16 9.58 4-5 58 34.73Above 60 2 1.20 Above 5 20 11.98
Education Elementary school 2 1.2 Rank Beginner 0 0.0Junior high school 6 3.59 Postgraduate 43 25.75High school 42 25.15 Expert 50 29.94University 91 54.5 Master 40 13.17Graduate school 22 13.17 Knowledgist 34 20.36Doctor of philosophy 4 2.40
Table IX.Demographic information
of respondents
Knowledgist Master Expert Postgraduate
Sample size 34 40 50 43Gender (M/F) 0.6 0.7 0.7 0.8Age (year) 32 21 28 21Education (year) 16 15 15 15Seniority (year) 3 3 2 2
Table X.Demographic information
of respondents indifferent levels
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Why peopleshare
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validity, we assessed the composite reliability and average variance extracted (AVE)from the measures. Previous studies have suggested that the threshold of thecomposite reliability of the measure should be above 0.7, and 0.5 is a recommendedvalue for a reliable construct (Fernback and Thompson, 1995; Hagerty, 1996). Table XIpresents the composite reliability values, which range from 0.848 to 0.976, and areabove the acceptable value. The AVE values range from 0.411 to 0.954, where the valueof the SOVC, however, is below the acceptable value. According to the factor analysis,only the SOVC was categorized as four factors.
Blanchard and Markus (2002) divided SOVC into the recognition of members,attachment, obligation, identity (self), identification (of others), and relationshipwith specific members. Based on the result of factor analysis, we defined these fourfactors: first, the obligation and recognition of members; second, emotional attachmentand relationship with specific members; third, identity (self) and identification(of others); and finally, exchange of support. The re-categorized items are listedin Table XII.
Knowledgeþ participants who share their knowledge can get reward points, whichcan be used to improve the rank of each individual. Although these reward points canimprove the individual’s rank, it does not directly connect with their daily life. In thisstudy, we defined these reward points as on-system rewards. However, from ourinterview with the knowledgists, one of them mentioned that some member ofKnowledgeþ reply questions because they will get some physical advantage, such asbenefits for their career. We also added one question about off-system reward tosee whether the benefit to their career motivated members to share. From the abovediscussion, we then divided reward items into on- and off-system rewards. Ultimately,we used the result of SOVC and rewards to model the constructs (Figure 3) and formthe hypotheses in Table XIII.
Table XIV presents the composite reliability values and AVE values for themoderated model; the previous values range from 0.828 to 0.976, which are above theacceptability value, and the AVE values range from 0.453 to 0.953, with the valuesfor recognition and obligation being below the acceptability value. Thus, the values forrecognition and obligation do not pass the acceptability value; the way in which weseparated the categories may have caused this result. We examined the factor analysisresult of SOVC and renamed the factor according to the extant studies because theprevious studies did not directly present the factor with question items, which may bethe reason why the AVE value did not reach the threshold.
Finally, we used the square root of the AVE values to verify the discriminantvalidity of the instrument (Fornell and Larcker, 1981). Table XV shows the
MeasuresNumber of
itemsCompositereliability
Average varianceextracted
Cronbach’sa
Reward (RE) 4 0.848 0.591 0.813Self-efficacy (SE) 2 0.900 0.818 0.781Altruism (AL) 3 0.885 0.719 0.805Sense of virtual community (SO) 16 0.914 0.411 0.891Attitude toward knowledge sharing (AT) 4 0.976 0.954 0.952Subjective norm (SN) 2 0.925 0.754 0.888Intension to knowledge sharing (IS) 4 0.878 0.643 0.813
Table XI.Results of confirmatoryfactor analysis
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confirmation of the discriminant validity; that is, the square root of the AVE value fromeach construct is greater than the level of correlation involving the construct.
6. Research results and discussionFrom the statistical analysis of survey results, we found that H1a, H2, H3, H5, and H6are supported; that is, users’ attitude toward knowledge sharing on Knowledgeþ waspositively influenced by on-system rewards (H1a), their self-efficacy (H2), and theiraltruism (H3), and their intention to share knowledge was positively influenced bytheir attitude toward knowledge sharing (H5) and their subjective norm (H6), as shownin Figure 4.
H1a describes that a user who received more on-system rewards has a morefavorable attitude toward knowledge sharing. This result matches the results fromprevious studies suggesting that verbal rewards have a positive influence on intrinsicmotivation (Deci et al., 1999).
H2 states that a greater the sense of self-efficacy through knowledge sharing leadsto a more favorable attitude toward knowledge sharing. It matches the results ofprevious studies that people with high self-efficacy are more likely to perform the
Factor Items
Obligation andrecognition of members
1. I think this group is a good place for me to be a member2. Other members and I want the same thing from Yahoo! Kimo
Knowledgeþ3. I feel at home on Yahoo! Kimo Knowledgeþ (corresponding to
original 4)4. If there is a problem on Yahoo! Kimo Knowledgeþ , there are members
here who can solve it (corresponding to original 6)5. It is very important to me to be a member of Yahoo! Kimo
Knowledgeþ (corresponding to original 7)6. I expect to stay on Yahoo! Kimo Knowledgeþ for a long time
(corresponding to original 8)7. I feel obligated to help others on Yahoo! Kimo Knowledgeþ
(corresponding to original 15)Emotional attachmentand relationship withspecific members
8. Some members of Yahoo! Kimo Knowledgeþ have friendship witheach other (corresponding to original 12)
9. I have friends on Yahoo! Kimo Knowledgeþ (corresponding tooriginal 13)
10. Some members of Yahoo! Kimo Knowledgeþ can be counted on tohelp others (corresponding to original 14)
11. I really like Yahoo! Kimo Knowledgeþ (corresponding tooriginal 16)
Identity (self) andidentification (of others)
12. I can recognize the names most members on Yahoo! KimoKnowledgeþ (corresponding to original l3)
13. I care about what other Yahoo! Kimo Knowledgeþ members think ofmy actions (corresponding to original l5)
Exchange of support 14. I get a lot out of being on Yahoo! Kimo Knowledgeþ (corresponding tooriginal l9)
15. I had questions that have been answered on Yahoo! KimoKnowledgeþ (corresponding to original 10)
16. I have gotten support from on Yahoo! Kimo Knowledgeþ(corresponding to original 11)
Table XII.The result of factoranalysis of sense ofvirtual community
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Why peopleshare
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related action (Howellsi et al., 2009). This result aligns with that of Lin (2007a, b) andother researchers stating that the self-efficacy of sharing knowledge, perceived relativeadvantage, and perceived compatibility significantly and positively influenceknowledge sharing behavior.
Reward
On-systemreward
Off-systemreward
Self-efficacy
Altruism
Sense of virtual community
Recognitionand obligation
Emotional andrelationship
Identification
Exchange ofsupport
Attitudetoward
knowledgesharing
Subjectivenorm
H1
H2
H3
H4
H5
H6
H7H8
H9
H10
Intention toshare
knowledge
Figure 3.The intention to shareknowledge model
Hypotheses
H1a. The more on-system rewards a sharer receives, the more favorable attitude toward knowledgesharing s/he will have
H1b. The more off-system rewards an answerer receives, the more favorable attitude towardknowledge sharing s/he will have
H2. The greater the sense of self-efficacy through knowledge sharing a sharer obtains, the morefavorable attitude toward knowledge sharing s/he will have
H3. The more altruism a sharer possesses, the more favorable attitude toward knowledge sharing s/he will have
H4a. The greater the extent to obligation and recognition of members a sharer has, the greater thesubjective norm to share knowledge s/he will have
H4b. The greater the extent to emotional attachment and relationship with specific members a sharerhas, the greater the subjective norm to share knowledge s/he will have
H4c. The greater the identity (self) and identification (of others) with specific members a sharer has,the greater the subjective norm to share knowledge s/he will have
H4d. The greater the exchange of support is a sharer has, the greater the subjective norm to shareknowledge s/he will have
H5. The more favorable attitude toward knowledge sharing a sharer has, the greater intention toshare knowledge s/he will have
H6. The greater the subjective norm to share knowledge a sharer has, the greater the intention toshare knowledge s/he will have.
H7. The greater the subjective norm to share knowledge a sharer has, the more favorable attitudetoward knowledge sharing s/he will have
Table XIII.The moderatedhypotheses
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The study of Wasko and Faraj (2005) indicates that individuals who enjoy helpingothers will contribute more to virtual communities of practice, and Hsu and Lin (2008)identified that altruism will positively affect users’ attitudes toward participatingon a blog. The results from this study confirm the previous studies supporting H3,which states that a higher level altruism leads to a more favorable attitude towardknowledge sharing.
H1b is not supported, which is consistent with the previous studies that extrinsicrewards will not affect the attitude toward knowledge sharing (Bock et al., 2005;Eisenberger and Cameron, 1996). H4a-d are based on SOVC, where H4a (the greater theextent of the obligation and recognition of the members, the greater that the subjectivenorm to share knowledge will be) and H4d (the greater the exchange of support,the greater that the subjective norm to share knowledge will be) are supported. Fromthese results, obligation and recognition and exchange of support show their influence
MeasuresNumberof items
Compositereliability
Averagevarianceextracted
Cronbach’sa
On-system reward (RE(On-S)) 4 0.841 0.580 0.813Self-efficacy (SE) 2 0.901 0.820 0.784Altruism (AL) 3 0.885 0.719 0.805Recognition and obligation (REOB) 7 0.845 0.453 0.754Emotional attachment and relationship withspecific members (EMORE) 4 0.797 0.511 0.667Identity (self) and identification (of others) (IDEN) 2 0.828 0.708 0.601Exchange of support (EX) 3 0.848 0.653 0.742Attitude toward knowledge sharing (AT) 4 0.872 0.631 0.804Subjective norm (SN) 2 0.976 0.953 0.951Intension to knowledge sharing (IS) 4 0.923 0.750 0.885
Note: *Reward (Off-S) only has one item, and no factor analysis is needed
Table XIV.The confirmatory analysis
of moderated model
RE(On-S) RE(Off-S) SE AL OBRE EMRE ID EX AT SN IS
RE(On-S) 0.627RE(Off-S) �0.111 1SE �0.258 0.066 0.904AL �0.066 �0.011 �0.424 0.848OBRE �0.261 0.015 �0.407 0.551 0.777EMRE �0.383 0.027 �0.317 0.402 �0.718 0.840ID �0.194 0.031 �0.352 0.528 �0.86 0.582 0.809EX �0.436 0.009 �0.339 0.305 �0.575 0.692 �0.449 0.780AT �0.207 0.075 �0.397 0.547 �0.694 0.779 �0.573 0.603 0.884SN �0.218 0.054 �0.162 0.234 �0.481 0.43 �0.436 0.445 0.496 0.997IS �0.078 �0.068 �0.229 0.606 �0.708 0.545 �0.67 0.382 0.677 0.436 0.868
Notes: RE(On-S), On-system reward; RE(Off-S), off-system reward; SE, self-efficacy; AL, altruism; SO,sense of virtual community; AT, attitude toward knowledge sharing; SN, subjective norm; IS, intensionto knowledge sharing; the shared numbers in the diagonal row are square roots of the average varianceextracted values
Table XV.Correlation between
constructs
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Why peopleshare
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on subjective norm. Bock et al. (2005) have defined subjective norm as “the degree towhich one believes that people who bear pressure on one’s actions expect one toperform the behavior in question multiplied by the degree of one’s compliance witheach of one’s referents,” and we can see the influence from the outcome that a greaterdegree of recognition and obligation perceived by members led to having a morepronounced feeling of subjective norm.
On the contrary, subjective norm is about others’ expectations for the individual;nevertheless, emotional attachment and relationship with specific members concernsrelationships between members. These two play different roles toward knowledgesharing. However, testing H4d confirms our expectation that if people receive moresupport from others, they will have a greater sense of subjective norm. Additionally,the questions on identification were framed as follows: “I can recognize the names ofmost members on Knowledgeþ ” and “I care about what other group members think ofmy actions.” It is unlikely that the members of Knowledgeþ could know everyonebecause there are thousands of members.
From the results, we reconfirm the findings from prior studies that the attitudetoward knowledge sharing and subjective norm positively influence the intention toshare knowledge (Ajzen and Fishbein, 1980; Bock et al., 2005). However, the results ofthis study come from a mix of the four levels of members (i.e. knowledgist, master,expert, and postgraduate). Additionally, the result of testing H7 is consistent with thatof previous studies (Wasko and Faraj, 2005). To distinguish the effects of factors
Reward
On-systemreward
Off-systemreward
Self-efficacy
Altruism
Recognitionand obligation
Emotional andrelationship
Identification
Exchange ofsupport
0.129**t =2.411
0.055t=1.030
0.160***t =2.254
0.472***t =7.893
Sense of virtual community 0.391*t =1.365
0.047t=0.5600.136t=1.170
0.241***t =2.513
Subjectivenorm
R2=0.290
R 2=0.419
Notes: *p�0.1; **p�0.05; ***p�0.01
0.133**t=1.734
R 2=0.618
Intention toshare
knowledge
Attitudetoward
knowledgesharing
0.611***t=8.080
Figure 4.Results of PLS analysiswith the whole samples
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considered by different user groups on the intention of knowledge sharing, we dividedsamples into four groups based on their levels of sharing knowledge, the results ofwhich are shown in Table XVI.
6.1 The result obtained from knowledgist levelFigure 5 shows that H2, H3, H4b, H5, and H7 are supported, whereas H1a, H4a, andH4d are not supported. H1b (the larger off-system reward, the more favorable thatattitude toward knowledge sharing will be), H4c (the greater the identity (self) andidentification (of others) with specific members, the greater that the subjective norm toshare knowledge will be), and H6 (the greater the subjective norm to share knowledge,the greater that the intention to share knowledge will be) have negative effects. Theresults of the statistical analysis for H2, H3, and H5 among knowledgists are similarto those for the total sample. However, we still can find a difference between thesetwo samples.
For knowledgists, both H1a and H1b are insignificant because users ranked asknowledgists have no higher ranks to climb. Additionally, the lack of significantsupport for H4c fits the previous prediction that they can spend more time helpingothers rather than being helped by others because they are at the highest rank.However, they are also influenced more by subjective norm when they perceive more ofthe factor of emotional and relationship rather than the level of recognition andobligation. H6 is insignificant (negative); nevertheless, the previous study indicatesthat the more individuals are motivated to conform to group norms, the more theirattitudes tend to be group-determined than individual-determined (Krebs, 1975). Bocket al. (2005) also identified that the greater the subjective norm to share knowledge,the more favorable that the attitude toward knowledge sharing will be. For users on theknowledgist level, the attitude toward knowledge sharing mediates subjective normand the intention to share knowledge. From Figure 5, we can see that the individual’ssubjective norm does not directly influence individuals’ intention to share; instead, itsets individuals’ attitudes as a mediator.
6.2 The result obtained from master levelFrom the statistical results from users on the master level, H1a, H3, H4d, H5, andH6 are supported, whereas H1b, H2, H4a-c are not supported. The result of H1a-b,H3, H4b-d, H5, H6, and H7 are similar to the results found for the total sample(Figure 4). Master is the second highest rank on Knowledgeþ . We expected thatthe behavior of the users on the master level would be similar to that of users onthe knowledgist level. However, the results from testing H1a (the moreon-system the rewards, the more favorable that attitude toward knowledge sharingwill be) and H2 (the greater the sense of self-efficacy through knowledge sharing, themore favorable that attitude toward knowledge sharing will be) are different from thoseof knowledgists.
This result can be interpreted as follows. According to the upgrading rule set byKnowledgeþ , a user on the master level can be upgraded to the knowledgist levelif s/he answers 2,000 additional questions (upgrading from expert to master levelonly requires answering 1,000 additional questions), and in turn, they will be grantedprestigious access on Knowledgeþ , such as asking/answering 100 questions a day(only 50 questions for master), voting, commenting and suggesting 50 timesa day (only 30 times for master), and sending 100 e-mails to other members a day (only50 for master). The double load of questions to answer needed to upgrade to the
151
Why peopleshare
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Hy
pot
hes
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aste
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H3
.T
he
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atti
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dk
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H4a
.T
he
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ater
the
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Not
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Not
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Not
sup
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H4b.
Th
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ten
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tan
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ipw
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sa
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ater
the
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H4c.
Th
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enti
ty(s
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and
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nor
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Not
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sup
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sup
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ted
(sig
nif
ican
tb
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ite
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.T
he
gre
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the
exch
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sup
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ash
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Not
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up
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Not
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H5
.T
he
mor
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atti
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dk
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ge
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arer
has
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ave
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up
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pp
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up
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ted
H6
.T
he
gre
ater
the
sub
ject
ive
nor
mto
shar
ek
now
led
ge
ash
arer
has
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eg
reat
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ten
tion
tosh
are
kn
owle
dg
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he
wil
lh
ave
Not
sup
por
ted
Su
pp
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up
por
ted
H7
.T
he
gre
ater
the
sub
ject
ive
nor
mto
shar
ek
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ge
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de
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Table XVI.The results of hypothesistesting for differentranks of users
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INTR23,2
knowledgist level may influence the self-efficacy of masters and the return onupgrading may affect the master’s attitude toward on-system reward.
6.3 The result obtained from expert levelThe statistical results from users on the expert level support H1a, H2, H3, H4b-d, H5,and H7 and do not support H1b, H4a, and H6. The results for H1a-b, H2, H3, H4d, H5,and H7 are similar to those for the total sample, and the statistical results for users onthe expert level are similar to those for users on the knowledgist level.
H6 is not supported, and we examined the mediating effect of subjective norm, theattitude toward knowledge sharing, and the intention to engage in knowledge sharing.The relationship between them is the same as that on the knowledgist level. InTable XVI, we can see that the individual’s subjective norm does not directly influencethe individual’s intention; instead, they require the individual’s attitude as mediator.For users on the expert level, the attitude toward knowledge sharing is the mediator ofsubjective norm and the intention to share knowledge.
6.4 The result obtained from postgraduate levelTable XVI shows that H1b, H3, H5, and H6 are supported and that H1a, H2, H4a-d,and H7 are not supported. H1a and H1b present a very different result from theprevious samples. From the results of testing H1a and H1b, we conclude that users on
Reward
On-systemreward
Off-systemreward
Self-efficacy
Altruism
Sense of virtual community
Recognitionand obligation
Emotional andrelationship
Identification
Exchange ofsupport
0.074t=0.555
–0.073t =0.604
0.248**t =1.803
0.336***t =2.520
Attitudetoward
knowledgesharing
R2=0.285
0.913***t =3.474
Intention toshare
knowledge
R2=0.695–0.111t =–0.676
0.247t =1.065
Subjectivenorm
R2=0.395
0.479**t =1.734
0.192t =–0.866
0.068t =0.252
Notes: *p�0.1; **p�0.05; ***p�0.01
Figure 5.Results of PLS analysis
with users onknowledgist level
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Why peopleshare
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the postgraduate level do not care about the upgrading incentive set by Knowledgeþ ;instead, they care about off-systems when sharing knowledge.
However, H4a-d considering the four factors from the SOVC (i.e. obligation andrecognition of members, emotional attachment and relationship with specific members,identity (self) and identification (of others), and exchange of support) are notsupported, which can be interpreted as follows. The tenure of users on thepostgraduate level on Knowledgeþ is shorter than that of users on other levels (e.g. ittakes two to five years to upgrade to the knowledgist level and zero to three years toupgrade to the postgraduate level); thus, they may have yet to perceive a SOVC.Additionally, the statistical results of hypotheses based on TRA (H5 and H6) are allsignificantly supported. We also discovered that on the postgraduate level, subjectivenorm does not affect the attitude toward knowledge sharing; therefore, H7 is notsupported. On this level, the intention to share knowledge is influenced significantly byusers’ attitude (H5) and subjective norm (H6).
6.5 The synthesis of results from four levelsFrom Table XVI, H3 and H5 are supported for users at each of the four levels.Altruistic people are more willing to share. On the contrary, H4a and H4c are notsupported for the whole sample across four levels. According to the results, peoplewho are willing to share are not affected by their obligation, recognition of members oridentity (self) and identification (of others).
Synthesizing the results of this study, we draw two conclusions. First, users asknowledge providers at different knowledge sharing levels are sensitive with differentreward systems. For users on higher levels, on-system reward affects them very little.On Knowledgeþ , on-system rewards do not influence users on the knowledgist level,the highest level. On the contrary, among users on the master and expert levels, on-system rewards can motivate them to pursue higher levels in the community.Nevertheless, users on the postgraduate level are not affected by on-system reward;instead, they are influenced by off-systems that can benefit them in their daily lives(e.g. careers).
Second, for users on the knowledgist and expert levels, their individual subjectivenorms directly influence their intention through their attitude toward knowledgesharing. Along with level upgrading, the motive for knowledge sharing changes. Forexample, the off-system for users on the postgraduate level affects their attitudetoward knowledge sharing, but the off-system has no effect on users on theknowledgist level. For users on the postgraduate levels, the on-system rewarddoes not influence their attitude toward knowledge sharing; however, for userson the master and expert levels, on-system rewards influence their attitude towardknowledge sharing.
7. Conclusions and future workConsider again our research question: Why did Google Answers fail and Knowledgeþsucceed? Among many possible reasons in different aspects, this study compared theirdifferences mainly in two characteristics: first, in their reward mechanisms and secondin their definitions of expert. Regarding reward systems, Google Answers usedmonetary compensation, but Knowledgeþ uses on-system rewards. The results ofour hypothesis testing indicate that users in higher knowledge sharing levels do notcare about off-systems; instead, they prefer on-system rewards, such as upgradinglevels and enjoying self-accomplishment. Additionally, for these knowledge providers,
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altruism is also an important factor. Therefore, they share not only becauseof rewards they can receive but also because of their feelings of altruism andself-fulfillment, which are similar to why Wikipedians shared mainly in intrinsicmotivations (Nov, 2007).
Google Answers identified experts by recruiting those who passed theircertification process. On Knowledgeþ , anybody who has the confidence to shareknowledge has the freedom to answer. This study shows that for a higher level ofknowledge providers, self-efficacy increases users’ intention to share.
Additionally, on Knowledgeþ , the identification of experts is conducted throughanswer seeking. To upgrade levels, one needs to achieve certain milestones basedon the number of answers they provide and the acceptance rate of these answers,which is calculated as the number of the best answers divided by the number ofanswers. The best answer is the most satisfactory answer and is determined by theanswer seekers, who can choose by themselves or let the public on Knowledgeþ vote.The results of this study indicate that for a user with high level of knowledgeprovision, receiving an on-system reward is more important than receiving aoff-system. Therefore, in contrast to the identification of experts by Google Answers,an answerer on Knowledgeþ is required to prove her/himself based on the answerss/he provides. However, users on a higher level of knowledge provision need to obtainthe ownership of sharing (the negative effect of H6), so that the closed method ofidentifying experts on Google Answers may cause a higher-ranked knowledgeprovider to feel a loss of ownership and become less willing to share. In summary, forknowledge providers and answer seekers on Google Answers, the only relationship isbuying and selling. Although the answer seekers can evaluate the answers via “stars,”the answer seekers cannot pick knowledge providers in Google Answers’ settings, andin turn, there is no chance for the service to become a virtual community. This may bethe reason why Google Answers failed.
For IS researchers, the findings of this research deepen the knowledge of why andhow people share knowledge in a virtual community. The important findings include:first, knowledge providers in different degrees of knowledge sharing are sensitive withdifferent reward systems; and second, users’ individual subjective norms directlyinfluence their intention through their attitude toward knowledge sharing. This impliesthat the design of reward system for a virtual community should consider its marginaleffects on users’ knowledge sharing intention.
For knowledge sharing platform practitioners, this study concludes that differentreward systems fit different people. There may be ways to establish reward systemsfor any kinds of knowledge sharing operation, whether it be in the context of a Q&Aweb site or that of an enterprise. When establishing reward systems, managers canalso consider the specialty of their members/employees (are they significant knowledgeproviders or not?). Through the understanding of members/employees, we shouldidentify a suitable way to reward and motivate these knowledge providers. Thedifferent effective incentives for different levels of knowledge providers can be used tomanage Q&A web services.
From the statistical result for users on the postgraduate level, the factors for SOVCare insignificant, which indicates that we did not find the proper constructs forinterpreting users’ subjective norm on the postgraduate level, whereas subjective normsignificantly affects users’ intention to share knowledge. For future studies, we willidentify additional constructs that are more effective to qualify subjective norm forusers on the postgraduate level.
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Notes
1. http://tw.knowledge.yahoo.com/
2. www.computerworld.com/s/article/9005481/Google_questions_need_for_Google_Answers?intsrc¼hm_list; http://arstechnica.com/business/news/2006/11/8317.ars
3. Developed by Jen-Ruie Fu, professor of Department of Information Management of NationalKaohsiung University of Applied Sciences, Taiwan.
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About the authors
Fu-ren Lin is the Professor and the Founding Director of the Institute of Service Science, NationalTsing Hua University, Taiwan. He graduated from University of Illinois at Urbana-Champaign in1996 with PhD degree in Information Systems. He taught in the Department of InformationManagement, National Sun Yat-sen University until 2004. He joined the Institute of TechnologyManagement in Tsing Hua until the Institute of Service Science was launched in 2008. He hasbeen doing research in various topics, such as supply chain management, business processre-engineering, e-commerce, knowledge management, and data/text mining since graduation.Based on the prior academic background and methodologies, he started to focus on serviceinnovation related issues in service science research, including new service development,healthcare service engagement, and service experience design, service value network analysis.His research results have been published in domain related journals, such as Service Science,International Journal of e-Commerce, Decision Support System, Information Systems and
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e-Business Management, Information Processing and Management, Electronic Commerce
Research and Application, etc. He also actively serves for academic communities. He co-chairs theminitrack of SSME in the Hawaii International Conference of System Sciences (HICSS) since2008. He also serves as the Associate Editor for INFORMS Service Science. He currently servesas the General Secretary for the Service Science Society of Taiwan (s3tw). Fu-ren Lin is thecorresponding author and can be contacted at: [email protected]
Hui-yi Huang graduated from the Institute of Service Science, National Tsing Hua Universityin June 2011. She is interested in knowledge management and e-service related topics. Sheobtained her bachelor degree from the Department of Information Management, National CentralUniversity in 2008.
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