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Advancing Non-compensatory Choice Models in Marketing

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Abstract

The extant choice literature has proposed different non-compensatory rules as a more realistic description of consumers’ choice than a standard compensatory model. Some research has further suggested a two-stage sequential decision process of non-compensatory consideration and then compensatory choice, where the determinants of each stage may differ. Some aspects of non-compensatory choice modeling are under-studied. In this article, we hope to advance the understanding of non-compensatory choice models with the following aims: (a) providing an overview of existing representations for non-compensatory choice decisions, (b) discussing how such choice decisions can manifest from the economic search theoretical perspective, (c) exploring the empirical identification of non-compensatory decisions using different data, and (d) presenting applications of non-compensatory choice models in novel domains.

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Notes

  1. Some researchers use the term lexicographic to describe a one-shot choice heuristic where the alternative with the highest value of the most important attribute is chosen without considering a tie [14, 47], as compared to EBA, which is described as a sequential choice process that involves lexicographic rules.

  2. Yee et al. [87] refer to an attribute as a feature and emphasize the difference between aspect and feature. Processing by feature implies that , for example, a brand was the feature chosen for evaluation first, then an ordering of all the aspects of that feature would take place before moving on to the next feature (e.g., price). Processing by aspect assumes that consumers split features into specific aspects and proceed through the decision process in a binary aspect-sorting manner. In this way, processing by feature is a special restricted case of processing by aspect.

  3. Lexicographic -by- aspects is a two-stage model with a non-compensatory decision in the first stage and compensatory decision in the second stage.

  4. Tversky [81] distinguishes unique and shared aspects in his original EBA paper. Moreover, McFadden [55] presents rigorous notions of the uniqueness of aspects defined over sets.

  5. For detailed search literature overviews, see Baye et al. [10] and Ratchford [64].

  6. Our discussion focuses primarily on search models for differentiated goods rather than on search models for homogeneous goods.

  7. For a detailed discussion, see Honka and Chintagunta [43].

  8. Learning over the distribution of the unknown alternative has been modeled (see, e.g., [67]), but learning preferences more broadly (i.e., the consumer might try to figure out which characteristics he wants a digital camera to have during his search process) has not been tackled to the best of our knowledge.

  9. Some progress in modeling search for multiple product attributes has been made under the assumption that the characteristics’ distributions are independent [70].

  10. Note that the paradigms illustrated in Reiss [65] are intended for compensatory models with standard generalized extreme value errors.

  11. See Heckman and Vytlacil [41] for related notions of “identification at infinity.”

  12. A complete treatment of the role of response times in identifying the underlying choice architecture is beyond the scope of this paper. We refer the interested reader to the recent survey by Gaissmaier et al. [30].

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Authors and Affiliations

  1. Ross School of Business, University of Michigan, Ann Arbor, USA

    Anocha Aribarg

  2. Goethe University, Frankfurt am Main, Germany

    Thomas Otter

  3. Fisher College of Business, The Ohio State University, Columbus, USA

    Daniel Zantedeschi & Greg M. Allenby

  4. McCombs School of Business, The University of Texas at Austin, Austin, USA

    Taylor Bentley & Ty Henderson

  5. Lindner College of Business, University of Cincinnati, Cincinnati, USA

    David J. Curry

  6. Marriott School of Management, Brigham Young University, Provo, USA

    Marc Dotson

  7. Anderson School of Management, University of California Los Angeles, Los Angeles, USA

    Elisabeth Honka

  8. Graduate School of Business, Columbia University, New York, USA

    Rajeev Kohli & Kamel Jedidi

  9. Graduate School of Business, Stanford University, Stanford, USA

    Stephan Seiler

  10. Ivey Business School, Western University, London, Canada

    Xin (Shane) Wang

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  1. Anocha Aribarg
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  2. Thomas Otter
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  13. Xin (Shane) Wang
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Correspondence to Daniel Zantedeschi.

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Aribarg, A., Otter, T., Zantedeschi, D. et al. Advancing Non-compensatory Choice Models in Marketing. Cust. Need. and Solut. 5, 82–92 (2018). https://doi.org/10.1007/s40547-017-0072-0

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