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Beyond price: how does trust encourage online group’s buying

intention?Edward C.S. Ku

Department of Travel Management,

National Kaohsiung University of Hospitality and Tourism, Kaohsiung City, Taiwan, R.O.C

AbstractPurpose – The purpose of this paper is to investigate how online trust affects group shopping intention in the Ihergo web site.Design/methodology/approach – Samples from the Ihergo web site were collected by mailing a questionnaire survey to those who agreed to participate.Findings – Group-buying operators need to understand their consumers and the scheduling shopping rules between internet shoppers and firms. Moreover, word-of-mouth (WOM) can be created online by offering web visitors the ability to access the opinions of satisfied customers.Practical implications – An online business may adopt different methods to enhance its customer satisfaction level. When people enter a significant amount of personal data at a web site, they are typically reluctant to change vendors and enter the data again.Social implications – Customers view a group-buying operator as a shopping expert, and expect that the group-buying operator can handle shopping problems before a dispute occurs.Originality/value – The findings of this study provide interesting insights for group-buying operators interested in group-buying commerce; consumers having a high level of interest in shopping possess a strong motivation and desire to interact with the group-buying operator.Keywords Internet, Consumer behaviour, Trust, Electronic commerce, Web sites, Internet marketing, Online shoppingPaper type Research paper

1. IntroductionGroup buying is a widely deployed price-discovery mechanism in a variety of markets and contexts (Chen et al., 2007; Demangeot and Broderick, 2010; Kauffman et al., 2010; Kauffman and Wang, 2001). Customers joined online purchasing together by consolidating similar demands and buying collectively to attain the goal of reducing sales cost; online group buying constitutes the noticeable proportion of the online customer market (Market Intelligence Center, 2010). Previous research found that pricing (Ba et al., 2007; Kauffman and Wang, 2001; Lin et al., 2009) and product characteristics are important factors that influenced the decision of joining a purchasing group (Li et al., 2010; Nollet and Beaulieu, 2005). However, in the study, the authors argued that beyond a price reduction is considered when online group buying is active, there are factors encourage online group’s buying intention from the virtual environment, and they focussed on signaling, which is the act of sending marketing signals from virtual environment to increase consumers’ attention.

Beyond price information, companies operating in the online environment should focus their attention on the trust formation process and its management as well as the creation and management of their relationships with important third parties ( Jiang et al., 2008). In view of the tendency, there is a greater chance for the combination

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Received 20 September 2011Revised 5 March 2012

14 March 2012Accepted 15 March 2012

Internet Research Vol. 22 No. 5,

2012pp. 569-590

r Emerald Group Publishing Limited

1066-2243DOI 10.1108/10662241211271554

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of web site plus physical presence to capture business than the web site – only presence because the former can provide better pre-purchase and post-sales services to lower consumer transaction costs and build trust in web stores. Moreover, many online web sites sell multiple products from multiple vendors because their key attraction is that they provide details of all the services available and thus offer consumers the ability to research and purchase an entire trip online from a single site. Likewise, beyond price information, consumers recognize differences in size and reputation among internet stores, and those differences influenced their assessments of the store trustworthiness and their perception of risk, as well as their willingness to patronize the store (Ganguly et al., 2010). Thus, trust makes consumers comfortable to sharepersonal information (Hsiao et al., 2010), shop online, and act on advice from virtualenvironment.

When joining a purchasing group, customers should consider the potential benefits, the size of the group, the potential impact of the buying group, and member characteristics (Nollet and Beaulieu, 2005). From the trust perspective, previous research analyzed buying group trends and concluded that groups became larger, showed more adaptation to group member preferences, managed more partnership- style types of relationship with suppliers, and have implemented electronic catalogues for their members (Chen et al., 2010; Kauffman et al., 2010; Kauffman and Wang, 2001). The other key differences include the means of obtaining product information (Chang and Chen, 2008; Ganguly et al., 2010; Yeh and Li, 2009), the greater perceived risk, and the ability for consumers to repurchase the same product through the use of a savable personal shopping list. This study aims to elucidate how non-price factors affect online group’s buying intention based on trust perspective.

A customer’s experience with an e-commerce environment extends beyond the interaction with the web site, including delivery of products, post-sales support, and consumption of products and services (Abdul-Muhmin, 2010; Demangeot and Broderick, 2010). From the perspective of trust, online vendors should ensure that they provide adequate utilitarian and hedonic value for customers instead of focussing on just one of these aspects in their web site development (Chen et al., 2007, 2010;Li et al., 2010) that is, trust fully mediates in the relationships between perceivedreputation, perceived capability of order fulfillment, and repurchasing intention and partially mediates in the relationship between perceived web site quality and repurchasing intention. Likewise, trust in the online environment intermediary that provides the overarching institutional context also builds buyer’s trust in the community of sellers.

Virtual communities have a social nature, and it is possible to increase the interactivity with electronic storefronts to attract more recreational shoppers (Chiu et al., 2006) and to serve as reference groups that can influence their members’ shopping preferences (Li et al., 2010). Recommendation sources can be used and promoted by three different types of web site: sellers, commercially linked third parties, and non-commercially linked third parties ( Jiang et al., 2008; Hsiao et al., 2010; Novak et al., 2000). The provision of more alternatives to choose from and more objective information should result in independent web sites being perceived as more useful by consumers.

One example is the Ihergo web site (www.ihergo.com.tw) centered on an online group-buying community in Taiwan; it is updated daily and has two major themes: upcoming multiple products and their highlighted attributes. The web site allows group-buying operators introduce the process of purchasing products and obtain

information from fellow customers. Moreover, customers receive tips from Ihergo on how best to purchase together from the web site as cheaply as possible.

Moreover, the group-buying operator announces, and commits to, the pricing scheme and a closing date for the sale in virtual shopping environment (Abdul-Muhmin, 2010; Chen et al., 2007; Kauffman and Wang, 2001). Likewise, the group-buying operator’s status is updated dynamically and displayed on the Ihergo web site. In addition, customers can buy online together and air their views in popular forums, and customers with different valuations for the product can follow the sales orbit and invest at an opportune moment. Furthermore, all customers get the product at the same clearing price.

The research goal of this study was to investigate how online trust affects shopping intention in the Ihergo web site (www.ihergo.com.tw/). In this study, a group online-buying model from the perspective of trust was formulated, and group online-buying samples from the Ihergo web site were collected by mailing a questionnaire survey to those who agreed to participate. The model and hypotheses were tested using structural equation modeling. This report first describes the motivation for this study. Section 2 describes the theoretical background, followed by a review of previous research in Section 3. The research design is then presented in Section 4, and finally, the research findings and conclusions are reported in Sections 5 and 6, respectively.

2. Theoretical background and literature reviewAs online buying became an increasingly common social phenomenon, researchers started to explore the reasons behind this trend and ways to utilize this electronic channel more effectively for commercial purposes. Web-based shopping removes many geographic barriers between consumers and merchants, allowing new distant merchant-consumer relationships to emerge.

2.1 TrustThis conceptualization of trust, which also is known as trust intentions (Lee et al., 2007) and trustworthiness (Benedicktus et al., 2010; Edelman, 2011; Hamer, 2011; Mun˜ oz-Leiva et al., 2010), is based on a set of beliefs that others upon whom one depends will behave in a socially acceptable manner (Constantinides et al., 2010) by showing appropriate integrity, benevolence, and ability.

A number of trust antecedents have been identified: knowledge-based trust (Walczuch and Lundgren, 2004), institution-based trust (Datta and Chatterjee, 2008; Lander et al., 2004), calculative-based trust (Suh and Kwon, 2006), cognition-based trust (Chua et al., 2008; Parayitam and Dooley, 2009), and personality-based trust (Walczuch and Lundgren, 2004). Knowledge-based trust occurs as a result of a history of interaction. Institution-based trust is a buyer’s perception that effective third-party institutional mechanisms are in place to facilitate transaction process, calculative- based trust is based on perceived economic outcomes and describes the kind of trustthat develops as a result of first impressions and cues from the environment, and personality-based trust describes trust tendencies that are developed during childhood.

Trust is a prerequisite of social behavior, especially regarding important decisions (Edelman, 2011). The customer-related determinants of trust include familiarity with the web site, online shopping experience, and entertainment experience (Abdul-Muhmin, 2010). Trust makes consumers comfortable sharing personal

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information, making purchases, and acting on web vendor advice – behaviors essential to widespread adoption of e-commerce (Lee et al., 2007). Likewise, the web site-related determinants of trust include privacy, security, navigation, presentation, order fulfillment, and others (Serrano-Cinca et al., 2010; Jeong et al., 2009). Consumers recognized differences in size and reputation among internet stores, and those differences influenced their assessments of store trustworthiness and their perception of risk, as well as their willingness to patronize the store (Holzwarth et al., 2006; Kwon and Chung, 2010; Forsythe and Shi, 2003). The online vendor-related determinants of trust include firm size and reputation and offline presence (Tan, 1999). Customers have limited information and cognitive resources available and thus seek to reduce the uncertainty and complexity of online transactions by applying mental shortcuts (Clemons and Gao, 2008; Gan et al., 2007; Novak et al., 2000). Trust is one of the main determinants of the success of e-retailers, and much research has dealt with web site features triggering consumer trust.

2.2 Reasons for shopping in a virtual environmentThe internet has significantly increased the bargaining power of consumers. Customers buy online for the goal of reducing sales cost (Chan and Li, 2010; Chen et al., 2009, 2010). Nevertheless, perceived risk is an important factor that affects consumers’ acceptance of business-to-commerce e-commerce (Chiu et al., 2006; Yen, 2010); online firms should try to identify sources of consumers’ perceived risk in addition to consequences.

A virtual community is a community of people with a common interest or shared purpose whose interactions are governed by policies in the form of tacit assumptions, rituals, protocols, rules, and laws and who use computer systems to support and mediate social interactions and to facilitate a sense of togetherness (Chiu et al., 2006; Hsiao et al., 2010). Because a virtual community is a social network that uses computer support, rather than face-to-face interaction, for its communication (Hsiao et al., 2010; Wu and Tsang, 2007), some virtual communities exist purely in cyberspace.

In the virtual environment, the attributes of virtual communities that make them similar to real-life groups include shared interests or goals; sustained social interaction; and shared values, membership rules, or norms (Talukder and Yeow, 2007). From the trust perspective, social networking refers to sites, such as Ihergo web site, where users set up a profile, create formal connections to people they know, communicate, and share preferences and interests about products.

In addition, price and non-price factors, such as e-service quality and brand recognition, contribute to the decision of making an online purchase (Kim et al., 2008; Kwon and Lennon, 2009; Shang et al., 2006); however, online retailer reputation can influence their pricing strategy and thus provide an explanation for online price dispersion (Bigne´-Alcan˜ iz et al., 2008; Karakaya and Barnes, 2010; Lin et al., 2009; Oh and Lucas, 2006).

Participation in the activities carried out in a virtual community is one of the most important factors for the development and sustainability of the virtual community. From the perspectives of balanced beliefs and emotion, commitment to virtual communities is an important factor for online firms (Kauffman and Wang, 2001; Lin, 2008); thus, a firm or individual credibly communicates the level of some unobservable element in a transaction by providing an observable signal. For some categories, the brand name is more important online than in a traditional shopping environment; however, this might depend on the available attribute information.

Service standards

communicatio

Online brand image (OBI)

Word of mouth (WOM)

H4

H3

H5

Furthermore, shopping value is particularly important in the e-commerce. Online competition is not usually based on product quality; to avoid excessive price competition, some other differentiation strategies, such as offering better shopping value through customization, are used (Demangeot and Broderick, 2010; Novak et al., 2000). Many online shopping search engines allow consumers to find most retailers that sell a specific product, compare product prices, and review detailed store ratings (Parra and Ruiz, 2009; Yang and Lai, 2006). Also, group-buying operators seek to aggregate buyers via the web by providing them price-based incentives for bulk purchases.

Although a traditional virtual community attracts a large number of people who become committed to the virtual community, online firms and vendors have been found to fail to instill significant commitment among their customers through a virtual community (Clemons and Gao, 2008). Commitment to a virtual community is characterized by the member’s helping behavior and active participation in the activities of the virtual community, based on a strong psychological attachment to it.

3. The research modelFrom the perspective of trust, the group shopping intention, based on the service standards communication of a web site and its word of mouth (WOM) in the virtual environment, resulted in the development of a research model of this study as depicted in Figure 1.

3.1 Service standards communicationThe service standards communication is valued as the degree to which the organization measures, controls, and communicates the standards of service quality (Gonza´lez and Garazo, 2006; Lytle and Timmerman, 2006). The effective functioning of the service system requires service standards or benchmarks that are understood by all members of the organization (Ku et al., 2011). Conformity to a set of standards is more likely if those standards are understood by every employee in the organization (Lin et al., 2006). These standards, when communicated to all employees, maximize internal benchmark achievement and minimize service failures. They also strengthen the firm’s ability to recover from such failures.

Customers rate delivery pricing guides, delivery guarantees, and delivery schedules as the most important delivery information they expect online prior to purchase (Maleyeff, 2006). Virtual community has to exhibit a good knowledge transfer on markets, customers, products and services, and methods and processes (Balasubramanian et al., 2003; Fan and Ku, 2010). Moreover, a group-buying operator that interplays between the members’ strengths and resources drives the type

H1

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H2Figure 1.

Research modelExpertise of

sender

Group online buying

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of partnership that the members use to enter the online market (Clemons and Gao, 2008; Kauffman et al., 2010). The decision regarding who controls the management and marketing largely depends on whether the expanding members can rely on interests to maintain the operators’ customer service standards.

From a trust perspective, the training and communication processes in implementing group-buying integration solutions, securing members’ commitment, and building a customer-centric culture between group members are important (Constantinides et al., 2010). The value of a strategic adaptation will improve the group’s communication and raise service standards. That is, when group-buying operators expand continuously, they must analyze the ownership strategy and the management strategy that will best maintain the firm’s competitive advantage.

3.2 Online brand image of a productBrand image, as overall perceptions of a brand, reflects all brand associations in the consumer’s mind (Kwon and Lennon, 2009). Consumers use brands as an important tool for organizing information and simplifying their decision making in both cyber and traditional marketplaces (Bruwer and Johnson, 2010). In brand extension contexts, consumers’ perceptions of the parent brand influence their attitudes toward a brand extension (Rui Vinhas Da and Sharifah Faridah Syed, 2008), and their evaluation of the brand extension performance also has a spillover effect on the parent brand image (Da Silva and Syed Alwi, 2008; Delgado-Ballester and Herna´ndez- Espallardo, 2008).

Previous studies on online shopping are focussed on the effects of following the action by positive or negative brand image of a product (Bruwer and Johnson, 2010; Kwon and Lennon, 2009). The power of brand image communication and its influence on consumer decision making suggest that strong tie referral sources are more influential than weak tie information sources on decision making. The following hypothesis is thus proposed:

H1. Service standard communication during online group-buying procedure in a virtual community is positively associated with the online brand image of a product.

3.3 Expertise of senderThe expertise of sender can be defined as the extent to which the source is perceived as being capable of providing correct information from the sender (Bansal and Voyer, 2000). If the sender’s expertise is high, the receiver, in attempting to attain information via WOM, will more actively seek the information from a sender who is perceived as having a high level of expertise (Karakaya and Barnes, 2010; Lin et al., 2005), that is, the senders’ level of expertise or knowledge in recommending the internet should be considered as a key influential variable in determining online-buying behaviors.

Virtual communities, or groups of people informally bound together by shared expertise, synthesize this internet evolution and the Web 2.0 technology. Users increasingly want to engage online with one another and with all kinds of organizations (Breitsohl et al., 2010; Hoffman et al., 2004). Virtual communities can affect consumer buying behavior because they also can be considered social groups (Koo, 2006); member expertise plays an important role between conformity in virtual communities and online compulsive buying tendencies. Perceived expertise is one of the interpersonal attraction factors in the loyalty of virtual communities

(Ganguly et al., 2010), and interpersonal interaction factors also provide insights into the design and branding strategies of social media.

In an online-buying environment, credibility refers to the believability of information and is an important consideration in internet shopping (Hamer, 2011; Herrero Crespo and Rodriguez del Bosque, 2010). Consumers’ internet expertise can play on the formation of both their affective and buying-related responses toward this medium (Clemons and Gao, 2008). Online customers with different levels of purchaser expertise also showed differing preference structures.

3.4WOM on virtual communitiesWOM is similar to personal selling in that it provides explicit information, tailored solutions, interactivity, and empathetic listening, but it has a shorter “distance” between the source of communication and the receiver, compared with marketer- generated information (Hsiao et al., 2010). Along with traditional marketing, WOM can then be linked to the number of new members subsequently joining the virtual community (Chen et al., 2009; Duan et al., 2008), that is, WOM is the most important source of influence in the purchase of household goods (Park and Kim, 2008), and advice from other consumers about a service exerts a greater influence than all marketer-generated information combined.

WOM communication is an important source of consumer information. It forms the basis of interpersonal communications and significantly influences product evaluations and purchase decisions (Duan et al., 2008). Previous research emphasized that WOM communications were found to have a strong impact on product judgments (Cheung et al., 2008; Tuzovic, 2010) and also underlined the importance of service differentiation in achieving high levels of relationship commitment and ultimate satisfaction and positive WOM. This leads to the hypothesis:

H2. Expertise of online group-shopping originator in a virtual community is positively associated with the WOM from virtual environment.

WOM communication has been recognized as a powerful marketing communication medium and a credible information-gathering tool; however, the WOM information direction and a web site’s reputation contribute to the WOM effect (Libai et al., 2010). Undoubtedly, consumers with high need for uniqueness are less willing to generate positive WOM for publicly consumed products that they own, and adoption of online communication by many consumers has facilitated a fundamental change to the structure of many WOM interactions by exposing consumers to electronic WOM from virtual communities.

Moreover, online advertising greatly involves recommendations to buy or try a brand when compared with other WOM discussions about brands (Gupta and

Harris, 2010; Hennig-Thurau and Walsh, 2003), and brands should redouble their efforts in using advertising to grow brand advocacy through the integration of online branded consumer contact points (Duan et al., 2008; Godes and Mayzlin, 2009).

Thus, marketers have trumpeted the importance of WOM in influencing purchase choice. Customers, in general, and the adventuresome, risk averse, inexperienced,

and disinclined decision makers, in particular, may employ a common external search strategy, conferring with a perceived expert on the web site when they make a

decision. This leads to the hypothesis:

H3. Online brand image is positively associated with the WOM in virtual environment.

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3.5 Group online-buying intentionsIn the context of e-commerce, online shopping intention is a major consequence of pre- purchase satisfaction (Hausman and Siekpe, 2009; Su and Huang, 2011; Sunil et al., 2006). Online shopping intention, an important predictor of actual buying behavior, refers to an outcome of criteria assessment of consumers regarding web site quality, information search, and product evaluation (Abdul-Muhmin, 2010; Yang and Lai, 2006). Online purchase intention reflects the desire of consumers to make a purchase through the web site. Previous research has explored the driving forces of online purchasing intention. Searching and purchasing within one channel (e.g. the internet) may be perceived as less costly than searching and purchasing in multiple channels (Demangeot and Broderick, 2010). Empirical research studies also supported that consumers were likely to search more information from the internet when purchasing products online.

Users of the internet behave with motives that differ from those associated with other existing technologies in that an information medium (i.e. the internet) involves the advent of new technology (Kim and Forsythe, 2009), quality of WOM, online trust, perceived usefulness, perceived ease of use, and online-buying intention (Bhatnagar and Ghose, 2004). Moreover, consumer opinions about customer care in web sites greatly affect consumer opinions and consumer choice of brand or company when making purchases. Therefore, we hypothesize that:

H4. Online brand image is positively associated with online-buying intentions.

Customers with an enduring involvement with shopping may receive hedonic pleasure directly from the time spent exploring the virtual shopping environment. Accordingly, customers who have reason to surf a virtual community (information seeking, entertainment, or socialization) are more motivated in that task (Nollet and Beaulieu, 2005). In fact, they develop enduring involvement and, by extension, site involvement. This is a motivational state that is influenced by their perception of the virtual community based on their needs, values, and interests. This state predicts behaviors, such as information search.

Customer online excitement leads to positive WOM and increases the intent to return. That is, the greater source expertise and trustworthiness, a more positive attitude toward the brand, and higher satisfaction with the retailer will influence greater online-buying intentions. This leads to the hypothesis:

H5. WOM from online group shopping environment is positively associated with the online-buying intentions.

4. Research methodology4.1 Data collection and sample characteristicsThe research goal of this study was to investigate how trust theory affects the online- buying intentions in the Ihergo web site of Taiwan. Empirical data were collected by conducting a field survey among the members of the above-mentioned web site. A survey program was developed to handle the data collection process using the My3q web site (www.my3q.com). The questionnaire is linked to an invitation message on the Ihergo web site. The message stated the purpose of this study and provided a hyperlink to the survey form. Subjects were selected by placing messages on more than five recommended members in the Ihergo web site.

In total, 2000 invitation messages were mailed to members of Ihergo web site, of which, 472 were returned (a return rate of 24.1 percent). The characteristics of the sample (37 percent male and 63 percent female) are described in Table I.

4.2 MeasuresWe first conducted literature reviews on related topics to examine the external validity of our research model. We then developed the questionnaire items based on the literature. The measures used to operationalize the constructs in the research model were mainly adopted from some of the related studies conducted in the past, with minor wording changes tailored to the interviewees. This resulted in the identification of 25 potential research items. These scales are summarized in Table II with their related literature. The different opinions are indicated by the following: 1, strongly disagree; 2, disagree to some extent; 3, uncertain; 4, agree to some extent; and 5, strongly agree. The constructs of the study were measured with a multi-item scale, as indicated in Table V.

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Table I.Sample description

Samples n %

GenderMale 179 37.9Female 293 62.1Age (years)o20 9 1.921-30 211 44.731-40 188 39.8441Education

64 13.6

Junior high school 6 1.3Senior high school 91 19.3University/college 349 73.9Graduate school 26 5.5Using internet hours per day (hours)o1 11 2.31-3 150 31.83-5 131 27.85-7 69 14.647Salary per month (NT$)

111 23.5

o5,000 65 13.85,001-15,000 37 7.815,001-25,000 101 21.425,001-35,000 214 45.3435,000Total amount of group online shopping per month

55 11.7

o200 107 22.7201-500 285 60.4501-800 43 9.1801-1,100 16 3.441,100 21 4.4

Note: n ¼ 472

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Table II.Scale development

(continued)

Factor Item Reference

Service communicatio(SSC)SSC1 The group-buying operators understood various firms’ Lytle and

service standard being sold in the Ihergo web site Timmerman (2006)SSC2 The group-buying operator can handle shopping

problem before a shopping dispute occursSSC3 The group-buying operator schedules the clear

purchasing rules to increase his/her shopping membersin the Ihergo web site

SSC4 The group-buying operator sets the public procedure toall the purchasing groups

Expertise ofsender (EOS)EOS1 The group-buying operator understands the group Bansal and Voyer

purchase process on the Ihergo web site (2000)EOS2 The group-buying operator has excellent

communication skills on the Ihergo web siteEOS3 I think the group-buying operator is an expert on the

Ihergo web siteEOS4 The group-buying operator is a veteran shopperOnline brandimage (OBI)OBI1 I feel that a company branded product fulfills its Yeh and Li (2009)

practical functionOBI2 I feel that a company branded product possesses a

positive symbolic meaningOBI3 I feel that a company branded product is associated with

pleasant experiencesOBI4 In general, my opinion about a product’s brand is goodWord of mouth(WOM)WOM1 Recommendations about shopping online are useful Cheung et al.

shopping information to meWOM2 Recommendations about shopping online will affect my

choice when I shop onlineWOM3 Recommendations about shopping online will provide

me with different advisory opinionWOM4 Recommendations about shopping online will change

my purchasing motivationWOM5 Recommendations about shopping online will increase

my interest to search for this productWOM6 Recommendations about shopping online will change

my purchasing intentionWOM7 I will make purchase decision by the recommendations

from virtual environmentWOM8 Recommendations about shopping online will change

the items I intend to purchaseGroup onlinebuying (GOBI)

Factor Item Reference Online group’s buying intention

GOBI1 I am willing to select the Ihergo web site as a shopping web site in the future

GOBI2 I am willing to visit the Ihergo web site to purchase products in the future

GOBI3 I am willing to use the Ihergo web site for sharing information in the future

GOBI4 I am willing to select the Ihergo web site as a channel for

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buying products in the future Table II.

4.2.1 Service standards communication. Conformity to a set of standards is more likely if those standards are understood by every employee in the organization (Berry et al., 2002; Clow et al., 2006; Parasuraman et al., 1994). The measure used in the present study was developed by modifying the scale of Lytle and Timmerman (2006) to suit the group online-buying experience of visiting Ihergo web site and consisted of four items.

4.2.2 Online brand image of product. Consumers use brands as an important tool for organizing information and simplifying their decision making when they shop online. The measure used here was modified from the scale of the online brand image (Yeh andLi, 2009) to suit the online brand image of product when visiting the Ihergo web site and consisted of four items.

4.2.3 Expertise of sender. Form the receiver’s perspective, the expertise of originator in a virtual community can be viewed in terms of a high degree of expertise. Theauthors argued that a buyer’s perception of shopping in a virtual community also may be affected by the influence of online group-buying operator. The measure used here was modified from the scale of Bansal and Voyer (2000) to suit the expertise of group-buying operator context of visiting Ihergo web site and consisted of four items.

4.2.4 WOM on virtual communities. Customers can search online WOM for products and information in the group online-buying web site; the measure used here was modified from the scale of the WOM (Cheung et al., 2008) to suit the WOM of visiting Ihergo web site and consisted of eight items.

4.2.5 Group online-buying intentions. Group online-buying intention, an importantpredictor of actual buying behavior, refers to an outcome of criteria assessment of consumers regarding web site quality, information search, and product evaluation, with the construct adapted from and Hausman and Siekpe (2009) to suit the shopping intentions of visiting Ihergo web site and consisted of four items.

5. Results5.1 Tests of the measuring scalesInternal consistency reliability is the accuracy or precision of a measuring instrument, which is the extent of unidimensionality, that is, the detailed items (questions) measure the same thing. The internal consistency reliability was assessed by calculatingCronbach’s a values. The reliability results of the constructs are summarized in Table III. The internal consistency (Cronbach’s a) of the construct is 40.9, which isabove the acceptable threshold.

Content validity means measuring what is supposed to be measured. In other words, if we aim at a good measure of visiting Ihergo web site, we should be convinced

INTR 22,5 Item Mean STD

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Table III.Reliability

The group-buying operators understood various firms’ servicestandard being sold in the Ihergo web site 3.76 0.827 0.854The group-buying operator can handle shopping problem before ashopping dispute occurs 4.01 0.745 0.823The group-buying operator schedules the clear purchasing rules toincrease his/her shopping members in the Ihergo web site 4.07 0.657 0.830The group-buying operator sets the public procedure to all thepurchasing groups 4.06 0.726 0.843The group-buying operator understands the group purchaseprocess on the Ihergo web site 4.03 0.737 0.846The group-buying operator has excellent communication skills onthe Ihergo web site 3.76 0.800 0.829I think the group-buying operator is an expert on the Ihergo website 3.70 0.831 0.832The group-buying operator is a veteran shopper 3.96 0.753 0.828I feel that a company branded product fulfills its practical function 4.19 0.650 0.884I feel that a company branded product possesses a positivesymbolic meaning 3.91 0.681 0.866I feel that a company branded product is associated with pleasantexperiences 3.68 0.84 0.943In general, my opinion about a product’s brand is good 4.03 0.74 0.924Recommendations about shopping online are useful shoppinginformation to me 4.28 0.613 0.876Recommendations about shopping online will affect my choicewhen I shop online 4.30 0.608 0.872Recommendations about shopping online will provide me withdifferent advisory opinion 4.29 0.547 0.873Recommendations about shopping online will change mypurchasing motivation 4.27 0.597 0.872Recommendations about shopping online will increase my interestto search for this product 4.26 0.616 0.871Recommendations about shopping online will change mypurchasing intention 4.32 0.567 0.872I will make purchase decision by the recommendations from virtualenvironment 4.24 0.605 0.874Recommendations about shopping online will change the items Iintend to purchase 4.27 0.584 0.871I am willing to select the Ihergo web site as a shopping web site inthe future 4.19 0.650 0.884I am willing to visit the Ihergo web site to purchase products in thefuture 3.91 0.681 0.866I am willing to use the Ihergo web site for sharing information inthe future 4.03 0.583 0.881I am willing to select the Ihergo web site as a channel for buyingproducts in the future 4.11 0.576 0.888

that the measurement instrument includes the essential features of success (Saarinen, 1996). Construct validity is established by relating a measuring instrument to a general theoretical framework to investigate whether the instrument is tied to the concepts and theoretical assumption being used. This can be analyzed, first, by correlating with the

detailed items and scale. However, a more powerful method for analyzing the construct validity is factor analysis.

Because each latent construct was measured by the multiple items, tests of construct validity were performed. To obtain evidence of the construct validity of an instrument, a researcher must make use of both convergent validity and discriminant validity. Discriminant validity was checked using factor analysis. Because multiple item constructs measure each variable, factor analysis with varimax was used to check unidimensionality among the items. The confirmatory factor analysis shown in Table IV was used with LISREL 8.50 software to examine the convergent validity of each construct. The range for factor loadings was 0.50-0.73. Table IV shows the results of factor analysis.

5.2 Measurement modelThis study assessed construct reliability by calculating composite reliability that assesses whether the specified indicators are sufficient in their representation of their respective latent factors, as suggested by Segars. These estimates of composite reliability of latent factors range from 0.70 to 0.87, which are all well above the threshold of 0.70, as suggested by Jo¨reskog and So¨rbom; thus, acceptable construct reliability is implied (as shown in Table V). However, composite reliability cannot reflect the amount of variance that is captured by the construct in relation to the amount of variance due to measurement error. Thus, average variance extracted (AVE) estimate was used to acquire this information.

AVE estimate of 0.50 or higher indicates acceptable validity for a construct’s measure. As shown in Table V, all AVE estimates are well above the cutoff value, thus suggesting that all measurement scales have convergent validity. To assess discriminant validity among the constructs, this study calculated the square root of AVE for each construct and compared the resulting value with interconstruct correlations for each pair of constructs. Results also show that the square root of all AVE estimates for each construct is greater than interconstruct correlations; thus, discriminant validity is supported.

5.3Test of the structural modelWe used the LISREL 8.50 software for this analysis. Structural equation modeling was performed to test the hypothesized model presented in Figure 1. The overall goodness- of-fit was assessed in terms of the following eight common model fit measures: GFI, 0.90; AGFI, 0.87; RMR, 0.05; RMSEA, 0.059; NFI, 0.91; CFI, 0.94, PNFI, 0.76; and PGFI,0.68. Thus, overall, the data indicate a favorable fit for our hypothesized model. The direct model shows an acceptable fit except w2 and CFI, but the full model seemed to besuperior to the direct model in explaining online group-buying intention. As presented in Table VI, the results of this hypothesized full virtual community participation model indicate a favorable fit of the model.

The significance and the relative strength of individual links specified by the research model also were evaluated. The results provide meaningful support for research hypotheses. Among these hypotheses, five are fully supported.

As our analysis, service standard communication during online-buying procedure in a virtual community is positively associated with the online brand image of the product. According to Lytle and Timmerman (2006), service standards communication has an important role related to online brand image of a product when customers consider joining a virtual shopping community. That is, firms develop enduring

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Constructs variables SSC EOS OBI WOM GOBI

The group-buying operators understood various firms’ service standard being sold in the Ihergoweb site 0.73The group-buying operator can handle shopping problem before a shopping dispute occurs 0.67The group-buying operator schedules the clear purchasing rules to increase his/her shoppingmembers in the Ihergo web site 0.64The group-buying operator sets the public procedure to all the purchasing groups 0.65The group-buying operator understands the group purchase process on the Ihergo web site 0.68The group-buying operator has excellent communication skills on the Ihergo web site 0.65I think the group-buying operator is an expert on the Ihergo web site 0.58The group-buying operator is a veteran shopper 0.52I feel that a company branded product fulfills its practical function 0.71I feel that a company branded product possesses a positive symbolic meaning 0.56I feel that a company branded product is associated with pleasant experiences 0.53In general, my opinion about a product’s brand is good 0.54Recommendations about shopping online are useful shopping information to me 0.72Recommendations about shopping online will affect my choice when I shop online 0.61Recommendations about shopping online will provide me with different advisory opinion 0.66Recommendations about shopping online will change my purchasing motivation 0.59Recommendations about shopping online will increase my interest to search for this product 0.52Recommendations about shopping online will change my purchasing intention 0.52I will make purchase decision by the recommendations from virtual environment 0.51Recommendations about shopping online will change the items I intend to purchase 0.51I am willing to select the Ihergo web site as a shopping web site in the future 0.73I am willing to visit the Ihergo web site to purchase products in the future 0.51I am willing to use the Ihergo web site for sharing information in the future 0.54I am willing to select the Ihergo web site as a channel for buying products in the future 0.50

INT

R 5Table IV.Confi

involvement, and by extension of the Ihergo web site involvement, a motivational state influenced by their standard of the virtual community based on the firm and group- buying operator’s communication.

In H2, expertise of online group-buying originator in a virtual community is positively associated with the WOM from virtual environment. Customers can search online products and information in popular forums; thus, expertise of theInhergo community will have an impact on WOM regarding products in virtual community.

In H3, online brand image is positively associated with the WOM in the virtualenvironment. The Ihergo community can provide real products and information that increase group-buying intention; thus, online brand image of products will have an impact on WOM when visiting the virtual community.

In H4, online brand image of a product is positively associated with the online-buying intentions. Consumers’ evaluation of the brand extension performance also has a spillover effect on the parent brand image (Kwon and Lennon, 2009). In this study, online brand image of products significantly influences the intention of joining the Ihergo web site.

In H5, WOM from online group shopping environment is positively associated with online shopping intentions. As Karakaya and Barnes (2010) discussed, the communityof Ihergo, which serves as a reference group, could exert a key influence on customers’ attitudes and choices.

6. Conclusions and implicationsAs virtual community becomes more important as a criterion for attracting and retaining customers, many firms are increasingly focussing on online-buying environment to increase their performance. This study has focussed on trust and its correlation with online shoppers and discussed the expertise of group-buying operators as another important factor for consideration.

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Mean SD SSC EOS OBI WOM GOBI AVE

SSC 3.98 0.74 0.81 0.66EOS 3.86 0.78 0.56 0.72 0.52OBI 3.95 0.73 0.56 0.57 0.75 0.56 Table

V.EWOM 4.28 0.59 0.50 0.33 0.32 0.80 0.64 MeasurementGOBI 4.06 0.62 0.27 0.32 0.23 0.59 0.81 0.66 model estimation

Hypothesis t-valueStandard

coefficients Results

Note: *p

o0.05

H1 Service standards communication4online brand image 9.52* 0.78 SupportedH2 Expertise of sender4word of mouth 3.38* 0.35 SupportedH3 Online brand image4word of mouth 8.68* 0.69 SupportedH4 Online brand image4group online buying intentions 5.54* 0.52 SupportedH5 Word of mouth4group online buying intentions 5.29* 0.51 Supported

Table VI.

Hypothesis and results

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6.1 Implication for researchThis study utilized a reliable multidimensional measure of factors that influence online group shopping intention. The results of the analysis of the measurement model indicated that the proposed metrics have an acceptable degree of validity and reliability. Overall, the results of the study provided reliable instruments for operationalizing the key effect constructs in the analysis of online group shopping decision making. From a theoretical perspective, our finding factors encourage online group’s buying intention in virtual environment; expertise of group-buying operator can contribute to online group’s buying to some extent, the group-buying operator’ level of expertise or knowledge in recommending the internet should be considered as a key influential variable in determining online-buying behaviors.

Our findings suggest that facets of trust are helpful in explaining online group’s buying intention in virtual environment. Compared prior research argues that group buying is a widely deployed price-discovery mechanism in a variety of markets and contexts (Demangeot and Broderick, 2010; Kauffman et al., 2010; Kauffman and Wang, 2001), our findings explore what factors influence the facets of trust in online group’s buying setting. The results imply that consumers’ evaluation of the non-price factors also has a spillover effect on their buying behavior. Thus, another direction for future research is to examine how credibility of a web or an online store is useful in motivating a customer to purchase repeatedly.

6.2 Implications for practiceIn the virtual environment, the attributes of virtual communities that make them similar to real-life groups include shared interests or goals. Our use of a structural equation model to test a theoretical model of online group’s buying could lead to a greater understanding of the nature and determinants of trust across different stages related to group’s buying analysis.

First, the findings of this study provide interesting insights for group-buying operators interested in group-buying commerce. Group-buying operators need to understand their consumers and the scheduling shopping rules between internet shoppers and firms. Convenience was one of the top reasons to shop online (Abdul-Muhmin, 2010). That is, online shoppers enjoy multiple forms of convenience, which include less shopping time, flexibility with regard to when they shop, less physical effort, and easier response to advertisement or promotions. Thus, group- buying operators will understand various firms’ service standard being offered on the Ihergo web site and focus on making the online purchasing process easier to encourage repeat purchases among internet shoppers.

For example, in maintaining a good relationship and clear communication between group-buying operators and their customers among Ihergo web site, the group-buying operators in online-buying environment should make, accept, and keep promises by providing full purchasing information about the products, delivering the right product to the customers, and offering good post-purchase service, which includes a clear return policy.

Second, our findings also predict that the expertise of group-buying operator will positively influence the WOM on virtual shopping environment. Consumers having a high level of interest in shopping possess a strong motivation and desire to interact with the group-buying operator. Likewise, customers view the group-buying operator as a shopping expert and expect that the group-buying operator can handle shopping problems before a shopping dispute occurs.

Expertise plays a role of knowledge contributor between conformity in virtual communities and online compulsive buying tendencies. For example, group-buying operators provide diverse means of viewing, and customizing several features of a product, and through the Ihergo web site, customers are likely to feel more satisfied with their experiences. Likewise, customers’ interest in the expertise of group-buying operator is brought about by intrigue, fascination, curiosity, and excitement with the group-buying operator. Additionally, these positive characteristics, combined with the strong motivational tendencies and heightened involvement with the group-buying operator previously described, are likely to generate positive WOM from customers.

Third, the finding of the study suggests that high brand recognition will lower the risk to the consumer when purchasing on the Ihergo web site. Brand consciousness is a shopping orientation, which is characterized by the degree to which a consumer is oriented toward buying well-known branded products (Delgado-Ballester and Herna´ndez-Espallardo, 2008). As our result, customers will feel more reassured purchasing online if they are buying from an entity with a name they know and trust. From the trust perspective, WOM can be created online by offering web visitors the ability to access the opinions of satisfied customers.

For example, a firm also can create a simple depiction, in advertisements, of consumers discussing brands or seeking information from group-buying operators as a strategy of WOM. Moreover, interpersonal influence not only flows from the opinion of group-buying operators to shoppers but also spreads as a result of relationships among shoppers, that is, group-buying operator promotion techniques that allow consumers to talk about brands are important on virtual shopping environment.

Finally, the result suggests that trust plays an important role in increasing buying intention among virtual environment. Consumers’ commitment to online businesses is difficult to develop and is not as strong as commitment in other contexts. This suggests that it is not practical for online businesses to expect persistent and long-term relationship orientations from online consumers. However, various incentive mechanisms other than those focussed on relationship building and maintenance may need to be initiated. An online business may adopt different methods to enhance its customer satisfaction level. For example, a company may use the Ihergo web site as its official web site to retain or “locks” a consumer by keeping personal data. When people enter a significant amount of personal data at a web site, they are typically reluctant to change vendors and enter the data again.

6.3 LimitationsThe first limitation of this study is that the products’ scopes of operation were not compared; perhaps different products have different signals and online strategies. Second, the customers included in the study were not selected according to their age and gender that may classify them into different categories. This may have different effects because group-buying intention may vary between different products. These limitations should be addressed in future studies.

ReferencesAbdul-Muhmin, A.G. (2010), “Repeat purchase intentions in online shopping: the role of

satisfaction, attitude, and online retailers’ performance”, Journal of International Consumer Marketing, Vol. 23 No. 1, pp. 5-20.

Online group’s buying

intention

585

INTR 22,5

586

Ba, S., Stallaert, J. and Zhang, Z. (2007), “Price competition in e-tailing under service and recognition differentiation”, Electronic Commerce Research and Applications, Vol. 6 No. 3, pp. 322-31.

Balasubramanian, S., Konana, P. and Menon, N.M. (2003), “Customer satisfaction in virtual environments: a study of online investing”, Management Science, Vol. 49 No. 7, pp. 871-89.

Bansal, H.S. and Voyer, P.A. (2000), “World-of-mouth processes within a services purchase decision context”, Journal of Service Research: JSR, Vol. 3 No. 2, pp. 166-77.

Benedicktus, R., Brady, M., Darke, P. and Voorhees, C. (2010), “Conveying trustworthiness to online consumers: reactions to consensus, physical store presence, brand familiarity, and generalized suspicion”, Journal of Retailing, Vol. 86 No. 4, pp. 322-55.

Berry, L.L., Carbone, L.P. and Haeckel, S.H. (2002), “Managing the total customer experience”,MIT Sloan Management Review, Vol. 43 No. 3, pp. 85-9.

Bhatnagar, A. and Ghose, S. (2004), “Segmenting consumers based on the benefits and risks of internet shopping”, Journal of Business Research, Vol. 57 No. 12, pp. 1352-60.

Bigne´-Alcan˜ iz, E., Ruiz-Mafe´, C., Alda´s-Manzano, J. and Sanz-Blas, S. (2008), “Influence of online shopping information dependency and innovativeness on internet shopping adoption”, Online Information Review, Vol. 32 No. 5, pp. 648-67.

Breitsohl, J., Khammash, M. and Griffiths, G. (2010), “E-business complaint management: perceptions and perspectives of online credibility”, Journal of Enterprise Information Management, Vol. 23 No. 5, pp. 653-60.

Bruwer, J. and Johnson, R. (2010), “Place-based marketing and regional branding strategy perspectives in the California wine industry”, The Journal of Consumer Marketing, Vol. 27 No. 1, pp. 5-16.

Chan, K.W. and Li, S.Y. (2010), “Understanding consumer-to-consumer interactions in virtual communities: the salience of reciprocity”, Journal of Business Research, Vol. 63 Nos 9/10, pp. 1033-40.

Chang, H.H. and Chen, S.W. (2008), “The impact of online store environment cues on purchase intention”, Online Information Review, Vol. 32 No. 6, pp. 818-41.

Chen, J., Chen, X. and Song, X. (2007), “Comparison of the group-buying auction and the fixed pricing mechanism”, Decision Support Systems, Vol. 43 No. 2, pp. 445-59.

Chen, J., Chen, X., Kauffman, R. and Song, X. (2009), “Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions”, Electronic Commerce Research and Applications, Vol. 8 No. 4, pp. 191-202.

Chen, J., Kauffman, R., Liu, Y. and Song, X. (2010), “Segmenting uncertain demand in group-buying auctions”, Electronic Commerce Research and Applications, Vol. 9 No. 3, pp. 126-47.

Cheung, C.M.K., Lee, M.K.O. and Rabjohn, N. (2008), “The impact of electronic word-of-mouth: the adoption of online opinions in online customer communities”, Internet Research, Vol. 18 No. 3, pp. 229-47.

Chiu, C.M., Hsu, M.H. and Wang, E.T.G. (2006), “Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories”, Decision Support Systems, Vol. 42 No. 3, pp. 1872-88.

Chua, R., Ingram, P. and Morris, M. (2008), “From the head and the heart: locating cognition- and affect-based trust in managers’ professional networks”, Academy of Management Journal, Vol. 51 No. 3, pp. 436-52.

Clemons, E. and Gao, G. (2008), “Consumer informedness and diverse consumer purchasing behaviors: traditional mass-market, trading down, and trading out into the long tail”, Electronic Commerce Research and Applications, Vol. 7 No. 1, pp. 3-17.

Clow, K.E., James, K.E., Kranenburg, K.E. and Berry, C.T. (2006), “The relationship of the visual element of an advertisement to service quality expectations and source credibility”, The Journal of Services Marketing, Vol. 20 No. 6, pp. 404-11.

Constantinides, E., Lorenzo-Romero, C. and Go´mez, M.A. (2010), “Effects of web experience on consumer choice: a multicultural approach”, Internet Research, Vol. 20 No. 2, pp. 188-209.

Da Silva, R. and Syed Alwi, S. (2008), “Online corporate brand image, satisfaction and loyalty”,

Journal of Brand Management, Vol. 16 No. 3, pp. 119-44.Datta, P. and Chatterjee, S. (2008), “The economics and psychology of consumer trust in

intermediaries in electronic markets: the EM-trust framework”, European Journal of Information Systems, Vol. 17 No. 1, pp. 12-28.

Delgado-Ballester, E. and Herna´ndez-Espallardo, M. (2008), “Effect of brand associations on consumer reactions to unknown on-line brands”, International Journal of Electronic Commerce, Vol. 12 No. 3, pp. 81-114.

Demangeot, C. and Broderick, A.J. (2010), “Consumer perceptions of online shopping environments: a gestalt approach”, Psychology and Marketing, Vol. 27 No. 2, pp. 117-40.

Duan, W., Gu, B. and Whinston, A.B. (2008), “The dynamics of online word-of-mouth and product sales – an empirical investigation of the movie industry”, Journal of Retailing, Vol. 84 No. 2, pp. 233-42.

Edelman, B. (2011), “Adverse selection in online ‘trust’ certifications and search results”,Electronic Commerce Research and Applications, Vol. 10 No. 1, pp. 17-25.

Fan, Y.-W. and Ku, E. (2010), “Customer focus, service process fit and customer relationship management profitability: the effect of knowledge sharing”, Service Industries Journal, Vol. 30 No. 2, pp. 203-23.

Forsythe, S.M. and Shi, B. (2003), “Consumer patronage and risk perceptions in internet shopping”, Journal of Business Research, Vol. 56 No. 11, pp. 867-75.

Gan, L., He, S., Huang, T. and Tan, J. (2007), “A comparative analysis of online grocery pricing in Singapore”, Electronic Commerce Research and Applications, Vol. 6 No. 4, pp. 474-83.

Ganguly, B., Dash, S., Cyr, D. and Head, M. (2010), “The effects of website design on purchase intention in online shopping: the mediating role of trust and the moderating role of culture”, International Journal of Electronic Business, Vol. 8 Nos 4/5, pp. 302-30.

Godes, D. and Mayzlin, D. (2009), “Firm-created word-of-mouth communication: evidence from a field test”, Marketing Science, Vol. 28 No. 4, pp. 721-40.

Gonza´lez, J.V. and Garazo, T.G. (2006), “Structural relationships between organizational service orientation, contact employee job satisfaction and citizenship behavior”, International Journal of Service Industry Management, Vol. 17 No. 1, pp. 23-50.

Gupta, P. and Harris, J. (2010), “How e-WOM recommendations influence product consideration and quality of choice: a motivation to process information perspective”, Journal of Business Research, Vol. 63 Nos 9/10, pp. 1041-9.

Hamer, L. (2011), “Reducing online buyers’ perceptions of risk: a study of online auctions”,Services Marketing Quarterly, Vol. 32 No. 2, pp. 83-95.

Hausman, A.V. and Siekpe, J.S. (2009), “The effect of web interface features on consumer online purchase intentions”, Journal of Business Research, Vol. 62 No. 1, pp. 5-13.

Hennig-Thurau, T. and Walsh, G. (2003), “Electronic word-of-mouth: motives for and consequences of reading customer articulations on the internet”, International Journal of Electronic Commerce, Vol. 8 No. 2, pp. 51-74.

Herrero Crespo, A. and Rodriguez del Bosque, I. (2010), “The influence of the commercial features of the internet on the adoption of e-commerce by consumers”, Electronic Commerce Research and Applications, Vol. 9 No. 6, pp. 562-75.

Online group’s buying

intention

587

INTR 22,5

588

Hoffman, D.L., Novak, T.P. and Venkatesh, A. (2004), “Has the internet become indispensable?”, Association for Computing Machinery. Communications of the ACM, Vol. 47 No. 7, pp. 37-42.

Holzwarth, M., Janiszewski, C. and Neumann, M.M. (2006), “The influence of avatars on online consumer shopping behavior”, Journal of Marketing, Vol. 70 No. 4, pp. 19-36.

Hsiao, K.L., Lin, J.C.C., Wang, X.Y., Lu, H.P. and Yu, H. (2010), “Antecedents and consequences of trust in online product recommendations”, Online Information Review, Vol. 34 No. 6, pp. 935-53.

Jeong, S.W., Fiore, A.M., Niehm, L.S. and Lorenz, F.O. (2009), “The role of experiential value in online shopping”, Internet Research, Vol. 19 No. 1, pp. 105-24.

Jiang, P., Jones, D.B. and Javie, S. (2008), “How third-party certification programs relate to consumer trust in online transactions: an exploratory study”, Psychology and Marketing, Vol. 25 No. 9, pp. 839-58.

Karakaya, F. and Barnes, N.G. (2010), “Impact of online reviews of customer care experience on brand or company selection”, The Journal of Consumer Marketing, Vol. 27 No. 5, pp. 447-57.

Kauffman, R.J. and Wang, B. (2001), “New buyers’ arrival under dynamic pricing market microstructure: the case of group-buying discounts on the internet”, Journal of Management Information Systems, Vol. 18 No. 2, pp. 157-88.

Kauffman, R., Lai, H. and Ho, C. (2010), “Incentive mechanisms, fairness and participation in online group-buying auctions”, Electronic Commerce Research and Applications, Vol. 9 No. 3, pp. 249.

Kim, J. and Forsythe, S. (2009), “Adoption of sensory enabling technology for online apparel shopping”, European Journal of Marketing, Vol. 43 Nos 9/10, pp. 1101-20.

Kim, J., Choi, J., Qualls, W. and Han, K. (2008), “It takes a marketplace community to raise brand commitment: the role of online communities”, Journal of Marketing Management, Vol. 24 Nos 3/4, pp. 409-31.

Koo, D.M. (2006), “The fundamental reasons of e-consumers’ loyalty to an online store”, Electronic Commerce Research and Applications, Vol. 5 No. 2, pp. 117-30.

Ku, E.C. (2011), “Recommendations from a virtual community as a catalytic agent of travel decisions”, Internet Research, Vol. 21 No. 3, pp. 282-303.

Kwon, S. and Chung, N. (2010), “The moderating effects of psychological reactance and product involvement on online shopping recommendation mechanisms based on a causal map”, Electronic Commerce Research and Applications, Vol. 9 No. 6, pp. 522-36.

Kwon, W. and Lennon, S. (2009), “Reciprocal effects between multichannel retailers’ offline and online brand images”, Journal of Retailing, Vol. 85 No. 3, pp. 376-90.

Lander, M.C., Purvis, R.L., McCray, G.E. and Leigh, W. (2004), “Trust-building mechanisms utilized in outsourced is development projects: a case study”, Information & Management, Vol. 41 No. 4, pp. 509-28.

Lee, K., Kang, I. and McKnight, D. (2007), “Transfer from offline trust to key online perceptions: an empirical study”, IEEE Transactions on Engineering Management, Vol. 54 No. 4, pp. 729-41.

Li, C., Sycara, K. and Scheller-Wolf, A. (2010), “Combinatorial coalition formation for multi-item group-buying with heterogeneous customers”, Decision Support Systems, Vol. 49 No. 1, pp. 1-13.

Libai, B., Bolton, R., Bu¨ gel, M., de Ruyter, K., Go¨tz, O., Risselada, H. and Stephen, A. (2010), “Customer-to-customer interactions: broadening the scope of word of mouth research”, Journal of Service Research: JSR, Vol. 13 No. 3, pp. 267-182.

Lin, H.-F. (2008), “Antecedents of virtual community satisfaction and loyalty: an empirical test of competing theories”, Cyber Psychology & Behavior, Vol. 11 No. 2, pp. 138-44.

Lin, K.J., Hsu, J.Y.J., Zhang, Y. and Tao, Y. (2006), “A distributed reputation broker framework for web service applications”, Journal of Electronic Commerce Research, Vol. 7 No. 3, pp. 164-77.

Lin, L., Geng, X. and Whinston, A.B. (2005), “A sender-receiver framework for knowledge transfer”, MIS Quarterly, Vol. 29 No. 2, pp. 197-219.

Lin, P., Chen, C. and Song, M. (2009), “Price dispersion of online air tickets for short distance international routes”, The Service Industries Journal, Vol. 29 No. 11, pp. 1597-613.

Lytle, R.S. and Timmerman, J.E. (2006), “Service orientation and performance: an organizational perspective”, The Journal of Services Marketing, Vol. 20 No. 2, pp. 136-47.

Market Intelligence Center (2010), “IT data bank annual report”, available at: www.mic.eee.org.tw Maleyeff, J. (2006), “Exploration of internal service systems using lean

principles”, ManagementDecision, Vol. 44 No. 5, pp. 674-89.

Mun˜ oz-Leiva, F., Luque-Mart´ınez, T. and Sa´nchez-Ferna´ndez, J. (2010), “How to improve trust toward electronic banking”, Online Information Review, Vol. 34 No. 6, pp. 907-34.

Nollet, J. and Beaulieu, M. (2005), “Should an organisation join a purchasing group?”, Supply Chain Management, Vol. 10 No. 1, pp. 11-7.

Novak, T.P., Hoffman, D.L. and Yung, Y.F. (2000), “Measuring the customer experience in online environments: a structural modeling approach”, Marketing Science, Vol. 19 No. 1, pp. 22-42.

Oh, W. and Lucas, H.C. Jr (2006), “Information technology and pricing decisions: price adjustments in online computer markets”, MIS Quarterly, Vol. 30 No. 3, pp. 755-75.

Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1994), “Alternative scales for measuring service quality: a comparative assessment based on psychometric and diagnostic criteria”, Journal of Retailing, Vol. 70 No. 3, pp. 201-30.

Parayitam, S. and Dooley, R. (2009), “The interplay between cognitive- and affective conflict and cognition- and affect-based trust in influencing decision outcomes”, Journal of Business Research, Vol. 62 No. 8, pp. 789-96.

Park, D. and Kim, S. (2008), “The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews”, Electronic Commerce Research and Applications, Vol. 7 No. 4, pp. 399-410.

Parra, J. and Ruiz, S. (2009), “Consideration sets in online shopping environments: the effects of search tool and information load”, Electronic Commerce Research and Applications, Vol. 8 No. 5, pp. 252-62.

Rui Vinhas Da, S. and Sharifah Faridah Syed, A. (2008), “Online brand attributes and online corporate brand images”, European Journal of Marketing, Vol. 42 Nos 9/10, pp. 1039-

58.Saarinen, T. (1996), “An expanded instrument for evaluating information system success”,

Information & Management, Vol. 31 No. 2, pp. 103-18.Serrano-Cinca, C., Fuertes-Calle´n, Y. and Gutie´rrez-Nieto, B. (2010), “Internet positioning

and performance of e-tailers: an empirical analysis”, Electronic Commerce Research and Applications, Vol. 9 No. 3, pp. 237-48.

Shang, R.A., Chen, Y.C. and Liao, H.J. (2006), “The value of participation in virtual consumer communities on brand loyalty”, Internet Research, Vol. 16 No. 4, pp. 398-418.

Su, D. and Huang, X. (2011), “Research on online shopping intention of undergraduate consumer in China-based on the theory of planned behavior”, International

Business Research, Vol. 4 No. 1, pp. 86-92.

Online group’s buying intention

589

INTR 22,5

590

Suh, T. and Kwon, I.G. (2006), “Matter over mind: when specific asset investment affects calculative trust in supply chain partnership”, Industrial Marketing Management, Vol. 35 No. 2, pp. 191-201.

Sunil, M., Narayan, R., Krishnan, M.S. and Claes, F. (2006), “Designing web sites for customer loyalty across business domains: a multilevel analysis”, Journal of Management Information Systems, Vol. 23 No. 3, pp. 97-127.

Talukder, M. and Yeow, P.H. (2007), “A comparative study of virtual communities in Bangladesh and the USA”, The Journal of Computer Information Systems, Vol. 47 No. 4, pp. 82-90.

Tan, S.J. (1999), “Strategies for reducing consumers’ risk aversion in internet shopping”, The Journal of Consumer Marketing, Vol. 16 No. 2, pp. 163-80.

Tuzovic, S. (2010), “Frequent (flier) frustration and the dark side of word-of-web: exploring online dysfunctional behavior in online feedback forums”, The Journal of Services Marketing, Vol. 24 No. 6, pp. 446-57.

Walczuch, R. and Lundgren, H. (2004), “Psychological antecedents of institution-based consumer trust in e-retailing”, Information & Management, Vol. 42 No. 1, pp. 159-77.

Wu, J.-J. and Tsang, A. (2007), “Factors affecting members’ trust belief and behaviour intention in virtual communities”, Behaviour & Information Technology, Vol. 27 No. 2, pp. 115-25.

Yang, T.C. and Lai, H.C. (2006), “Comparison of product bundling strategies on different online shopping behaviors”, Electronic Commerce Research and Applications, Vol. 5 No. 4, pp. 295-304.

Yen, Y.S. (2010), “Can perceived risks affect the relationship of switching costs and customer loyalty in e-commerce?”, Internet Research, Vol. 20 No. 2, pp. 210-24.

Yeh, Y.S. and Li, Y.M. (2009), “Building trust in m-commerce: contributions from quality and satisfaction”, Online Information Review, Vol. 33 No. 6, pp. 1066-86.

Corresponding authorEdward C.S. Ku can be contacted at: edwardku@mail.nkuht.edu.tw

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