Columbia Business School Research

Generative Artificial Intelligence (GenAI) tools based on Large Language Models (LLMs) are quickly reshaping how researchers conduct surveys and experiments. From reviewing the literature and designing instruments, to administering studies, coding data, and interpreting results, these tools offer substantial opportunities to improve research productivity and advance methodology. Yet with this potential comes a critical challenge: researchers often use these systems without fully understanding how they work.

This paper addresses a simple theoretical question of high substantive relevance: What makes a consumption experience special in a consumer’s mind?

Any judgment about population heterogeneity depends on the definition of the sampling frame (1). In a recent paper, Holzmeister et al. (2) (HJBK hereafter) compare different sources of heterogeneity to population heterogeneity. They find that population heterogeneity is much smaller compared to design and analytic heterogeneity as a source of variation in effect sizes.

We aim to stimulate discussion on how innovation research within marketing can use a better world (BW) perspective to help innovation become a driver of positive change in the world. In this "Challenging the Boundaries" series paper, we hope to provide purposeful research opportunities for scholars seeking to bridge innovation research with the BW movement. We frame our discussion with four areas of innovation research in marketing that are particularly relevant to BW objectives.

One of the most crucial aspects and significant levers that gaming companies possess in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression.

Language is a uniquely human trait at the core of human interactions. The language people use often reflects their personality, intentions and state of mind. With the integration of the Internet and social media into everyday life, much of human communication is documented as written text. These online forms of communication (for example, blogs, reviews, social media posts and emails) provide a window into human behaviour and therefore present abundant research opportunities for behavioural science.

Advancements in data analytics and increased access to consumer data have revolutionized companies’ price discrimination capabilities. These technological advancements have not only changed how prices are determined but also who determines them, with companies increasingly relying on algorithms rather than humans to set prices. We examine consumers’ fairness perceptions of demographic price discrimination—a prevalent yet controversial practice that can trigger considerable consumer backlash—and find that it depends on who is responsible for setting prices.

One of the most significant levers available to gaming companies in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression. In this paper, we leverage a large randomized control trial to assess the effect of dynamically adjusting game difficulty on players’ behavior and game monetization in the context of a popular F2P mobile game.

We propose that social-media users’ own post histories are an underused yet valuable resource for studying fake-news sharing. By extracting textual cues from their prior posts, and contrasting their prevalence against random social-media users and others (e.g., those with similar socio-demographics, political news-sharers, and fact-check sharers), researchers can identify cues that distinguish fake-news sharers, predict those most likely to share fake news, and identify promising constructs to build interventions. Our research includes studies along these lines.

Non-informational cues, such as facial expressions, can significantly influence judgments and interpersonal impressions. While past research has explored how smiling affects business outcomes in offline or in-store contexts, relatively less is known about how smiling influences consumer choice in e-commerce settings even when there is no face-to-face interaction.

Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products.

As concern with climate change increases, people seek to behave and consume sustainably. This requires understanding which behaviours, firms and industries have the greatest impact on emissions. Here we ask if people are knowledgeable enough to make choices that align with growing sustainability intentions.

Whether speaking, writing, or thinking, almost everything humans do involves language. But can the semantic structure behind how people express their ideas shed light on their future success? Natural language processing of over 40,000 college application essays finds that students whose writing covers more semantic ground, while moving more slowly (i.e. moving between more semantically similar ideas), end up doing better academically (i.e. have a higher college grade point average). These relationships hold controlling for dozens of other factors (e.g.

Using publicly available data from 299 pre-registered replications from the social sciences, we find that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the paper characteristics, study design and results, author information, and replication effort. To understand why, we analyze the textual differences between replicable and nonreplicable studies.

Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines — which we define as repeated behaviors with recurring, temporal structures — for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. We propose a new approach for doing so, which we apply in the context of ridesharing.

Policymakers increasingly rely on behavioral science in response to global challenges, such as climate change or global health crises. But applications of behavioral science face an important problem: Interventions often exert substantially different effects across contexts and individuals. We examine this heterogeneity for different paradigms that underlie many behavioral interventions. We study the paradigms in a series of five preregistered studies across one in-person and 10 online panels, with over 11,000 respondents in total.

Should consumer researchers employ silicon samples and artificially generated data based on large language models, such as GPT, to mimic human respondents' behavior? In this paper, we review recent research that has compared result patterns from silicon and human samples, finding that results vary considerably across different domains. Based on these results, we present specific recommendations for silicon sample use in consumer and marketing research.

Making good health insurance decisions is important for health outcomes and longevity, but consumers’ errors are well documented. The authors examine whether targeted choice architecture interventions can reduce these mistakes. The article examines the interaction of two choice architecture tools on improved consumer insurance decisions in online health care exchanges: (1) ordering the options from best to worst based on a high-quality user model and (2) partitioning the total set of options.

In response to increasing societal divisions, an extensive literature has emerged examining the construct of receptiveness. This literature suggests that signaling receptiveness to others confers a variety of interpersonal benefits, such as increased persuasiveness. How do people signal their receptiveness to others? The current research investigates whether one of the most fundamental aspects of language—pronoun use—could shape perceptions of receptiveness.

In the months before the 2020 U.S. election, several political campaign websites added prechecked boxes (defaults), automatically making all donations into recurring weekly contributions unless donors unchecked them. Since these changes occurred at different times for different campaigns, we use a staggered difference-in-differences design to measure the causal effects of defaults on donors’ behavior. We estimate that defaults increased campaign donations by over $43 million while increasing requested refunds by almost $3 million.

In the past decades, as traditional luxury goods and conspicuous consumption have become more mainstream and lost some of their signaling value, new alternative signals of status (e.g., vintage, inconspicuous consumption, sustainable luxury) have progressively emerged. This research applies the grounded theory method to establish a novel framework that systematically unifies existing conceptualizations, findings, and observations on alternative signals of status.

How can firms encourage consumers to adopt smartphone apps? The authors show that several inexpensive choice architecture techniques can make users more likely to enable important app features and complete app onboarding. In six preregistered experiments (n = 5,968) and a field experiment (n = 594,997), choice architecture interventions manipulating choice sequence, color, and wording of app adoption decisions dramatically increased app adoption. Across experiments, integrating multiple feature decisions into a single choice increased adoption.

In light of the widely discussed political divide and increasing societal polarization, we investigate in this paper whether the polarization of political ideology extends to consumers’ preferences, intentions, and purchases. Using three different data sets—the publicly available social media data of over three million brand followerships of Twitter users, a YouGov brand-preference survey data set, and Nielsen scanner panel data—we assess the evolution of brand-preference polarization.

Researchers and practitioners in marketing, economics, and public policy often use preference elicitation tasks to forecast realworld behaviors. These tasks typically ask a series of similarly structured questions.

Recently, defaults have become celebrated as a low-cost and easy-to-implement nudge for promoting positive outcomes, both at an individual and societal level. In the present research, we conducted a large-scale field experiment (N = 32,508) in an educational context to test the effectiveness of a default intervention in promoting participation in a potentially beneficial achievement test. We found that a default manipulation increased the rate at which high school students registered to take the test but failed to produce a significant change in students’ actual rate of test-taking.

This paper focuses on teaching the application of anthropology in business to marketing students. It begins with the premise that consumer marketers have long used ethnography as a component of their qualitative market research toolkit to inform their knowledge about and empathy for consumers. A question for market research educators who include ethnography in their curricula is if and how to teach the richness of anthropologically based approaches, especially given a decoupling of ethnographic method from anthropological theory in much consumer research practice.

An important challenge for many firms is to identify the life transitions of its customers, such as job searching, expecting a child, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firms opportunities to be more relevant to their customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely to be job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms.

Infrastructure professionals (N = 261) were randomly assigned to either a future or present-framed project description and asked to recommend design attributes for an infrastructure project. The future-framed condition led professionals to propose a significantly longer infrastructure design life, useful life to the community, and acceptable return on financial investment. The findings suggest a straightforward and inexpensive way to lessen present bias in various design contexts

Community input matters in long-term decisions related to climate change, including the development of public infrastructure. In order to assess the effect of different ways of informing the public about infrastructure projects, a sample of people in the United States (n = 630) was provided with a case study concerning the redevelopment of the San Diego Airport. Participants received the same written information about the projected future condition of the airport.

Minimalism in consumption can be expressed in various forms, such as monochromatic home design, wardrobe capsules, tiny home living, and decluttering. This research offers a unified understanding of the variegated displays of minimalism by establishing a conceptual definition of consumer minimalism and developing the twelve-item Minimalist Consumer Scale to measure the construct.

With heightened concerns regarding user privacy, there is a recent movement for empowering consumers with the ability to control how their private data are collected, stored, used and shared. Notably, between 2018 and 2020, the General Data Protection Regulation (GDPR) has been implemented in the European Union (EU), and the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA) have been implemented/passed in the state of California in the United States. These regulations address both consumer data security and consumer privacy rights.