Following trends on social media has become increasingly popular. But what is the best way to do so? Should brands and other creators copy the trend as closely as possible, or should they put a more unique spin on it? To answer this question, the authors develop a multimodal, unsupervised video analytics tool (MUVID) to quantify the typicality of over 85,000 TikTok dance videos. Results indicate that more atypical videos (i.e., more differentiated from the trend) generate more engagement. Consistent with the notion that atypicality drives engagement, this relationship is amplified when atypicality is easier to observe (i.e., when audiences have seen more trend videos). Follow-up experiments, including a content-creator field experiment, manipulate atypicality and confirm its causal impact. The findings provide practical guidance on how to create more impactful content, shed light on effective trend-following, and offer a tool (available through an app) that researchers and practitioners can use to quantify typicality and analyze short videos more generally.

People turn to generative artificial intelligence (AI) to make ideation less effortful. Turning a vague idea into a mature concept is hard work, and offloading it to AI is tempting. However, our research shows that this strategy can backfire; the lower-effort form of AI support often produced less creative ideas. In an online experiment, 276 people refined ideas for an innovation challenge working alone or with AI-generated text or images. Image-based generative AI had a double-edged effect; it significantly reduced effort compared with working alone or using text-based support. However, ideas from image-based support were also 18% less creative than those from the more effortful text-based support. The reason lies in what each format leaves for the human to do. Images specify all details of an idea, leaving less creative room for humans, but text leaves gaps that humans can complete with their imagination—a process that is effortful yet stimulates creativity. Crucially, the effect also depends on how far the idea is developed. Text-based input helps most when the idea is already well developed because humans can then make the most of it. The major takeaway is do not use AI just to save effort but use it where the effort pays off.

As autonomous products increasingly take over tasks and decisions, consumers often experience a perceived loss of control that undermines adoption and satisfaction. This research investigates different nicknaming strategies for autonomous products as a remedy to regain control and improve attitudes. A field study and web-scraped reviews demonstrate that consumer-created nicknaming (e.g., a consumer uses the nickname "Robbie" for their robot vacuum) is positively associated with attitudes. Six preregistered experiments support the hypothesized causality and show that this nicknaming strategy can indeed counter a perceived loss of control, while also triggering other mechanisms. Of managerial importance, consumer-selected nicknaming (a consumer selects from a predefined list of nicknames) yields similar benefits, while companycreated nicknaming (the company assigns a nickname) does not. In line with a control account, the nicknaming effect is mitigated when nicknames signal high power (e.g., "Boss") rather than low power (e.g., "Assistant"). These findings contribute to consumer research on new technologies and nicknaming, with practical implications for the marketing of autonomous products.

As influencer marketing evolves into a dominant force in the marketing landscape, it necessitates a deeper theoretical exploration to understand its strategic implementations and impacts. This article examines the dynamics of influencer marketing within the growing creator economy, emphasizing the interactions among firms, influencers, followers, and digital platforms. We introduce a novel, equity-driven framework that analyzes how influencers contribute to customer equity, how influencers manage and leverage the value from their followers, and how platforms maximize the value from their users. We detail the complex relationships and value exchanges within the influencer marketing ecosystem, highlighting the challenges of measuring the return on investment and influencers’ strategic use of content to maintain authenticity and influence. By synthesizing diverse academic literature and current industry practices, this manuscript provides a comprehensive overview of the mechanisms of value creation and exchange in influencer marketing, offers strategic implications for marketers aiming to optimize their influencer engagements, and outlines future work in the form of the eleven “INFLUENCERS” research directions.

Dehumanization by algorithms raises important issues for business and society. Yet, these issues remain poorly understood due to the fragmented nature of the evolving dehumanization literature across disciplines, originating from colonialism, industrialization, post-colonialism studies, contemporary ethics, and technology studies. This article systematically reviews the literature on algorithms and dehumanization (n = 180 articles) and maps existing knowledge across several clusters that reveal its underlying characteristics. Based on the review, we find that algorithmic dehumanization is particularly problematic for human resource management and the future of work, managerial decision-making, consumer ethics, hard- and soft-law regulation, and basic values, including privacy and consumer rights. From the literature synthesis, we also derive the following definition of algorithmic dehumanization: the act of using algorithms and data in a way that results in the intentional or unintentional treatment of individuals and/or groups as less than fully human, thereby violating human rights, including liberty, equality, and dignity. Ultimately, we present a dehumanization avoidance model that serves as a structured research agenda and practical guide to address the challenges raised by algorithmic dehumanization. Thus, the model indicates promising pathways for future research at the intersection of AI and society and facilitates reflection on organizational processes that support improving corporate capabilities and managerial decision-making to avoid algorithmic dehumanization.

In influencer marketing, the differing incentives of advertisers and influencers necessitate a delicate balance between control and creativity. Investigating this trade-off, we explore the impact of contractual constraints on advertiser outcomes, addressing the empirical challenges posed by the fact that contracts are rarely observed by researchers and are strategically offered by advertisers and selectively accepted by influencers. Our analysis uses a unique dataset comprising a thousand contracts offered by hundreds of brands to thousands of influencers to examine the prevalent advertiser-imposed constraints. We relate this data to information on influencer participation and follower responses, assessing how contractual constraints influence these outcomes. Our findings demonstrate that influencers are significantly averse to contractual constraints, which create a relational cost for them. Additionally, the audiences respond less favorably in terms of the advertiser's outcomes to content produced under more restrictive conditions, implying a creativity suppression cost borne by the advertiser. A uniquely designed two-stage field experiment shows that the creativity suppression cost for advertisers outweighs the relational cost for influencers. Relaxing these constraints allows advertisers to triple their influencer retention rate within the same budget. We highlight the critical need for balancing managerial direction and influencer autonomy in designing influencer marketing campaigns.

Over the last half-century, consumer research has often depicted scarcity as a dominant factor increasing price. But should we assume that scarcity's upward pressure on price remains intact, in a world where novel forms of digital products proliferate? In this article, we propose that blockchain-encrypted digital goods, in particular, non-fungible tokens (NFTs), offer good reason to revisit this assumption. In this context, we argue and find that social value can outweigh intrinsic value as a determinant of willingness-to-pay. As a result, when scarcity threatens access to high levels of social value, its effect on price can be negative rather than positive-an inversion of a pattern typically observed for offline collectibles. Secondary data taken from the NFT platform Opensea and a set of experimental studies support this social value-based lens. Given these findings, we propose a research agenda to ground future work in this area. We also suggest that NFTs offer a laboratory in which past theories related to social value, scarcity, and price can be reconsidered and future theories developed, hopefully allowing consumer researchers to lead knowledge development in these domains over the next 50 years.

In social media, remaining authentic while taking advantage of monetizing opportunities is a key dilemma for online content creators-the creator's dilemma. Individuals typically start as small-scale creators, creating the content they enjoy. As they grow, the temptations of monetization increase, creating tension with their sense of authenticity. This can affect how genuine their content appears, potentially altering how they and others perceive their authenticity. Five in-depth interviews with mega-creators (content creators with more than one million followers) provide qualitative insights into how this dilemma can evolve and what strategies creators use to balance authenticity and monetization. The interviewed creators suggest three strategies, by moving (1) from paid advertising to cocreation with brands, (2) from staging content to improvising content, and (3) from producing content to growing their own creator brand. This non-representative and conceptual discussion motivates future research into the creator's dilemma. (c) 2024 Published by Elsevier B.V.

Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers.

In this article, we argue that non-fungible tokens (NFTs) challenge established marketing understanding of digital ownership, uniqueness, and value; authenticity, status, and sharing; and branding and distribution. We propose a set of preliminary research questions rooted in these areas, in hopes of offering entry points to future programmatic investigation of the broader field of “crypto-marketing.” This emerging subdiscipline offers opportunities to expand our understanding of consumer behavior, pricing, and product design and may be crucial in predicting the future of our discipline as NFTs further evolve.

Literature on dehumanization by algorithms has grown substantially in recent years, making it a highly important topic also for a managerial and business ethics audience as it touches upon several interlinked ethical issues in the business and society nexus. However, scattering across di-verse disciplines makes algorithmic dehumanization challenging to approach. It stretches from his-torical terminological roots from colonialism, industrialization and post-colonialism and today is deeply rooted in technology studies and ethics. We review the literature on algorithms and dehu-manization (n=180 articles) and map existing knowledge across several clusters that reveal its under-lying characteristics. Based on the review, we derive a business-ethics informed definition: Algo-rithmic dehumanization is defined as: organizational actors integrating algorithms and data analytics that result in the intentional or unintentional treatment of individuals and/or groups as less than fully human, thus infringe on human rights and particularly the three foundational principles of liberty, equality and dignity. We find that algorithmic dehumanization applies particularly to employees and the future of work, managerial decision making, consumer ethics, hard- and soft-law regulation and basic values such as privacy or human rights of consumers. Further, from the review, we develop a dehumanization avoidance model, which may help researchers and business practitioners to deal with the ethical challenges raised by algorithmic dehumanization. The three stages of the model show that several research topics remain unaddressed so far. Unraveling these topics, we suggest a set of open questions and how future research and practice can build on the model to reflect about organizational processes and improve corporate capabilities and managerial decision-making to avoid algorithmic dehumanization, thus contributing to outline the emerging concept of Corporate Digital Responsibility.