Journal of Consumer Psychology最新文献

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.

User reviews are now an essential source of information for consumers, exerting strong influence on purchase decisions. Broadly speaking, reviews rated by consumers as more helpful exert a greater influence downstream. The current research examines how the linguistic characteristics of a review affect its helpfulness score. Using a convolutional neural network (CNN), this research analyzes the linguistic subjectivity and objectivity of over 2 million reviews on Amazon. The results show that, ceteris paribus, both linguistic subjectivity and objectivity have a positive impact on review helpfulness. However, contrary to consumers' intuition, when subjectivity and objectivity are combined in the same review, review helpfulness increases less than their respective separate effects would predict, especially for hedonic products. We conceptualize that this results from the increased complexity of messages mixing subjective and objective sentences, which requires more effortful processing. The findings extend the literature on online reviews, word-of-mouth, and text analysis in marketing, and offer practical implications for marketing communication and facilitation of reviews.

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.

Organizations face growing pressure from their consumers and stakeholders to take public stances on sociopolitical issues. However, many are hesitant to do so lest they make missteps, promises they cannot keep, appear inauthentic, or alienate consumers, employees, or other stakeholders. Here we investigate consumers' impressions of firms that respond quickly or slowly to sociopolitical events. Using data scraped from Instagram and three online experiments (N = 2452), we find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. Unlike other types of public firm decision making such as product launch, where careful deliberation can be appreciated, consumers treat firm response time to sociopolitical events as an informative cue of the firm's authentic commitment to the issue. We identify an important boundary condition of this main effect: speedy responses bring limited benefits when the issue is highly divisive along political lines. Our findings bridge extant research on brand activism and communication, and offer practical advice for firms.

The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. It has been examined in relation to psychological distance, attribution, credibility, and deception. Most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. We introduce an automated tool to identify passive voice from large-scale text data, PassivePy, a Python package (readymade website: https://passivepy.streamlit.app/). This package achieves 98% agreement with human-coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss how PassivePy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving. Future research can build on this work and further explore the potential relevance of passive voice to consumer psychology and beyond.

Consumers are choosing to purchase food products from retailers through online channels rather than brick-and-mortar channels. While online reviews play a crucial role in influencing online purchases, scant work has examined how consumers write reviews for food products. We argue that the nutritional value of the food is a key aspect of product performance and apply expectation-disconfirmation theory to examine whether pre-purchase expectations about a food product's nutritional value and disconfirmation of these expectations have a significant effect on online review content and linguistic characteristics. Using text-mining approaches to analyze Amazon data, we find that pre-purchase expectations, postpurchase performance, and disconfirmation regarding nutritional value affect both review content and linguistic characteristics, including review length, diversity, readability, subjectivity, and sentiment. While research suggests that postpurchase product performance is the main influence on online review writing behavior, this research shows that the pre-purchase phase also plays a key role.

How can social media managers engage consumers to share posts with others? Extending regulatory mode theory, we demonstrate that high construal levels enable the integration of regulatory mode complementarity orientations, resulting in engagement and shares. Regulatory mode complementarity refers to the combination of high assessment (i.e., the motivation to “be right” by critically evaluating options) and high locomotion (i.e., the motivation to “act” by moving toward a goal). Specifically, this research proposes that an abstract (vs. concrete) construal allows these two orientations to work together, resulting in regulatory fit. Three text analysis field studies on marketer- and consumer-generated Facebook and Twitter posts show that construal–regulatory mode fit increases social media sharing. Three follow-up studies then show generalizability, establish causality, and demonstrate the role of engagement as the underlying mechanism driving the fit effect.