I’m an Assistant Professor of Marketing at the University of Illinois Chicago. I’m interested in the ways that people look for products and information, particularly on the internet.

And here is my CV and a link to my ResearchGate Profile.

Areas of Interest

I have three main areas of research. The first is in how people search for products and information on the internet. Questions I’m interested in include:

  • How do people navigate as they search for products?

  • How does the availability of tools like filters, sorting, “saving for later,” and recommendation engines impact how people shop?

  • What are some of the cognitive processes that people use when choosing where they want to shop for something? And how effective are these processes in different environments?

The second is AI methods in education and social science research. Interests include:

  • How can we use Large Language Models to improve the education experience?

  • How can we use Large Language Models to understand social phenomena that have previously been hard to research?

I’m also interested in understanding the ways that people think about morality in their everyday lives. I’m interested in questions like:

  • How do expectations about morality that people have for others change depending on their relationships with them?

  • How do consumers think about privacy issues?

  • How do people naturally categorize moral situations?

  • What types of moral dilemmas do people face in their everyday lives?

Research Projects and Papers

Navigating Choices: A Framework of Consumer Search

Alexander K. Moore and Reid Hastie

Published in Marketing Letters, 2024

Consumers are inundated with choices, yet they effectively find products that satisfy them. In this review, we draw from the marketing, economics, psychology, and ecology literatures to outline a general framework for consumer search, implicit in many prior descriptions of consumer behavior. Within the framework, we particularly focus on navigation, which we define as the act of deciding the order in which to inspect products with the intention of learning more about them. We focus on navigation because it has received relatively little attention in marketing. We also highlight opportunities for future research in understanding interactions between stages of the framework. By understanding this framework, researchers and marketers can gain key theoretical insights into the consumer experience when looking for a product. Further, understanding these processes is likely to yield practical results that can help in the design of more effective search experiences, ultimately increasing consumer satisfaction.

Benevolent Friends and High Integrity Leaders: How Preferences for Benevolence and Integrity Change Across Relationships

Alexander K. Moore, Joshua Lewis, Emma E. Levine, and Maurice Schweitzer

Published in Organizational Behavior and Human Decision Processes (OBHDP), 2023

People value benevolence and integrity in their relational partners. Across 7 experiments (and 4 supplemental studies), we demonstrate that the relative importance people attach to benevolence and integrity systematically shifts across relationships. We introduce the Size-Closeness-Hierarchy (SCH) Model, a theoretical framework to characterize moral preferences in workplace relationships across group size, emotional closeness, and hierarchy. According to our model, as relationships involve more people, become more emotionally distant, and become more hierarchical (relational features common for leaders), people prefer partners who privilege integrity over benevolence. However, as relationships involve fewer people, become less emotionally distant, and become less hierarchical (relational features common for friends), people prefer partners who privilege benevolence over integrity. Our findings advance our understanding of the interplay between moral values, leadership, and interpersonal perceptions.

Everyday dilemmas: New directions on the judgment and resolution of benevolence-integrity dilemmas

Alexander K. Moore, David M. Munguia Gomez, and Emma E. Levine

Published in Social and Personality Psychology Compass, 2019

Many everyday dilemmas reflect a conflict between two moral motivations: the desire to adhere to universal principles (integrity) and the desire to improve the welfare of specific individuals in need (benevolence). In this article, we bridge research on moral judgment and trust to introduce a framework that establishes three central distinctions between benevolence and integrity: (1) the degree to which they rely on impartiality, (2) the degree to which they are tied to emotion versus reason, and (3) the degree to which they can be evaluated in isolation. We use this framework to explain existing findings and generate novel predictions about the resolution and judgment of benevolence–integrity dilemmas. Though ethical dilemmas have long been a focus of moral psychology research, recent research has relied on dramatic dilemmas that involve conflicts of utilitarianism and deontology and has failed to represent the ordinary, yet psychologically taxing dilemmas that we frequently face in everyday life. The present article fills this gap, thereby deepening our understanding of moral judgment and decision making and providing practical insights on how decision makers resolve moral conflict.

Seen and Not Seen: How People Judge Ambiguous Behavior During the COVID-19 Pandemic

Andras Molinar, Alexander K. Moore, Carman Fowler, and George Wu

Published in the Journal of Risk and Uncertainty, 2023

How do we judge others’ behavior when they are both seen and not seen—when we observe their behavior but not their underlying traits that moderate the riskiness of their behavior? In this paper we investigate this question in the context of the COVID-19 pandemic: How people make sense of, and judge, vaccination-contingent behaviors—behaviors, such as going to the gym or a bar, which are considered to be more or less risky and appropriate, depending on the target’s vaccination status. While decision theoretic models suggest that these judgments should depend on the probability that the target is vaccinated (e.g., the positivity of judgments should increase linearly with the probability of vaccination), in a large-scale pre-registered experiment (N = 936) we find that both riskiness and appropriateness judgments deviate substantially from such normative benchmarks. Specifically, when participants judge a stranger’s behavior, without being asked to think about the stranger’s vaccination status, they tend to judge these behaviors similarly positively to behaviors of others who are known to be fully vaccinated. By contrast, when participants are explicitly prompted to think about the vaccination status of others, they do so, leading them to view others more disparagingly, at times even more negatively than what a normative benchmark would imply. This finding—that vaccination status may not come naturally to mind when judging the riskiness of vaccination-contingent behaviors—is not only striking given how ubiquitous discussions, debates, and recommendations of vaccination against COVID-19 have become but also have important practical and policy implications.

Evaluating Large Language Models in Psychological Research: A Guide for Reviewers

Suhaib Abdurahman, Alireza Salkhordeh Ziabari, Alexander K. Moore, Daniel M. Bartels , and Morteza Dehghani

Large Language Models (LLMs) are being increasingly used in scientific research, be it to analyze data, generate synthetic data, or even to write scientific papers. This trend necessitates that journal reviewers are able to evaluate the quality of works that utilize LLMs. We provide reviewers of psychological research with a comprehensive guide on evaluating research that uses LLMs, examining their dual roles of automating data processing and simulating human data. Essential considerations for reviewers are highlighted, focusing on the evaluation of methodological rigor, the importance of replicability, and the validity of results when employing LLMs. We offer practical advice on assessing the appropriateness of LLM applications in submitted studies, emphasizing the need for transparency in methodological reporting and the challenges posed by the non-deterministic and continuously evolving nature of these models. By providing a framework for critical review, this guide aims to ensure high-quality, innovative research within the evolving landscape of psychological studies utilizing LLMs.

The Determinants of Acceptable Privacy Behaviors by Organizations

Alexander K. Moore, Jordyn Schor, and Daniel Bartels

Working Paper

Growing consumer concerns about data privacy necessitate organizations to consider what kind of data usage is deemed acceptable. Failure to address this can harm brands and products. Our research comprises 14 studies (1 pretest, 4 main studies, 1 supplemental study, and 3 manipulation checks and 5 norming studies; Total N= 7,559), examining factors that determine acceptability in data privacy situations. In our first study, we crowdsource common situations and dimensions surrounding data privacy related situations. Our second study examines how these dimensions impact acceptability across 60 situations. Our third study manipulates these dimensions to test causal relationships. Our fourth study looks at how our dimensions impact consumer intentions to switch to other products with better privacy protections. Organizational security efforts, appropriate consent procedures for data collection, and the degree to which data collected and used are permanently associated with a consumer or not impacted appropriateness most. Sentiment towards an organization and whether a company gave consumers an option to opt out of providing their data while using the service also impacted acceptability. Finally, consumers are most likely to switch products if those products fail to have good security or fail to gain adequate consent for their use and collection of data.

Optimal vs Heuristic Navigation in Search

Alexander K. Moore and Reid Hastie

In Prep

We report on three experiments studying participants’ search for high-valued options in an experimental resource environment. Participants either stated their stopping rule or made decisions about where to search next. We systematically manipulated participants’ beliefs about the distributions of values available at different locations and the cost of inspecting more locations. Our research shifts from the previous emphasis on the optimality of searchers’ stopping rules to study their navigation decisions about where to search next. At the aggregate level, human searchers appeared to approximate normative navigation and stopping rules based on the principle that the searcher should maximize expected gain, implied by the Weitzman Model for search. Although, underneath the average behavior there was considerable reliable variation in search strategies, with more than half of the human searchers following non-optimal strategies. We found that about 40% of participants were relying on inferences about expected gain to stop, 40% relied on expected value, which is sensible but not optimal, and 20% were following a truly anomalous strategy. We conclude that navigation decisions are also essentially normative, with about 80% of the overall responses consistent with the Weitzman Model.

How Just Noticeable Differences Lead to Sub-Optimal Shopping Behaviors

Alexander K. Moore and Daniel Bartels

In Prep (to be submitted soon)

Consumers typically want high quality products at low prices. In many cases, they also have the option to visit several locations to find products at good prices. Drawing on insights from an influential economic search model (the Weitzman Model), we investigate how price expectations and search costs impact what stores consumers choose to visit, and the order in which they visit those stores. Across 4 studies, we find that consumers are sensitive to search costs and differences in price variance between stores in ways that are not predicted by optimal search models. We present evidence that consumers are representing the value of visiting locations consistently with normative models, but that just noticeable differences in these representations lead to errors consistent with our findings.

More About Me

I grew up in Yokohama, Japan before moving to Washington D.C. for High School. I received a BA in Economics from the University of Chicago. After graduation, I worked as a consultant for Nielsen, specializing in market research, machine learning and data analysis. I worked in a psychology lab specializing judgment and decision making. For two years, I made little metal widgets at a tool and die maker in Japan. Then for two more years I managed it. And finally I worked as a freelance consultant specializing in the management of data analytics projects. I received my PhD from the University of Chicago Booth School of Business

Also, I’m a pretty devoted Cinephile, love to travel, enjoy cooking, and am pretty knowledgeable about the history of Chicago Architecture.