Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls
Abstract
:1. Introduction
2. Theoretical Background and Research Hypothesis
2.1. Consumer Behavior Model
2.2. Proposed Information Systems Model
2.2.1. TAM Fusion
2.2.2. User Gratification Integration
2.2.3. Integration of Website Design and Website Irritation
3. Methodology
3.1. Pretest
3.2. Sample and Procedure
- (a)
- Respondent submitted the survey more than once.
- (b)
- Incomplete response.
- (c)
- Constant response to all questions.
- (d)
- Respondent had not purchased at least twice each month for the previous 6 months.
3.3. Measurements
4. Empirical Results
4.1. Exploratory Factory Analysis
4.2. Confirmatory Factor Analysis
4.3. Hypothesis Testing
5. Discussion and Implications
- Enrich understanding predecessors of online shopping acceptance and customer behavior in terms of IS and CB viewpoint.
- Examines the proposed unified IS-CB model.
- Provide valuable and practical guidelines for online retail managers.
5.1. Customer Belief Constituents
5.2. Customer Beliefs and Technology Acceptance
5.3. Actual Online-Shopping Purchase and Post-Purchase Estimations
5.4. Online Shopping Perceived Value
5.5. Website Design and Website Irritation impacts
6. Conclusions
- Immediate indicators of customer acceptance of online shopping are PV, PEOU, PU, WI, and SF.
- EG influences customer acceptance of online shopping indirectly through PEOU and PU.
- WI influences customer acceptance of online shopping indirectly through PEOU.
- PEOU influences customer acceptance of online shopping indirectly through PU.
6.1. Theoretical Implications
6.2. Managerial Implications
6.3. Limitations and Directions for Future Research
- The data was gathered from exclusively Korean shoppers, and future research should extend the customer base to include European, American, Chinese, and other regions, to allow cross-culture aspects to be considered, and recognize potential similarities and contrasts between the various relationships.
- We did not examine the demographical impacts, e.g., impact differences between males and females or income levels, only the overall effects. This extended analysis could be productive to identify moderating impacts (e.g., age, sexual orientation, and race), providing deeper insight into online shopping.
- We considered individual online purchases exclusively. However, several online retailers offer group purchases, where customers gather together to attract larger discounts. Hence, future work should expand the unified model to inspect generalizing to group purchasing.
- Online stores have many different layouts, including trees, pipelines, and guiding pathways [65]. A more comprehensive inspection of these different design factors would be fascinating to identify which impact of online customers trust and WI.
- Previous studies used trust as a mediator [13] between consumers’ perceived risk and online buying intention. Ganguly et al. [99] also showed that trust mediated positive effects of information design, visual design, and navigational design on purchase intention. Thus, many mediators significantly affect this relationship. Since the present study primarily investigated significant attributes of online shopping, determining other significant mediators requires further study focusing on mediating effects of the variables.
- The present study took data from an online shopping mall used by customers of all ages. However, the sample was restricted to only single online shopping mall. Further studies should expand data collection to include several online shopping malls that sell diverse products, to augment generalizability of the current findings.
- Future studies will also examine total and indirect effects of mediators to assist better understanding of customer behavior.
Author Contributions
Funding
Conflicts of Interest
Appendix A
Construct | Item | Measure |
Perceived value (PV) | PV1 | While shopping online I feel a sense of adventure. |
PV2 | While shopping I find just the items I look for. | |
PV3 | Shopping to me is truly a source of pleasure. | |
Attitude (AT) | AT1 | I am comfortable shopping at online-shopping sites. |
AT2 | I like to purchase what I need from online-shopping sites. | |
AT3 | I hold a positive evaluation of shopping online. | |
Online-shopping intention (OSI) | OSI-1 | I prefer to shop online. |
OSI-2 | I plan to do more of my shopping via online retailers. | |
OSI-3 | I am likely to recommend websites to my friends. | |
Online-shopping experience (OSE) | OSE1 | I am pleased with my shopping activity. |
OSE2 | I have a delightful shopping experience over the internet. | |
OSE3 | I feel comfortable using the website. | |
Trust (TR) | TR1 | I feel safe in purchasing from online websites that safeguard my privacy. |
TR2 | I believe a safe online retailer will safeguard my private information | |
TR3 | I am satisfied in buying what I want from secured online retailers. | |
Perceived ease of use (PEOU) | PEOU1 | Learning to operate the online store that I have recently gone to is easy. |
PEOU2 | My interaction with the online store I have recently gone to is clear and understandable. | |
PEOU3 | The online store that I have recently gone to is easy to use. | |
PEOU4 | The shopping through the online store that I have recently gone to would be fun for its own sake. | |
Perceived usefulness (PU) | PU1 | The online store that I have recently gone to would be useful in buying what I want. |
PU2 | The online store that I have recently gone to would improve my shopping ability. | |
PU3 | The online store that I have recently gone to is convenient for searching and buying products. | |
PU4 | The online store that I have recently gone to makes it easier to search for and purchase products. | |
Entertainment gratification (EG) | EG1 | I find it entertaining to shop at online retailers. |
EG2 | I find that online-shopping sites are fun to use. | |
EG3 | Using online-shopping sites to purchase products provides me with lots of enjoyment. | |
Social factors (SF) | SF1 | I am anxious about what others say when I shop online |
SF2 | I prefer to shop at online-shopping sites where my friends shop. | |
SF3 | I prefer to shop online for products that are recommended by my friends in their postings on social networking sites | |
Web-irritation (WI) | WI-1 | All product options, product attributes and product information are well presented. |
WI-2 | I often feel annoyed when shopping online. | |
WI-3 | I feel that most online-shopping sites are confusing. | |
Navigation design (ND) | ND1 | I can easily navigate through the site. |
ND2 | I find this website easy to use. | |
ND3 | This site provides good navigation facilities to information content. | |
Visual design (VD) | VD1 | The degree of interaction (video, demos) offered by this site is sufficient. |
VD2 | This site allowed me to efficiently tailor the information for my specific needs. | |
VD3 | This website looks professionally well designed. | |
Information design (ID) | ID1 | The website animations are meaningful. |
ID2 | In online store, I find the information on this site to be well organized. | |
ID3 | In online store, I find the information to be logically presented. | |
Perceived risk (PR) | PR1 | I feel personal data might be lost or used incorrectly by the website. |
PR2 | The information provided on the website may be exaggerated for advertising purposes. | |
PR3 | Time required to buy and obtain the travel items will be longer on the website. | |
Actual online-shopping purchase (AOP) | AOP1 | I regularly make online purchases. |
AOP2 | I make online purchases extensively | |
AOP3 | Overall, I have made many online purchases. |
References
- Global E-Commerce Market 2016–2020. Available online: https://www.technavio.com/report/global-media-and-entertainment-services-global-e-commerce-market-2016-2020 (accessed on 29 August 2018).
- Statista. Online Shopping Behavior in the United States—Statistics and Facts. 2017. Available online: https://www.statista.com/topics/2477/online-shopping-behavior/ (accessed on 29 August 2018).
- Zheng, X.; Lee, M.; Cheung, C.M. Examining e-loyalty towards online shopping platforms: The role of coupon proneness and value consciousness. Internet Res. 2017, 27, 709–726. [Google Scholar] [CrossRef]
- Pappas, I.O.; Kourouthanassis, P.E.; Giannakos, M.N.; Lekakos, G. The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach. Telemat. Inform. 2017, 34, 730–742. [Google Scholar] [CrossRef]
- Zhou, L.; Dai, L.; Zhang, D. Online shopping acceptance model-A critical survey of consumer factors in online shopping. J. Electron. Commer. Res. 2007, 8, 41–62. [Google Scholar]
- Ha, S.; Stoel, L. Consumer e-shopping acceptance: Antecedents in a technology acceptance model. J. Bus. Res. 2009, 62, 565–571. [Google Scholar] [CrossRef]
- Che, T.; Peng, Z.; Lim, K.H.; Hua, Z. Antecedents of consumers’ intention to revisit an online group-buying website: A transaction cost perspective. Inf. Manag. 2015, 52, 588–598. [Google Scholar] [CrossRef]
- Hasan, B. Perceived irritation in online shopping: The impact of website design characteristics. Comput. Hum. Behav. 2016, 54, 224–230. [Google Scholar] [CrossRef]
- Azeem, M.A. Consumer’s attitudes toward commercial e-mail spam and web pop-ups: Interference, perceived loss of control, and irritation. Inf. Knowl Manag. 2012, 2, 21–33. [Google Scholar]
- Dennis, C.; Merrilees, B.; Jayawardhena, C.; Wright, L.T. E-consumer behavior. Eur. J. Mark. 2009, 43, 1121–1139. [Google Scholar] [CrossRef] [Green Version]
- Lim, W.M. Antecedents and consequences of e-shopping: An integrated model. Internet Res. 2015, 25, 184–217. [Google Scholar] [CrossRef]
- Fazal-e-Hasan, S.M.; Ahmadi, H.; Mortimer, G.; Grimmer, M.; Kelly, L. Examining the role of consumer hope in explaining the impact of perceived brand value on customer–brand relationship outcomes in an online retailing environment. J. Retail. Consum. Serv. 2018, 41, 101–111. [Google Scholar] [CrossRef]
- Bashir, S.; Anwar, S.; Awan, Z.; Qureshi, T.W.; Memon, A.B. A holistic understanding of the prospects of financial loss to enhance shopper’s trust to search, recommend, speak positive and frequently visit an online shop. J. Retail. Consum. Serv. 2018, 42, 169–174. [Google Scholar] [CrossRef]
- Wang, Y.M.; Lin, H.H.; Tai, W.C.; Fan, Y.L. Understanding multi-channel research shoppers: An analysis of Internet and physical channels. Inf. Syst. e-Bus. Manag. 2016, 14, 389–413. [Google Scholar] [CrossRef]
- Wu, I.L.; Wu, S.M. A strategy-based model for implementing channel integration in e-commerce: An empirical examination. Internet Res. 2015, 25, 239–261. [Google Scholar] [CrossRef]
- Zhang, H.; Zhao, L.; Gupta, S. The role of online product recommendations on customer decision making and loyalty in social shopping communities. Int. J. Inf. Manag. 2018, 38, 150–166. [Google Scholar] [CrossRef]
- Weisberg, J.; Te’eni, D.; Arman, L. Past purchase and intention to purchase in e-commerce: The mediation of social presence and trust. Internet Res. 2011, 21, 82–96. [Google Scholar] [CrossRef]
- Cyr, D. Modeling web site design across cultures: Relationships to trust, satisfaction, and e-loyalty. J. Manag. Inf. Syst. 2008, 24, 47–72. [Google Scholar] [CrossRef]
- Kim, D.J.; Ferrin, D.L.; Rao, H.R. A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decis. Support Syst. 2008, 44, 544–564. [Google Scholar] [CrossRef]
- Fishbein, M.; Ajzen, I. Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research; Addison-Wesley: Reading, MA, USA, 1975. [Google Scholar]
- Ng, S.; Paladino, A. Examining the influences of intentions to purchase green mobile phones among young consumers: An empirical analysis. In Proceedings of the 2009 ANZMAC Annual Conference, Melbourne, Australia, 30 November–2 December 2009; pp. 212–230. [Google Scholar]
- Ajzen, I.; Fishbein, M. Understanding Attitudes and Predicting Social Behavior; Prentice-Hall: Englewood-Cliffs, NJ, USA, 1980. [Google Scholar]
- Teng, H.J.; Ni, J.J.; Chen, H.H. Relationship between e-servicescape and purchase intention among heavy and light Internet users. Internet Res. 2018, 28, 333–350. [Google Scholar] [CrossRef]
- Zeithaml, V.A. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
- Wang, C. An empirical study on consumer’s perceived value and attitude toward advertising. In Proceedings of the 6th Global Information Technology and Management (GITM) World Conference, Anchorage, AK, USA, 5–7 June 2005. [Google Scholar]
- Salehzadeh, R.; Pool, J.K. Brand attitude and perceived value and purchase intention toward global luxury brands. J. Int. Consum. Mark. 2017, 29, 74–82. [Google Scholar] [CrossRef]
- Swait, J.; Sweeney, J.C. Perceived value and its impact on choice behaviour in a retail setting. J. Retail. Consum. Serv. 2000, 7, 77–88. [Google Scholar] [CrossRef]
- Al-Rafee, S.; Cronan, T.P. Digital piracy: Factors that influence attitude toward behavior. J. Bus. Ethics 2006, 63, 237–259. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behaviour. Organ. Behav. Hum. Decis. Process 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Cheung, C.M.K.; Chan, G.W.W.; Limayen, M. A critical review of online consumer behaviour: Empirical research. J. Electron. Commer. Organ. 2005, 3, 1–19. [Google Scholar] [CrossRef]
- Lim, W.M. Toward a theory of online buyer behavior using structural equation modeling. Mod. Appl. Sci. 2013, 7, 34–41. [Google Scholar] [CrossRef]
- Laroche, M.; Yang, Z.; McDougall, G.H.G.; Bergeron, J. Internet versus brick-and mortar retailers: An investigation into tangibility and its consequences. J. Retail. 2005, 81, 251–267. [Google Scholar] [CrossRef]
- Broekhuizen, T.; Huizingh, E.K.R.E. Online purchase determinants: Is their effect moderated by direct experience? Manag. Res. News 2009, 329, 440–457. [Google Scholar] [CrossRef]
- Venkatesh, V.; Agarwal, R. Turning visitors into customers: A usability-centric perspective on purchase behaviour in electronic channels. Manag. Sci. 2006, 52, 367–382. [Google Scholar] [CrossRef]
- Salam, A.F.; Iyer, L.; Palvia, P.; Singh, R. Trust in e-commerce. Commun. ACM 2005, 48, 73–77. [Google Scholar] [CrossRef]
- Delgado-Ballester, E.; Munuera-Aleman, J.L. Brand trust in the context of consumer loyalty. Eur. J. Mark. 2001, 35, 1238–1258. [Google Scholar] [CrossRef]
- Fukuyama, F. Trust: The Social Virtues and the Creation of Prosperity; Free Press: New York, NY, USA, 1995. [Google Scholar]
- Morgan, R.M.; Hunt, S.D. The commitment-trust theory of relationship marketing. J. Mark. 1994, 58, 20–38. [Google Scholar] [CrossRef]
- Kimery, K.; McCord, M. Third-party assurances: Mapping the road to trust in e-retailing. J. Inf. Technol. Theory Appl. 2002, 4, 63–82. [Google Scholar]
- McKnight, D.; Chervany, N. What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. Int. J. Electron. Commer. 2002, 6, 35–39. [Google Scholar] [CrossRef]
- Kim, E.Y.; Kim, Y.K. Predicting online purchase intentions for clothing products. Eur. J. Mark. 2004, 38, 883–897. [Google Scholar]
- Liu, X.; Wei, K.K. An empirical study of product differences in consumers’ e-commerce adoption behavior. Electron. Commer. Res. Appl. 2003, 2, 229–239. [Google Scholar] [CrossRef]
- Featherman, M.S.; Valacich, J.S.; Wells, H.D. Is that authentic or artificial? Understanding consumer perceptions of risk in e-service encounters. Inf. Syst. J. 2006, 16, 107–134. [Google Scholar] [CrossRef]
- Pavlou, P.A. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 2003, 7, 101–134. [Google Scholar]
- Forsythe, S.; Shi, B. Consumer patronage and risk perceptions in Internet shopping. J. Bus. Res. 2003, 56, 867–875. [Google Scholar] [CrossRef]
- Park, J.; Lennon, S.J.; Stoel, L. On-line product presentation: Effects on mood, perceived risk, and purchase intention. Psychol. Mark. 2005, 22, 695–719. [Google Scholar] [CrossRef]
- Choi, J.; Lee, K.-H. Risk perception and e-shopping: A cross-cultural study. J. Fash. Mark. Manag. 2003, 7, 49–64. [Google Scholar] [CrossRef]
- Jarvenpaa, S.L.; Tractinsky, N. Consumer trust in an internet store a cross-cultural validation. J. Comput.-Mediat. Commun. 1999, 5, 1–35. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
- Zhao, J.; Fang, S.; Jin, P. Modeling and quantifying user acceptance of personalized business modes based on TAM, trust and attitude. Sustainability 2018, 10, 356. [Google Scholar] [CrossRef]
- Gao, L.; Bai, X. A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pac. J. Mark. Logist. 2014, 26, 211–231. [Google Scholar] [CrossRef]
- Garaus, M.; Wolfsteiner, E.; Wagner, U. Shoppers’ acceptance and perceptions of electronic shelf labels. J. Bus. Res. 2016, 69, 3687–3692. [Google Scholar] [CrossRef]
- Ijaz, M.F.; Tao, W.; Rhee, J.; Kang, Y.S.; Alfian, G. Efficient Digital Signage-Based Online Store Layout: An Experimental Study. Sustainability 2016, 8, 511. [Google Scholar] [CrossRef]
- Jang, S.; Lee, C. The Impact of Location-Based Service Factors on Usage Intentions for Technology Acceptance: The Moderating Effect of Innovativeness. Sustainability 2018, 10, 1876. [Google Scholar] [CrossRef]
- Lee, Y.; Kozar, K.A.; Larsen, K.R. The technology acceptance model: Past, present, and future. Commun. Assoc. Inf. Syst. 2003, 12, 752–780. [Google Scholar]
- McCloskey, D. Evaluating electronic commerce acceptance with the technology acceptance model. J. Comput. Inf. Syst. 2004, 44, 49–57. [Google Scholar]
- Barkhi, R.; Belanger, F.; Hicks, J. A model of determinants of purchasing from virtual stores. J. Organ. Comput. Electron. Commer. 2008, 18, 177–196. [Google Scholar] [CrossRef]
- Kim, S.; Williams, R.; Lee, Y. Attitude toward online shopping and retail website quality: A comparison of US and Korean consumers. J. Int. Consum. Mark. 2003, 16, 189–203. [Google Scholar] [CrossRef]
- Buton-Jones, A.; Hubona, G.S. Individual differences and usage behaviour: Revisiting a technology acceptance model assumption. Data Base Adv. Inf. Syst. 2005, 36, 58–77. [Google Scholar] [CrossRef]
- Selamat, Z.; Jaffar, N.; Ong, B.H. Technology acceptance in Malaysian banking industry. Eur. J. Econ. Financ. Adm. Sci. 2009, 1, 143–155. [Google Scholar]
- Teo, T.S.H. Demographic and motivation variables associated with Internet usage activities. Internet Res. 2001, 11, 125–137. [Google Scholar] [CrossRef]
- Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef]
- Choi, J.; Lee, H.J.; Sajjad, F.; Lee, H. The influence of national culture on the attitude towards mobile recommender systems. Technol. Forecast. Soc. Chang. 2014, 86, 65–79. [Google Scholar] [CrossRef] [Green Version]
- Saghafi, F.; Moghaddam, E.N.; Aslani, A. Examining effective factors in initial acceptance of high-tech localized technologies: Xamin, Iranian localized operating system. Technol. Forecast. Soc. Chang. 2017, 122, 275–288. [Google Scholar] [CrossRef]
- Chang, M.K. Predicting unethical behavior: A comparison of the theory of reasoned action of the theory of planned behavior. J. Bus. Ethics 1998, 17, 1825–1834. [Google Scholar] [CrossRef]
- Swanson, D.L. Understanding audiences: Continuing contributions of gratifications research. Poetics 1992, 21, 305–328. [Google Scholar] [CrossRef]
- Joinson, A.N. Looking at, looking up or keeping up with people? Motives and use of Facebook. In Proceedings of the CHI’08 SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy, 5–10 April 2008; pp. 1027–1036. [Google Scholar]
- Li, D. Why do you blog: A uses-and-gratifications inquiry into bloggers’ motivations. In Proceedings of the 57th Annual Conference of the International Communication Association, San Francisco, CA, USA, 24–28 May 2007. [Google Scholar]
- Liu, Y.; Li, H.; Hu, F. Website attributes in urging online impulse purchase: An empirical investigation on consumer perceptions. Decis. Support Syst. 2013, 55, 829–837. [Google Scholar] [CrossRef]
- Luo, M.M.; Chea, S.; Chen, J.-S. Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decis. Support Syst. 2011, 51, 21–30. [Google Scholar] [CrossRef]
- Chiu, C.-M.; Fang, Y.-H.; Wang, E.T.G. Building community citizenship behaviors: The relative role of attachment and satisfaction. J. Assoc. Inf. Syst. 2015, 16, 947. [Google Scholar] [CrossRef]
- Papacharissi, Z.; Rubin, A. Predictors of Internet use. J. Broadcast. Electron. Media 2000, 44, 175–196. [Google Scholar] [CrossRef]
- Huang, L.-Y.; Hsieh, Y.-J.; Wu, Y.-C.J. Gratifications and social network service usage: The mediating role of online experience. Inf. Manag. 2014, 51, 774–782. [Google Scholar] [CrossRef]
- Shao, G. Understanding the appeal of user-generated media: A uses and gratification perspective. Int. Res. 2009, 19, 7–25. [Google Scholar] [CrossRef]
- Chung, C.; Austria, K. Social media gratification and attitude toward social media marketing messages: A study of the effect of social media marketing messages on online shopping value. In Proceedings of the Northeast Business & Economics Association, Morristown, NJ, USA, 30 September 30–2 October 2010; pp. 581–586. [Google Scholar]
- Raacke, J. Bonds-Raacke, J. MySpace and Facebook: Applying the uses and gratifications theory to exploring friend-networking sites. CyberPsychol. Behav. 2008, 11, 169–174. [Google Scholar] [CrossRef] [PubMed]
- Lim, W.M.; Ting, D.H. E-shopping: An analysis of the uses and gratifications theory. Mod. Appl. Sci. 2012, 6, 48–63. [Google Scholar] [CrossRef]
- Hooff, B.V.D.; Weenen, F.D.L.; Soekijad, M.; Huysman, M. The value of online networks of practice: The role of embeddedness and media use. J. Inf. Technol. 2010, 25, 205–215. [Google Scholar] [CrossRef]
- Huang, E. Use and gratification in e-consumers. Internet Res. 2008, 18, 405–426. [Google Scholar] [CrossRef]
- Kim, J.; Forsythe, S. Factors affecting adoption of product virtualization technology for online consumer electronics shopping. Int. J. Retail Distrib. Manag. 2010, 38, 190–204. [Google Scholar] [CrossRef]
- Luo, X. Uses and gratifications theory and e-consumer behaviors: A structural equation modeling study. J. Interact. Advert. 2002, 2, 34–41. [Google Scholar] [CrossRef]
- Stafford, T.F.; Stafford, M.R. Identifying motivations for the use of commercial websites. Inf. Resour. Manag. J. 2001, 14, 22–30. [Google Scholar] [CrossRef]
- Gao, Y.; Koufaris, M.; Ducoffe, R.H. An experimental study of the effects of promotional techniques in web-based commerce. J. Electron. Commer. Organ. 2004, 2, 1–20. [Google Scholar] [CrossRef]
- Li, H.; Edwards, S.M.; Lee, J.H. Measuring the intrusiveness of advertisements: Scale development and validation. J. Advert. 2002, 31, 37–47. [Google Scholar] [CrossRef]
- Ducoffe, R.H. Advertising value and advertising on the web. J. Advert. Res. 1996, 36, 21–35. [Google Scholar]
- Ahn, T.; Ryu, S.; Han, I. The impact of Web quality and playfulness on user acceptance of online retailing. Inf. Manag. 2007, 44, 263–275. [Google Scholar] [CrossRef]
- Chang, H.H.; Chen, S.W. Consumer perception of interface quality, security, and loyalty in electronic commerce. Inf. Manag. 2009, 46, 411–417. [Google Scholar] [CrossRef]
- Chen, Q.; Wells, W.D. Attitude toward the site. J. Advert. Res. 1999, 39, 27–38. [Google Scholar]
- Baty, J.B.; Lee, R.M. InterShop: Enhancing the vendor/customer dialectic in electronic shopping. J. Manag. Inf. Syst. 1995, 11, 9–31. [Google Scholar] [CrossRef]
- Tilson, R.; Dong, J.; Martin, S.; Kieke, E. Factors and principles affecting the usability of four e-commerce sites. In Proceedings of the 4th Conference on Human Factors & the Web, Basking Ridge, NJ, USA, 5 June 1998. [Google Scholar]
- Nielsen, J. User interface directions for the Web. Commun. ACM 1999, 42, 65–72. [Google Scholar] [CrossRef] [Green Version]
- Fan, W.-S.; Tsai, M.-C. Factors driving website success—The key role of Internet customisation and the influence of website design quality and Internet marketing strategy. Total Qual. Manag. 2010, 21, 1141–1159. [Google Scholar] [CrossRef]
- Gao, Y.; Wu, X. A cognitive model of trust in e-commerce: Evidence from a field study in China. J. Appl. Bus. Res. 2010, 26, 37. [Google Scholar] [CrossRef]
- Thota, S.C. A resolution model of consumer irritation consequences and company strategies: Social networking and strategy implications. J. Appl. Bus. Econ. 2012, 13, 114. [Google Scholar]
- Jarvenpaa, S.L.; Todd, P.A. Consumer reactions to electronic shopping on the World Wide Web. Int. J. Electron. Commer. 1996, 1, 59–88. [Google Scholar] [CrossRef]
- Gao, Y.; Koufaris, M. Perceptual antecedents of user attitude in electronic commerce. Database Adv. Inf. Syst. 2006, 37, 42–50. [Google Scholar] [CrossRef]
- Wells, J.D.; Valacich, J.S.; Hess, T.J. What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Q. 2011, 35, 373–396. [Google Scholar] [CrossRef]
- Li, Y.-M.; Yeh, Y.-S. Increasing trust in mobile commerce through design aesthetics. Comput. Hum. Behav. 2010, 26, 673–684. [Google Scholar] [CrossRef]
- Ganguly, B.; Dash, S.B.; Cyr, D.; Head, M. The effects of website design on purchase intention in online shopping: The mediating role of trust and the moderating role of culture. Int. J. Electron. Bus. 2010, 8, 302–330. [Google Scholar] [CrossRef]
- Wulf, K.D.; Schillewaert, N.; Muylle, S.; Rangarajan, D. The role of pleasure in web site success. Inf. Manag. 2006, 43, 434–446. [Google Scholar] [CrossRef]
- Chang, S.-H. The Influence of Green Viral Communications on Green Purchase Intentions: The Mediating Role of Consumers’ Susceptibility to Interpersonal Influences. Sustainability 2015, 7, 4829. [Google Scholar] [CrossRef]
- Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef] [PubMed]
- Ramayah, T.; Rouibah, K.; Gopi, M.; Rangel, G.J. A decomposed theory of reasoned action to explain intention to use internet stock trading among Malaysian investors. Comput. Hum. Behav. 2009, 25, 1222–1230. [Google Scholar] [CrossRef]
- Chiu, C.-M.; Chang, C.-C.; Cheng, H.-L.; Fang, Y.-H. Determinants of customer repurchase intention in online shopping. Online Inf. Rev. 2009, 33, 761–784. [Google Scholar] [CrossRef]
- Alam, S.S.; Yasin, N.M. What factors influence online brand trust: Evidence from online tickets buyers in Malaysia. J. Theor. Appl. Electron. Commer. Res. 2010, 5, 78–89. [Google Scholar]
- Sihombing, S.O. Predicting environmentally purchase behaviour: A test of the value-attitude-behaviour hierarchy. In Proceedings of the 2nd Indonesian Business Management Conference, Jakarta, Indonesia, 15–16 August 2007; pp. 73–82. [Google Scholar]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Hair, J.F.; Tatham, R.L.; Anderson, R.E. Multivariate Data Analysis; Prentice Hall: Upper Saddle River, NJ, USA, 1998. [Google Scholar]
- Carrigan, M.; Attalla, A. The myth of the ethical consumer—Do ethics matter in purchase behaviour? J. Consum. Mark. 2001, 18, 560–578. [Google Scholar] [CrossRef]
- Ping Zhang, G.M. User expectations and rankings of quality factors in different web site domains. Int. J. Electron. Commer. 2001, 6, 9–33. [Google Scholar]
- Perea y Monsuwé, T.; Dellaert, B.G.; De Ruyter, K. What drives consumers to shop online? A literature review. Int. J. Ser. Ind. Manag. 2004, 15, 102–121. [Google Scholar] [CrossRef] [Green Version]
- Koo, D.M.; Ju, S.H. The interactional effects of atmospherics and perceptual curiosity on emotions and online shopping intention. Comput. Hum. Behav. 2010, 26, 377–388. [Google Scholar] [CrossRef]
- Mikalef, P.; Giannakos, M.; Pateli, A. Shopping and word-of-mouth intentions on social media. J. Theor. Appl. Electron. Commer. Res. 2013, 8, 17–34. [Google Scholar] [CrossRef]
- Mikalef, P.; Giannakos, M.N.; Pappas, I.O. Designing social commerce platforms based on consumers’ intentions. Behav. Inf. Technol. 2017, 36, 1308–1327. [Google Scholar] [CrossRef]
Characteristic | Items | N | Percentage |
---|---|---|---|
Gender | Male | 381 | 60.2 |
Female | 252 | 39.8 | |
Age Range | 0–25 | 237 | 37.4 |
26–30 | 270 | 42.7 | |
31–40 | 104 | 16.4 | |
41–50 | 22 | 3.50 | |
Education | Undergraduate | 337.4 | 53.3 |
Postgraduate | 232.3 | 36.7 | |
Ph.D. | 63.3 | 10 | |
Online-shopping Frequency | Less than once a month | 212 | 33.5 |
A few times a month | 133 | 21 | |
A few times per week | 175.3 | 27.7 | |
About once a day | 112.7 | 17.8 | |
Monthly Income | 500 USD and below | 178.5 | 28.2 |
501–1000 USD | 137.4 | 21.7 | |
1001–1500 USD | 91.8 | 14.5 | |
1501 USD and above | 225.3 | 35.6 |
Construct | Sources | Construct | Sources |
---|---|---|---|
Perceived value | Zeithaml [24] | Entertainment gratification | Huang [79] |
Attitude | Kim and Forsythe [80] | Social factors | Ramayah et al. [103] |
Online-shopping intention | Chiu et al. [104] | Web-irritation | Gao and Koufaris [96] |
Online-shopping experience | Alam and Yasin [105] | Navigation design | Ganguly et al. [99] |
Trust | Broekhuizen and Huizingh [33] | Visual design | Ganguly et al. [99] |
Perceived ease of use | Davis [49] | Information design | Ganguly et al. [99] |
Perceived usefulness | Davis [49] | Actual online-shopping purchase | Sihombing [106] |
Perceived risk | Park et al. [46] |
Constructs | Mean | SD | EFA | CFA | AVE | Composite Reliability | Cronbach’s Alpha |
---|---|---|---|---|---|---|---|
Perceived value (PV) | 5.34 | 0.61 | 0.70 | 0.87 | 0.91 | ||
PV1 | 5.42 | 0.74 | 0.63 | 0.86 | |||
PV2 | 5.39 | 0.59 | 0.58 | 0.74 | |||
PV3 | 5.26 | 0.84 | 0.61 | 0.91 | |||
Attitude (AT) | 5.25 | 0.64 | 0.65 | 0.85 | 0.89 | ||
AT1 | 5.32 | 0.91 | 0.74 | 0.81 | |||
AT2 | 5.48 | 0.67 | 0.71 | 0.87 | |||
AT3 | 4.96 | 0.81 | 0.73 | 0.73 | |||
Online-shopping intention (OSI) | 5.50 | 0.61 | 0.75 | 0.89 | 0.87 | ||
OSI-1 | 5.14 | 0.75 | 0.50 | 0.81 | |||
OSI-2 | 5.64 | 0.68 | 0.54 | 0.86 | |||
OSI-3 | 5.71 | 0.85 | 0.56 | 0.92 | 0.93 | ||
Online-shopping experience (OSE) | 5.57 | 0.71 | 0.76 | 0.90 | 0.93 | ||
OSE1 | 5.63 | 0.78 | 0.67 | 0.79 | |||
OSE2 | 5.41 | 0.83 | 0.61 | 0.93 | |||
OSE3 | 5.68 | 0.81 | 0.59 | 0.89 | |||
Trust (TR) | 5.67 | 0.81 | 0.61 | 0.82 | 0.87 | ||
TR1 | 5.47 | 0.69 | 0.54 | 0.78 | |||
TR2 | 5.71 | 0.82 | 0.51 | 0.83 | |||
TR3 | 5.84 | 0.76 | 0.59 | 0.74 | |||
Perceived ease of use (PEOU) | 5.52 | 0.83 | 0.74 | 0.92 | 0.90 | ||
PEOU1 | 5.11 | 0.67 | 0.56 | 0.94 | |||
PEOU2 | 5.48 | 0.59 | 0.63 | 0.83 | |||
PEOU3 | 5.63 | 0.78 | 0.58 | 0.90 | |||
PEOU4 | 5.87 | 0.73 | 0.65 | 0.77 | |||
Perceived usefulness (PU) | 5.45 | 0.83 | 0.69 | 0.90 | 0.94 | ||
PU1 | 5.35 | 0.72 | 0.57 | 0.91 | |||
PU2 | 5.38 | 0.76 | 0.53 | 0.82 | |||
PU3 | 5.46 | 0.83 | 0.58 | 0.78 | |||
PU4 | 5.63 | 0.87 | 0.59 | 0.81 | |||
Entertainment gratification (EG) | 5.44 | 0.84 | 0.58 | 0.80 | 0.85 | ||
EG1 | 5.23 | 0.91 | 0.53 | 0.88 | |||
EG2 | 5.37 | 0.83 | 0.55 | 0.94 | |||
EG3 | 5.74 | 0.86 | 0.51 | 0.86 | |||
Social factors (SF) | 5.55 | 0.87 | 0.68 | 0.87 | 0.84 | ||
SF1 | 5.63 | 0.79 | 0.61 | 0.79 | |||
SF2 | 5.48 | 0.86 | 0.65 | 0.83 | |||
SF3 | 5.38 | 0.73 | 0.74 | 0.83 | |||
Website Irritation (WI) | 2.73 | 0.67 | 0.61 | 0.82 | 0.78 | ||
WI-1 | 2.62 | 0.72 | 0.62 | 0.74 | |||
WI-2 | 2.85 | 0.69 | 0.63 | 0.81 | |||
WI-3 | 2.73 | 0.86 | 0.67 | 0.79 | |||
Navigation design (ND) | 5.58 | 0.76 | 0.70 | 0.87 | 0.83 | ||
ND1 | 5.38 | 0.73 | 0.61 | 0.79 | |||
ND2 | 5.64 | 0.86 | 0.63 | 0.84 | |||
ND3 | 5.73 | 0.91 | 0.62 | 0.89 | |||
Visual design (VD) | 5.57 | 0.84 | 0.77 | 0.91 | 0.87 | ||
VD1 | 5.42 | 0.68 | 0.53 | 0.85 | |||
VD2 | 5.61 | 0.77 | 0.64 | 0.87 | |||
VD3 | 5.68 | 0.69 | 0.68 | 0.91 | |||
Information design (ID) | 5.73 | 0.82 | 0.72 | 0.89 | 0.89 | ||
ID1 | 5.74 | 0.81 | 0.54 | 0.90 | |||
ID2 | 5.66 | 0.68 | 0.59 | 0.84 | |||
ID3 | 5.79 | 0.79 | 0.51 | 0.81 | |||
Perceived risk (PR) | 2.55 | 0.86 | 0.56 | 0.79 | 0.80 | ||
PR1 | 2.61 | 0.70 | 0.51 | 0.82 | |||
PR2 | 2.57 | 0.84 | 0.56 | 0.89 | |||
PR3 | 2.49 | 0.91 | 0.52 | 0.93 | |||
Actual online-shopping purchase (AOSP) | 5.52 | 0.67 | 0.72 | 0.89 | 0.83 | ||
AOSP1 | 5.42 | 0.77 | 0.52 | 0.85 | |||
AOSP2 | 5.66 | 0.64 | 0.53 | 0.89 | |||
AOSP3 | 5.49 | 0.84 | 0.56 | 0.81 |
Attributes | PV | AT | OSI | OSE | TR | PEOU | PU | EG | SF | WI | ND | VD | ID | PR | AOSP |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PV | 0.84 | ||||||||||||||
AT | 0.51 | 0.81 | |||||||||||||
OSI | 0.41 | 0.62 | 0.86 | ||||||||||||
OSE | 0.18 | 0.52 | 0.61 | 0.87 | |||||||||||
TR | 0.48 | 0.57 | 0.47 | 0.61 | 0.78 | ||||||||||
PEOU | 0.36 | 0.27 | 0.35 | 0.47 | 0.51 | 0.86 | |||||||||
PU | 0.22 | 0.34 | 0.36 | 0.42 | 0.57 | 0.44 | 0.83 | ||||||||
EG | 0.52 | 0.33 | 0.48 | 0.35 | 0.19 | 0.39 | 0.49 | 0.76 | |||||||
SF | 0.42 | 0.49 | 0.35 | 0.43 | 0.22 | 0.27 | 0.46 | 0.14 | 0.82 | ||||||
WI | −0.27 | −0.47 | −0.33 | −0.46 | −0.19 | −0.26 | −0.34 | −0.46 | −0.37 | 0.78 | |||||
ND | 0.15 | 0.24 | 0.23 | 0.41 | 0.35 | 0.33 | 0.37 | 0.36 | 0.49 | −0.31 | 0.84 | ||||
VD | 0.37 | 0.61 | 0.52 | 0.49 | 0.17 | 0.24 | 0.32 | 0.19 | 0.11 | −0.36 | 0.14 | 0.88 | |||
ID | 0.67 | 0.39 | 0.46 | 0.24 | 0.37 | 0.29 | 0.14 | 0.14 | 0.24 | −0.42 | 0.23 | 0.41 | 0.85 | ||
PR | −0.41 | −0.22 | −0.48 | −0.17 | −0.12 | −0.15 | −0.26 | −0.29 | −0.35 | −0.19 | −0.32 | −0.36 | −0.48 | 0.75 | |
AOSP | 0.49 | 0.38 | 0.48 | 0.45 | 0.37 | 0.16 | 0.51 | 0.18 | 0.26 | −0.46 | 0.14 | 0.45 | 0.27 | 0.14 | 0.85 |
Fit Indices | The Measurement Model | The Unified Model | Recommended Values |
---|---|---|---|
χ2/df | 1.74 | 1.92 | <3 |
GFI | 0.93 | 0.98 | >0.09 |
CFI | 0.91 | 0.96 | >0.09 |
TLI | 0.94 | 0.97 | >0.09 |
RMSEA | 0.05 | 0.06 | <0.08 |
Hypothesis/Structural Relationship | Path Coefficients (β) | Standard Error | t-Values | Results |
---|---|---|---|---|
A → B | ||||
Descendants of perceived value (R2 = 0.13) | ||||
H1: attitude → perceived value | 0.168 | 0.051 | 3.29 *** | Supported |
Descendants of attitude (R2 = 0.74) | ||||
H11: perceived risk → attitude | −0.164 | 0.046 | −3.56 *** | Supported |
H12: perceived ease of use → attitude | 0.346 | 0.041 | 4.56 *** | Supported |
H14: perceived usefulness → attitude | 0.401 | 0.051 | 7.86 *** | Supported |
H15: social factors → attitude | 0.271 | 0.067 | 4.04 *** | Supported |
H16: entertainment gratification → attitude | 0.363 | 0.056 | 6.48 *** | Supported |
H19: web-irritation → attitude | −0.391 | 0.041 | −9.53 *** | Supported |
Descendants of online-shopping intention (R2 = 0.45) | ||||
H2: perceived value →online-shopping intention | 0.512 | 0.076 | 6.74 *** | Supported |
H3: attitude → online-shopping intention | 0.468 | 0.073 | 6.41 *** | Supported |
H6: online-shopping experience → online-shopping intention | 0.518 | 0.063 | 8.22 *** | Supported |
H8: trust → online-shopping intention | 0.625 | 0.071 | 8.80 *** | Supported |
H10: perceived risk→ online-shopping intention | −0.469 | 0.059 | −7.95 *** | Supported |
Descendants of actual online-shopping purchase (R2 = 0.15) | ||||
H4: online-shopping intention → actual online-shopping purchase | 0.508 | 0.061 | 8.32 *** | Supported |
Descendants of online-shopping experience (R2 = 0.22) | ||||
H5: actual online-shopping purchase → online-shopping experience | 0.421 | 0.037 | 11.37 *** | Supported |
Descendants of trust (R2 = 0.18) | ||||
H7: online-shopping experience → trust | 0.326 | 0.046 | 7.08 *** | Supported |
H24: navigation design → trust | 0.228 | 0.062 | 3.68 *** | Supported |
H25: visual design → trust | 0.540 | 0.063 | 8.57 *** | Supported |
H26: information design → trust | 0.310 | 0.058 | 5.34 *** | Supported |
Descendants of perceived risk (R2 = 0.13) | ||||
H9: trust → perceived risk | −0.301 | 0.045 | −6.69 *** | Supported |
Descendants of web-irritation (R2 = 0.19) | ||||
H21: navigation design → web-irritation | −0.329 | 0.084 | −3.91 *** | Supported |
H22: visual design → web-irritation | −0.506 | 0.077 | −6.57 *** | Supported |
H23: information design → web-irritation | −0.464 | 0.092 | −5.04 *** | Supported |
Descendants of perceived ease of use (R2 = 0.25) | ||||
H17: entertainment gratification → perceived ease of use | 0.601 | 0.055 | 10.93 *** | Supported |
H20: web-irritation → perceived ease of use | −0.502 | 0.061 | −8.23 *** | Supported |
Descendants of perceived usefulness (R2 = 0.31) | ||||
H13: perceived ease of use → perceived usefulness | 0.391 | 0.048 | 8.14 *** | Supported |
H18: entertainment gratification → perceived usefulness | 0.456 | 0.066 | 6.90 *** | Supported |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ijaz, M.F.; Rhee, J. Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls. Sustainability 2018, 10, 3756. https://doi.org/10.3390/su10103756
Ijaz MF, Rhee J. Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls. Sustainability. 2018; 10(10):3756. https://doi.org/10.3390/su10103756
Chicago/Turabian StyleIjaz, Muhammad Fazal, and Jongtae Rhee. 2018. "Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls" Sustainability 10, no. 10: 3756. https://doi.org/10.3390/su10103756