Abstract
User-customization is increasingly common in electronic commerce, because both the buyer and seller potentially benefit. The user interface to implement and the influence of the interface on various process and outcome measures, however, are not well understood. We developed a Flow-based model consisting of seven hypotheses regarding the user interface and its consequents. We conducted a field experiment to test an attribute-based interface vs. a question-based interface on three variables (perceived control, shopping enjoyment and choice satisfaction) as well as two web site intentions: intention to return and intention to purchase. Six of the seven hypotheses were supported in a parsimonious model. Variance explained was 16.3% for perceived control, 45.6% for shopping enjoyment, 59.3% for choice satisfaction and 63.1% for web site intentions. The main finding is that an attribute-based interface for retail e-shopping increases the shopper’s sense of control and feeling of enjoyment in the process more than a question-based interface, and thereby increases satisfaction with the outcome. This combination of influences increases the intention of the shopper to return to the web site and to purchase the item. We discuss the results and suggest areas for future research in user-customization, which may apply to many different industries that engage in online commerce.
Similar content being viewed by others
References
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694.
Agarwal, R., & Venkatesh, V. (2002). Assessing a firm’s web presence: A heuristic evaluation procedure for the measurement of usability. Information Systems Research, 13(2), 168–186.
Bagozzi, R. P. (1979). The role of measurement in theory construction and hypothesis testing: Toward a holistic model. In O. C. Ferrell, S. W. Brown, & C. W. Lamb (Eds.), Conceptual and theoretical developments in marketing (pp. 15–32). Chicago: American Marketing Association.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.
Bakos, J. Y. (1997). Reducing buyer search costs: implications for electronic marketplaces. Management Science, 43(12), 1676–1692.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood, NJ: Prentice-Hall.
Bauer, H. H., Falk, T., & Hammerschmidt, M. (2006). eTransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59(7), 866–875.
Berman, B. (2002). Should your firm adopt a mass customization strategy? Business Horizons, 45(4), 51–60.
Bharati, P., & Chaudhury, A. (2004). An empirical investigation of decision-making satisfaction in web-based decision support systems. Decision Support Systems, 37(2), 187–197.
Bo, X., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.
Bowen, D. E. (1986). Managing customers as human resources in service organizations. Human Resource Management, 25, 371–383.
Brynjolfsson, E., & Smith, M. D. (2000). Frictionless commerce? A comparison of internet and conventional retailers. Management Science, 46(4), 563–585.
Cai, S., & Xu, Y. (2006). Effects of outcome, process and shopping enjoyment on online consumer behaviour. Electronic Commerce Research & Applications, 5(4), 272–281.
Chenoweth, T., Dowling, K. L., & Louis, R. D. S. (2004). Convincing DSS users that complex models are worth the effort. Decision Support Systems, 37(1), 71–82.
Chin, W. (2000). Frequently asked questions—partial least squares & PLS-graph home page. Retrieved April 12, 2002 from http://disc-nt.cba.uh.edu/chin/plsfaq/plsfaq.htm.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.
Chin, W. W., & Newsted, P. R. (1998). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical strategies for small-sample research. Thousand Oaks, CA: Sage.
Cohen, S. L., & Pine, B. J. (2007). Mass customizing the training industry. T+D, 61(6), 50–54.
Csikszentmihalyi, M. (1975). Plan and intrinsic rewards. Journal of Humanistic Psychology, 15(3), 41–63.
Csikszentmihalyi, M. (1977). Beyond boredom and anxiety. San Francisco: Jossey-Bass.
Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (1988). Optimal experience: Psychological studies of flow in consciousness. Cambridge: Cambridge University Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Detlor, B., Sproule, S., & Gupta, C. (2003). Pre-purchase online information seeking: Search versus browse. Journal of Electronic Commerce Research, 4(2), 72–84.
Dhar, R. (1996). The effect of decision strategy on deciding to defer choice. Journal of Behavioral Decision Making, 9, 265–281.
Diehl, K., Kornish, L. J., Lynch Jr., J. G., Mick, D. G., & Lehmann, D. R. (2003). Smart agents: when lower search costs for quality information increase price sensitivity. Journal of Consumer Research, 30(1), 56–71.
Dietrich, A. J., Kirn, S., & Sugumaran, V. (2007). A service-oriented architecture for mass customization a shoe industry case study. IEEE Transactions on Engineering Management, 54(1), 190–204.
Dwivedi, Y. K., Williams, M. D., & Venkatesh, V. (2008). Guest editorial: A profile of adoption of Information and Communication Technologies (ICT) research in the household context. Information Systems Frontiers, 10(4), 385–390.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18, 39–50.
Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human-computer interaction. Journal of Psychology, 128(4), 381–391.
Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science, 23(3), 391–406.
Gilbride, T. J., & Allenby, G. M. (2006). Estimating heterogeneous EBA and economic screening rule choice models. Marketing Science, 25(5), 494–509.
Gretzel, U., & Fesenmaier, D. R. (2005). Persuasiveness of preference elicitation processes in destination recommendation systems. In A. J. Frew (Ed.), Information and communication technologies in tourism 2005 (pp. 194–204). Innsbruck, Austria: Springer.
Gretzel, U., & Fesenmaier, D. R. (2006). Persuasion in recommender systems. International Journal of Electronic Commerce, 11(2), 81–100.
Group, T. I. (2002). Business-to-Consumer E-Commerce Report.
Hampton-Sosa, W., & Koufaris, M. (2005). The effect of web site perceptions on initial trust in the owner company. International Journal of Electronic Commerce, 10(1), 55–81.
Hassanein, K., & Head, M. (2005). The impact of infusing social presence in the web interface: An investigation across product types. International Journal of Electronic Commerce, 10(2), 31–55.
Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 4–21.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68.
Holzwarth, M., Janiszewski, C., & Neumann, M. M. (2006). The Influence of Avatars on Online Consumer Shopping Behavior. Journal of Marketing, 70(4), 19–36.
Hong, S.-J., Thong, J. Y. L., Moon, J.-Y., & Tam, K.-Y. (2008). Understanding the behavior of mobile data services consumers. Information Systems Frontiers, 10(4), 431–445.
Hong, W., Thong, J. Y. L., & Tam, K. Y. (2005). The effects of information format and shopping task on consumers’ online shopping behavior: A cognitive fit perspective. Journal of Management Information Systems, 21(3), 149–184.
Huffman, C., & Kahn, B. E. (1998). Variety for sale: Mass customization or mass confusion? Journal of Retailing, 74(4), 491–513.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.
Ives, B., & Piccoli, G. (2003). Custom made apparel and individualized service at lands’ end. Communications of AIS, 2003(11), 79–93.
Jahng, J., Jain, H. K., & Ramatnurthy, K. (2006). An empirical study of the impact of product characteristics and electronic commerce interface richness on consumer attitude and purchase intentions. IEEE Transactions on Systems, Man & Cybernetics: Part A, 36(6), 1185–1201.
Jarvis, C. B., Mackenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.
Jiang, Z., & Benbasat, I. (2005). Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. Journal of Management Information Systems, 21(3), 111–148.
Johnson, D., & Wiles, J. (2003). Effective affective user interface design in games. Ergonomics, 46(13/14), 1332–1345.
Kahn, B. E. (1998). Dynamic relationships with customers: High-variety strategies. Journal of the Academy of Marketing Science, 26(1), 45–53.
Kamis, A. (2006). Search strategies in shopping engines: An experimental investigation. International Journal of Electronic Commerce, 11(1), 63–84.
Kamis, A., Koufaris, M., & Stern, T. (2008). Using an attribute-based DSS for user-customized products online: An experimental investigation. MIS Quarterly, 32-1, 159–177.
Keaveney, S. M. (1995). Customer switching behavior in service industries: An exploratory study. Journal of Marketing, 59(2), 71–85.
Kelley, S. W., Donnelley J. H., Jr., & Skinner, S. J. (1990). Customer participation in service production and delivery. Journal of Retailing, 66(3), 315–335.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223.
Koufaris, M., Kambil, A., & LaBarbera, P. A. (2001-2002). Consumer behavior in web-based commerce: An empirical study. International Journal of Electronic Commerce, 6(2), 115–138.
Lee, J.-N., Pi, S.-M., Kwok, R. C.-w., & Huynh, M. Q. (2003). The contribution of commitment value in internet commerce: An empirical investigation. Journal of the Association for Information systems, 4(2003), 39–64.
Lingyun, Q., & Benbasat, I. (2005). Online consumer trust and live help interfaces: The effects of text-to-speech voice and three-dimensional avatars. International Journal of Human-Computer Interaction, 19(1), 75–94.
Lovelock, C. H., & Young, R. F. (1979). Look to consumers to increase productivity. Harvard Business Review, 57(3), 168–178.
Mills, P. K., Chase, R., & Margulies, N. (1983). Motivating the client/employee system as a service production strategy. Academy of Management Review, 8(2), 301–310.
Mills, P. K., & Morris, J. H. (1986). Clients as ‘partial’ employees of service organizations: Role development in client participation. Academy of Management Review, 11(4), 726–735.
Moe, W. W. (2001). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1&2), 29–40.
Moe, W. W., & Fader, P. S. (2004). Dynamic conversion behavior at E-commerce sites. Management Science, 50(3), 326–335.
Nielsen, J. (2000). Designing web usability. Indianapolis, IN: New Riders.
Novak, T. P. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42.
Pavlou, P. A., & Fygenson, M. (2006). Understanding and prediction electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143.
Payne, J. W. (1976). Task complexity and contingent processing in decision-making—an information search and protocol analysis. Organizational Behavior and Human Decision Processes, 16(2), 366–387.
Pennington, R., Wilcox, H. D., & Grover, V. (2003-2004). The role of system trust in business-to-consumer transactions. Journal of Management Information Systems, 20(3), 197–226.
Pine, J. P. (1993). Mass customization. Boston, MA: Harvard Business School Press.
Polaine, A. (2005). The flow principle in interactivity. Australasian conference on Interactive entertainment. Sydney, Australia: Creativity & Cognition Studios Press.
Pu, P., & Chen, L. (2005). Integrating tradeoff support in product search tools for e-commerce sites. ACM conference on Electronic commerce.
Pu, P., & Kumar, P. (2004). Evaluating example-based search tools. ACM Conference on Electronic Commerce New York.
Qiu, L., & Benbasat, I. (2005). An investigation into the effects of text-to-speech voice and 3d avatars on the perception of presence and flow of live help in electronic commerce. ACM Transactions on Computer-Human Interaction, 12(4), 329–355.
Reichheld, F. F. (1996). Learning from customer defections. Harvard Business Review, 74(2), 56–69.
Ricci, F., & Werthner, H. (2006–2007). Introduction to the special issue: Recommender systems. International Journal of Electronic Commerce, 11(2), 5–9.
Richard, R.-B. (2007). Four keyboards to sustainable mass customization in architecture & construction. MCPC 2007 World Conference on Mass Customization & Personalization, Cambridge, MA.
Ro, Y. K., Liker, J. K., & Fixson, S. K. (2007). Modularity as a strategy for supply chain coordination: The case of U.S. auto. IEEE Transactions on Engineering Management, 54(1), 172–189.
Ryan, R., Rigby, C., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation & Emotion, 30(4), 344–360.
Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1–2), 115–153.
Silpakit, P., Fiskin, R. P., Eds. (1985). ’Participatizing’ the service encounter: A theoretical framework. Services marketing in a changing environment. Chicago, American Marketing Association.
Simon, H. A. (1972). Theories of bounded rationality. In R. a. Radner (Ed.), Decision and organisation. Amsterdam: North Holland.
Simon, H. A. (1982). Models of bounded rationality. Cambridge, MA: MIT Press.
Sweetser, P., & Wyeth, P. (2005). GameFlow: a model for evaluating player enjoyment in games. Computers in Entertainment, 3, 3.
Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381–396.
Todd, P., & Benbasat, I. (1992). The use of information in decision making: An experimental investigation of the impact of computer-based decision aids. MIS Quarterly, 16(3), 373–393.
Tu, Q., Vonderembse, M. A., Ragu-Nathan, T. S., & Ragu-Nathan, B. (2004). Measuring modularity-based manufacturing practices and their impact on mass customization capability: A customer-driven perspective. Decision Sciences, 35(2), 147–168.
Urbany, J. E. (1986). An experimental examination of the economics of information. Journal of Consumer Research, 13(2), 257–271.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704.
Venkatesh, V., & Agarwal, R. (2006). Turning visitors into customers: a usability-centric perspective on purchase behavior in electronic channels. Management Science, 52(3), 367–382.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Wan, G. (2000). Internet based customer decision support systems for mass customization. International Journal of Management, 17(3), 386–393.
Wang, W., & Benbasat, I. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 72–101.
White, R. W. (1959). Motivation considered: The concept of competence. Psychological Review, 66(5), 297–333.
Wind, J., & Rangaswamy, A. (2001). Customerization: the next revolution in mass customization. Journal of Interactive Marketing, 15(1), 13–32.
Wujin, C., Beomjoon, C., & Mee Ryoung, S. (2005). The role of on-line retailer brand and infomediary reputation in increasing consumer purchase intention. International Journal of Electronic Commerce, 9(3), 115–127.
Yao, A. C., & Carlson, J. G. H. (2003). Agility and mixed-model furniture production. International Journal of Production Economics, 81, 8295.
Yen, B. P. C., & Ng, K. (2007). Virtual objects in electronic catalogs: A human-computer interface issue. IEEE Transactions on Systems, Man & Cybernetics: Part A, 37(4), 599–608.
Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362–375.
Zhang, X., & Maruping, L. M. (2008). Household technology adoption in a global marketplace: Incorporating the role of espoused cultural values. Information Systems Frontiers, 10(4), 403–413.
Ziegler, C.-N., & Golbeck, J. (2007). Investigating interactions of trust and interest similarity. Decision Support Systems, 43(2), 460–475.
Zigurs, I., & Buckland, B. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313–334.
Zipkin, P. (2001). The limits of mass customization. MIT Sloan Management Review, 42(3), 81–87.
Author information
Authors and Affiliations
Corresponding author
Additional information
Note: Yogesh K. Dwivedi, Michael D. Williams and Viswanath Venkatesh were the guest editors accepting the article as part of the special issue on Adoption and Use of Information & Communication Technologies (ICT) in the Residential/Household Context (see Dwivedi et al. 2008 for editorial).
Appendix
Appendix
Screen Shots of the Question-Based UID and Attribute-Based UID
Rights and permissions
About this article
Cite this article
Kamis, A., Stern, T. & Ladik, D.M. A flow-based model of web site intentions when users customize products in business-to-consumer electronic commerce. Inf Syst Front 12, 157–168 (2010). https://doi.org/10.1007/s10796-008-9135-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-008-9135-y