About

Roland T. Rust is a Distinguished University Professor and the David Bruce Smith Chair in Marketing at the Robert H. Smith School of Business at the University of Maryland. He is the founder and Executive Director of the Center for Excellence in Service. Rust has been recognized as one of the top 100 'Best Scientists in Business and Management' worldwide based on research impact, and he has received numerous lifetime achievement honors including the AMA Irwin/McGraw-Hill Distinguished Marketing Educator Award, the EMAC Distinguished Marketing Scholar Award, and fellowships in the American Marketing Association, the European Marketing Academy, the INFORMS Society for Marketing Science, and the American Statistical Association. His research spans marketing strategy, service marketing, and the application of artificial intelligence in marketing, with a focus on advancing both theory and practice. Rust has served as Editor-in-Chief for several prominent journals, including the Journal of Marketing and the Journal of Service Research, and has consulted with leading companies worldwide. His contributions have significantly influenced the development of marketing disciplines, particularly in services, marketing research, and strategic marketing, earning him numerous awards and honors throughout his career.

Research topics

• Artificial Intelligence
• Computer Science
• Marketing
• Business
• Psychology
• Knowledge management
• Social psychology
• Process management

Selected publications

• The GenAI Future of Consumer Research

  Journal of Consumer Research · 2025-05-15 · 31 citations

  articleOpen accessSenior author

  Abstract 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.

  PublisherOA PDFDOI

• GENERATIVE AI FOR CREATIVE FASHION DESIGN

  Global Fashion Management Conference · 2024-07-03

  articleSenior author
  PublisherDOI

• Automating Creativity

  arXiv (Cornell University) · 2024-05-11 · 6 citations

  preprintOpen accessSenior author

  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.

  PublisherOA PDFDOI

• The Caring Machine: Feeling AI for Customer Care

  Journal of Marketing · 2023-12-21 · 247 citations

  articleOpen accessSenior author

  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.

  PublisherDOI

• Service Marketing Models

  World Scientific-Now Publishers series in business · 2023-06-19

  book-chapter1st authorCorresponding

  The following sections are included:IntroductionHistorical OverviewService Quality and Customer SatisfactionService Word-of-MouthSatisfaction/Productivity Trade-offReturn on InvestmentCustomer EquityService DesignPersonalizing ServiceService PricingArtificial Intelligence in ServiceMacro and Societal IssuesHow Service Informs Other Marketing DecisionsConclusion and Directions for Future ResearchReferences

  PublisherDOI

• AI as customer

  Journal of service management · 2022-01-20 · 43 citations

  articleSenior author

  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.

  PublisherDOI

• Executive confidence and myopic marketing management

  Journal of the Academy of Marketing Science · 2022-11-30 · 6 citations

  articleSenior author
  PublisherDOI

• Real-Time Brand Reputation Tracking Using Social Media

  Journal of Marketing · 2021-02-01 · 163 citations

  article1st authorCorresponding

  How can we know what stakeholders think and feel about brands in real time and over time? Most brand reputation measures are at the aggregate level (e.g., the Interbrand “Best Global Brands” list) or rely on customer brand perception surveys on a periodical basis (e.g., the Y&R Brand Asset Valuator). To answer this question, brand reputation measures must capture the voice of the stakeholders (not just ratings on brand attributes), reflect important brand events in real time, and connect to a brand’s financial value to the firm. This article develops a new social media–based brand reputation tracker by mining Twitter comments for the world’s top 100 brands using Rust–Zeithaml–Lemon’s value–brand–relationship framework, on a weekly, monthly, and quarterly basis. The article demonstrates that brand reputation can be monitored in real time and longitudinally, managed by leveraging the reciprocal and virtuous relationships between the drivers, and connected to firm financial performance. The resulting measures are housed in an online longitudinal database and may be accessed by brand reputation researchers.

  PublisherDOI

• AI for Feeling

  2021-01-01 · 3 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Politics That Feel

  2021-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Ming‐Hui Huang

  National Taiwan University

  76 shared

• William Rand

  44 shared

• Gillian Brooks

  King's College School

  36 shared

• Andrew T. Stephen

  36 shared

• Timur Chabuk

  Perceptronics Solutions (United States)

  36 shared

• Katherine N. Lemon

  Boston College

  15 shared

• Mark A. Cohen

  13 shared

• Valarie A. Zeithaml

  University of North Carolina at Chapel Hill

  11 shared

Awards & honors

• AMA Irwin/McGraw-Hill Distinguished Marketing Educator Award
• EMAC Distinguished Marketing Scholar Award
• Fellow of the INFORMS Society for Marketing Science
• Paul D. Converse Award
• Honorary Doctorate in Economics from the University of Neuch…