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Investigating Drivers of Repeated Behaviors in Field Data

Citation

Buyalskaya, Anastasia (2021) Investigating Drivers of Repeated Behaviors in Field Data. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/wbrp-ca13. https://resolver.caltech.edu/CaltechTHESIS:04042021-172613123

Abstract

This dissertation investigates the influences on frequently repeated human behaviors (e.g. eating, exercising, washing hands) using empirical tests on field data. While some of the phenomena discussed have been studied in lab settings (e.g., self-regulation failures, insensitivity to reward devaluation), these studies present some of the first tests of these behavioral phenomena in the field. This dissertation also assembles a number of methodologies which can be used to study individual-level field data, informed by an interdisciplinary perspective on social and decision science research.

The first chapter uses field data to study spillovers across behavioral domains, namely exercise and food choice. This work joins a small group of papers which document field evidence related to domain spillovers and failures of self-regulation. Most of the existing research on self-regulation has been conducted in controlled laboratory settings, where participants are either asked to imagine making hypothetical restrained choices or exert effort on a laboratory task as a proxy for making a restrained choice. As is the critique of many lab studies without direct field equivalents however, it is debatable whether the self-regulation behaviors observed in survey and laboratory settings necessarily generalize to the field. We fill this gap by looking at how natural (rather than incentivized) changes in exercise systematically affect food choice, thus empirically identifying spillovers across two behavioral domains in field data. We find that, even after controlling for individual fixed effects, there is a robust effect of morning exercise on the healthiness of a lunch choice. We complement the analysis of field data with surveys to better understand the mechanism driving this result.

The second chapter presents a novel methodology for identifying behaviors that are highly and predictably context-sensitive, and thus candidates for being habitual. While there is a large body of laboratory research documenting the mechanisms underlying well-developed habits in animals and humans, there is much less field research on how human habits naturally develop over time. Using two large datasets on gym attendance and handwashing behavior, we use machine learning to statistically classify when choices are predicted by an identifiable set of context variables. This technique generates a person-specific measure of behavioral predictability, which can then be used to study individual differences in predictability and speed of habit formation. This allows us to establish two important discoveries. First, the sets of context cues that are predictive of individual-level behavior are different for different people. Specifically, while historical behavior is an important universal predictor, other context variables such as day of the week or month of the year have more heterogeneous effects. Second, contrary to common wisdom, there is no "magic number" for how long it takes to form a habit. Instead, the speed of habit formation appears to vary significantly, both between behavioral domains and between individuals within domains.

The third chapter uses a novel methodology to run a field experiment testing the effect of a price promotion on consumer behavior. The goal of this "pilot study" is to credibly dissociate predictions made by brand loyalty/habit formation from reference-dependence theories. A customizable vending machine serves as a "mini-retailer," allowing for full control of price promotion details in an ecologically valid setting. The vending machine allows controlling for stockpiling behavior, an important concern for empirical work analyzing price promotions in the marketing literature. Analysis of the data collected from this pilot study suggests that price promotions increase the sales of both discounted and non-discounted items, as well as the total number of unique customers making purchases. Furthermore, in line with the loss leader hypothesis, more items are purchased during the sale period overall.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Consumer behavior, marketing, behavioral economics, field experiment, field data
Degree Grantor:California Institute of Technology
Division:Humanities and Social Sciences
Major Option:Social and Decision Neuroscience
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Camerer, Colin F.
Thesis Committee:
  • Shum, Matthew S. (chair)
  • Agranov, Marina
  • Wood, Wendy
  • Camerer, Colin F.
Defense Date:14 May 2021
Record Number:CaltechTHESIS:04042021-172613123
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:04042021-172613123
DOI:10.7907/wbrp-ca13
ORCID:
AuthorORCID
Buyalskaya, Anastasia0000-0002-1848-1661
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14116
Collection:CaltechTHESIS
Deposited By: Anastasia Buyalskaya
Deposited On:02 Jun 2021 23:20
Last Modified:09 Jun 2021 15:52

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