Recommendations in the digital era : a psychological perspective

In the digital era, many of people's choices for what to buy, what to read, what to watch, and where to eat are made on online platforms. Given the abundance of available options, consumers often rely on recommendations to make their choices. Although reliance on recommendations is not a new phenomenon, novel technologies and recommendation systems have transformed the ways that people receive recommendations as well as the type of content that gets recommended to them. This dissertation explores the psychological and behavioral impacts of technology-mediated recommendations in the digital era, including how the personalization and modality of delivery influence consumers. In Chapter 1 of this dissertation, I examine how the modality through which a recommendation is delivered affects the recommendation recipient. Specifically, I explore the effect of recommendation modality on recommendation adherence. Given the rise of digital voice assistants (the number of voice assistants in use is expected to overtake the world's population by 2024), I focus on auditory recommendations from automated voices (e.g., like Amazon's Alexa and Apple's Siri). I find that people are more likely to adhere to recommendations that they hear (auditory) than recommendations that they read (visual). I show that the effect is in part driven by the relative need for closure—manifested in a sense of urgency—that is evoked by the ephemerality of auditory messages. As voice assistants revolutionize the way people receive information and interact with the internet, this work suggests that differences in the physical properties of auditory and visual modalities can lead to meaningful psychological and behavioral consequences for the information recipient. In Chapter 2, I consider the algorithms that companies use to predict consumer preferences and thereby recommend content. One challenge these algorithms face is that consumers often demonstrate discrepancies between what they actually want and what they ideally want. In this research, I show that the decisions of recommendation algorithms regarding which types of preferences to target can have important consequences for individuals' consumption patterns and consumer well-being. I utilize machine learning algorithms to build an online recommendation agent that can generate personalized recommendations tailored to people's actual or ideal preferences. I find that targeting ideal rather than actual preferences results in somewhat fewer clicks, but it also increases the extent to which people feel better off and that their time is well spent. Moreover, of note to companies, targeting ideal preferences increases users' willingness to pay for the service, the extent to which they feel the company has their best interest at heart, and their likelihood of using the service again. This research has profound implications for companies, highlighting their ability to leverage recommendation algorithms for their own benefit while helping consumers live in line with their ideals. Together, these projects offer insights into how new technologies and recommendation systems affect and interact with consumer psychology to impact behavioral outcomes. As policymakers grapple with how to best regulate tech companies to improve societal welfare, my hope is that researchers continue to explore the effects of emerging technologies on consumer behavior, uncovering insights that might transcend the obvious and that could shape policy decisions. Chapter 1 (Mariadassou, Bechler, & Levav, 2023) was published in Psychological Science. Chapter 2 (Khambatta, Mariadassou, Morris, & Wheeler) is forthcoming in Scientific Reports. I am the first author on the first essay and joint first author on the second essay. Each chapter employs the use of first-person plural pronouns to emphasize the contributions made by my collaborators

Keywords: Business.
Advisor: Levav Jonathan
Wheeler S. Christian
Huang Szu-chi