Modeling and measurement of consumers' decision strategies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.This thesis consists of three related essays which explore new approaches to modeling and measurement of consumer decision strategies. The focus is on decision strategies that deviate from von Neumann-Morgenstern utility theory. Essays 1 and 2 explore decision rules that consumers use to form their consideration sets. Essay 1 proposes disjunctions-of-conjunctions (DOC) decision rules that generalize several well-studied decision models. Two methods are proposed for estimating the model. Consumers' consideration sets for global positioning systems are observed for both calibration and validation data. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of profiles. Essay 2 develops and tests an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a "configurator." Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question the posterior is approximated with a variational distribution and uses belief-propagation. The method runs sufficiently fast to select new queries in under a second and provides significantly and substantially more information per question than existing methods based on random, market-based, or orthogonal questions. The algorithm is tested empirically in a web-based survey conducted by an American automotive manufacturer to study vehicle consideration. Adaptive questions outperform market-based questions when estimating heuristic decision rules. Heuristics decision rules predict validation decisions better than compensatory rules. Essay 3 proposes a model of product search when preferences are constructed during the process of search: consumers learn what they like and dislike as they examine products. Product recommendations, whether made by sales people or online recommendation systems, bring products to the consumer's attention and impact his/her preferences. Changing preferences changes the products the consumer will choose to search; at the same time, the products the consumer chooses to search will determine the future shifts in preferences. Accounting for this two-way relationship between products and preferences is critical in optimizing recommendations.by Daria Dzyabura.Ph.D

Unstructured Direct Elicitation of Decision Rules

The authors investigate the feasibility of unstructured direct elicitation (UDE) of decision rules consumers use to form consideration sets. They incorporate incentives into the tested formats that prompt respondents to state noncompensatory, compensatory, or mixed rules for agents who will select a product for the respondents. In a mobile phone study, two validation tasks prompt respondents to indicate which of 32 mobile phones they would consider from a fractional design of features and levels. The authors find that UDE predicts consideration sets better, across both profiles and respondents, than a structured direct-elicitation method. It predicts comparably to established incentive-aligned compensatory, noncompensatory, and mixed decompositional methods. In a more complex automotive study, noncompensatory decomposition is not feasible and additive-utility decomposition is strained, but UDE scales well. The authors align incentives for all methods using prize indemnity insurance to award a chance at $40,000 for an automobile plus cash. They conclude that UDE predicts consideration sets better than either an additive decomposition or an established structured direct-elicitation method (CASEMAP)

Unstructured Direct Elicitation of Decision Rules

We investigate the feasibility of unstructured direct-elicitation (UDE) of decision rules consumers use to form consideration sets. With incentives to think hard and answer truthfully, tested formats ask respondents to state non-compensatory, compensatory, or mixed rules for agents who will select a product for the respondents. In a mobile-phone study two validation tasks (one delayed 3 weeks) ask respondents to indicate which of 32 mobile phones they would consider from a fractional 4[superscript 5]x2[superscript 2] design of features and levels. UDE predicts consideration sets better, across profiles and across respondents, than a structured direct-elicitation method (SDE). It predicts comparably to established incentive-aligned compensatory, non-compensatory, and mixed decompositional methods. In a more-complex (20x7x5[superscript 2]x4x3[superscript 4]x2[superscript 2]) automobile study, non-compensatory decomposition is not feasible and additive-utility decomposition is strained, but UDE scales well. Incentives are aligned for all methods using prize indemnity insurance to award a chance at $40,000 for an automobile plus cash. UDE predicts consideration sets better than either additive decomposition or an established SDE method (Casemap). We discuss the strengths and weaknesses of UDE relative to established methods.Research Grants Council (Hong Kong, China) (SAR (9041182, CityU 1454/06H))Pennsylvania State University (Smeal Small Research Grant

Recommending Products When Consumers Learn Their Preference Weights

Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each time a product is searched, updated preference weights affect the expected utility of all products and the value of subsequent optimal search. Product recommendations, which take preference-weight learning into account, help consumers search. We motivate a model in which consumers learn (update) their preference weights. When consumers learn preference weights, it may not be optimal to recommend the product with the highest option value, as in most search models, or the product most likely to be chosen, as in traditional recommendation systems. Recommendations are improved if consumers are encouraged to search products with diverse attribute levels, products that are undervalued, or products for which recommendation-system priors differ from consumers’ priors. Synthetic data experiments demonstrate that proposed recommendation systems outperform benchmark recommendation systems, especially when consumers are novices and when recommendation systems have good priors. We demonstrate empirically that consumers learn preference weights during search, that recommendation systems can predict changes, and that a proposed recommendation system encourages learning