Journal of Consumer Behaviour最新文献

The conversational, social and intelligent capabilities of Artificial Intelligence (AI) enabled voice assistants (VAs) to assist humans have grown. However, their use still remains limited for complex tasks such as shopping. While some studies find that consumers are willing to use them, others report negative reactions that lead to rejection, especially for complex activities. This study aims to bridge the gap between the dichotomous streams of literature by investigating the overall attitude towards the use of AI VAs for a multi-step task that is, shopping. We first identify 27 attitudinal criteria through a comprehensive literature review in light of the Uncanny Valley Theory and the Self Determination Theory. The criteria are organized using the cognitive, affective and conative framework of attitude, and ranked using the Fuzzy Analytical Hierarchy Process. Sensitivity analysis is done to affirm the robustness of the framework. Findings reveal that the behavioral intentions to use (conative criteria) are the strongest, followed by feelings evoked during use (affective criteria), followed by beliefs people hold regarding AI VAs (cognitive criteria). Intention to use AI voice assistants during all stages of the purchase journey takes precedence over negative feelings such as loss of control, unfulfillment and vulnerability. This study reconciles the existing stream of literature on conversational AI and provides managerial implications based on the dimensions of attitude.

This article sheds light on the profound impact of technology on consumer behavior, specifically focusing on the rise of generative AI tools. It highlights how these advancements have revolutionized consumer engagement, purchase decision-making, and technology interaction. The article underscores the transformative potential of generative AI in shaping consumer behavior through personalized recommendations and interactive shopping experiences. It emphasizes the need for continued research and exploration to comprehend and effectively navigate the ever-evolving landscape of consumer behavior influenced by generative AI. Additionally, the article identifies implications for research and practice, offers valuable strategies for brands, and presents a comprehensive research agenda to delve deeper into this field. Ultimately, it provides valuable insights into the challenges and opportunities presented by generative AI in consumer behavior, serving as a guiding resource for advancing theory, practice, and policy in this domain.

Whereas consumer satisfaction is critical for the success of services, we research how providers evaluate customers in the sharing economy represented by the rideshare marketplace. We examine anticipated customer evaluation (ACE) as the underlying link and the provider's sense of power as a moderator for the relationship between customer ingratiation and provider evaluations. We first conducted a field pilot study and analyzed the content of rideshare trips described by drivers. Then, we tested the conceptual framework in four experiments that manipulated different rideshare customer behaviors (self-presentation, other-enhancement, and customer conformity). Our study contributes to the consumer research literature by examining provider evaluation and discovering the peer-to-peer (P2P) dynamic evaluation mechanisms that adopt a two-way rating system.

Feelings of belongingness threat are common nowadays. The lack of social support received from friends and family leads to social disconnection. Our objective is to investigate the effect of unmet need to belong on preference for crowdedness. Expecting the prospects to interact and reconnect with others, socially disconnected consumers may be more receptive to crowdedness. By contrast, social disconnection may sensitize consumers to potential social threat and thus drive them away from the crowds. To find out how socially disconnected consumers react to crowdedness, three quasi-experiments with more than 1500 participants (M_age = 35.9, 64% female) from an online panel were conducted. Our findings support the latter hypothesis. We manipulate the crowdedness of the service setting, while the natural variation of social disconnectedness was captured by measuring participants' unmet need to belong. Socially disconnected consumers are averse to social crowding (Studies 1 and 2). Specifically, regression analyses reveal that crowdedness has a negative impact on attitude and word-of-mouth recommendation. Importantly, the effect of crowdedness is found exaggerated by unmet need to belong. Disconnected consumers avoid social crowding since they may consider getting too close an invasion of personal space. The findings on preference for space are extended to time in Study 3. In particular, busyness interacts with unmet need to belong such that disconnected consumers avoid busy schedules in package tours. Findings from the three studies together suggest a general preference for more psychological space when need to belong is not met.

The purpose of this study is twofold: to examine the mediating effect of a number of barrier-breakers on the relationship between barriers and the intention to fully adopt digital payment methods; and to identify adopters-resisters' commenting behaviour when facing a full-adoption scenario. Mixed methods were applied. The PROCESS macro method was used to analyse 388 survey responses and test the mediating effects. The main study reveals that the privacy, security, and access barriers can be reduced by increasing the credibility and usefulness barrier-breakers, respectively. However, the impersonalisation barrier is found to be unaffected by the social influence barrier-breaker. Based on 91 collected and analysed comments, the netnographic-based follow-up study identifies social media commenting behaviours caused by a major bank's decision to stop handling cash. Among the more frequent such behaviours are boycotting and aggressive comments.

The field of using neural data to forecast aggregate consumer choice has garnered attention in the past decade, holding substantial promise for both researchers and practitioners. However, a comprehensive understanding of this emerging field is lacking. This paper aims to bridge that gap by summarizing existing research, encompassing relevant theories, metrics, progress, and future directions. We begin by introducing the concept of neuroforecasting within the field of neuromarketing. We then delve into theories that leverage neural data for forecasting aggregate choice, including affect-integration-motivation framework, frontal asymmetry, and inter-subject correlation. Subsequently, we review various metrics, including self-reported, behavioral, and neural metrics employed to forecast market-level behavior, presenting key findings from relevant studies. Furthermore, we examine the strengths and weaknesses of this field. Advantages of this approach include its ability to offer effective predictions of consumer behavior and provide enhanced insights into consumer preferences and choices, while its weaknesses encompass relatively high cost, sample size constraints, issues of ecological validity, and challenges related to reverse inference. In conclusion, future research should prioritize integrating diverse data types with machine learning techniques to forecast the outcomes of marketing campaigns in advance. Additionally, a deeper exploration of the psychological and cognitive processes underlying successful predictions can augment predictive accuracy and effectiveness. This review provides a systematic overview for researchers and practitioners in this field, offering valuable insights and guidance for future research endeavors and industry applications.

This research is an empirical investigation of the interaction effect between self-deficit and benefit-appeal message framing upon subsequent environmental sustainability decision-making. Based on the self-discrepancy theory, the current research demonstrates that behavioral intentions that encourage environmental sustainability could serve as a means of compensating for the self-deficit appraisal. Two online experiments empirically test that self-deficit moderated the effect of benefit-appeal (self vs. other) on ad evaluation and purchasing intention toward an environmentally sustainable brand. In particular, Study 1 demonstrated that when in a self-deficit state, self-benefit appeals are more effective than altruistic other-benefit appeals in enhancing positive advertising evaluation and purchase intention toward a brand that advocates environmental sustainability. On the other hand, the opposite pattern was captured when self-deficit was minimized. Study 2 replicated these findings using a different brand, confirming the interaction effect between self-deficit and benefit appeal, and further investigated whether consumers' positive ad evaluation mediates the interaction effect of benefit appeals and self-deficit on purchase intention. The findings also offer managerial implications recommending that advertisers and marketers tailor their ad messaging to match consumers' desires and wants.

As products are constantly updated, brands launch increasingly different versions thereof, and consumers frequently face upgrade choices. However, when and why consumers choose to upgrade has received limited attention. This article thus sheds new light on consumer upgrade intention by distinguishing a novel antecedent, product type (material vs. experiential). Three online studies with 642 participants from the US were conducted; we used t-tests and bootstrapped mediation analysis via PROCESS Model 4 to analyze the data. The results of these studies reveal that consumers are more likely to upgrade experiential products than material products. Moreover, this effect is mediated by a heightened sense of anticipated regret rather than upgrade degree or perceived product closeness. Specifically, consumers feel greater anticipated regret if they do not upgrade experiential products (vs. material products), which leads to their higher upgrade intentions toward experiential products. This research therefore significantly extends regret regulation theory, provides important insights into the relationship between product type and upgrade intention, and offers valuable knowledge for brands seeking to optimize their marketing strategies.

The aim of this study is to analyze whether disvalues—that is, undesirable traits and emotions that have a negative impact on the individual and are reflected in the Seven Deadly Sins (anger, envy, gluttony, greed, lust, pride, and sloth) and the Dark Triad traits (narcissism, machiavellianism, and psychopathy)—have a predictive power for pathological and uncontrollable shopping behavior (compulsive shopping). Two studies were conducted on as many consumer samples to examine the relationship between disvalues and compulsive shopping behavior and the influence of personality traits on this relationship. The results of Study 1 confirmed that anger, envy, gluttony, and pride, as well as psychopathy directly influence compulsive shopping behavior. In addition, two types of consumers were identified: those who are guided by values (Sober and Light consumers) and those who are guided by disvalues (Vicious and Dark consumers). Only the latter exhibit a high levels of compulsive shopping behavior. Study 2 found that personality traits (in terms of the Big Five factors) moderate the relationship between disvalues and compulsive shopping behavior. More specifically, this relationship is negatively moderated by conscientiousness and agreeableness, and positively moderated by neuroticism.