Journal of Consumer Psychology最新文献

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.

Consumer-brand relationships are important predictors of consumption, but the psychology surrounding the different roles brands occupy within these relationships is not fully understood. Three experiments and one field study investigate how preferences for two of these brand roles, partner and servant, depend on consumers' implicit theories of self-change. Counter to what prior literature might suggest, findings show that consumers who believe that self-traits are relatively malleable (incremental theorists) and fixed (entity theorists) prefer partner and servant brands, respectively. Results demonstrate that a partner brand signals an equal effort by both the consumer and the brand, whereas a servant brand signals less effort by the consumer and more effort by the brand. The relatively greater consumer effort signals by partner (vs. servant) brands align with the effort beliefs associated with consumers' implicit theories, thereby mediating preferences. Findings are demonstrated across different product categories and samples (Taiwan and US). The focus on dyadic effort signals of brand roles in consumer-brand relationships, and the resulting interactive effect with implicit theories, provide novel contributions to theory and practice.

Scholarship on when and why humans are willing to rely on algorithms rather than other humans has made substantial progress in recent years, although virtually all such research is based on Western, educated, industrialized, rich, and democratic (WEIRD) research participants. This limits efforts to understand the cultural generalizability of attitudes toward algorithms. In this paper, I study algorithm aversion among participants from over 30 countries on all inhabited continents, thereby significantly increasing the diversity of this field's knowledge base. Furthermore, I leverage this diversity to test a theoretically derived prediction: that perceived corruption makes algorithmic decision-making more appealing. I find that participants who are born or raised in countries with high levels of perceived corruption are much less averse to algorithmic decision-making (or, in some studies, are not at all algorithm averse), relative to those from countries with low perceived corruption. Furthermore, experimentally varying corruption salience causes a decrease in algorithm aversion. I explore mechanisms and boundary conditions of these effects and discuss the implications in the context of algorithms that can both increase and decrease injustice.

Communication is an integral part of everyday life. Consumers chat with friends, search for information, and complain to customer service. Salespeople pitch products, employees answer questions, and market researchers ask them. But communication does not occur in a vacuum. Modalities (e.g., speaking or writing), channels (e.g., text, phone call, or email), and devices (e.g., smartphone or computer) are the mediums through which communicators communicate. While these mediums often seem incidental, might they impact what gets communicated? And if so, how? This paper offers a comprehensive framework for understanding how mediums shape the message. Specifically, we argue that modality, devices, and channels all shape communication through the same two key drivers: deliberation and audience salience. As a result, the mediums communicators use to communicate impact everything from the thoughtfulness and concreteness of communicated content to the degree to which it is self-enhancing or honest. This work sheds light on the psychology of content production, provides insight into the drivers and consequences of communication, and highlights how emerging technologies may shape communication in the future.