David Gal

Researchers are increasingly challenged by this question: On what basis can they claim their work contributes to research progress? We propose that contemporary consumer research relies on three broad strategies for warranting such claims. An establishing‐an‐effect strategy claims progress based on confidence that a focal relationship between variables X and Y reliably replicates. A theory‐of‐the‐effect strategy claims progress based on confidence that a local mechanistic account specifies when and how a particular effect occurs. A theoretical framework strategy instead claims progress based on confidence in an explanation that transcends any single effect paradigm. We (a) clarify how these strategies differ in their core logic, methodological emphases, and vulnerabilities; (b) document via a coding of recent articles in leading consumer research journals that the field relies overwhelmingly on the theory‐of‐the‐effect strategy and rarely advances theoretical frameworks; and (c) develop actionable guidance for progressing from theories of effects to frameworks. We argue that recognizing the merits and vulnerabilities of each strategy—and deliberately leveraging the strategies in combination—provides a stronger basis for claiming research progress.

Researchers are increasingly challenged by this question: On what basis can researchers claim confidence in making research progress? Here, we categorize studies that base their reliance on one of three different strategies to answer this question. One involves establishing an effect where progress is the extent of support that an effect will be observed in subsequent studies that follow similar or conceptually close procedures. A second strategy involves a theory of the effect where progress is the extent that the mechanism or process by which an effect occurs can be understood sufficiently to identify key conditions under which the effect will or will not occur. A third strategy entails constructing a theoretical framework, where progress is the extent that a theory explains why effects occur at a level that transcends individual effect paradigms. We discuss issues that should be explicitly considered in selecting a strategy for justifying claims of research progress. Although there is potential for synergy across studies where the strategies build on one another, each strategy represents an alternative basis for claiming confidence in a study.

Digital platforms increasingly mediate how individuals share personal information. While prior research has documented that people selectively disclose based on social relationships, conflicting findings remain: some studies show individuals restrict strangers’ access to private data, while others reveal a surprising willingness to share sensitive information with them. We propose that these seemingly contradictory behaviors are explained by contextually dominant privacy concerns—specifically, security versus identity concerns. We theorize that when concerns over security are dominant, individuals are less willing to disclose personal information to strangers, whereas when concerns over identity are salient, individuals are less inclined to share with acquaintances. To test these predictions, we conducted five studies, including a secondary dataset (N = 11,123 posts) and four pre-registered experiments (N = 1,019). We find consistent evidence that the dominant component of privacy concerns predicts individuals’ willingness to share private information across relationship types. These findings contribute to the understanding of selective self-disclosure and offer marketing-relevant implications for digital platforms—guiding how platforms can be positioned and designed to reduce dominant privacy concerns, encourage deeper engagement, and support more effective data-driven personalization.

Large language models (LLMs) such as ChatGPT, Gemini, and Claude are increasingly being used in aid or place of human judgment and decision making. Indeed, academic researchers are increasingly using LLMs as a research tool. In this paper, we examine whether LLMs, like academic researchers, fall prey to a particularly common human error in interpreting statistical results, namely ‘dichotomania’ that results from the dichotomization of statistical results into the categories ‘statistically significant’ and ‘statistically nonsignificant’. We find that ChatGPT, Gemini, and Claude fall prey to dichotomania at the 0.05 and 0.10 thresholds commonly used to declare ‘statistical significance’. In addition, prompt engineering with principles taken from an American Statistical Association Statement on Statistical Significance and P-values intended as a corrective to human errors does not mitigate this and arguably exacerbates it. Further, more recent and larger versions of these models do not necessarily perform better. Finally, these models sometimes provide interpretations that are not only incorrect but also highly erratic.

Almaatouq et al. view the purpose of research is to map variable-to-variable relationships (e.g., the effect of X on Y). They also view theory as this mapping of variable-to-variable relationships rather than an explanation of why the relationships occur. However, it is theory as explanation that allows us to reconcile disparate findings and that should guide application.

Chater & Loewenstein thoughtfully express their disillusionment with contemporary applied behavioral science, particularly as it pertains to public policy. Although they fault an overemphasis on i-frame approaches, their proposed alternatives leave doubt regarding whether behavioral science has much, if anything, useful to offer policy. We offer two critical principles to guide and motivate more relevant behavioral science.

A crisis of confidence in research findings in consumer psychology and other academic disciplines has led to various proposals to abandon, replace, strengthen, or supplement the null hypothesis significance testing paradigm. The proliferation of such proposals, and their often‐conflicting recommendations, can increase confusion among researchers. We aim to bring some clarity by proposing five simple principles for the new era of data analysis and reporting of research in consumer psychology. We avoid adding to researchers' confusion and proposing more onerous or rigid standards. Our goal is to offer straightforward practical principles that are easy for researchers to keep in mind while analyzing their data and reporting their findings. These principles involve (1) interpreting p‐values as continuous measures of the strength of evidence, (2) being aware of assumptions that determine whether one can rely on p‐values, (3) using theory to establish the applicability of findings to new settings, (4) employing multiple measures of evidence and various processes to obtain them, but assigning special privilege to none, and (5) reporting procedures and findings transparently and completely. We hope that these principles provide researchers with some guidance and help to strengthen the reliability of the conclusions derived from their data, analyses, and findings.

Consumers generally prefer natural to synthetic drugs, a phenomenon known as the “natural preference.” Through six experiments and one archival study, the current research shows that while consumers have a general preference for natural drugs over synthetic drugs, this preference is stronger when the goal is to treat psychological rather than physical conditions. Process evidence indicates an important mechanism that explains the amplified natural preference for treating psychological conditions: Consumers are more concerned about their true selves being altered when treating psychological conditions, and they perceive natural drugs to be less likely than synthetic drugs to affect their true selves. The current research provides novel insights into the natural preference. It also offers policy and managerial implications for marketing natural remedies and pharmacological treatments for mental health conditions.

A crisis of confidence in research findings in consumer psychology and other academic disciplines has led to various proposals to abandon, replace, strengthen, or supplement the null hypothesis significance testing paradigm. The proliferation of such proposals, and their often- conflicting recommendations, can increase confusion among researchers. We aim to bring some clarity by proposing five simple principles for the new era of data analysis and reporting of research in consumer psychology. We avoid adding to researchers’ confusion and proposing more onerous or rigid standards. Our goal is to offer straightforward practical principles that are easy for researchers to keep in mind while analyzing their data and reporting their findings. These principles involve 1) interpreting p-values as continuous measures of the strength of evidence, 2) being aware of assumptions that determine whether one can rely on p-values, 3) using theory to establish the applicability of findings to new settings, 4) employing multiple measures of evidence and various processes to obtain them, but assigning special privilege to none, 5) reporting procedures and findings transparently and completely. We hope that these principles provide researchers with some guidance and help to strengthen the reliability of the conclusions derived from their data, analyses, and findings.

Almaatouq et al. view the purpose of research is to map variable-to-variable relationships (e.g., the effect of X on Y). They also view theory as this mapping of variable-to-variable relationships rather than an explanation of why the relationships occur. However, it is theory as explanation that allows us to reconcile disparate findings and that should guide application.