Journal of Service Research

Artificial intelligence (AI) is likely to spawn revolutionary transformational effects on service organizations, including by impacting the ways in which firms engage with their customers. In parallel, customer engagement (CE), which reflects customer interactions with brands, offerings, or firms, has risen to the top of many managers’ strategic wish lists in the last decade. However, despite literature-based advances made in both areas, AI and CE are largely investigated in isolation to date, yielding a paucity of insight into their interface. In response to this gap, this Special Issue offers a pioneering exploration of CE in automated or AI-based service interactions. Our editorial first reviews AI’s Industry 4.0 underpinnings, followed by an important AI typology that comprises robotic process automation (RPA), machine learning (ML), and deep learning (DL) applications. We then offer a high-level synopsis of existing CE research, followed by the development of a set of integrative propositions of CE in automated service interactions. Next, we introduce the Special Issue papers, which feature particular RPA, ML, or DL applications. We conclude with an overview of further research avenues in this growing area, which has the potential to develop into a powerful service research substream in the coming years.

We propose a conceptual framework that includes the antecedents and consequences of firms’ adopting and integrating robotics into their customer service operations. Drawing insights from literature on customer service, technology marketing, and computer science, our proposed framework elaborates on the concept of the degree of robotics adoption (DRA) as well as the antecedents (employee acceptance of robots and customer acceptance of robots) and multiple sequential consequences (service quality, customer long-term performance, and customer engagement) of DRA. We also discuss how the nature of the firm (Business to Consumer versus Business-to-Business, i.e., B2C vs. B2B), service characteristics (utilitarian vs. hedonic), and brand positioning (low equity vs. high equity) might moderate the relationship between DRA and service quality. Further, we provide actionable guidance for managers to adopt and integrate robotics into their customer service operations.

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.

Machine-age technologies, including automation, robotics, and artificial intelligence, are profoundly expanding the variety of service interfaces and therefore the possible ways that customers and firms can interact across customer journeys. This expansion challenges service firms’ capabilities to deliver coherent streams of interactions for effective customer engagement. This article develops a conceptual framework of firm capabilities that enable firms to operate with “one voice” to deliver seamless, harmonious, and reliable interactions across diverse interfaces in a customer journey. The proposed framework integrates three themes: (1) service interaction space to capture the interrelationship among devices, interfaces, interactions, and journeys; (2) learning and coordination as core capabilities for generating and using intelligence, respectively, to enhance customer engagement in subsequent interactions; and (3) one-voice strategy to configure learning and coordination capabilities in combinations that meet conditions of fitness and equifinality for effective customer engagement. We provide several research questions and priorities to guide research and practice.

Technology is changing frontline service scripts. Businesses are now using mobile point-of-sale applications (e.g., Square) and mobile technology (e.g., iPad) to prompt customers for tips. Tip requests are occurring more frequently at the start of service transactions, before any service has been provided. This research examines how requesting a tip either before or after service completion affects customers and service providers. We test the effects of preservice versus postservice tip sequence in four studies (a natural experiment in the field and three controlled experiments) across food and beauty service contexts. Findings reveal that requesting a tip before (vs. after) completing a service leads to smaller tips, reduced return intentions, diminished word-of-mouth intentions, and lower online ratings. Inferred manipulative intent is revealed as the psychological mechanism underlying the harmful effects of requesting a tip before service. Findings suggest that emphasizing the benefits of automated point-of-sale systems can reduce, but not eliminate, the negative effects of preservice tip requests. Contrary to norms within the service industry, we find that service providers should avoid requesting tips before serving customers.

The rise of augmented reality (AR) technology, which overlays digital content to alter customers’ views of a physical service setting, using mobile and wearable computing, drives the digital automation of physical services. In particular, it promises to achieve tangibility even in service encounters delivered in digital formats. However, customer engagement with AR is falling short of expectations. Managers lack an integrated framework of AR service automation and therefore tend to focus on the technology rather than on the process of customer engagement with AR service automation. To address this problem, the current study proposes a technology-enabled engagement process that integrates multiple stages of customer engagement, as a service-centric process. To establish that engagement with AR service automation requires the inclusion of service tangibility, as part of the process, the authors decompose the steps of interactive service engagement, the spatial presence of the service, customers’ emotional and cognitive engagement with the service, and perceived value-in-use, which lead to emergent behavioral forms of engagement.

Service organizations, emboldened by the imperative to innovate, are increasingly introducing robots to frontline service encounters. However, as they augment or substitute human employees with robots, they may struggle to convince a distrusting public of their brand’s ethical credentials. Consequently, this article develops and tests a holistic framework to ascertain a deeper understanding of customer perceptions of frontline service robots (FLSRs) than has previously been attempted. Our experimental studies investigate the effects of the (1) role (augmentation or substitution of human employees or no involvement) and (2) type (humanoid FLSR vs. self-service machine) of FLSRs under the following service contexts: (a) value creation model (asset-builder, service provider) and (b) service type (experience, credence). By empirically establishing our framework, we highlight how customers’ personal characteristics (openness-to-change and preference for ethical/responsible service provider) and cognitive evaluations (perceived innovativeness, perceived ethical/societal reputation, and perceived innovativeness-responsibility fit) influence the impact that FLSRs have on service experience and brand usage intent. Our findings operationalize and empirically support seminal frameworks from extant literature, as well as elaborate on the positive and negative implications of using robots to complement or replace service employees. Further, we consider managerial and policy implications for service in the age of machines.

Professional service jobs exist at the high end of the skill ladder; thus, some have assumed that highly trained professional workers are relatively immune to being replaced by automation. However, this assumption is a bit dubious because automation does not occur at the job level but rather at the task level, and some tasks within a professional job might be highly susceptible to automation disruption. This research builds on prior research by (1) empirically testing a model for automation of professional services and (2) developing a professional task-automation framework that shows how individual tasks within a given job can be enhanced or disrupted by automation in very different ways. Some tasks are augmented by automation and remain in the purview of professionally trained workers. Other tasks are deskilled by automation, allowing the tasks to be transferred to lower cost workers (who are aided by automation). Other tasks are moved directly to customers through self-service technologies, reducing or eliminating the need to interact with professionals or other workers. Finally, some professional tasks are centralized, which leverages professional workers’ distinctive expertise. Our framework shows precipitating conditions for each task-automation strategy and outlines logic for reconfiguring tasks within professional service systems.

The role of virtual salesperson (VS) and the importance of customer reviews in facilitating online purchase decisions and sales have recently received much attention from technology companies, marketing practice professionals, and academics. However, customers’ willingness to follow the purchase advice of the VS when there is a conflict between these recommendations and those of other online customers is less understood. This research theorizes and investigates the extent to which customers’ relationship satisfaction with, and trust in, the VS helps explain customer willingness to follow VS advice in the context of recommendation conflict. Using four studies, our research explores how and when the VS’s avatar design mitigates the negative influence of conflict. An important theoretical and managerial implication of this research is that VS avatar designs that are high in automated social presence (ASP) help reduce the negative impact of conflict. However, we find that ASP mitigates the negative effects of conflict only for avatars that score low (vs. high) on cuteness.