Social media platforms often use tiered status systems to categorize influencers into different tiers based on their follower count. However, few studies have examined the effects of these follower-based status systems on influencers’ content-creation behaviors. This study addresses this gap using data from a leading Chinese social media platform. We explore the platform’s influencer status-assignment rule that grants “mid-tier” status to influencers with more than 50,000 but less than 500,000 followers; specifically, we estimate the impacts of gaining mid-tier status on influencers’ content creation using a regression discontinuity (RD) approach. The results show that obtaining mid-tier status positively impacts the quantity of sponsored but not organic content. However, status change does not affect the quality (measured through user engagement) of sponsored or organic content. Further analysis reveals that the positive effect on sponsored content quantity operates through multi-channel networks (MCNs), which selectively allocate sponsorship opportunities based on influencers’ follower-based status.

Influencer livestreaming has become a key marketing strategy for firms to engage with consumers. A recent trend, co-livestreaming, features the influencer and the brand representative jointly presenting and promoting products. However, the effectiveness of co-livestreaming remains unclear. Building on signaling theory, in this study we investigate how co-livestreaming (vs. single-influencer livestreaming) affects livestream performance. Using secondary data analysis and three experiments, we find that co-livestreaming outperforms single-influencer livestreaming, significantly enhancing both transactional (product sales) and relational (new follower count) performance. This effect is serially mediated by perceived brand endorsement and influencer credibility. However, the positive impact of co-livestreaming on performance diminishes as the popularity of the influencer and brand increases. These findings reveal the mechanism and boundary conditions of co-livestreaming effectiveness, providing actionable insights for firms and influencers to adopt more effective livestreaming strategies.

Artificial intelligence (AI) is increasingly praised for its superior performance compared to humans. Some firms now strategically adopt a better-than-human framing to promote their AI products. Across four experimental studies, we investigate consumer responses to this better-than-human framing and identify significant risks associated with the strategy. Specifically, presenting AI through a better-than-human frame can trigger existential concerns, as reminders of AI dominance evoke perceived threats to humanity. Given that many consumers already experience anxiety about AI’s advancement, such better-than-human framing can intensify these fears—particularly among those high in general AI anxiety. However, recognizing that firms may continue to use this approach for strategic purposes, we develop and test an intervention message designed to accompany the better-than-human frame, aiming to mitigate perceived threats to humanity. Our findings provide critical insights for AI marketing and communication, highlighting the need for nuanced messaging that balances AI performance signals with consumer reassurance.

The study explores how and why an embedded implausible tensile price claim (TPC) affects consumers’ decisions as to whether to click on the associated online advertisement and their subsequent purchases. The authors conduct a field experiment by designing a structured set of search advertisements with implausible TPCs for a global apparel brand and launching them on Baidu, the largest search engine in China. Using a hierarchical Bayesian model, the authors quantify how implausible TPCs influence consumers’ two-stage decisions: clicking on the advertisement and purchasing the product. Using two laboratory experiments, the authors further demonstrate that the purchase decision is mediated by consumers’ perceptions of deception in the advertisement, despite the fact that the click decision is not. Results show that implausible TPCs in a search advertisement motivate consumers to click on the promotion. However, the discrepancy between the advertised and the actual discounts makes the advertisement seem deceptive to consumers, which reduces their likelihood of purchasing the product and leads them to search for other products and promotions. The results address advertising deception in online price claims, offer critical policy implications regarding implausible TPCs, and shed light on designing and executing implausible TPCs in online advertising.

Artificial intelligence (AI) recommendations are becoming increasingly prevalent, but consumers are often reluctant to trust them, in part due to the “black-box” nature of algorithm-facilitated recommendation agents. Despite the acknowledgment of the vital role of interpretability in consumer trust in AI recommendations, it remains unclear how to effectively increase interpretability perceptions and consequently enhance positive consumer responses. The current research addresses this issue by investigating the effects of the presence and type of post hoc explanations in boosting positive consumer responses to AI recommendations in different decision-making domains. Across four studies, the authors demonstrate that the presence of post hoc explanations increases interpretability perceptions, which in turn fosters positive consumer responses (e.g., trust, purchase intention, and click-through) to AI recommendations. Moreover, they show that the facilitating effect of post hoc explanations is stronger in the utilitarian (vs. hedonic) decision-making domain. Further, explanation type modulates the effectiveness of post hoc explanations such that attribute-based explanations are more effective in enhancing trust in the utilitarian decision-making domain, whereas user-based explanations are more effective in the hedonic decision-making domain.

Demand spillovers between distribution channels are critical for revenue streams in the music industry. Drawing on consumption capital theory, this study investigates the impact of a subscription (unsubscription) or upgrade (downgrade) in music streaming on live, physical, and digital music consumption. The propensity score matching approach applied to a biannual panel with six waves indicates that conversion in music subscriptions spills over to the live market and changes willingness to pay (WTP) and demand for live events. Converting from no streaming or free streaming to a premium subscription leads to a particularly high surplus in WTP and demand for live music. Unsubscribing or downgrading a music subscription decreases WTP and demand for live events. However, changes in music subscriptions are rather unrelated to physical and digital purchases. A scenario analysis indicates that deleting the free streaming option outperforms restricting the features available to free users. The authors provide contingencies of this finding.

Marketers and researchers recognize the importance and impact on consumer behavior of marketer-generated content (MGC) in social media channels. In this study, the authors present a method to classify MGC using a combination of unsupervised and supervised machine learning. They gather a large data set of posts from Facebook, Instagram, and Twitter and use a time-series model (panel-data vector autoregression) to demonstrate how MGC can be used to explain average toxicity on the part of users. They contribute to the field by examining what types of MGC lead to toxic comments and how these toxic comments impact product usage. The authors find that MGC that demonstrates the quality of products and MGC that is aimed at creating a sense of belonging to a group are more likely to increase average toxicity. Furthermore, the authors find that higher average toxicity in social media communities leads to an increase in usage of the focal product. Finally, the results contribute to the literature by providing insights on the impact of MGC on product usage.

Artificial intelligence (AI) in medicine offers a unique opportunity to improve the global health system. However, consumers remain skeptical about AI's ability to accurately assess their medical condition. The five studies here provide insights into consumers’ reluctance to use AI-produced health care recommendations. Consumers are less willing to follow a medical recommendation from AI (vs. from a human) when the medical diagnosis provides health results that are good (i.e., symptoms do not require medical care) versus bad (i.e., symptoms are worrisome and may require urgent care) (Study 1a). The effect is mediated by consumers’ perception of diagnosis trustworthiness (Study 1b) and enhanced by consumers’ health anxiety score (Study 2). Providing social proof (e.g., number of satisfied customers recommending the service) reduces the negative effect of health anxiety on consumers’ trust in the medical diagnosis and increases their willingness to follow the AI's recommendations (Study 3a). The findings provide insights into the psychological drivers of acceptance of automated health care and suggest possible actions to overcome consumers’ reluctance to follow AI medical recommendations.

During the COVID-19 pandemic, older consumers have increased their usage of social networking services (SNSs) to avoid social isolation, yet this behavior remains unexplored. Through selective optimization with compensation theory, the authors combine concepts from gerontology and marketing to investigate the following research question: How does older consumers’ usage of SNSs during the pandemic interrelate with the constructs of social well-being? The research draws on qualitative data collated during lockdown in the United Kingdom, including 14 semistructured interviews from participants age 65–80 and six months of netnographic data from an online forum geared toward older people. The findings reveal how older consumers leverage three strategies—selection, optimization, and compensation—to improve their use of SNSs and social interactions during lockdowns. Such behaviors in turn interrelate with the dimensions of social well-being: social acceptance, social integration, social contribution, social actualization, and social coherence. This research contributes to the marketing literature by (1) introducing a framework for transformative SNSs into transformative service research, (2) utilizing theory from gerontology studies to further understand the older consumer, and (3) enhancing the sparse understanding of older consumers’ use of SNSs. Future research directions and managerial implication are suggested for both marketers and developers of SNSs for aging consumers.