Journal of Consumer Psychology最新文献

Language, whether spoken or written, is fundamental to the consumer experience. It is how consumers express their thoughts, articulate choices, negotiate with others, and receive information about products or services. And it is how marketers deliver persuasion attempts, make apologies, and build relationships with consumers.

Language has also long been a powerful research tool. Scholars have used content analysis methods like ethnography in interviews, observational studies, and interpretation of language-based artifacts to advance our understanding of consumer culture (e.g., Arnould & Thompson, 2005; Stern, 1989). Research on the psychology of language has studied how people respond to a range of semantic, syntactic, and rhetorical aspects of verbal communication (Kronrod, 2022).

But something new has emerged in the last decade or so—something that has helped foster a genuine explosion in the analysis of text in consumer research (Packard & Berger, 2023). First, language data has become more accessible. While consumers, companies, and other marketplace actors are constantly producing language, only recently has much of this content become digitized (or digitizable), making it far easier to collect and analyze. Every day, billions of consumers share attitudes and opinions online. Customer service calls, depth interviews, and Zoom meetings can be transcribed with the push of a button, and the shift from paper and pencil surveys to online data collection means open-ended participant responses are ready-made for automated text analysis. Similarly, massive online repositories of human conversations, product reviews, books, movie scripts, newspaper articles, and other cultural content provide easy ways to explore ideas in language.

Second, new tools have changed how language can be analyzed. Previously, language data could only be coded manually. Researchers, or research assistants, would read or listen to language and score it on various dimensions. While manual coding is helpful, it is often subjective and difficult to scale for both lab and field research. Manually reading and carefully evaluating just 10 conversations, online reviews, or thought listings takes a fair amount of time—and reading 1000 takes 100 times as long.

In recent years, though, psychologists and computer scientists have developed tools that allow language data to be processed and analyzed quickly and easily. Dictionaries like Linguistic Inquiry and Word Count (LIWC, Tausczik & Pennebaker, 2010) allow researchers to count the presence of different words linked to psychological constructs and approaches like latent Dirichlet allocation (Blei et al., 2003). Word embeddings (cf. Bakarov, 2018) and large language model approaches (e.g., BERT, GPT) make it possible to measure almost any construct. And these tools are becoming more user-friendly every day.

Research suggests that mystery products can be appealing to consumers and can motivate interest and purchase. In this paper, we examine a different benefit of these offerings—their effect on driving conversation. We propose that such products can prompt a conversation due to their ability to motivate joint speculation, or the process of thinking about possible resolutions of the uncertainty with others. We define this novel driver of conversation, delineate it from related constructs, and situate it in the literature. We then provide initial evidence for the proposed theory in seven studies (n = 2835), demonstrating that mystery products increase the desire for conversation (Studies 1, 3–4, Supplemental Studies A–C) and generate joint speculation (Studies 2–4, Supplemental Study B–C). We also rule out alternative explanations (such as information acquisition and savouring, Study 3; novelty, Supplemental Study B). These effects, however, are attenuated for closed-minded consumers (Study 4), who are less open to considering multiple perspectives and thereby less interested in joint speculation. We conclude with directions for future research and implications for marketers.

We investigate how duration of storage affects food waste behavior. We propose and demonstrate a negative impact of duration of storage: even when two packaged food products are otherwise identical (i.e., same manufacturing/expiration dates, not expired, and unopened), the product that has been stored for a longer duration under current ownership is more likely to be wasted and is likely to be wasted in greater quantities. This occurs because duration of storage lowers consumers' perceptions of food freshness, even when normative justification is not possible. The duration of storage has an independent effect over and above those due to manufacturing and expiration dates, and the effect persists in both single and joint evaluations. We draw from research in several areas, including ownership, mental accounting, food science, and waste management to develop our theory. We report findings from six pre-registered studies, provide process evidence, and identify interventions to lower food waste.

Would highlighting that a customer review can be completed in 3 min influence a customer's decision to either promptly submit their review or delay it, with the potential risk of forgetting it altogether? Despite the popular approach of using task duration to mitigate task delay, the empirical support for this method is scant. This study investigates the effect of considering task duration (i.e., how long a task may take) on task delay (i.e., postponing the task until later). Across four studies, we demonstrate that making task duration salient decreases the likelihood of postponing a short task that can be achieved in one sitting. This effect occurs because considering task duration strengthens the implemental mindset, but only when task duration information is more evaluable. The findings of this paper suggest an easily implementable method that could customer engagement. Finally, we propose a set of promising avenues for future research.

Some retailers round up price discounts, such as displaying a 7.7% discount as an 8% discount. In such instances, lay beliefs would suggest that displaying an 8% discount (vs. a 7.7% discount) would increase purchase intentions. In this research report, however, we show that displaying a rounded-up, higher-value discount (8%) versus a more precise but lower-value discount (7.7%) reduces purchase intentions. Specifically, we show that using a more precise discount framing increases perceptions that the discount duration is shorter, in turn increasing purchase intentions. This research report presents a relevant and counterintuitive effect, and we propose contributions to work on both behavioral pricing and numerical information processing. Furthermore, this work has implications for practice, showing how to optimally display price discounts.

Our research develops a framework that explores how to fuel the climate movement by accelerating grassroots, community-based climate action. Drawing on insights from consumer psychology, our framework identifies the psychological mechanisms that encourage and motivate people, both individually and collectively, to take climate action, thereby contributing to our understanding of how to advance social action and propel a social movement. Our climate action framework builds on: (1) individuals we describe as climate upstanders who rise up to take climate action with like-minded others, and (2) communities of climate upstanders who engage in collective action aimed at addressing the climate crisis. Our framework expands the field of consumer psychology by redefining the role of consumers to include the practice of social action and broadening the study of consumers to include collective, community-based action. We call on consumer psychologists to research individual and collective consumer practices related to social action and contribute to making social good central to the study of consumer psychology.

This paper examines product-facilitated conversations. In three studies, we show that the products consumers publicly display influence how other consumers start conversations with them and how enjoyable and self-disclosing these conversations are. Study 1 is an experiment in the field that shows that product-facilitated conversations are deeper and more enjoyable than non-product-facilitated ones. Study 2 examines the characteristics of products that, when mentioned, lead to good conversations and identifies uniqueness and commonality as key characteristics. Study 3 is an additional experiment in the field that tests these characteristics and shows that products with those characteristics are better conversation starters than the weather. Overall, these studies show novel social benefits to talking about products and generate new ideas about how talking about products can help consumers meet new people, smooth awkward social situations, and build relationships.

American consumers tip $36bn annually, predominantly using small sums of cash. Yet, little is known about how the denominations of cash affect tipping behavior. In contrast to existing findings on the spending of different denominations (i.e., the denomination effect), we posit that consumers are less likely to tip smaller (vs. larger) denominations (e.g., $1 in 4 × 25¢ coins vs. a $1 banknote) to the same total value. We term this the “denomination-tipping” effect and predict that it occurs because it is more embarrassing to tip with smaller denominations than larger denominations. Consistent with this prediction, we find across one field study and four online studies (N = 1402) that consumers are less likely to tip smaller (vs. larger) denominations, and that this “denomination-tipping” effect is mediated by feelings of embarrassment regarding tipping smaller denominations. Our findings add to the literature on how cash denomination affects consumers' usage of money in the context of tipping, and we provide practical guidance on how service providers can minimize the adverse impact of smaller denominations on tips to their service staff.

Prior research revealed a striking heterogeneity of how consumers view smart objects, from seeing them as helpful partners to merely a useful tool. We draw on mind perception theory to assess whether the attribution of mental states to smart objects reveals differences in consumer–smart object relationships and device usage. We train a language model to unobtrusively predict mind perception in smart objects from consumer-generated text. We provide a rich set of interpretable linguistic markers for mind perception, drawing on a diverse collection of text-mining techniques, and demonstrate that greater mind perception is associated with expressing a more communal (vs. instrumental) relationship with the device and using it more expansively. We find converging evidence for these associations using over 20,000 real-world customer reviews and also provide causal evidence that inducing a more communal (vs. instrumental) relationship with a smart object enhances mind perception and in turn increases the number of tasks consumers engage in with the device. These findings have important implications for the role of mind perception as a novel lens to study consumer–smart object relationships. We offer an easy-to-use web interface to access our language model using researchers own data or to fine-tune the model to entirely new domains.

Three commentaries below provide different perspectives on data analysis and reporting. They generally focus on how the quality of the measures and manipulations determines the value of the analysis. Norbert Schwarz and Fritz Strack's comment is less on the right statistic and more on “sloppy reasoning, gaps between theoretical concepts and their operationalizations, and blissful ignorance of the situated nature of human thinking, feeling, and doing contribute more to the limited reproducibility of empirical findings than the choice of a particular test statistic.” They propose that particular effects are contextual and inappropriately labeled as true or false. Instead, our job is to focus on general constructs that make sense of the diversity of human experience and psychological reactions. Too often studies replicating psychological effects in the noisy and confounded conditions of the marketplace result in statistical uncertainty of garbage in, garbage out. Researchers instead need to look toward tests of specific interactions, which can clarify the influencing factors based on theoretical considerations. The second comment is by Andrew Gelman, an outstanding psychological statistician. He proposes that “once the data have been collected, the most important decisions have already been done.” He then provides four recommendations that enable the statistics to work appropriately. The first requirement of an effective study is to be sure that the measures address the construct of interest. Similar to the position of Schwarz and Strack, it is important to articulate the relevance of a statistically significant finding. The second recommendation seeks to curb large number of studies with inflated effect sizes built from narrow studies and unwarranted optimism. The third recommendation is to simulate data from a model and consider the distribution of possible results. That is often done to test a new analysis method, but it can be even more important in marketplace studies where novel characteristics of the sample and experimental conditions are included in the analysis. Finally, he recommends that one consider likely analyses needed before getting the data. Such foresight would encourage, for example, thinking about the kind of data needed to defend the equality of the control demographics against the treatment. The final commentary is by Stijn van Osselaer. He agrees that p-values reflect the detailed methods from a given study but do not focus on the problem of generalizability. Like Gelman, he sees designs focused on effect sizes may have generated too many studies that do not replicate. He contrasts broad explorations with narrowly defined existence tests that provide evidence that an effect exists somewhere but are mute on other contexts where they may apply. For theoretical problems relevant to applications, it is important to identify moderators through broad sampling across population characteristics, stimuli, and situations. He p