PurposeThe objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.Design/methodology/approachCombining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.FindingsThe results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.Research limitations/implicationsThe review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.Originality/valueThe authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

We define simulated satiation through reality‐enhancing technology as any attenuation in perceived benefits that occurs within or results from vicarious and simulated intermediary sources. We examine simulated satiation as a factor that underlies consumer experiences with reality‐enhancing technologies and presents nine testable propositions. Each proposition is aimed at determining how simulated satiation can unlock implications in terms of engaging consumers for the right amount of time to improve marketing outcomes. We further conduct a proof‐of‐concept study to test proposition 1, that physiological drivers and sensory overload increases simulated satiation. The empirical results show that shorter (vs. longer) exposure to virtual reality content raises perceived usefulness for virtual reality (VR), which in turn mediates stickiness for VR experiences alongside consumers’ subjective well‐being. Finally, we carry forward key theoretical contradictions and areas for future empirical testing based on simulated satiation in reality‐enhanced environments, including experiences, that are reshaping consumer decision making.

Artificial intelligence (AI) allows business actors to exchange resources, particularly information and knowledge, to strengthen their businesses. These AI-enabled value co-creation processes are playing a substantial role in the business-to-business (B2B) sales context. However, little is known about the mechanisms and the process of value co-creation enabled by AI. On this basis, this study addresses this gap by employing Service-Dominant Logic to understand value co-creation with AI. This study identifies the value co-creation process, and provides an understanding of the actors, activities and resources during the usage of AI to create value in B2B sales. The study also identifies several limitations of AI, such as, value co-creation is heavily dependent on human activities and resources. Lastly, we suggest that managers continue to manage customer expectations when using AI for value cocreation and highlight the necessity of human actors and resources in the value co-creation process.

Killer applications, or killer apps, are technology applications that profoundly change the way any society thinks, works, and functions. This paper explores Artificial Intelligence (AI) as a killer app, with specific application to marketing. Specifically, this paper employs the lens of technology history to explore the relationship between marketing and AI. Using Kranzberg’s six laws of technology, this paper sheds light on all manner of innovations, how technologies have shaped and impacted society, and how marketers can respond to this. This inquiry offers two main contributions: First, it suggests a number of implications for marketing practice and scholars, derived from each of Kranzberg’s laws. These suggestions are intended to guide marketing practice when implementing or using AI. In addition, this article offers a number of research directions that might be fruitful and important areas for investigation in future scholarly work regarding technology’s impact among marketing scholars.

No abstract