Knowledge that Transforms

Despite relatively little attention and a generally negative view among both academic researchers and managers, we propose that imitation should be an explicit and important consideration in marketing strategy-making and may often be a more attractive option than widely believed. Synthesizing fragmented extant knowledge and expanding upon recent work regarding the nature of imitation we propose a new conceptualization offering a balanced and comprehensive view of imitation and its role in firm strategy-making. Building upon this we offer a new typology of different imitation options that managers may usefully consider as part of their strategy-making and identify their associated costs, benefits, and risks that can be evaluated in doing so. We also provide initial insights into marketplace contingencies impacting which of these imitation options are likely to be relatively more or less attractive as part of a business strategy. To further this new thinking about imitation, we develop an allied research agenda that offers a platform for a new stream of marketing strategy research. By changing the framing of imitation, we seek to provoke managers to alter their strategy-making in ways that will not only benefit customers, imitators, and even imitatees, but may also improve economic growth and social welfare.

National public health awareness campaigns that emphasize peer-to-peer support are increasingly adopted, but evidence on the effects of peer-based programs at scale remains limited. Using quasi-experimental methods, we examine whether the prominent nationwide “R U OK? Day” campaign affects short-term mental health outcomes in Australia. Leveraging survey and administrative data, we find R U OK? Day leads to a 4% of a standard deviation increase in self-reported mental wellbeing, with the effect particularly pronounced among middle-aged males who experience a 9% of a standard deviation increase. We find no detectable effects on mental health care utilization, and we detect no statistically significant changes in suicide-related deaths in the short run, though the mortality outcomes are rare and power is limited. Our results underscore that peer-based campaigns can improve mental wellbeing, especially for high-risk groups, and point to a distinction between short-run psychological responses and outcomes that require behavioural follow-through.

Given influencers’ greater social influence, managers are often advised to target influencers alone in social media marketing. However, the spread of word-of-mouth (WOM) may be enhanced by social affirmation (i.e., cues a message is valuable to others). The authors show that an influencer + follower seeding strategy, in which the best influencer-follower pair in a social network is chosen to tweet and retweet a message, respectively, outperforms an influencer-only strategy in spreading word-of-mouth. Lab experiments show that the likelihood that a user chooses to share or engage with a promoted tweet is higher under an influencer + follower strategy than under an influencer-only or influencer-twice strategy. Greater perceived message value to others explains these results. A field study involving promoted tweets shows that an influencer + follower strategy leads to greater event attendance than a two-influencer strategy. Simulation results show that the advantages of influencer + follower seeding strategies over influencer-only strategies are greater for social networks characterized by a stronger response to social affirmation. When social affirmation is not important to WOM diffusion, an influencer-only seeding strategy is superior. These findings contribute to research on seeding strategies and provide firms a way to improve return on investments in influencer marketing.

Digital platforms commonly ask customers to select interest categories (e.g., genres/topics) as input for personalized recommendations. Twelve main studies and two pilot studies (total N = 8,824) reveal that customers share less diverse preferences with algorithms (versus human curators or when merely listing preferences for themselves); they focus on core preferences while omitting tangential ones, a phenomenon termed preference filtering . It is driven by customers’ expectation that algorithms weigh their preferences more uniformly than human curators (i.e., expected uniformity). A mathematical model, as well as interviews and a survey with practitioners show that preference filtering appears rational ex ante yet leads to negative consequences for both customers and firms ex post. The authors examine key design dimensions of the preference elicitation task— when preferences are elicited, how customers articulate them, what purpose is made salient, and who customers believe they are interacting with—that determine the extent to which customers engage in preference filtering. Two studies on self-developed video-streaming websites show that alleviating preference filtering can boost engagement and enhance customer reviews of recommendation services. These findings offer valuable insights for firms that rely on algorithms to engage customers.

When choosing from a product assortment, consumers often compare options on multiple product attributes (e.g., energy usage, processor speed). These attributes may be displayed with more precise numbers (e.g., 12.34) or less precise numbers (e.g., 12). The present research shows that when attribute values are communicated more (vs. less) precisely, consumers weight smaller-is-better attributes (e.g., energy usage)—for which smaller numbers are generally preferred—relatively more in their decision making. Furthermore, building on an association between precise numbers and smaller magnitudes, the authors provide evidence that this difference in attribute weighting occurs because numerical precision orients consumers’ attention toward the smaller (and therefore more favorable) values of smaller-is-better attributes. This shift in attention causes smaller-is-better attributes to be weighted more heavily during multi-attribute comparison, which affects consumer evaluations and choice. Findings from nine studies (four of which are reported in the Web Appendix), including a field study with a real estate website, offer triangulating evidence for the proposed theorizing and contribute to several streams of research within judgment and decision making, including attribute weighting and numerical cognition.

#MeToo is one of the most influential social movements of recent decades, yet its marketplace effects remain underexplored. The authors investigate how #MeToo reshaped both consumer responses to gender portrayals and short-run content change in the U.S. entertainment industry. Drawing on social role theory, social movement framing and consumer behavior research, they analyze a unique data set of 1,523 top-grossing films released between 2010 and 2023. Using artificial intelligence–assisted content analysis validated by human coders and supplemented with text analysis of 334,917 crowdsourced movie keywords, the authors measure portrayals of sexual misconduct, female objectification, and stereotypical and counter-stereotypical gender roles. The findings show little evidence of meaningful short-run content change following #MeToo. While audiences penalize films featuring problematic behaviors targeted by the movement, they have enduring preferences for traditional gender roles, showing resistance to counter-stereotypical male portrayals while only selectively embracing counter-stereotypical female representations. These findings reveal the complex and uneven ways the #MeToo movement reshaped marketplace behavior, and they provide implications for entertainment firms, marketers and social movement organizers navigating changing cultural norms.

Although mental health conditions have worsened globally, many consumers remain insufficiently engaged in mental healthcare. Intuitively, practitioners can address this challenge by applying strategies that research has shown to promote physical healthcare. However, this approach might not be optimal because the decision-making process for seeking mental healthcare differs significantly from that for seeking physical healthcare. This research examines how consumers’ relative preference for ingroup (i.e., consumers’ own groups) versus outgroup healthcare providers varies when seeking healthcare for mental versus physical illness. Using a large-scale dataset comprising approximately 25 million medical consultations and 11 randomized controlled experiments, we consistently found that when seeking healthcare for mental (vs. physical) illness, consumers placed greater importance on healthcare provider empathy. This resulted in a stronger preference for ingroup over outgroup healthcare providers. The effect of illness type on healthcare provider preference was reduced when consumers were presented with ratings that demonstrated healthcare provider empathy; the main effect was attenuated among consumers with higher global identity. This research contributes to the literature on mental health, healthcare decision-making, empathy, and accessible healthcare. It offers practical implications for marketers, healthcare professionals, and policymakers for promoting mental healthcare and mitigating health disparities.

An idea is more than a simple collection of words or ingredients that make up the idea. What makes an idea original or appealing is how these elements are combined in the context in which they appear. This research leverages representation learning methods, specifically word embeddings, to measure internal coherence among the components of a creative idea and capture the relationship between its coherence and idea evaluation or success. Using a large-scale online recipe dataset with over 57K recipes, as well as an ideation dataset of 1,057 ideas, the authors investigate how the fit among idea components relates to downstream measures such as popularity and evaluation. The results consistently show that internal coherence of an idea promotes its popularity, trial, and post-trial evaluation. Counter to prior research on creativity, which suggests that creativity is mostly associated with positive outcomes, the study finds that ideas with unique ingredients have lower popularity or trial, but higher ratings given trial in the context of food recipes. Based on these findings, the authors develop a generative recipe tool that suggests recipe improvements by adding, removing, or substituting ingredients ( http://recipecreativity.com/ ).

Personalization in marketing communication may increase relevance, and thus lead to successful persuasion, but at the same time may trigger privacy concerns, thus leading to persuasion failure. These contradictory effects are described by the personalization paradox. Prior research has provided diverse results on personalization effects and what they depend on. It lacks a generalizable empirical pattern describing the paradox, leaving researchers with ambiguous theoretical implications and practitioners with little guidance on whether and when personalization is a useful marketing communication tool. To address these gaps, this meta-analysis integrates 1,536 effect sizes of personalization effects in marketing communication extracted from 290 studies found in 229 papers. The results reveal, first, that personalization leads to persuasion success and has negligible negative effects. Second, personalization effects depend on various moderators including the data type used: personalization is more persuasive when these data are closely tied to the consumer’s self-concept. Third, the level of personalization leads to a J-shaped pattern of persuasion, describing a modified model of the personalization paradox, with the most persuasive effects at the end of the personalization spectrum. These findings provide novel empirical generalizations of personalization effects and recommend marketers to use personalization and when personalized marketing communication is most effective.

How do consumers form preferences when they cannot rely on established quality standards? We examined this question in the context of non-fungible token (NFT) art markets, where buyers lack evaluative anchors such as provenance, conventional aesthetic criteria, or institutional endorsement. We demonstrated that view counts, a social cue that reflects others' attention, are one key attribute. Across a 150-day observational dataset (N = 1,459,979), a pre-registered 20-wave panel study, and six experiments, we documented that artworks that attract early views accumulate disproportionately greater attention over time, while other artworks fall further behind. We showed that this “snowballing” effect is driven by investment motives. When consumers approach markets with the motive to invest rather than collect, they rely on others' attention as a proxy for market value. Two structural features of the NFT market amplify this reliance: high price volatility and purely digital ownership, both of which heighten the salience of investment outcomes. By attenuating volatility or introducing tangible ownership cues, we diminished this reliance on social signals. Our findings extend theory on social influence and digital consumption, demonstrating that in the absence of objective quality standards, social cues shift from supplementary information to the primary basis for preference formation.