Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors

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  • This article was downloaded by: [University of Newcastle (Australia)]On: 06 October 2014, At: 09:50Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

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    Understanding online group buyingintention: the roles of sense of virtualcommunity and technology acceptancefactorsMing-Tien Tsai a , Nai-Chang Cheng a & Kun-Shiang Chen a ba Department of Business Administration and Institute ofInternational Business , National Cheng Kung University , Tainan,Taiwanb Department of Optometry , Chung Hwa University of MedicalTechnology , Tainan, TaiwanPublished online: 20 Sep 2011.

    To cite this article: Ming-Tien Tsai , Nai-Chang Cheng & Kun-Shiang Chen (2011) Understandingonline group buying intention: the roles of sense of virtual community and technologyacceptance factors, Total Quality Management & Business Excellence, 22:10, 1091-1104, DOI:10.1080/14783363.2011.614870

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  • Understanding online group buying intention: the roles of senseof virtual community and technology acceptance factors

    Ming-Tien Tsaia, Nai-Chang Chenga and Kun-Shiang Chena,b

    aDepartment of Business Administration and Institute of International Business, National ChengKung University, Tainan, Taiwan; bDepartment of Optometry, Chung Hwa University of MedicalTechnology, Tainan, Taiwan

    The purpose of this paper is to provide a research model to examine the impact oftechnology acceptance factors and social factors on online group buying (OGB).Based on an empirical survey of 346 online adopters of OGB in Taiwan, the paperuses structural equation modelling to investigate the research model. The findingsindicate that perceived usefulness (PU), a sense of virtual community (SOVC) andtrust in the VC (virtual community) are determinants of OGB intention. In addition,perceived ease of use and website quality influence PU. To sustain a successfulgroup buying website, attention must be paid to enhancing users SOVC, websitefunctions and usability. Practitioners can apply the findings of this study to focus onthe determinants of success for their online shopping websites. Theoretically, whiledrawing upon technology acceptance relevant studies, this paper provides a modelthat is capable of lending an understanding of the determinants of OGB intention.From a managerial perspective, the findings should provide further insight intomembers behaviours, leading to more effective strategies for increasing the numberof customers.

    Keywords: online group buying; technology acceptance model; sense of virtualcommunity; trust

    1. Introduction

    Group buying is when an item must be bought in a minimum quantity or dollar amount,

    and several people agree to approach the vendor in order to obtain discounts. The shoppers

    benefit by paying less, and the business benefits by selling multiple items at once (Kauffman

    & Wang, 2002). Because customers choose collective procurement to obtain lower prices

    and to enhance bargaining power, group buying behaviour has become extremely popular

    (Umit Kucuk & Krishnamurthy, 2007). Group buying also is a shopping strategy originating

    in societies with predominately Chinese cultures, and the phenomenon has been most suc-

    cessful in China, where buyers have leveraged the power of this approach (Montlake, 2007).

    The rise of the Internet has caused a rapid increase in online group buying (OGB).

    OGB members are connected over the Internet, and most of them are strangers to each

    other. The rising popularity of OGB is evidenced by the doubling of revenues at one

    well-known group buying website between August and December of 2008. An average

    of more than 700 new groups are established each day (Cameron, 2009). These figures

    demonstrate that more and more people are using the Internet in innovative ways to

    save money. Using OGB, it is easy to find people in a short period of time to share

    ISSN 1478-3363 print/ISSN 1478-3371 online

    # 2011 Taylor & Francishttp://dx.doi.org/10.1080/14783363.2011.614870

    http://www.tandfonline.com

    Corresponding author. Email: chengnaichang@gmail.com

    Total Quality Management

    Vol. 22, No. 10, October 2011, 10911104

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  • freight costs and to buy in bulk. It is also easier to obtain larger discounts when more

    people take part in a group purchase.

    The concept of the TAM (the Technology Acceptance Model), an information systems

    theory that models how users come to accept and use a technology, is widely used in con-

    sumer electronics, communication and website design. Technology acceptance factors

    include methods for measuring usability (ease of use) and the study of the principles

    behind an objects perceived efficiency (website quality). Based on the humancomputer

    interaction perspective, researchers have noted that website usability and website quality

    are the key factors for predicting users intention to use a website (Kuo, 2003).

    Furthermore, online group buyers will normally take the recommendations, warnings

    and comments that appear on relevant virtual communities (VCs) into consideration

    before making a purchase. Such VCs thus function as the main source of social influences

    in this process, since they will influence online shopping decisions. Therefore, since the

    focus of this study is OGB, it follows that the idea of the sense of virtual communities

    (SOVC) should be taken into consideration when modelling OGB behaviour (Grabner-

    Kraeuter, 2002; Hsu & Lu, 2004; Yu, Ha, Choi, & Rho, 2005).

    The purpose of this study is to better understand the motivations behind a customers

    decision to purchase through OGB websites. We begin with the technology acceptance

    factors (perceived ease of use (PEOU) and perceived usefulness (PU)) and social

    factors (trust in VC and sense of VC) to investigate customer purchase motivation, as

    this should enable a more comprehensive examination of the acceptance of OGB. We

    then present the research methods and findings. Finally, we conclude the paper with a dis-

    cussion of the implications of our study for theory and practice, pointing out limitations

    and areas for future research.

    2. Theoretical background and hypotheses

    The decision to undertake OGB may be influenced by potential antecedents such as social

    influence and technology acceptance factors. The TAM is widely used to discuss the

    effects of these antecedents on behaviour. However, technology acceptance relevant

    factors will also allow a more comprehensive understanding of group buying behaviour.

    Table 1 summarises relevant studies on technology acceptance.

    2.1 Technology acceptance model (TAM)

    As indicated above, the TAM is an information systems theory that models how users

    come to accept and use a technology. The model suggests that when users are presented

    with a new technology, a number of factors influence their decision about how and

    when they will use it. It has been applied to studies of the relations among beliefs, atti-

    tudes, intentions and behaviours in various fields. This model suggests that a persons be-

    havioural intention depends on the persons attitude about the behaviour. If a person

    decides on a behaviour, then it is likely that the person will do it. Furthermore, a

    persons intentions are themselves guided by his/her attitude towards the behaviour.

    TAM asserts that attitudes towards new technology are determined by PU and PEOU.

    In this study, PU is defined as Attitudes towards using an online buying system will

    enhance behavioural intention. In contrast, PEOU is defined as The degree to which a

    person believes that using a particular system will be free from effort. In the previous

    research, PU can be described as an attitude towards intention. Furthermore, the PEOU

    1092 M.-T. Tsai et al.

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  • of a website is positively related to its PU. Accordingly, this paper thus proposes the fol-

    lowing hypotheses:

    H1: PU has a positive effect on OGB intention.

    H2: PEOU has a positive effect on PU.

    Table 1. Summary of relevant technology acceptance studies on online behaviour.

    Author Study content Findings

    Point of view: extended TAM in online behavior(Gefen Karahanna, &

    Straub, 2003)An integrated model of Trust and

    TAM in online shoppingThe finding shows that consumer

    trust is as important to onlinecommerce as the widely acceptedTAM use-antecedents, PU andPEOU

    (Hsu & Lu, 2004) This study applies the TAM thatincorporates social influencesand flow experience as belief-related constructs to predictusers acceptance of onlinegames

    The proposed model wasempirically evaluated usingsurvey data collected from 233users about their perceptions ofonline games. Overall, the resultsreveal that social norms, attitudeand flow experience explainabout 80% of game playing. Theimplications of this study arediscussed

    (Pikkarainen,Pikkarainen,Karjaluoto, &Pahnila, 2004)

    Investigates online-bankingacceptance in the light of thetraditional TAM, which isapplied to the onlineenvironment

    The findings of the study indicatethat PU and information ononline banking on the websiteswere the main factors influencingonline-banking acceptance

    (Wu & Chen, 2005) An extension of trust and TAMmodel with TPB in the initialadoption of an online tax

    A more comprehensive extension ofthe Trust and TAM model withTPB to understand behaviouralintention to use an online tax

    (Chu & Lu, 2007) Provide an explanation of factorsinfluencing the online musicpurchase intention of earlyadopters of online music

    The findings show that theperceived value of online musicis a significant factor inpredicting purchaser intention ofbuying online music in Taiwan.Furthermore, the beneficialfactors of PU and playfulness areidentified in addition to thesacrificing factor of the perceivedprice for assessing value

    (Moon & Kim, 2001) Besides ease-of-use andusefulness, the studyintroduces playfulness as anew factor that reflects theusers intrinsic belief in WWWacceptance

    The study introduces playfulness asa new factor that reflects theusers intrinsic belief in WWWacceptance. Using it as anintrinsic motivation factor, theauthor extends and empiricallyvalidates the TAM for the WWWcontext. The finding may explainthe users behaviour towardsnewly emerging ITs

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  • 2.2 Website quality

    Besides website usability, Kuo (2003) proposed that website quality is the key factor for

    predicting users intention to use a website. Website features are the quality measure for

    web-based information systems or services provided by a website. Among the various

    studies addressing website quality factors, those involving the dimensions suggested

    by DeLone and McLean (2003, 2004) have received the most attention. They found

    that information quality, system quality and service quality are important constructs

    making a successful information system. In the context of e-commerce, website

    quality factors have the potential to directly affect the PU of websites (Ahn, Ryu, &

    Han, 2004).

    In the field of online shopping, specific website quality factors are also believed to be

    critical in affecting the usage of VCs (Chen & Cheng, 2009; Gefen et al., 2003; Lian &

    Lin, 2008). If consumers perceive that the website is of high quality, they perceive high

    usefulness towards the website and will develop a willingness to purchase (Van der

    Heijden, Verhagen, & Creemers, 2003). Accordingly, we hypothesise:

    H3: Website quality has a positive effect on PU.

    2.3 Sense of virtual community (SOVC)

    Taiwans earliest form of OGB can be traced back to the Bulletin Board Systems that pro-

    liferated in universities in the mid-1990s. Since those early days, and with the rapid spread

    of the Internet, VCs structured around consumer interests have grown significantly and

    have reshaped the way buyers and sellers conduct electronic commerce (Hsu & Lu,

    2007; Williams & Cothrel, 2000).

    SOVC is an important component of successful VCs. Defined as members feelings of

    belonging, identity and attachment to each other in computer-mediated communication,

    SOVC distinguishes VCs from mere virtual groups. SOVC is believed to come from

    exchange of social support among members as well as from the creation of their own iden-

    tities and their learning the identity of other members.

    McMillan proposed that sense of community is defined as members feelings of

    belonging and being important to each other as well as a shared faith that members

    needs will be met by the commitment to be together (McMillan & Chavis, 1986). In

    order to develop a more appropriate measurement for the VC context, Koh and Kim

    (2003) proposed the SOVC measurement, characterised by three key dimensions:

    membership, influence and immersion. Membership indicates that people experience

    a feeling of belonging to their VC. Influence implies that people influence other

    members of their community. Immersion suggests that people feel that they are in a

    state of flow during VC navigation. These three dimensions of SOVC reflect the affec-

    tive, cognitive and behavioural aspects of VC members, respectively, as does the

    general construct of attitude in the area of marketing or behavioural science (Koh &

    Kim, 2003).

    According to the TPB (Theory of Planned Behavior) and the Theory of Reasoned

    Action, if individuals think others are important to them (e.g. part of their VC) and

    want them to perform a given behaviour, a higher intention (motivation) results,

    making them more likely to perform the behaviour. Thus, the SOVC can be seen as a

    major source of social influences, which clearly affect OGB intention. This study proposes

    that the SOVC has an influence on intention:

    H4: SOVC has a positive effect on OGB intentions.

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  • 2.4 Trust in VC

    Trust is a defining feature of most economic and social interactions in which uncertainty is

    present. Practically all interactions require an element of trust, especially those conducted

    in the uncertain environment of e-commerce (Ba & Pavlou, 2002). Trust has long been

    regarded as a catalyst in consumermarketer relationships because it provides expec-

    tations of successful transactions (Pavlou, 2003). Several researchers, in fact, have pro-

    posed trust as an important element of B2C e-commerce (Awad & Ragowsky, 2008;

    Gefen et al., 2003; Martin & Camarero, 2008).

    Recent studies have included the construct of trust in the extended TAM to explore

    consumer acceptance of Internet services (Gefen et al., 2003; Wu & Chen, 2005). Trust in

    VC could increase the willingness to use online shops or services. Hence, we hypothesize:

    H5: Trust in a VC has a positive effect on OGB intention.

    Trust can be naturally attributed to relationships between people. Conceptually, trust is

    also attributable to relationships within and between social entities such as families,

    friends, communities, organisations and companies. According to the social exchange

    theory, individuals usually expect reciprocal benefits, such as trust, when they act accord-

    ing to social influences (Gefen & Ridings, 2002). In other words, trust will create a SOVC,

    making it easier for community members to do things together (Blanchard, 2007; Ellonen,

    Kosonen, & Henttonen, 2007; Lin, 2008). Additionally, trust in a VC is positively related

    to the SOVC. This paper thus proposes the following hypothesis:

    H6: Trust in a VC has a positive effect on SOVC.

    3. Research methodology

    3.1 The research model

    The research model was built based on the beliefs regarding technology acceptance factors

    and social factors. This model decomposes the TAM component into PU and PEOU.

    In combination, SOVC, trust in VC and PU lead to the formation of a behavioural

    intention. Each of the constructs in this research model and the hypotheses are detailed

    in Figure 1.

    3.2 Measurement

    In constructing the measurement instrument, measures were selected from validated ques-

    tionnaires used in prior research when possible. PU and PEOU were measured using items

    Figure 1. Research model.

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  • derived from Davis (1989) and Van der Heijden (2004). The items measuring the three

    website quality factors, namely information quality, system quality and service quality,

    were taken from DeLone and McLean (2003) and Lin (2007). Trust in VC was measured

    using items based on Suh and Han (2002, 2003). SOVC was measured using items based

    on Blanchard (2007). Finally, purchase intentions were measured using items based on

    Ajzen and Fishbein (1975).

    Table 2 lists the construct definitions for the instruments and the relevant literature. In

    this study, items used to operationalise the constructs included in each investigated model

    were largely adapted from previous studies for use in the online shopping context. This

    study measured eight constructs: purchase intentions, PU, and PEOU, trust in VC,

    SOVC and website quality. Multiple items were used to measure all of the constructs,

    and all items were measured using a seven-point Likert scale (ranging from 1 = strongly

    disagree, to 7 = strongly agree). Terms such as likely, acceptable and needed were

    used to assess user intentions.

    3.3 Sampling and data collection

    This study focuses on OGB users in Taiwan. We primarily used online field surveys

    because they have several advantages over traditional paper-based mail-in-surveys

    (Tan & Teo, 2000). Specifically, they are cheaper to conduct, elicit faster responses and

    are geographically unrestricted. Moreover, such surveys have been widely used in

    recent years, and Internet researchers are coming to accept the validity of online research

    (Wright, 2005).

    The online survey yielded 346 usable responses out of 500 online field questionnaires,

    giving a response rate of 70%. Nearly 80% of the respondents were male, and 20% were

    female; 42% were under 25 years of age; 30% were between 26 and 30; 13% were between

    31 and 35, and 14% were over 36. The respondents had a wide variety of occupations, as

    can be seen from the details shown in Table 3.

    Table 2. Operational definitions.

    Constructs Operational definition References

    PEOU The degree to which an individual believes thatusing a particular system would be free fromeffort

    (Davis, 1989; Van derHeijden, 2004)

    PU The degree to which an individual believes thatusing a website system will enhance his or herbehavioural intention

    (Davis, 1989; Van derHeijden, 2004)

    Website quality Information quality, system quality, servicequality

    Trust in VC The trust between users and the online groupbuying VCs

    (Gefen et al., 2003; Wu& Chen, 2005)

    Sense of virtualcommunity(SOVC)

    VC members feelings of belonging and beingimportant to each other and a shared faith thatmembers needs will be met by thecommitment to be together

    (Blanchard, 2007; Koh& Kim, 2003)

    OGB intention The degree to which an individual believes theywill adopt OGB to make a purchase

    (Ajzen & Fishbein,1975)

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  • 4. Data analysis and findings

    4.1 Statistical analyses

    This study tested the proposed model using structural equation modeling (SEM), a power-

    ful second-generation multivariate technique for analysing causal models involving an

    estimation of the following two components: the measurement and the structural

    models (Hair, Black, Babin, Anderson, & Tatham, 2006; Joreskog & Sorbom, 1997;

    Maruyama, 1997). In our study, the Amos 16 software package was used to evaluate

    the measurement and the structural models, with the former tested before the latter. The

    measurement model specifies how the hypothetical constructs are measured in terms of

    the observed variables, while the structural model specifies the causal relationships

    among the latent variables (Anderson & Gerbing, 1988).

    4.2 The measurement model

    The results of the measurement model are presented in Table 4. The data show that internal

    construct reliability, measured by Cronbachs a, ranges from 0.868 to 0.953, whichexceeds the acceptable value of 0.7. The internal consistency of the measurement

    model was assessed by computing the composite reliability (CR). The CR in the present

    study consists of the validity of the latent variables, with higher CR values indicating

    better reliability. According to the suggestion of Fornell and Larcker (1981) and

    Bagozzi and Yi (1988), the CR value should exceed 0.6. Table 4 shows all of the CR

    values to be above 0.7, which is the commonly accepted level for explanatory research.

    Additionally, the convergent validity of the scales was verified by using two criteria

    suggested by Fornell and Larcker (1981): (1) all indicator loadings should be significant

    and exceed 0.7, and (2) the average variance extracted (AVE) for each construct

    should exceed 0.50 (Anderson & Gerbing, 1988). For the current measurement model

    Table 3. Demographic details of the respondents (n = 346).

    Measure Items Frequency Percentage (%)

    Gender Male 274 79.2Female 72 20.8

    Age Under 25 146 42.22630 104 30.13135 46 13.33640 36 10.441 (or above) 14 4.0

    Marriage Single 272 78.6Married 74 21.4

    Education High school 131 37.9College 45 13.0University 128 37.0Graduate school 42 12.1

    Occupation IT related 25 7.2Public servants 21 6.1Banking 18 5.2Service industry 84 24.3Manufacturing 38 11.0Unemployed 42 12.1Others 118 34.1

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  • (Table 4), all loadings are above the 0.7 threshold and AVE ranges from 0.668 to 0.819.

    Hence, both conditions for convergent validity are met.

    Table 5 presents the means and standard deviations of the constructs. It also shows that

    the variances extracted for the constructs are greater than any squared correlation among

    the constructs, implying that the constructs are empirically distinct. In sum, the results

    of the measurement mode test, including convergent and discriminant validity measures,

    are satisfactory.

    Table 5. Correlations among the latent variables.

    Mean SD 1 2 3 5 6 7

    PU 5.05 1.09 0.741PEOU 5.08 1.10 0.681 0.781Trust in VC 4.73 1.06 0.144 0.211 0.668SOVC 4.82 1.09 0.092 0.134 0.336 0.760Website quality 4.96 1.18 0.321 0.471 0.184 0.146 0.819OGB intention 4.98 1.11 0.136 0.163 0.257 0.462 0.154 0.793

    Note: Diagonals represent the AVE, while the other matrix entries represent the square correlations.aVariance extracted: (summation of the square of the factor loadings)/{(summation of the square of the factorloadings)} + (summation of error variances)}. For discriminant validity, diagonal elements should be larger thanthe off-diagonal elements.

    Table 4. Internal reliability and convergent validity test results.

    Latentvariable Item

    Internal reliability Convergent validity

    Item-totalcorrelation

    Cronbachsa

    Factorloadinga

    Compositereliabilityb AVE

    PEOU PE1 0.918 0.931 0.828 0.935 0.781PE2 0.92 0.929PE3 0.868 0.937PE4 0.858 0.836

    PU PU1 0.907 0.934 0.884 0.92 0.741PU2 0.909 0.879PU3 0.895 0.85PU4 0.882 0.828

    Websitequality

    WQ1 0.907 0.953 0.921 0.931 0.819WQ2 0.928 0.935WQ3 0.869 0.857

    Trust in theVC

    TR1 0.732 0.868 0.793 0.889 0.668TR2 0.813 0.73TR3 0.721 0.869TR4 0.786 0.868

    SOVC VC1 0.824 0.91 0.935 0.904 0.76VC2 0.881 0.871VC3 0.759 0.804

    OGBintention

    IN1 0.856 0.933 0.902 0.92 0.793IN2 0.886 0.916IN3 0.844 0.852

    Note: All t-values are significant at P , 0.001.aFactor loadings come from the confirmatory factor analysis.bComposite reliability: (square of the summation of the factor loadings)/{(square of the summation of the factorloadings) + (summation of error variances)}.

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  • 4.3 The structural model

    We examined the structural equation model by testing the hypothesised relationships

    among the various constructs, as shown in Figure 2. The results support the influence of

    PU on OGB intention (b = 0.16, P , 0.01), supporting H1.Consistent with our expectations, the PEOU and website quality were positively

    related to the PU (b = 0.66; P , 0.001, b = 0.22; P , 0.001). The path from PEOUand website quality explains 68% of the observed variance in PU. Therefore, Hypotheses

    H2 and H3 are supported. This reveals OGB intention can be predicted by the proposed

    model.

    The effect of SOVC on OGB intention was also significant (b = 0.48; P , 0.001), sup-porting H4. The hypothesised path from trust in VC is significant in the prediction of OGB

    intentions (b = 0.28, P , 0.001), and SOVC (b = 0.68, P , 0.001), supporting H5 andH6. More specifically, SOVC, trust in VC and PU explain 66% of the variance in OGB

    intention.

    Table 6 shows the SEM analysis has a good fit, as seen from the goodness-of-fit indices

    (GFI = 0.886; AGFI = 0.852; CFI = 0.959; RMSEA = 0.068), and the chi-square index is

    significant (x2 = 459.574; df = 178; x2/df = 2.582). The results indicate that the researchmodel exhibited a satisfactory overall fit to the collected data and was capable of providing

    a reasonable explanation of user acceptance of OGB.

    5. Discussion

    5.1 Technology acceptance variables

    This study examines OGB based on technology acceptance variables that were theoreti-

    cally justified to influence PU and PEOU. Previous research has successfully applied

    TAM in the context of general web-based information systems (Vijayasarathy, 2004).

    This studys findings strongly support the appropriateness of using some technology

    acceptance variables to understand the factors that contribute to OGB intention. Both

    website quality and PEOU were observed to have significant effects on users PU. PU

    in turn significantly was shown to affect users intention to purchase online.

    Therefore, users are most likely to participate in a buying group when they perceive the

    website as useful for OGB. Additionally, members are willing to use a website for online

    group buying if they find that it is easy to use. PEOU also exerted an indirect effect on

    adoption intention via PU, indicating that members tend to rate a shopping website as

    not useful if they find that it is difficult to use. Therefore, for websites to be successful,

    Figure 2. Results of SEM analysis.

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  • online businesses and community providers have to focus on designing both useful and

    easy-to-use websites.

    5.2 VC variables

    OGB intention is primarily positively influenced by SOVC and trust in the VC. In other

    words, attitude towards the VC plays a determinant role in purchase intentions as com-

    pared with technological acceptance factors. Further, trust in the VC is also an antecedent

    of the SOVC, and this, in turn, influences OGB intention. Trust appears to be the important

    determinant of a users SOVC. This highlights the critical role of trust in VC growth. In the

    process of satisfying individuals needs, such as achieving interdependence and building

    relationships, users are likely to perceive members attraction to the VC or that towards

    each other. Trust in the VC will develop SOVC and consequently form positive OGB

    intention.

    Overall, findings from the study suggest the proposed model to be an appropriate

    model to explain individual OGB behavioural intention. The model provides a conceptual

    depiction of what motivates people to use an OGB website with reasonably strong empiri-

    cal support.

    6. Conclusions and suggestions for future research

    This study provides a theoretical understanding of the factors contributing to OGB behav-

    ioural intention. It also offers a compelling theoretical framework for conducting an

    empirical study in this field of research, from which future work may extend to better

    understanding of online group shopping.

    The results of this paper enrich our understanding of factors that encourage and impede

    the purchase intention of adopters of OGB in Taiwan. A key contribution of this is its

    establishment of a theoretical model incorporating an integration of the technology

    Table 6. Overall fit indices of the CFA model.

    Fit index ScoresRecommendedcut-off value Reference

    Absolute fitmeasures

    x2 459.574 Near to degreeof freedom

    d.f. 178 The higher,the better

    GFI 0.886a 0.80 Etezadi-Amoli andFarhoomand (1996)

    RMR 0.077 0.05 Browne and Cudeck (1992)RMSEA 0.068b 0.08AGFI 0.852b 0.9 Ullman and Bentler (2004)

    Incremental fitmeasures

    NFI 0.936a 0.9TLI 0.952a 0.9CFI 0.959a 0.9RFI 0.924b 0.9

    Parsimonious fitmeasures

    PNFI 0.793a .0.5PCFI 0.813a .0.5X2/d.f. 2.582a Between 1 and 3

    Acceptability: a(acceptable), b(marginal).

    1100 M.-T. Tsai et al.

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  • acceptance variables and social factors to investigate the purchase behaviour of OGB. The

    results of this study may help OGB platform managers in Taiwan and other Asian

    countries with cultures similar to Taiwan in creating greater customer satisfaction and

    benefits.

    This study also provides several practical suggestions for OGB behaviour. Prac-

    titioners can apply the findings of this study to focus on the determinants of success for

    their online shopping websites. First, designers should improve the user friendliness of

    OGB systems, making them both easier to use and more accessible. Based on the

    humancomputer interaction perspective, practitioners and designers should note that

    website usability and website quality are the key factors for predicting user intention

    with regard to use of group buying websites. Second, to sustain a successful group

    buying website, attention must be paid to the enhancement of user attitudes towards

    VCs (SOVC and trust in VCs). We recommend that website practitioners build trust

    and feedback mechanisms into their sites to increase the effect of SOVC on users. In

    addition, VCs should focus on bringing people together to interact through chat rooms

    and forums, where they can share personal information and ideas about various OGB

    topics. In our research, SOVC appears to lead to positive outcomes such as increased sat-

    isfaction and communication with the VC as well as to greater trust and social interaction.

    Future research will be able to further develop the theoretical and empirical contributions

    of SOVC in computer-mediation communication research.

    There is a need for further research efforts focused on accumulating empirical data and

    addressing the limitations of the present work. First, since this study only considered

    buying intention with regard to inexpensive items (such as daily supplies and snacks), it

    is unclear whether these analytical results can be generalised to other merchandise.

    Further research can apply this research model to examine expensive items such as con-

    sumer electronics. Second, this study only collects Taiwanese data. Therefore, the

    results might not be generalisable due to the unique characteristics of such organisations.

    In order to generalise the results from this study, we thus need to collect data from a wider

    variety of countries and cultures. Third, it is always possible that some degree of common

    method bias may exist given the nature of perceptual data using a single source of infor-

    mation (Podsakoff & Organ, 1986). To mitigate this problem, additional data should be

    collected from different sources. For example, actual behaviour was measured using

    two items from shopping frequency and quantity. Fourth, OGB is being widely used for

    both business-to-business (B2B) and business-to-consumer (B2C) transactions. We cur-

    rently only survey the B2C OGB markets. Future research should explore B2B markets.

    Finally, OGB is usually coordinated in the VC. Users of OGB are strongly influenced

    by the opinions of other VC members regarding the items they want to buy together. Users

    also obtain a great deal of information from the VC. Thus, social presence or social inter-

    action can be included in our future research model. This phenomenon is probably related

    to social influences, but it has also distinctive effects on attitude. In addition, the major

    purpose of using OGB is to obtain discounts by buying together. Therefore, for future

    models, constructs or items regarding monetary value should be considered.

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  • Appendix 1. Questionnaire

    Questionnaire Items

    Construct Item

    Perceived ease-of-use PE1 Using the OGB service is easy for me.PE2 I find my interaction with the OGB services clear

    and understandable.PE3 It is easy for me to become skillful in the use of the

    OGB services.PE4 Overall, I find the use of the OGB services easy.

    Perceived usefulness PU1 OGB enables me to save money.PU2 OGB makes it easier for me to obtain goods.PU3 I find OGB useful.PU4 Overall, I find OGB to be advantageous.

    Website quality WQ1 Information qualityThe information provided by the website is

    accurate.The website provides me with a complete set of

    information.The information from the website is always up to

    date.WQ2 System quality

    The website operates reliably.The website allows information to be readily

    accessible to me.The website can be adapted to meet a variety of

    needs.WQ3 Service quality

    I feel very confident about the website.The website does not give prompt service

    (reversed).The website has personalized information.

    Trust in VC TR1 I trust OGB website information to be true.TR2 I trust OGB communities forum to be true.TR3 The people who set up the community are

    trustworthy.TR4 I trust the OGB mechanism to be reliable.

    SOVC VC1 MembershipI feel as if I belong to OGB communities.I feel as if OGB members are my close friends.I like the members of my OGB group.

    VC2 InfluenceI am well known as a member of OGB

    communities.My postings on OGB communities are often

    reviewed by other members.Replies to my postings appear on OGB

    communities frequently.VC3 Immersion

    I spend more time than I expected navigating inOGB communities.

    I feel as if I am addicted to OGB communities.OGB intention IN1 I would use OGB for my needs.

    IN2 It is worth participating in OGB.IN3 I will frequently return to OGB site in which I

    participated in the future.

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