This study adapted the Unified Theory of Acceptance and Use of Technology (UTAUT) and innovation diffusion to show how virtual community building, website performance expectancy, effort expectancy, trialability, and two mediators (familiarity and perceived risk) interact and affect online shopping intention. A total of 430 valid participants were collected, all of whom had the goal of buying a smart phone. The results of the structural equation model analysis show that performance expectancy and effort expectancy positively affect website familiarity; virtual community building and trialability positively influence product familiarity; and perceived risk negatively affects purchase intention. In addition to the effects demonstrated by the regression results, website familiarity and product familiarity also have mediation effects. Finally, this study suggests that managers should provide a complete community to consumers in order to enable them to share information; furthermore, they should also provide details of product trials on the website, so that consumers can increase their familiarity with both the website and the products, as this can then raise consumer purchase intention.
BartYShankarVSultanFUrbanGL (2005) Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of Marketing69(4): 133–152.
2.
BurkeRR (2002) Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science30(4): 411–432.
3.
ChangHH (2008) Intelligent agent’s technology characteristics applied to online auctions’ task: A combined model of TTF and TAM. Technovation28(9): 564–579.
4.
ChangHHChenSW (2009) Consumer perception of interface quality, security and loyalty in electronic commerce. Information & Management46(7): 411–417.
5.
ChangHHChuangSW (2011) Social capital and individual motivations on knowledge sharing: participant involvement as a moderator. Information & Management48(1): 9–18.
6.
ChenR (2013) Member use of social networking sites – an empirical examination. Decision Support Systems54, 1219–1227.
7.
CompeauDRHigginsCA (1995) Computer self-efficacy: Development of a measure and initial test. MIS Quarterly19(2): 189–211.
8.
CrespoÁHdel BosqueIRde los Salmones SánchezMMG (2009) The influence of perceived risk on Internet shopping behavior: A multidimensional perspective. Journal of Risk Research12(2): 259–277.
9.
CunninghamS (1967) The major dimensions of perceived risk. In CoxD (ed.) Risk Taking and Information Handling in Consumer Behavior. (pp. 82–108). Boston: Harvard Business School.
10.
d’AstousAGargouriE (2001) Consumer evaluations of brand imitations. European Journal of Marketing35(1/2): 153–167.
11.
DoneyPMCannonJP (1997) An examination of the nature of trust in buyer-seller relationships. Journal of Marketing61(2): 35–51.
12.
DwyerFRTannerJF (2009) Business Marketing: Connecting strategy relationships and learning. New York, NY: McGraw Hill.
13.
FaroughianFFKalafatisSPLeddenLSamouelPTsogasMH (2012) Value and risk in business-to-business e-banking. Industrial Marketing Management41(1): 68–81.
14.
FornellCLarckerDF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research18: 39–50.
15.
FullerJMatzlerK (2007) Virtual product experience and customer participation – A chance for customer-centred, really new products. Technovation27(6/7): 378–387.
16.
GallaugherJMWangYM (2002) Understanding network effects in software markets: Evidence from web server pricing. MIS Quarterly26(4): 303–327.
17.
GefenDKarahannaEStraubDW (2003) Trust and tam in online shopping: An integrated model. MIS Quarterly27(1): 51–90.
18.
GeorgeDMalleryM (2010) SPSS for Windows Step by Step: A simple guide and reference 17.0 update. Boston: Pearson.
19.
GreggDGWalczakS (2008) Dressing your online auction business for success: An experiment comparing two eBay businesses. MIS Quarterly32(3): 653–670.
20.
HairJFBlackWCBabinBJdersonRETathamRL (2010) Multivariate Data Analysis . 7th ed. Upper Saddle River, NJ: Pearson Prentice Hall.
21.
HauserJRWernerfeltB (1990) An evaluation cost model of consideration sets. Journal of Consumer Research16(4): 393–408.
JiangZBenbasatI (2005) Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. Journal of Management Information Systems21(3): 111–148.
24.
JiangZBenbasatI (2007) The effects of presentation formats and task complexity on online consumers’ product understanding. MIS Quarterly31(3): 475–500.
25.
JoaquínAMCarlaRMSilviaSB (2009) Exploring individual personality factors as drivers of m-shopping acceptance. Industrial Management & Data Systems109(6): 739–757.
26.
JohnsonEJRussoJE (1984) Product familiarity and learning new information. Journal of Consumer Research11(1): 542–551.
27.
JohnsonMPZhengKPadmanR (2014) Modeling the longitudinality of user acceptance of technology with an evidence-adaptive clinical decision support system. Decision Support Systems57(1): 444–453.
28.
KellerKL (1993) Conceptualizing, measuring and managing customer-based brand equity. Journal of Marketing57(1): 1–22.
29.
KempfDSSmithRE (1998) Consumer processing of product trial and influence of prior advertising: A structural modeling approach. Journal of Marketing Research35(3): 325–338.
30.
KimDJFerrinDLRaoHR (2008) A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk and their antecedents. Decision Support Systems44(2): 544–564.
31.
KimJForsytheS (2008) Sensory enabling technology acceptance model (SE-TAM): A multiple-group structural model comparison. Psychology & Marketing25(9): 901–922.
32.
KingRCSenRXiaM (2004) Impact of web-based e-commerce on channel strategy in retailing. International Journal of Electronic Commerce8(3): 103–130.
33.
LeeKSTanSJ (2003) E-retailing versus physical retailing: A theoretical model and empirical test of consumer choice. Journal of Business Research56(11): 877–886.
34.
LiHDaughertyTBioccaF (2002) Impact of 3-D advertising on product knowledge, brand attitude and purchase intention: The mediating role of presence. Journal of Advertising31(3): 43–57.
35.
LiHDaughertyTBioccaF (2003) The role of virtual experience in consumer learning. Journal of Consumer Psychology13(4): 395–408.
36.
LinHF (2007) Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications6(4): 433–442.
37.
MachQHHunterMDGrewalRS (2010) Neurophysiological correlates in interface design: An HCI perspective. Computers in Human Behavior26(3): 371–376.
38.
MacInnisMHeslopLA (1990) Marketing planning in a high tech environment. Industrial Marketing Management19(2): 107–16.
39.
McKinneyVYoonKZahediF (2002) The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research13(3): 296–315.
40.
MiltgenCLPopovičAOliveiraT (2013) Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context, Decision Support Systems56(4): 103–114.
41.
NadkarniSGuptaR (2007) A task-based model of perceived website complexity. MIS Quarterly31(3): 501–524.
42.
NepomucenoaMVLarochebMRichardcMO (2014) How to reduce perceived risk when buying online: The interactions between intangibility, product knowledge, brand familiarity, privacy and security concerns. Journal of Retailing and Consumer Services21(4): 619–629.
43.
PavlouPA (2003) Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce7(3): 101–134.
44.
PavlouPAGefenD (2004) Building effective online marketplaces with institution-based trust. Information Systems Research15(1): 37–59.
45.
PodsakoffPMMacKenzieSBLeeJYPodsakoffNP (2003) Common method bias in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
46.
RabanDRRafaeliS (2007) Investigating ownership and the willingness to share information online. Computers in Human Behavior23(5): 2367–2382.
47.
RaniPSarkarNAdamsJ (2007) Anxiety-based affective communication for implicit human–machine interaction. Advanced Engineering Informatics21(3): 323–334.
48.
RileyFDScarpiDManaresiA (2009) Purchasing services online: A two-country generalization of possible influences. Journal of Services Marketing23(2): 93–103.
49.
RogersEM (1983) The Diffusion of Innovations. New York: Free Press.
50.
SlykeCVShimJTJohnsonRJiangJ (2006) Concern for information privacy and online consumer purchasing. Journal of the Association for Information Systems7(6): 415–444.
51.
SobelME (1982) Asymptotic confidence intervals for indirect effects in structural equations models. In LeinhartS. (ed.) Sociological Methodology. San Francisco: Jossey-Bass.
52.
TeoHHChanHCWeiKKZhangZ (2003) Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. International Journal of Human-Computer Studies59(5): 671–697.
53.
TsaiJYEgelmanSCranorLAcquistiA (2011) The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research22(2): 254–268.
54.
UrbanGHauserJR (2004) Listening in’ to find and explore new combinations of customer needs. Journal of Marketing68(2): 72–87.
55.
VanceAElie-dit-CosaqueCStraubD (2008) Examining trust in IT artifacts: The effects of system quality and culture on trust. Journal of Management Information Systems24(4): 73–100.
56.
VeloutsouCBianXM (2008) A cross-national examination of consumer perceived risk in the context of non-deceptive counterfeit brands. Journal of Consumer Behavior7: 3–20.
57.
VenkateshVBrownSAMarupingLMBalaH (2008) Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions and behavioral expectation. MIS Quarterly32(3): 483–502.
58.
VenkateshVMorrisMGDavisGB (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27(3): 425–47.
59.
VenkateshVThongJYLXuX (2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly36(1): 157–178.
60.
von HippelEKatzR (2002) Shifting innovation to users via toolkits. Management Science48(7): 821–833.
61.
WangYSLinHHLuarnP (2006) Predicting consumer intention to use mobile service. Information Systems Journal16(2): 157–179.
62.
WangYSWuMCWangHY (2009) Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology40(1): 92–118.
63.
WuJHWangSC (2005) What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management42(5): 719–29.
64.
WuWPChanTSLauHH (2008) Does consumers’ personal reciprocity affect future purchase intentions?Journal of Marketing Management24(3/4): 345–360.
65.
YangQPangCLiuLYenDCTarnJM (2015) Exploring consumer perceived risk and trust for online payments: an empirical study in China’s younger generation. Computers in Human Behavior50: 9–24.
66.
YoonVYHostlerRCGuoZGuimaraesT (2013) Assessing the moderating effect of consumer product knowledge and online shopping experience on using recommendation agents for customer loyalty. Decision Support Systems55(4): 883–893.
