Price beliefs and experience: Do consumers’ beliefs converge to empirical distributions with repeated purchases?

Matsumoto, Brett; Spence, Forrest

doi:10.1016/j.jebo.2016.03.008

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…Actually, if decision-makers are ex ante optimistic and ex post lucky, they could be better off by following a weighted regret and rejoice model so to reinforce their bias in the short-run (Chorus, 2014). See Matsumoto & Spence (2016) for findings based on reported expectations for real online textbook prices from a survey of 1224 college students, which is consistent with learning in beliefs about price distribution. Without loss of generality, I will assume A = 1, but see Analytis et al (2014) for a discussion of multiattribute alternatives.…”

Section: The Learning Mechanismmentioning

confidence: 70%

Learning and Dynamic Choices Under Uncertainty: From Weighted Regret and Rejoice to Expected Utility

Zagonari

2016

SSRN Journal

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This paper identifies the globally stable conditions under which an individual facing the same choice in many subsequent times learns to behave as prescribed by the expected-utility model. To do so, the analysis moves from the relevant behavioural models suggested by psychology (i.e., weighted probabilities applied to regret and rejoice theory), and by updating probability estimations and outcome preferences according to the learning models suggested by neuroscience (i.e., adaptive learning aimed at reducing surprises), and analogous to Bayesian updating. The search context is derived from experimental economics, whereas the learning framework is borrowed from theoretical economics. Analytical results show that obstinate and lucky individuals are better off in the short-run (i.e., a low density of events in the reference period), but they do not learn, and this is true to a greater extent in a simple context; in contrast, reactive and unlucky individuals are worse off in the short-run, but they learn and are better off in the long-run (i.e., all individuals are equally lucky or unlucky), and this is true to a greater extent in a complex context. The expected-utility model explains real behaviours in the long-run whenever unlucky events are more likely than lucky events.

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Section: The Learning Mechanismmentioning

confidence: 70%

Learning and Dynamic Choices Under Uncertainty: From Weighted Regret and Rejoice to Expected Utility

Zagonari

2016

SSRN Journal

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“…Actually, if decision‐makers are ex ante optimistic and ex post lucky, they could achieve greater satisfaction by following a weighted regret and rejoice model so as to reinforce their bias in the short run (Chorus, ). See Matsumoto and Spence () for findings based on reported expectations for real online textbook prices from a survey of 1,224 college students, which is consistent with how students establish beliefs about price distributions. Without loss of generality, I will assume A = 1, but see Analytis, Kothiyal, and Katsikopoulos () for a discussion of multiattribute alternatives.…”

Section: The Modelmentioning

confidence: 73%

Learning and dynamic choices under uncertainty: From weighted regret and rejoice to expected utility

Zagonari

2019

Manage Decis Econ

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This paper identifies the globally stable conditions under which an individual facing the same choice in many subsequent times learns to behave as prescribed by the expected‐utility model. The analysis moves from the relevant behavioural models suggested by psychology, by updating probability estimations and outcome preferences according to the learning models suggested by neuroscience, in a manner analogous to Bayesian updating. The search context is derived from experimental economics, whereas the learning framework is borrowed from theoretical economics. Analytical results show that the expected‐utility model explains real behaviours in the long run whenever bad events are more likely than good events.

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“…In light of this, the study proposes that although product rating could guide decision-making, consumers may pay more attention to other related factors (for instance, price, brands, etc.) [37,38].…”

Section: Discussionmentioning

confidence: 99%

Empirical Research on the Impact of Personalized Recommendation Diversity

Lin¹,

Yan²,

Lü³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Personalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers' adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system.

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Price beliefs and experience: Do consumers’ beliefs converge to empirical distributions with repeated purchases?

Cited by 19 publications

References 15 publications

Learning and Dynamic Choices Under Uncertainty: From Weighted Regret and Rejoice to Expected Utility

Learning and Dynamic Choices Under Uncertainty: From Weighted Regret and Rejoice to Expected Utility

Learning and dynamic choices under uncertainty: From weighted regret and rejoice to expected utility

Empirical Research on the Impact of Personalized Recommendation Diversity

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