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This article was downloaded by: [University of Newcastle (Australia)] On: 06 October 2014, At: 09:50 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Total Quality Management & Business Excellence Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ctqm20 Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors Ming-Tien Tsai a , Nai-Chang Cheng a & Kun-Shiang Chen a b a Department of Business Administration and Institute of International Business , National Cheng Kung University , Tainan, Taiwan b Department of Optometry , Chung Hwa University of Medical Technology , Tainan, Taiwan Published online: 20 Sep 2011. To cite this article: Ming-Tien Tsai , Nai-Chang Cheng & Kun-Shiang Chen (2011) Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors, Total Quality Management & Business Excellence, 22:10, 1091-1104, DOI: 10.1080/14783363.2011.614870 To link to this article: http://dx.doi.org/10.1080/14783363.2011.614870 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

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

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Page 1: Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors

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

Total Quality Management & BusinessExcellencePublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/ctqm20

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

To link to this article: http://dx.doi.org/10.1080/14783363.2011.614870

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Page 2: Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors

Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

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Page 3: Understanding online group buying intention: the roles of sense of virtual community and technology acceptance factors

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 user’s 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 & Francis

http://dx.doi.org/10.1080/14783363.2011.614870

http://www.tandfonline.com

∗Corresponding author. Email: [email protected]

Total Quality Management

Vol. 22, No. 10, October 2011, 1091–1104

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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 object’s perceived efficiency (website quality). Based on the human–computer

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 customer’s

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 person’s be-

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

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

person’s 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

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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’ and‘usefulness’, the studyintroduces ‘playfulness’ as anew factor that reflects theuser’s intrinsic belief in WWWacceptance

The study introduces playfulness asa new factor that reflects theuser’s intrinsic belief in WWWacceptance. Using it as anintrinsic motivation factor, theauthor extends and empiricallyvalidates the TAM for the WWWcontext. The finding may explainthe user’s 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)

Taiwan’s 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 consumer–marketer 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 Cronbach’s a, ranges from 0.868 to 0.953, which

exceeds 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 42.226–30 104 30.131–35 46 13.336–40 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

Cronbach’sa

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 PEOU

and 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 and

H6. 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 research

model 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 study’s 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 user’s 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).

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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

human–computer 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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