We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapse occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation while also embedding real-world biases. The average trap, caused by next-token prediction models, leads to generic outputs that lack individuality. Additionally, model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human–machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.

Generative AI (GenAI) has spurred the expectation of being creative, due to its ability to generate content, yet so far, its creativity has somewhat disappointed, because it is trained using existing data following human intentions to generate outputs. The purpose of this paper is to explore what is required to evolve AI from generative to creative. Based on a reinforcement learning approach and building upon various research streams of computational creativity, we develop a triple prompt-response-reward engineering framework to develop the creative capability of GenAI. This framework consists of three components: 1) a prompt model for expected creativity by developing discriminative prompts that are objectively, individually, or socially novel, 2) a response model for observed creativity by generating surprising outputs that are incrementally, disruptively, or radically innovative, and 3) a reward model for improving creativity over time by incorporating feedback from the AI, the creator/manager, and/or the customers. This framework enables the application of GenAI for various levels of creativity strategically.

Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI’s emotion understanding, employing “response engineering” to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.

Purpose The purpose of the paper is to note that customers are not necessarily human and to figure out how best to serve artificial intelligence (AI) customers. The authors also propose several major research streams, as examples, to help launch research on AI customers and how to serve them. Design/methodology/approach The current paper is a conceptual one that draws upon research from many areas to support the ideas proposed. Findings AI customer are proliferating. AI as customers can augment or replace human customers and can be the customer itself. Service providers may also be AI, which means that both humans serving AI customers and AI serving AI customers are relevant here. The authors show that even truly autonomous AI customers are likely to be more common in the future. The authors conclude that reverse engineering will probably not be successful in understanding AI customers and that an approach similar to how we research human consumer behavior is likely to be more useful. Originality/value Virtually, the entire literature on customers and how to serve them assumes that customers are human. With the rapid advancement of AI, purchase decisions are increasingly made by AI, suggesting that it is now important and necessary to consider the possibility of AI customers and how best to serve them. This paper opens the door for such research.

Prior literature does not provide a clear prediction of how executive confidence affects the degree to which a firm engages in “myopic marketing management,” the tendency to decrease current marketing spending to mitigate any potential earnings shortfall. We propose that highly confident CEOs are more likely to cut marketing spending to raise current earnings numbers because they believe in their ability to generate high future firm earnings that can cover the long-term losses arising from their current short-term actions. The effect is heightened when the board of directors is more independent and monitors more, but attenuated if CMOs are more confident and thus are better able to convince their CEOs and boards of directors to support continued investments in marketing. The moderating impact of CMO confidence is proposed to be stronger as the CMO becomes more powerful. Using secondary data from a broad cross-section of firms, we provide robust empirical support for our model. Our results highlight situations in which CMOs need to be wary of cuts to their marketing budget, and also provide a potential mechanism through which marketers can protect their budget in the presence of highly confident CEOs—through their own confidence levels.

We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence. (C) 2021 New York University. Published by Elsevier Inc. All rights reserved.

This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.

Many companies focus considerable resources on managing and enhancing positive word of mouth (WOM). WOM management, however, has become increasingly complex given the rise of online channels and the corresponding increasing breadth of connections giving and receiving WOM. Given the generally believed importance of WOM to business outcomes, managers seek to leverage key drivers that they believe will enhance positive and minimize negative WOM.Implicit in these actions is the belief that leveraging key drivers to enhance positive (or minimize negative) WOM results in generally positive outcomes across channels and connections. This research investigates whether this belief is correct. We examined WOM behaviors from over 15,000 consumers from 10 different countries in eight industry categories, as well as consumer attitudes toward the various brands investigated. Our findings indicate that efforts to enhance positive WOM typically have mixed effects – enhancing positive WOM in some channels while decreasing it (or even enhancing negative WOM) in other channels. Therefore, managers need to have a greater understanding of the complexity of leveraging attitudinal key drivers when seeking to enhance WOM to minimize potential negative outcomes.