22677429

7/28/2019 22677429

1/9

Understanding Behavioral Intention to Participatein Virtual Communities

HSIU-FEN LIN, Ph.D.

ABSTRACT

Virtual communities are formed on the Internet and are expected to serve the needs of mem-bers for communication, information, and entertainment. Online businesses should considervirtual communities as a new market place since their members are current or future cus-

tomers. Thus, there is a need to understand the determinants of member intentions to partici-pate in virtual communities. Based on the extended theory of planned behavior (TPB), thisstudy develop a research model to identify the attitudinal, social, and perceived behavioralcontrol factors that would influence members intentions to participate in virtual communi-ties. Specifically, the research model decomposes the attitude component into perceived use-fulness, perceived ease of use and perceived trust, and the perceived behavioral controlcomponent into Internet self-efficacy and facilitating conditions. Based on a survey of 165community members, this study uses structural equation modeling (SEM) approach to inves-tigate the research model. The results indicate that attitude and perceived behavioral controlsignificantly influence member behavioral intentions, while subjective norms do not. Fi-nally, this study discusses the implications of these findings and offer directions for futureresearch.

540

INTRODUCTION

GIVEN THE GROWING number of Internet users, anincreasing number of online businesses and In-ternet service providers are forming the virtualcommunity as a method of developing new socialrelationships through Internet-based technology.13

Virtual communities are having a major impact onenhancing Internet user online experiences. For ex-ample, activities conducted in virtual communities

range from chatting, making friends, exchangingideas, and sharing knowledge on particular sub-

jects. All these computer-mediated communicationshave led individuals to change their communica-tion and collaboration methods. On the other hand,Igbaria et al.4 suggested that sustaining the virtualcommunity does not only motivate knowledge

sharing, it also has significant impact on onlinebusiness activity.5 Moreover, online businesses andcommunity providers are under increasing pressureto identify the antecedents of member loyalty in vir-tual communities. Therefore, the factors essential tomembers participating in virtual communities must

be thoroughly understood.Theory of planned behavior (TPB) has received

attention from many researchers,6,7 and is exten-sively adopted in improving understanding of the

determinants of information technology (IT)usage.8,9 Previous studies have increased the ex-planatory power of TPB by considering the multi-dimensionality of its components.10,11 Furthermore,the extended TPB has been successfully used inpredicting IT usage from decomposing attitudinal,normative and control beliefs.1214 Virtual commu-

CYBERPSYCHOLOGY & BEHAVIORVolume 9, Number 5, 2006 Mary Ann Liebert, Inc.

Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung, Taiwan.

7/28/2019 22677429

2/9

nities are similar to general Internet-based informa-tion systems that have a significant impact on indi-vidual decision-making behaviors and Internetmarketing strategies.15 Hence, this study suggeststhat the extended TPB is an appropriate model forexplaining the determinants of member intentionsto participate in virtual communities.

Theory and hypotheses

This study extends the TPB to develop a researchmodel to identify the determinants of member in-tentions to participate in virtual communities (Fig.1). First, the research model derives predictions re-garding member intentions to participate in virtualcommunities based on the TPB. Second, using theliterature on virtual communities and extendedTPB,14,16,17 the research model decomposes the atti-tude component into perceived usefulness, per-

ceived ease of use and perceived trust, anddecomposes the perceived behavioral control intocomponents of into Internet self-efficacy and facili-tating conditions. Each of the constructs in the re-search model and hypotheses is detailed below.

TPB and member participate in virtual communities.In the TPB, Ajzen6 hypothesized that attitudes to-ward behavior, subjective norms and perceptionsof behavioral control generally can accurately pre-dict individual behavioral intentions. Recently,most applications of TPB in contexts related to In-ternet service acceptance or adoption have recog-

nized that attitudes, subjective norms, and per-ceived behavioral control are important for under-standing and predicting behavioral intentions.1820

Applying the TPB to the virtual community con-text, this study hypothesizes that member inten-tions will be determined by individual attitudestowards participation in virtual communities, indi-vidual perceived opinions of other groups that areimportant to an individual, and perceived controlover the act of participation in virtual communities.The following hypotheses thus are formulated:

Hypothesis 1: Attitude of members towards par-ticipation in virtual communities positively af-fects behavioral intentions.

Hypothesis 2: Subjective norms of members inrelation to participation in virtual communi-ties positively affect behavioral intentions.

Hypothesis 3: Perceived behavioral control of

members in relation to participation in virtualcommunities positively affects behavioral in-tentions.

Decomposition of attitude. The TAM asserts thatattitude toward the system are determined by per-ceived usefulness and perceived ease of use.21,22

Additionally, perceived ease of use of the system ispositively related to perceived usefulness of thesystem. Previous research has also suggested thatthe TAMs fundamental salient beliefs, such as per-ceived usefulness and perceived ease of use, maynot fully reflect the user attitude towards behav-

BEHAVIORAL INTENTION TO PARTICIPATE VIRTUAL COMMUNITIES 541

Behavioralintention

Subjective norms

H1

H3

H2

Perceivedusefulness

Perceived trust

Perceived ease ofuse

Attitude

H4a

H4b

H4d

H4c

Internetself-efficacy

Perceived behaviorcontrol

Facilitatingconditions

H5b

H5a

FIG. 1. Research model.

7/28/2019 22677429

3/9

ioral intention, necessitating a search for additionalfactors that has better explanatory power for sys-tem usage intentions.23 Recent studies have in-cluded the construct of perceived trust in theextended TAM to explore consumer acceptance ofInternet services.24 Moreover, Ridings et al.16 alsoproposed that trust is crucial in virtual communi-ties where the absence of workable rules creates re-liance on others behaving in a socially acceptablemanner, that is trust, essential for community conti-nuity. Consequently, this study proposes to decom-pose attitude into three components: perceivedusefulness, perceived ease of use, and perceivedtrust. Applying, the TAM to the virtual communitycontext, this study proposes the following hypothe-ses predicting member attitudes towards participa-tion in virtual communities.

Hypothesis 4a: Perceived usefulness positively

affect attitudes towards participation in virtualcommunities.

Hypothesis 4b: Perceived ease of use positivelyaffect attitudes towards participation in virtualcommunities.

Hypothesis 4c: Perceived ease of use positivelyaffect perceived usefulness of virtual commu-nities.

Hypothesis 4d: Perceived trust positively affectattitudes towards participation in virtual com-munities.

Decomposition of perceived behavioral control. Ac-

cording to the TPB, perceived behavioral control isdefined as individual perceptions of the ease or dif-ficultly of performing a specific behavior.6,7 Per-ceived behavioral control thus reflects individualperceptions towards internal and external behav-ioral constraints.7 Regarding internal constraints,increased Internet self-efficacy required to success-fully execute a given behavior will better representperceptions of behavioral control. In the context ofvirtual community, Internet self-efficacy describesmember self-assessments of their capabilities toparticipate in virtual communities. Moreover,Hung et al.13 found that Internet self-efficacy couldpredict user perceptions of behavioral control to-wards Internet services. On the other hand, userperceptions of external behavioral constraints in-fluenced perceived behavioral control. For in-stance, an empirical study on IT adoption by Taylorand Todd14 found that resource-facilitating condi-tions are an important predictor of perceptions of

behavioral control. Moreover, Bhattacherjee25

found that Internet resource availability influenceuser perceptions of behavioral control toward e-

commerce services. Hence, this study expects Inter-net self-efficacy and facilitating conditions to posi-tively affect member perceptions of behavioralcontrol of virtual communities.

Hypothesis 5a: Internet self-efficacy of participa-tion in virtual communities positively affectsperceived behavioral control.

Hypothesis 5b: Facilitating conditions of partici-pation in virtual communities positively affectperceived behavioral control.

METHODS

Sample and data collection

This study utilized virtual community literatureand interviews with leaders of diverse virtual com-

munities. Pre-testing focused on questionnaire clar-ity, question wording and question applicability.During the pre-testing, 15 members from differentcommunities taken as subjects were invited to com-ment on the questions and their wording. Com-ments of these 15 subjects then provided a basis forquestionnaire revisions. Leaders of 20 virtual com-munities willing to participate in this study wereselected from a group of very successful virtualcommunities (e.g., http://tw.club.yahoo.com,http://club.yam.com, and http://club.pchome.com.tw), which were the three highest-ranking vir-tual communities in Taiwan by Alexa.com in July

2005. The community leaders were requested torandomly distribute the paper-based question-naires to ten community members and to collectthe questionnaires when completed. Of the 200questionnaires distributed, 165 completed and us-able questionnaires were received, representing aresponse rate of 82.5%.

Measures

In this study, items used to operationalize theconstructs were mainly adapted from previousstudies and modified for use in the virtual commu-nity context. All constructs were measured usingmultiple items. All items were measured using aseven-point Likert-type scale (1 = strongly dis-agree; 7 = strongly agree). Table 1 lists all of the sur-vey items used to measure each construct.

This study measured perceived usefulness andperceived ease of use of virtual communities usingscales modified from Davies et al.22 Moreover, per-ceived trust was measured by two-item measuresadapted from Jarvenpaa et al.26, representing mem-

542 LIN

7/28/2019 22677429

4/9


TABLE 1. MEASUREMENT MODEL LOADINGS

Factor CompositeConstruct/item loading reliabilitya

Perceived usefulness 0.82PU1: Participation in virtual communities enhances my ability to get infor- 0.83

mation from community members.PU2: Participation in virtual communities enables me to share knowledge 0.75

with community members.PU3: Participation in virtual communities helps satisfy my social needs. 0.74PU4: Overall, participation in virtual communities meets my needs. 0.65

Perceived ease of use 0.78PEU1: Learning to operate virtual communities is easy for me. 0.79PEU2: My interaction with virtual communities is clear and understandable. 0.81PEU3: It is easy for me be become skillful at participation in virtual communities. 0.65PEU4: Overall, participation in virtual communities is easy for me. 0.69

Perceived trust 0.81PT1: I feel more confident about discussion skills that the other members of 0.77

virtual communities.

PT2: The members of virtual communities will do everything within their 0.78capacity to help others.

Internet self-efficacy 0.86ISE1: I feel confident finding information through participation in virtual 0.86

communities.ISE2: I feel confident exchanging information with other members of virtual 0.80

communities.ISE3: I feel confident chatting on the virtual community. 0.78ISE4: I feel confident downloading files from the virtual community. 0.70ISE5: I feel confident uploading files to the virtual community.

Facilitating conditions 0.83FC1: I have the Internet equipment (modems, ADSL, etc.) required to use the 0.86

virtual community.FC2: I have convenient access to virtual community resources. 0.72

Attitude 0.90A1: Participation in virtual communities is a good idea. 0.84A2: I like the idea of participation in virtual communities. 0.82A3: Participation in virtual communities is a pleasant experience. 0.81A4: Participation in virtual communities is a foolish idea. (Reverse coded.) 0.85

Subjective norms 0.85SN1: People who influence my behavior would encourage me to participate 0.82

in virtual communities.SN2: People who are important to me would encourage me to participate in 0.86

virtual communities.Perceived behavioral control 0.87

PBC1: I would be able to participate in virtual communities. 0.87PBC2: I am control my participation in virtual communities. 0.83Behavioral intention 0.86

BI1: I plan to participate in the virtual community in the future. 0.84BI2: I intend to participate in the virtual community in the future. 0.80BI3: I expect to participate in virtual communities in the future. 0.83

aComposite reliability: (square of the summation of the factor loadings)/{(square of the summation ofthe factor loadings) + (summation of error variances)}.

7/28/2019 22677429

5/9

ber trust in the ability and benevolence of othermembers of virtual communities. A five-item scalemeasuring Internet self-efficacy of participation invirtual communities was adapted from a measuredeveloped by Compeau and Higgins.27 Internetself-efficacy assesses member judgments of theircapability to participate in virtual communities. Fa-cilitating conditions were measured using a two-item measure derived from Taylor and Todd14 thatassessed the extent to which a member had readilyavailable technical resources and easy access to thevirtual community. Finally, attitude, subjectivenorms, perceived behavior control, and behavioralintention were measured using a total of 11 items,derived from those proposed by Taylor and Todd.14

Statistical analysis

The measurement model represents a confirmatory

factor analysis of all scales used in the study. Valuesthat exceed 0.90 for goodness-of-fit index (GFI),normed comparative fit index (NFI), and compara-tive fit index (CFI) are generally considered to indi-cate acceptable fit.28

RESULTS

Measurement model

The measurement model had an initial 2 = 479.32and showed a good fit with a GFI of 0.91, NFI of

0.92 and CFI of 0.95. The root mean square error ofapproximation (RMSEA) for the model was 0.051,which is below the cut-off level of 0.08 recom-mended by Browne and Cudeck.29 Also, the 2/df=2.04 (p < 0.001), which is well below the suggested3.0 value, indicating a good fit.30 Overall, the mea-surement model indicated good fit with the data.

The measurement model was further assessed forconstruct reliability and validity. Computing com-posite reliability assessed construct reliability. Thecomposite reliability for each construct of this studyis presented in Table 1. The values range from 0.78(for perceived ease of use) to 0.90 (for attitude). Thecomposite reliability of all latent constructs ex-ceeded the benchmark of 0.7 recommended byNunnally and Bernstein.31 Moreover, convergentvalidity is the degree to which multiple attempts tomeasure the same concept in agreement. Table 1also presented the factor loadings of the measure-ment items. The factor loading for all items exceedsthe recommended level of 0.6.32 In summary, themeasurement model demonstrated adequate relia-

bility and convergent validity.

Structural model

The structural model shows a good fit with 2 =416.56, GFI of 0.90, NFI of 0.92, CFI of 0.94, RMSEAof 0.061, and a 2/df= 2.27 (p < 0.001). All fit indicesindicate good fit of the structural model.30

Properties of the casual paths, including stan-

dardized path coefficients andp-values for each ca-sual path in the hypothesized model are presentedin Figure 2.

All the hypothesized paths, with the exception ofthe paths (1) from subjective norms to behavioralintention (Hypothesis 2) and (2) from Internetself-efficacy to perceived behavioral control (Hy-pothesis 5a), were significant. The influence of be-havioral intention was found to be stronglypositively associated with attitude and perceived

behavioral control of members in relation to partic-ipation in virtual communities. Three belief vari-ables (perceived usefulness, perceived ease of use,and perceived trust) were significantly positivelyrelated to perceived usefulness of virtual commu-nities. Further, perceived ease of use was signifi-cantly positively related to perceived usefulness ofvirtual communities. As expected, facilitating con-ditions of participation in virtual communities aresignificant determinants of perceived behavioralcontrol.

DISCUSSION

Based on the empirical findings, this study hasreached several conclusions. First, findings of thisstudy reveal that member attitudes toward thevirtual community were important in predicting

behavioral intention. To attract the participationin the virtual community, online businesses andcommunity providers need to devise strategies forcultivating positive attitudes towards using vir-tual communities. In this regard, favorable per-ceptions of the virtual communitys usefulnessand ease of use, as well as the trust concept, areimportant in increasing usability in virtual com-munity environments. The results indicated thatestablishing mutual trust among communitymembers (such as trust in ability, benevolence andintegrity of other members) and providing user-friendly website systems was important for ma-nipulating favorable member attitudes and onlinecommunication behavior.

Second, the insignificant effects of subjectivenorms on intention suggest that members of virtualcommunities might make their own decisionsrather than being influenced by the opinions and

544 LIN

7/28/2019 22677429

6/9

suggestions of others. This result can be explainedby the fact that virtual community characteristicssuch as anonymity, addictive behavior, and volun-tary behavior may imply the state of flow and con-sequently may place less emphasis on the opinionsof others.33,34 Also, a possible explanation for thisfinding may be that in the present study, around43% of respondents spent over 5 h using the virtualcommunity every week, and may have exhibitedInternet symptoms, such as heavy preoccupation

with the Internet, excessive time spent online, com-pulsive behavior, and time-management problems.As suggested by Leung,35 problematic Internet usewas associated with reduced participant communi-cation with family members or colleagues, reducedsocial circle, and increased depression and loneli-ness. This finding thus indicates the need of online

businesses and virtual community providers topromote awareness of over involvement with thevirtual community through being in a position to

both assess the needs of members, and formulatepreventive policies to reduce excessive use of vir-tual communities.

Finally, the small but significant effect of per-ceived behavioral control on intention, thoughweaker than that of attitude, indicates that per-ceived behavioral control remains an importantdeterminant of member intention to participate invirtual communities. However, this study foundthat Internet self-efficacy did not significantly in-fluence perceived behavioral control of membersin relation to participation in virtual communities.A possible explanation may lie in the fact that

more than 80% of respondents had at least 3 yearsof experience of using the Internet. That is, Internetknowledge and skills may be an obstacle they havealready overcome and, hence, may no longer becrucial to such individuals. Furthermore, thisstudy found that facilitating conditions signifi-cantly influenced perceived behavioral control ofmembers in relation to participation in virtualcommunities. This finding correlates with that re-ported by Lederer et al.,36 in which an individual

had readily available technical resources and easyaccess to a new system, and thus is more inclinedto use that new system. Therefore, to be successfula virtual community must meet member expecta-tions regarding easy access to virtual communityresources.

Several limitations should be considered in thisstudy: First, the sample used in this study targetedthe virtual community chosen for convenient sam-pling. Analytical results presented may thereforehave limited generalizability. Second, since thisstudy only considered non-profit virtual communi-ties, it is unclear whether these analytical resultscan be generalized to other virtual communities.Further research can apply this research model toexamine profit-oriented virtual communities, suchas eBay.com.tw or brand communities.37 Third, thesample may have been biased since all the samplecommunities voluntarily participated in the survey.A more sophisticated sample collection method isneeded to eliminate this potential shortcoming. Fi-nally, since the sample was collected in Taiwan,generalizability to other countries might be limited


Perceived trust

Behavioralintention

Subjective norms

Attitude

p

7/28/2019 22677429

7/9

due to cultural differences in member behavior inthe virtual community.

REFERENCES

1. Flanagin, A.J., & Metzger, M.J. (2001). Internet use inthe contemporary media environment. Human Com-munication Research 27:153181.

2. Matei, S. (2004). The impact of state-level social capi-tal on the emergence of virtual communities. Journalof Broadcasting and Electronic Media 48:2340.

3. Nie, N.H. (2001). Sociability, interpersonal relations,and the Internet: reconciling conflicting findings.

American Behavioral Scientist 45:420435.4. Igbaria, M., Shayo, C., & Olfman, L. (1998). Virtual so-

cieties: their prospects and dilemmas. San Diego, CA:Academic Press.

5. Kim, W.G., Lee, C., & Hiemstra, S.J. (2004). Effects ofan online virtual community on customer loyalty

and travel product purchases. Tourism Management25:343355.

6. Ajzen, I. (1988). Attitudes, personality and behavior.Milton Keynes, UK: Open University Press.

7. Ajzen, I. (1991). The theory of planned behavior. Or-ganizational Behavior and Human Decision Processes50:179211.

8. Harrison, D., Mykytyn Jr., P.P., & Riemenschneider,C. K. (1997). Executive decision about adoption ofinformation technology in small business: theoryand empirical tests. Information Systems Research 8:171195.

9. Venkatesh, V., Morris, M.G., & Ackerman, P.L. (2000).A longitudinal field investigation of gender differ-

ences in individual technology adoption decision-making processes. Organizational Behaviour and

Human Decision Processes 83:3360.10. Armitage, C.J., Conner, M., Loach, J., et al. (1999).

Different perceptions of control: applying an ex-tended theory of planned behavior to legal and ille-gal drug. Basic and Applied Social Psychology 21:301316.

11. OConnor, R.C., & Armitage, C.J. (2003). Theory ofplanned behaviour and parasuicide: an exploratorystudy. Current Psychology 22:196205.

12. Hsu, M.H., & Chiu, C.M. (2004). Predicting electronicservice continuance with a decomposed theory ofplanned behaviour. Behaviour and Information Technol-

ogy 23:359374.13. Hung, S.Y., Ku, C.Y., & Chang, C. M. (2003). Critical

factors of WAP services adoption: an empirical study.Electronic Commerce Research and Applications 2:4260.

14. Taylor, S., & Todd, P.A. (1995). Understanding infor-mation technology usage: a test of competing mod-els. Information Systems Research 6:144176.

15. Romm, C., Pliskin, N., & Clarke, R. (1997). Virtualcommunities and society: toward an integrativethree phase model. International Journal of Information

Management 17:261270.

16. Ridings, C.M., Gefen, D., & Arinze, B. (2002). Someantecedents and effects of trust in virtual communi-ties. Journal of Strategic Information Systems 11:271295.

17. Teo, H.H., Chan, H.C., Wei, K.K., et al. (2003). Evalu-ating information accessibility and communityadaptivity features for sustaining virtual learning

communities. International Journal of Human-Computer Studies 59:671697.

18. Athiyaman, A. (2002). Internet users intention topurchase air travel online: an empirical investiga-tion. Marketing Intelligence and Planning 20:234242.

19. Liao, S., Shiao, Y., Wang, H., et al. (1999). The adop-tion of virtual banking: an empirical study. Interna-tional Journal of Information Management 19:6374.

20. Shim, S., Eastlick, M.A., Lotz, S.L., et al. (2001). Anonline prepurchase intentions model: the role of in-tention to research.Journal of Retailing 77:397416.

21. Davis, F.D. (1989). Perceived usefulness, perceivedease of use, and end user acceptance of information

technology.MIS Quarterly 13:319340.22. Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989).

User acceptance of computer technology: a compari-son of two theoretical models. Management Sciences35:9821003.

23. Moon, J.W., & Kim, Y.G. (2001). Extending the TAMfor a World-Wide-Web context. Information and Man-agement 38:217230.

24. Suh, B., & Han, I. (2002). Effect of trust on customeracceptance of Internet banking. Electronic CommerceResearch and Applications 1:247263.

25. Bhattacherjee, A. (2000). Acceptance of Internet ap-plications services: the case of electronic Brokerages.IEEE Transaction on Systems, Man, and Cybernetics

Part A: Systems and Humans 30:411420.26. Jarvenpaa, S.L., Knoll, K., & Leidner, D.E. (1998). Is

anybody out there? Antecedents of trust in globalvirtual teams.Journal of Management Information Sys-tems 14:2964.

27. Compeau, D.R., & Higgins, C.A. (1995). Computerself-efficacy: development of a measure and initialtest.MIS Quarterly 19:189211.

28. Venkatesh, V., & Davis, F.D. (1996). A model of theantecedents of perceived ease of use: developmentand test. Decision Sciences 27:451481.

29. Browne, M.W., & Cudeck, R. (1993). Alternativeways of assessing model fit. Newbury Park, CA: SagePublications.

30. Kline, R.B. (1998). Principles and practice of structuralequation modeling. New York: Guilford Press.

31. Nunnally, J.C., & Bernstein, I.H. (1994). Psychometrictheory. New York: McGraw-Hill.

32. Chin, W.W., Gopal, A., & Salisbury, W.D. (1997). Ad-vancing the theory of adaptive structuration: thedevelopment of a scale to measure faithfulness ofappropriation. Information Systems Research 8:342367.

33. Wang, Y., & Fesenmaier, D.R. (2004). Towards under-standing members general participation in and ac-

546 LIN

7/28/2019 22677429

8/9

tive contribution to an online travel community.Tourism Management 25:709722.

34. Yung, K.S. (1996). Psychology of computer use: XL.Addictive use of the Internet: a case that breaks thestereotype. Psychology Reports 79:899902.

35. Leung, L. (2004). Net-generation attributes and se-ductive properties of the Internet as predictors of

online activities and Internet addiction. CyberPsy-chology and Behavior 7:333348.

36. Lederer, A.L., Maupin, D.J., Sena, M.P., et al. (2000).The technology acceptance model and the WorldWide Web. Decision Support Systems 29:269282.

37. McWilliam, G. (2000). Building stronger brands

through online communities. Sloan Management Re-view 41:4354.

Address reprint requests to:Dr. Hsiu-Fen Lin

Department of Shipping and Transportation

ManagementNational Taiwan Ocean UniversityNo. 2, Beining Road

Keelung 202-24, Taiwan R.O.C.

E-mail: [email protected]


7/28/2019 22677429

9/9

Documents

22677429