Journal of Interactive Marketing

Artificial Intelligence (AI) and 5G connectivity have been identified as drivers of the so-called Fourth Industrial Revolution (FIR). AI and 5G, through emerging technologies such as blockchain, gene editing, Internet of Things sensors, nanotechnology, or 3D printing accelerate a blurring of boundaries between digital, biological, and physical spheres. In this editorial, we introduce the term boundary object, or boundary technology, that can help process more information (syntactic boundary) for enhanced learning (semantic boundary) and that can create a higher-level intelligence (pragmatic boundary). Boundary objects are also a means of representing, learning about, and transforming knowledge at a given boundary. We propose that crossing syntactic, semantic, and pragmatic boundaries is facilitated by three FIR phenomena (big data, machine learning, and AI). Each of these phenomena possesses a unique capability (processing, learning, and adaptation) to help communities learn about their differences and dependences. We also show how the six articles in this special issue are related to boundary and sphere challenges, and we provide an overview of directions for future research. All in all, marketing scholars should focus on enhancing their abilities in knowledge integration across boundaries to sustain their role as cutting-edge scientists.

Human enhancement technology (HET) is advancing a host of industries, yet it remains limited in its retail, sales, and service applications. Soon, however, these technologies appear likely to have notable impacts on customer experiences. To address their potential influences on customer–employee interactions and the customer experience, this article leverages insights about the human–robot continuum to predict that a mid-range approach may be realistic for retail, sales, and service encounters (cf. pure robot range), at least in the near term. Furthermore, the authors detail the likelihood of both beneficial (elevated warmth and competence) and detrimental (dehumanization) mediating effects of HET on customer experiences. They also propose four moderators: information about the HET, gain/loss frames, persuasion ability, and strategic fit between HET and firm orientations. These insights suggest various implications and research directions pertaining to HET, as well as ways that these technologies might be used effectively in different industries.

Firms are increasingly turning towards new-age technologies such as artificial intelligence (AI), the internet of things (IoT), blockchain, and drones, among others, to assist in interacting with their customers. Further, with the prominence of personalization and customer engagement as the go-to customer management strategies, it is essential for firms to understand how to integrate new-age technologies into their existing practices to aid seamlessly in the generation of actionable insights. Towards this end, this study proposes an organizing framework to understand how firms can use digital analytics, within the changing technology landscape, to generate consumer insights. The proposed framework begins by recognizing the forces that are external to the firm then lead to the generation of specific capabilities by the firm. Further, the firms capabilities can lead to the generation of insights for decision-making that can be data-driven and/or analytics-driven. Finally, the proposed framework identifies the creation of value-based outcomes for firms and customers resulting from the insights generated. Additionally, we identify moderators that influence: (a) the impact of external forces on the development of firm capabilities, and (b) the creation of insights and subsequent firm outcomes. This study also identifies questions for future research that combines the inclusion of new-age technologies, generation of strategic insights, and the achievement of established firm outcomes.

In light of the emerging discourse on AI systems’ effect on society, whose perception swings widely between utopian and dystopian, we conduct herein a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM). To do so, we survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development, and retention. We highlight in particular how AI-CRM's improving ability to predict customer lifetime value will generate an inexorable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. We further consider the consequences for firms and the challenges to regulators.

New technologies such as Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), virtual assistants, chatbots, and robots, which are typically powered by Artificial Intelligence (AI), are dramatically transforming the customer experience. In this paper, we offer a fresh typology of new technologies powered by AI and propose a new framework for understanding the role of new technologies on the customer/shopper journey. Specifically, we discuss the impact and implications of these technologies on each broad stage of the shopping journey (pre-transaction, transaction, and post-transaction) and advance a new conceptualization for managing these new AI technologies along customer experience dimensions to create experiential value. We discuss future research ideas emanating from our framework and outline interdisciplinary research avenues.

Digital business platforms (DBPs) such as eBay, Google, and Uber Technologies have seen enormous growth; this paper explores their salient characteristics, the role of marketing in helping DBPs succeed, and important research topics for theory and practice. A new conceptual framework based on insights from transaction cost analysis outlines the role and impact of marketing in DBPs. A key role for marketing is to increase the number and quality of interactions on a DBP while reducing transaction costs for users and production costs for the DPB. The DBPs’ interactions and the data thus generated are key enablers of value creation and value appropriation on these platforms. However, there are several challenges to resolve in value creation and value appropriation because DBPs cater to the needs of many different types of users. Therefore, DBPs should carefully coordinate and manage interactions among users on different sides of a platform. For researchers, there are many opportunities to reconceptualize some of the traditional roles of marketing in the context of DBPs.

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.