Summary and Future Research

Our proposed recommendation system generates real-time data on consumer purchasing behavior, especially consumers' WTP, which are the basis for many models of market share estimation, pricing or product design. Because such data are (theoretically) easy to collect online, the increasing popu- larity of e-commerce has led to renewed interest in developing methods for estimating consumers' preferences and WTP in operations research (Abbas and Bell 2011; Gensler et al. 2012; Miller et al. 2011; Rusmevichientong et al. 2010). Specifically, a low-effort method for repeated measurements of consumers' WTP and attribute-level utilities is needed. Our approach is a promising step towards the development of such a method, extending utility-based recommendation systems.

The empirical evaluation shows that our proposed recommendation system predicts consumers' utility functions and, ultimately, their WTP with high accuracy. Considering that prior studies which reported similar levels of accuracy used more complex and cognitively exhausting measurement methods (e.g. choice-based or ranking-based conjoint analysis), our approach performed very well. Our results indicate that, contrary to prior suppositions, linear SAU functions are better suited for estimating WTP. Exponential SAU functions provide better approximations of consumers' preference structures and are therefore better suited for predicting product ranks. That overall utility accuracy for exponential functions is lower than for linear functions, although SAU functions were predomi- nantly concave, suggests that more complex and therefore challenging methods lead to higher error levels in SAU function specification, which then accumulate in the overall utility function. This suppo- sition is supported by the fact that complex conjoint analysis methods often do not attain much higher accuracy levels than simpler approaches although they too model utility at the attribute level. We suggest that for utility estimation, other approaches are as well suited as ours, but for combined individual utility and WTP estimation, our approach seems to perform above average.

Our research is subject to some limitations. First, our approach is designed for products with at least ordinal attributes for which it is possible to estimate reliable SAU functions, not for experience goods with nominal attributes such as “design” or “color”. Second, as shown in our supplementary experi- ment, SAU functions can be of (inverted) U-shape if consumers think about trade-offs between two attributes during the evaluation of one attribute (e.g. between “ease of transportation” and “fragili- ty” when evaluating the attribute “size”). Exponential approximation of SAU functions was particular- ly robust when consumers did not consider within-attribute trade-offs during evaluation. Splitting

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multi-dimensional attributes into atomic attributes would likely improve accuracy and reduce effort. Third, the accuracy of utility estimates on the product level was only slightly better in the exponential system than in the linear system although preference structure (SAU functions) was approximated much better with exponential functions. It is possible that the 10-point scale we used to determine SAU function curvature was not precise enough, resulting in small discrepancies at the SAU function level (see Figure 8) which might then have added up to a larger discrepancy between estimated and actual, or perceived, utilities at the product level. Fourth, user preferences may be influenced by exogenous factors like the decision support system used for measuring utility functions and WTP (Adomavicius et al. 2013). Our proposed approach for estimating exponential utility functions is in- teractive, requiring user input for several parameters, and may affect user preference building. The extent to which and the circumstances under which decision support systems change users' prefer- ences are as yet not fully understood and provide an avenue for future research.

The practical implications from our findings are threefold. First, it is relatively easy to use and gener- ates good recommendations. These results suggest that online consumers could benefit from using it in their purchasing process, saving effort in the process of finding attractive products. Since multi- dimensional attributes make utility estimation more difficult and increase consumer effort without improving utility or WTP estimates, designers of utility-based decision support systems ought to make sure their systems are based on atomic attributes only. Second, online retailers could easily extend existing utility based recommendation systems, such as the Dell Computer Advisor, to include WTP estimation. As illustrated in Section 3.6, retailers could use this information as a basis for a number of business decisions, e.g. product pricing. Third, online retailers could generate new reve- nue streams by selling the recommendation data to product manufacturers, who can then determine individual profit-maximizing product configurations more easily. Open questions that remain in this area are the degree of consumer acceptance and the profitability of WTP-based pricing strategies. Both still need to be evaluated in field studies to shed more light on consumers' reactions and deci- sion processes in real-life situations.

Research into collaborative recommendation systems could profit from our approach. Currently, one of the most commonly used collaborative recommendation algorithms is matrix factorization (e.g. Ge et al. 2014), which helps identify latent product features that contribute most to product utility. However, these features are generally not identical to product attributes, which makes it more diffi- cult to use the information in business decisions and to compute WTP. Combining our approach, which produces data on the individual level, with collaborative data on the user group level could generate better insights into the composition of latent features and preference differences between consumers. Conversely, integrating collaborative recommendation system data in our approach may

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further increase recommendation quality and decrease the level of consumer effort during the pur- chasing process, especially if consumer preferences can be estimated to a satisfactory degree during the specification process and recommendations provided at an early stage.