Cocreation with AI: Consumer, managerial, and societal perspectives
(A2026-133636)

AUTHORS

Koen Pauwels, Northeastern University; Yakov Bart, Northeastern University; Johanna Zimmermann, University of Cologne; Jan Schumann, University of Passau; Fabian Buder, Nuremberg Institute for Market Decisions; Timo Schreiner, Nuremberg Institute for Market Decisions

ABSTRACT

Much has been said about GenAI in marketing, but most work focuses on how consumers react to simple AI disclosure or on the role of AI literacy. This session instead examines consumer co-creation with GenAI: what drives trust and willingness to engage, how self-investment reshapes perceived privacy value, how benefit framing influences responses, and where along the customer journey GenAI is most welcome. Four papers in a transatlantic collaboration address these questions. Paper 1: Toward More Effective AI Disclosure in Advertising explores how beneficiary framing can soften the typically negative reaction to AI-disclosed ads. Building on beneficiary-linked framing and social distance research, it proposes a structured approach that goes beyond transparency to articulate credible motives. Pretests and experiments using realistic social media ads compare consumer-, company-, and society-focused explanations, and test moderators such as visual focus, message tone, and individual differences in AI beliefs and experience. Paper 2: Drivers of AI Trust challenges the view that AI literacy alone explains marketing professionals’ use of GenAI tools. Survey data from Germany, the UK, and the US show that organizational position, perceived career risk, and psychological safety are equally important. Role characteristics including autonomy and decision power are shown to moderate the literacy-usage relationship, with implications for training and AI governance. Paper 3: Artificial Intelligence in the Customer Journey from a Holistic Perspective responds to fragmented AI research by proposing an integrative framework spanning all journey stages. It distinguishes “sweet spots” where AI reliably creates mutual value, “hot spots” where powerful tools risk manipulation or autonomy loss, and “white spots” where applications remain underdeveloped, offering guidance for both systems-level research and managerial practice. Paper 4: Co-creating Privacy Value revisits the assumption that more data disclosure to AI necessarily erodes privacy value. Using surveys and experiments, it shows how interactive GenAI can transform data sharing from a necessary evil into a vehicle for self-expansion and co-creation. Greater self-investment in GenAI interactions increases psychological ownership of outputs and perceived benefits, offsetting heightened privacy risks and informing the design of AI tools and privacy regulation in collaborative AI contexts.