Attention and preference measurement

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This dissertation contains two essays examining the role of attention and information processing in stated choices under choice-based preference measurement tasks. While choice experiments have long been used in marketing as a way to measure consumer preferences, full rationality of consumers is always assumed, meaning consumers are able to process all the choice relevant information before making a decision. Moreover, conditioned on the premise that consumers process all the choice-relevant information, incentive-alignment mechanism introduced in choice experiments are considered the gold standard for inducing consumers to choose as they would in real-life situations. However, if consumers are boundedly rational and processing information is costly, we expect consumers to maximize not only the utility derived from the option they choose but also the utility derived from the process. Therefore, given a certain incentive structure, the amount of information processed by consumers is endogenized by individual preference toward the focal product in a choice experiment. Furthermore, research has shown that varying incentives in experiments might also result in changes in attention, which implies that the amount of attention paid in real-life choice situations (the probability of realizing a choice is 1) is different than the attention paid to choices paired with smaller incentives in most preference-measurement tasks (the probability of realizing a choice is strictly greater than 0 but lower than 1). In this dissertation, we first focus in Chapter 1 on the link between information processing and stated choices in an incentive-alignment choice experiment by developing a new preference measurement. We explore the impact of incentives on attention, information processing, and stated choices by conducting an experiment described in Chapter 2. In Chapter 1, we develop a dynamic discrete choice model of information search and choice under bounded rationality, that we calibrate using a combination of eye-tracking and choice data. Our model extends the directed cognition model of Gabaix et al. (2006) by capturing fatigue, proximity effects, and imperfect memory encoding and by estimating individual-level parameters and partworths within a likelihood-based, hierarchical Bayesian framework. We show that modeling eye movements as the outcome of forward-looking utility maximization improves out-of-sample predictions, enables researchers and practitioners to use shorter questionnaires, and allows better discrimination between attributes. In Chapter 2, we empirically investigate whether incentives impact attention, information processing, and stated choices. We vary the probability that the respondent's choice will be realized from 0 (hypothetical) to 0.01, 0.50, 0.99, and 1 (deterministic) and collect data on both response times and eye tracking. We find a U-shaped relationship between the probability that the choice will be realized and the level of attention. Hypothetical questions and deterministic questions induce similar attention and information processing but different choices.