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Product fit uncertainty and its effects on vendor choice: an experimental study

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Abstract

Recommender systems and other Internet-enabled technologies have changed the surrounding conditions of pre-purchase evaluations on the Internet. In some cases consumers can now sample entire products prior to a purchase – hereby removing all uncertainty about whether a product fits their taste. While previous research has mainly focused on vendor and product quality uncertainty, it is still not clear how declining product fit uncertainty affects consumers. To close this gap, we conducted a laboratory experiment to analyze the effects on consumers’ vendor selection. We find that full elimination of product fit uncertainty is beneficial for vendors, as it increases both the number of purchases and consumer loyalty. Interestingly, if product fit uncertainty is only partially eliminated, consumers do not necessarily show differential behavior for different levels of remaining product fit uncertainty. This has important implications for online vendors that consider the implementation of additional means to reduce product fit uncertainty.

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Notes

  1. In the past, the IS literature has frequently discussed fit in the context of task technology fit (e.g., Beaudry and Pinsonneault 2005; Goodhue and Thompson 1995; Lee et al. 2007).

  2. In accordance with random utility theory, a product’s overall utility is a single value that integrates all attributes of a product including its price (McFadden 1986).

References

  • Abdellaoui, M., Bleichrodt, H., & Paraschiv, C. (2007). Loss aversion under prospect theory: a parameter-free measurement. Management Science, 53(10), 1659–1674.

    Article  Google Scholar 

  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

    Article  Google Scholar 

  • Ba, S., & Pavlou, P. A. (2002). Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior. MIS Quarterly, 26(3), 243–268.

    Article  Google Scholar 

  • Bakos, J. Y. (1997). Reducing buyer search costs: implications for electronic marketplaces. Management Science, 43(12), 1676–1692.

    Article  Google Scholar 

  • Bearden, W. O., & Teel, J. E. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21–28.

    Article  Google Scholar 

  • Beaudry, A., & Pinsonneault, A. (2005). Understanding user responses to information technology: a coping model of user adaptation. MIS Quarterly, 29(3), 493–524.

    Google Scholar 

  • Benbasat, I., Gefen, D., & Pavlou, P. A. (2008). Special issue: trust in online environments. Journal of Management Information Systems, 24(4), 5–11.

    Article  Google Scholar 

  • Benlian, A., Titah, R., & Hess, T. (2012). Differential effects of provider recommendations and consumer reviews in e-commerce transactions: an experimental study. Journal of Management Information Systems, 29(1), 237–272.

    Article  Google Scholar 

  • Bergemann, D., & Välimäki, J. (2006). Dynamic pricing of new experience goods. Journal of Political Economy, 114(4), 713–743.

    Article  Google Scholar 

  • Bhattacharjee, S., Gopal, R. D., Lertwachara, K., & Marsden, J. R. (2006). Consumer search and retailer strategies in the presence of online music sharing. Journal of Management Information Systems, 23(1), 129–159.

    Article  Google Scholar 

  • Bock, G. W., Lee, J., Kuan, H. H., & Kim, J. H. (2012). The progression of online trust in the multi-channel retailer context and the role of product uncertainty. Decision Support Systems, 53(1), 97–107.

    Article  Google Scholar 

  • Brynjolfsson, E., Hu, Y. J., & Simester, D. (2011). goodbye pareto principle, hello long tail: the effect of search costs on the concentration of product sales. Management Science, 57(8), 1373–1386.

    Article  Google Scholar 

  • Burkard, C., Widjaja, T., & Buxmann, P. (2012). Software ecosystems. Wirtschaftsinformatik, 54(1), 43–47.

    Article  Google Scholar 

  • Camerer, C. F., & Kunreuther, H. (1989). Decision processes for low probability events: policy implications. Journal of Policy Analysis and Management, 8(4), 565–592.

    Article  Google Scholar 

  • Chellappa, R. K., & Shivendu, S. (2005). Managing piracy: pricing and sampling strategies for digital experience goods in vertically segmented markets. Information Systems Research, 16(4), 400–417.

    Article  Google Scholar 

  • Chiu, C.-M., Wang, E. T. G., Fang, Y.-H., & Huang, H.-Y. (2014). Understanding customers' repeat purchase intentions in b2c e-commerce: the roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85–115.

    Article  Google Scholar 

  • Clemons, E. K. (2007). An empirical investigation of third-party seller rating systems in e-commerce: the case of buysafe. Journal of Management Information Systems, 24(2), 43–71.

    Article  Google Scholar 

  • Clemons, E. K., Jin, F., Wilson, J., Ren, F., Matt, C., Hess, T., & Koh, N. (2013). The role of trust in successful ecommerce websites in China: Field observations and experimental studies. In Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS 2013), Maui, HI, USA.

  • Dellarocas, C. (2003). The digitization of word of mouth: promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  • Dellarocas, C., & Wood, C. A. (2008). The sound of silence in online feedback: estimating trading risks in the presence of reporting bias. Management Science, 54(3), 460–476.

    Article  Google Scholar 

  • Dimoka, A., Hong, Y., & Pavlou, P. (2012). On product uncertainty in online markets: theory and evidence. MIS Quarterly, 36(2), 395–426.

    Google Scholar 

  • Duncan, R. B. (1972). Characteristics of organizational environments and perceived environmental uncertainty. Administrative Science Quarterly, 17(3), 313–327.

    Article  Google Scholar 

  • Erdem, T., Swait, J., & Valenzuela, A. (2006). Brands as signals: a cross-country validation study. Journal of Marketing, 70(1), 34–49.

    Article  Google Scholar 

  • Farrell, J., & Rabin, M. (1996). Cheap talk. The Journal of Economic Perspectives, 10(3), 103–118.

    Article  Google Scholar 

  • Fox, C. R., & Tversky, A. (1995). Ambiguity aversion and comparative ignorance. The Quarterly Journal of Economics, 110(3), 585–603.

    Article  Google Scholar 

  • Gale, D., & Rosenthal, R. W. (1994). Price and quality cycles for experience goods. The Rand Journal of Economics, 25(4), 590–607.

    Article  Google Scholar 

  • Ghose, A. (2009). Internet exchanges for used goods: an empirical analysis of trade patterns and adverse selection. MIS Quarterly, 33(2), 163–291.

    Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  • Halbheer, D., Stahl, F., Koenigsberg, O., & Lehmann, D. R. (2014). Choosing a digital content strategy: how much should be free? International Journal of Research in Marketing, 31(2), 192–206.

    Article  Google Scholar 

  • Hess, T., & Matt, C. (2013). The internet and the value chains of the media industry. In S. Diehl, & M. Karmasin (Eds.), Media and convergence management (pp. 37–55). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  • Hinz, O., & Eckert, J. (2010). The impact of search and recommendation systems on sales in electronic commerce. Business & Information Systems Engineering, 2(2), 67–77.

  • Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655.

    Article  Google Scholar 

  • Hong, Y., & Pavlou, P. A. (2014). Product fit uncertainty in online markets: nature, effects, and antecedents. Information Systems Research, 25(2), 328–344.

    Article  Google Scholar 

  • Huang, Z., Zeng, D. D., & Chen, H. (2007). Analyzing consumer-product graphs: empirical findings and applications in recommender systems. Management Science, 53(7), 1146–1164.

    Article  Google Scholar 

  • Johnson, E. J., Moe, W. W., Fader, P. S., Bellman, S., & Lohse, G. L. (2004). On the depth and dynamics of online search behavior. Management Science, 50(3), 299–308.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47(2), 263–291.

    Article  Google Scholar 

  • Kamis, A., Koufaris, M., & Stern, T. (2008). Using an attribute-based decision support system for user-customized products online: an experimental investigation. MIS Quarterly, 32(1), 159–177.

    Google Scholar 

  • Kim, D., & Benbasat, I. (2006). The effects of trust-assuring arguments on consumer trust in internet stores: application of toulmin’s model of argumentation. Information Systems Research, 17(3), 286–300.

    Article  Google Scholar 

  • Kim, D., & Benbasat, I. (2009). Trust-assuring arguments in b2c e-commerce: impact of content, source, and price on trust. Journal of Management Information Systems, 26(3), 175–206.

    Article  Google Scholar 

  • Kim, D., & Benbasat, I. (2010). Designs for effective implementation of trust assurances in internet stores. Communications of the ACM, 53(2), 121–126.

    Article  Google Scholar 

  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564.

    Article  Google Scholar 

  • Kunreuther, H., Novemsky, N., & Kahneman, D. (2001). Making low probabilities useful. Journal of Risk and Uncertainty, 23(2), 103–120.

    Article  Google Scholar 

  • Kwark, Y., Chen, J., & Raghunathan, S. (2014). Online product reviews: implications for retailers and competing manufacturers. Information Systems Research, 25(1), 93–110.

    Article  Google Scholar 

  • Lee, Z., & Gosain, S. (2002). A longitudinal price comparison for music cds in electronic and brick-and-mortar markets: pricing strategies in emergent electronic commerce. Journal of Business Strategies, 19(1), 55–71.

    Google Scholar 

  • Lee, H.-J., & Huddleston, P. (2006). Effects of e-tailer and product type on risk handling in online shopping. Journal of Marketing Channels, 13(3), 5–28.

    Article  Google Scholar 

  • Lee, B.-C., Ang, L., & Dubelaar, C. (2005). Lemons on the web: A Signalling Approach to the Problem of Trust in Internet Commerce. Journal of Economic Psychology, 26(5), 607–623.

    Article  Google Scholar 

  • Lee, C.-C., Cheng, H. K., & Cheng, H.-H. (2007). An empirical study of mobile commerce in insurance industry: task–technology fit and individual differences. Decision Support Systems, 43(1), 95–110.

    Article  Google Scholar 

  • Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Information Systems Research, 19(4), 456–474.

    Article  Google Scholar 

  • Matt, C., & Hess, T. (2012). Facilitating consumers’ evaluation of experience goods and the benefits for vendors. Proceedings of the 18th Americas Conference on Information Systems (AMCIS 2012), Seattle, WA, USA.

  • Matt, C., Hess, T., & Weiß, C. (2013). The differences between recommender technologies in their impact on sales diversity. In Proceedings of the 2013 International Conference on Information Systems (ICIS 2013), Italy, Milan.

  • McFadden, D. L. (1986). The choice theory approach to market research. Marketing Science, 5(4), 275–297.

    Article  Google Scholar 

  • Moorthy, S., & Srinivasan, K. (1995). Signaling quality with a money-back guarantee: the role of transaction costs. Marketing Science, 14(4), 442–466.

    Article  Google Scholar 

  • Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34 (1), 185–200.

  • Nelson, P. (1970). Information and consumer behavior. The Journal of Political Economy, 78(2), 311–329.

    Article  Google Scholar 

  • Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.

    Article  Google Scholar 

  • Pavlou, P., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective. MIS Quarterly, 31(1), 105–136.

    Google Scholar 

  • Rao, A. R., & Monroe, K. B. (1996). Causes and consequences of price premiums. Journal of Business, 69(4), 511–535.

    Article  Google Scholar 

  • Reimann, M., Lünemann, U. F., & Chase, R. B. (2008). Uncertainty avoidance as a moderator of the relationship between perceived service quality and customer satisfaction. Journal of Service Research, 11(1), 63–73.

    Article  Google Scholar 

  • Resnick, P., Zeckhauser, R., Swanson, J., & Lockwood, K. (2006). The value of reputation on ebay: a controlled experiment. Experimental Economics, 9(2), 79–101.

    Article  Google Scholar 

  • Sha, W. (2009). Types of structural assurance and their relationships with trusting intentions in business-to-consumer e-commerce. Electronic Markets, 19(1), 43–54.

    Article  Google Scholar 

  • Shapiro, C., & Varian, H. R. (1999). Information rules: a strategic guide to the network economy. Boston: Harvard Business Press.

    Google Scholar 

  • Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696–707.

    Article  Google Scholar 

  • Sutton, J. (1986). Vertical product differentiation: some basic themes. The American Economic Review, 76(2), 393–398.

    Google Scholar 

  • Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: an extension. Journal of Marketing Research, 25(2), 204–212.

    Article  Google Scholar 

  • Tushman, M. L., & Nadler, D. A. (1978). Information processing as an integrating concept in organizational design. The Academy of Management Review, 3(3), 613–624.

  • Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: a reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.

    Article  Google Scholar 

  • Urbany, J. E., Dickson, P. R., & Wilkie, W. L. (1989). Buyer uncertainty and information search. Journal of Consumer Research, 16(2), 208–215.

    Article  Google Scholar 

  • Wang, W., & Benbasat, I. (2008). Attributions of trust in decision support technologies: a study of recommendation agents for e-commerce. Journal of Management Information Systems, 24(4), 249–273.

    Article  Google Scholar 

  • Weathers, D., Sharma, S., & Wood, S. L. (2007). Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods. Journal of Retailing, 83(4), 393–401.

    Article  Google Scholar 

  • Wu, J., Wu, Y., Sun, J., & Yang, Z. (2013). User reviews and uncertainty assessment: a two stage model of consumers' willingness-to-pay in online markets. Decision Support Systems, 55(1), 175–185.

    Article  Google Scholar 

  • Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.

    Google Scholar 

  • Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.

    Article  Google Scholar 

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Acknowledgments

The authors are very grateful to the senior editor and two anonymous reviewers for their encouragement and their excellent comments during the development of this manuscript. They also appreciate the very helpful feedback of the AMCIS 2012 participants on earlier versions of this manuscript.

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  1. Institute for Information Systems and New Media, Ludwigstr. 28 VG, 80539, Munich, Germany

    Christian Matt & Thomas Hess

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  1. Christian Matt
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  2. Thomas Hess
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Correspondence to Christian Matt.

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Matt, C., Hess, T. Product fit uncertainty and its effects on vendor choice: an experimental study. Electron Markets 26, 83–93 (2016). https://doi.org/10.1007/s12525-015-0199-5

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