Search in service of choice: An agenda for comparing, extending, and integrating consumer search models

International Journal of Research in Marketing · 2025-09-01

Measuring population heterogeneity requires heterogeneous populations

Proceedings of the National Academy of Sciences · 2025-02-18 · 5 citations

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.This is important because, if true, it presents an optimistic picture for the generalization of results from one sample to another.However, this claim is puzzling given calls to increase attention to heterogeneity in social science ( 3 ).A closer examination of their data and related work ( 4 ) (KSJ hereafter) suggests a modification to their conclusion.Table 1 lists the studies included in HJBK suggesting a predominance of similar, somewhat narrow sample frames.University students are the most common respondents.More generally, McShane et al. ( 5 ) argue that using large replication studies as in HJBK might provide limited evidence for the investigation of heterogeneity.Panel A of Fig. 1 shows the estimates of population, design, and analysis heterogeneity in HJBK.HJBK found a median H of 1.08, far below "the 2.0 threshold indicative of large heterogeneity," and only 4 of the 70 studies exceeded that threshold.KSJ employed purposive variation of the sampling frame, using 11 different panels-including frequently used panels like MTurk, Prolific, and a student sample.They kept design and analysis variance minimal.Panel B reports the population heterogeneity in KSJ.We also included heterogeneity across time of day and weekday as a benchmark, varied in Study 3 in KSJ.Some researchers have hypothesized that this changes effect sizes ( 7 ).KSJ's estimates of population heterogeneity are markedly larger than those provided by HJBK and are about as large as HJBK's estimates for analytic and design heterogeneity.The median H across the nine estimates of population

Search in Service of Choice: An Agenda for Integrating Consumer Search Models

2025-02-20

Understanding consumer search behavior is crucial for businesses and policymakers aiming to effectively engage with consumers through product offerings and interventions. Consumer search in service for choice remains a significant topic in marketing, economics, and psychology although existing theories differ in fundamental assumptions and methodological approaches. Theoretical assumptions range from the order in which items are searched being independent of their value, to search being influenced by cognitive and visual factors. Methodological approaches vary from directly observing search (with eye tracking or click stream data), to inferring search from choice data alone. This article proposes a framework for integrating theoretical and methodological approaches to consumer search. This integration will help marketers and policymakers compare and apply theories to different contexts. Ultimately, it paves the way to a unified framework for understanding and guiding consumer behavior as well as strategic decisions in marketing and policy. We review five prominent theoretical models, each with distinct terminologies. For each model, we review its background, aims, core assumptions, and provide illustrative examples. Our contribution is twofold: (1) we initiate a synthesis to resolve terminological differences and outline a path toward theory integration, and (2) we discuss how our framework can guide future research and foster a more comprehensive understanding of consumer search and choice.

Digital Twins as Funhouse Mirrors: Five Key Distortions

arXiv (Cornell University) · 2025-09-23

Scientists and practitioners are increasingly moving to deploy digital twins--LLM-based models of real individuals--across social science and policy research. We conduct 19 pre-registered studies spanning 164 diverse outcomes (e.g., attitudes toward hiring algorithms, intentions to share misinformation), comparing human responses to those of their corresponding digital twins, which are trained on each individual's prior responses to over 500 questions. We establish an empirical benchmark for digital twin performance: their predictions are only modestly more accurate than those of a homogeneous base LLM and exhibit weak correlation with human responses (average $r = 0.20$). To inform future development, we identify five systematic distortions in digital twin behavior: (i) insufficient individuation, (ii) stereotyping, (iii) representation bias, (iv) ideological bias, and (v) hyper-rationality. Finally, we release our full dataset and code as a standardized testbed for evaluating and improving digital twin methodologies. Together, our findings caution against premature deployment while laying the groundwork for a transparent, replicable, and iterative science of responsible digital twin development.

Consumers misestimate the greenhouse gas emissions associated with sustainable behaviors, firms, and industries

Journal of Environmental Psychology · 2025-07-29

Consumers are increasingly aware of the ecological impact of their consumption. Many intend to reduce their personal carbon footprint by adopting more environmentally friendly behaviors, like reducing meat consumption, avoiding short-distance flights, or switching to green energy providers. But do consumers know which behaviors contribute most to reducing emissions? Do they know which businesses and industries emit more greenhouse gases than others? We examine the accuracy of German consumers’ carbon emissions judgments, replicating and extending work suggesting widespread lack of carbon competence in the Unites States. Across six studies ( N = 2028), we demonstrate that German consumers often make inaccurate judgments about the emissions associated with sustainable behaviors, firms, and industries. We investigate judgmental biases that can distort the emissions judgments. Corroborating earlier observations, our results suggest that emissions judgments can be biased by cognitive processes of attribute substitution. Decision makers can answer the complex question of carbon emissions by substituting easier attributes instead, like how many people within their social circle adopt a sustainable behavior, or how much they like a firm. We discuss how better understanding biases in carbon emissions judgments can contribute to improving consumer carbon competence. • Replicates and extends earlier findings of emissions judgment inaccuracy • German respondents had slightly greater carbon competence than U.S. respondents • Cognitive processes of attribute substitution can distort emissions judgments

From Crisis to Revolution: Leveraging Heterogeneity in Consumer Research for Generalizability

2025-06-23

Some consumer research aims to affect marketing practice with rapidly applied insights. To do this, consumer research findings need to be generalized from the settings of the original research to the application. However, research results reflect both the manipulation of interest and a myriad of unobserved sources of heterogeneity that affect whether and how strong an effect occurs. To generalize, we need to understand this heterogeneity. We propose a toolbox supported by the re-analysis of existing data and simulations to help consumer researchers leverage heterogeneity for generalization. We propose five levers that consumer researchers can use to increase the generalizability: 1) Measuring proximal moderators that describe respondents' interaction with the setting. 2) Exploiting purposive variation to increase the range of observed moderators and settings. 3) Measuring manipulation intensity and measurement error. 4) Using survey para-data to estimate moderators and 5) Harnessing proximal moderators and purposive variation to predict generalized effect sizes. We suggest that our toolbox can help advance our field towards higher practical impact by moving beyond understanding what works to understanding what works when, where, and why.

Search in Service of Choice

2024-06-11

Understanding consumer search behavior is crucial for businesses and policymakers aiming to effectively engage with consumers through product offerings and interventions. Consumer search remains a significant research topic across disciplines such as marketing, economics, and psychology. However, differences in theoretical assumptions and methodological approaches across these fields pose challenges that hinder interdisciplinary knowledge and theory integration. Theoretical assumptions about search range from randomness in relation to choice value to search being influenced by cognitive and visual factors. Methodological approaches vary from direct observations, like eye tracking, to inferences from observed choices. To address the lack of consensus and facilitate scientific progress, we propose a framework for integrating theoretical and methodological approaches across disciplines. We review six prominent theoretical models, each with distinct terminologies and focus areas. For each model, we review its background, aims, core assumptions, and provide illustrative examples. Our contribution is a synthesis initiative that resolves terminological differences and outlines a path toward theory integration. We discuss how our framework can guide future research and foster a more comprehensive understanding of consumer search and choice.

More than Money Over Time: A Verbal Self-Report Scale for Measuring Intertemporal Preferences

2024-06-18

IIntertemporal preferences have become an increasingly important construct for understanding individual behavior. Most measures of intertemporal preferences have focused on tradeoffs between smaller monetary amounts that are available sooner and larger later monetary amounts. But individuals make time-value tradeoffs in many other domains, too, like health and work. We developed a new approach, the Intertemporal Preferences in Choice Behavior (IPICB) scale, which uses verbal self-report items derived from crowdsourcing and previous research. The IPICB measures preferences in three domains: Health: maintaining a healthy lifestyle; Personal Finance: saving, investing and spending wisely; and Effort: a preference for getting things done rather sooner than later. We find domain-specific predictive validity for a large variety of behaviors and indices (e.g., Credit scores, intentions to get vaccinated, filing tax returns on time). The IPICB explains additional variance beyond standard elicited intertemporal preferences, even when self-control and other covariates (numeracy, social desirability and parents’ SES). The IPICB’s accessible verbal format allows widespread application for studying individuals’ intertemporal preferences.

Search in Service of Choice: An Agenda for Integrating Consumer Search Models

2024-06-11

Understanding consumer search behavior is crucial for businesses and policymakers aiming to effectively engage with consumers through product offerings and interventions. Consumer search in service for choice remains a significant topic in marketing, economics, and psychology although existing theories differ in fundamental assumptions and methodological approaches. Theoretical assumptions range from the order in which items are searched being independent of their value, to search being influenced by cognitive and visual factors. Methodological approaches vary from directly observing search (with eye tracking or click stream data), to inferring search from choice data alone. This article proposes a framework for integrating theoretical and methodological approaches to consumer search. This integration will help marketers and policymakers compare and apply theories to different contexts. Ultimately, it paves the way to a unified framework for understanding and guiding consumer behavior as well as strategic decisions in marketing and policy. We review five prominent theoretical models, each with distinct terminologies. For each model, we review its background, aims, core assumptions, and provide illustrative examples. Our contribution is twofold: (1) we initiate a synthesis to resolve terminological differences and outline a path toward theory integration, and (2) we discuss how our framework can guide future research and foster a more comprehensive understanding of consumer search and choice.

Regulating robo-advice for consumers’ financial decisions: The interplay between algorithm quality & digital choice architecture

Behavioral Science & Policy · 2024-10-01 · 4 citations

Rapid advances in digital interfaces, the quality of predictive algorithms, and the availability of high-speed internet are allowing firms to offer robo-advice : automated online guidance about which financial products are suitable for a consumer. At their best, robo-advisors feature algorithms that accurately predict which financial products are the optimal match for a given consumer’s needs and use digital choice architectures (that is, ways of presenting the product recommendations online) that are designed to strongly guide consumers to those best-fit products. Yet when algorithms lack great accuracy, strong guidance in favor of products incorrectly predicted to have the best fit could lead consumers to select options that do not meet their needs well. The possibility that strong guidance results in different financial outcomes depending on the quality of the algorithms has important implications for regulating robo-advice. We propose that, consistent with current regulatory practice for human advisors, regulators should require any firm wishing to implement a robo-advisor for its clients to demonstrate both that the robo-advisor is honest (that is, it works in the best interests of the client) and that its algorithms meet some established level of accuracy. In addition, regulators should require any firm offering a robo-advisor to demonstrate that the strength of the guidance provided by the digital choice architecture aligns with the predictive accuracy of the advisor’s algorithms. We offer several practical suggestions for implementing such a regulatory strategy. The need for regulations is becoming increasingly urgent because many robo-advisors now use artificial intelligence (AI) to make predictions. AI has made robo-advisors more powerful and easier to use, which is likely to expand their adoption—and, by extension, the harm that would be caused if subpar robo-advisors are allowed on the market.