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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.

Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for "lower" (easier for AI) intelligence tasks first. AI first replaces some of a service job's tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job's tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the "softer" intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.

Information technology (IT)-related service is the strategic management of the creation and delivery of service in which information and communication technology (referred to as IT here) plays a substantial role. IT can serve as a facilitator (e.g., facilitates access to customer information and customer communication) or enabler (e.g., enables value cocreation), serves as the context (e.g., mobile phone market or e-commerce), or is itself service (e.g., social networking sites or information goods). There are three essential characteristics of IT-related service—it is information-intensive, customer-centric, and multidisciplinary. IT-related service is information-intensive. The ability to communicate (firm-to-customer, firm-to-firm, and customer-to-customer) anytime, anywhere, and to anyone, is significantly facilitated by the recent technology trends of big data, cloud computing, and mobile and networking platforms. IT-related service is customer-centric. The use of IT, both by firms and by customers, alleviates the common observation that there is a trade-off between customer satisfaction and productivity improvement for service. Customers are able to talk back to firms with their new communication power and firms are better able to cost-efficiently satisfy their customers. The study of IT-related service is thus inherently multidisciplinary involving such fields as marketing, strategic management, computer science/information systems, and operations management/organizational research. The results of the IT-service transformation are that IT blurs the distinction between goods and service; service is becoming more goods-like and goods are acquiring the characteristics of service. The Journal of Service Research special issue on IT-related service brings together all of these elements, and provides rich strategic implications for managers.

ABSTRACTA typical firm is operated by multiple functional managers who may collaborate as well as compete to achieve firm performance. In the digital age, firm performance is essentially customer‐dependent and technology‐dependent, with both marketing and information technology (IT) playing key roles. Unfortunately the two functions often have very different worldviews. We show how these differences can damage firm performance, and suggest ways to mitigate this damage. We build a worldview difference model, synthesized from multiple disciplines. The model is tested using both matched and nonmatched observations from marketing and IT managers, and is analyzed with hierarchical linear models using both perceptual and objective firm performance data over a 4‐year period. We find that differences between the beliefs and perceptions of marketing managers and IT managers generate a negative impact on firm performance, and suggest appropriate technology‐culture associations to effectively align their worldviews for firm performance. To improve firm performance, a cross‐functional appreciation for market and technology drivers can be achieved by making marketing managers more learning‐oriented and by providing IT managers a culture that is congruent with technology.

AI in service can be for routine mechanical tasks, analytical thinking tasks, or empathetic feeling tasks. We provide a conceptual framework for the customer, firm, and interactional use of AI for empathetic tasks at the micro-, meso-, and macro-levels. Emotions resulting from AI service interactions can include basic emotions (e.g., joy, sadness, and fear), self-conscious emotions (e.g., pride, guilt, embarrassment), and moral emotions (e.g., contempt, righteous anger, social disgust). These emotions are mostly likely to occur during frontline interactions in which both firms and customers use AI, a phenomenon called "AI as customer." The analysis level of AI service and emotion can be at the macro-level in which AI is transforming the service economy into a feeling economy, at the meso-level in which firms can use "thoughtful AI" to make the employees' and customers' lives a little bit better by brightening their days, and at the micro-level in which customers can experience basic, self-conscious, and moral emotions from interactions with service AI.

This editorial outlines the vision that the new Journal of Service Research editorial team has about moving service research forward, which requires more than just duplicating the service research of the past. We encourage authors to be forward-looking and futuristic in their orientation. In this way, JSR can help guide the service research of the future.

Smart technologies are rapidly transforming frontline employee-customer interactions. However, little academic research has tackled urgent, relevant questions regarding such technology-empowered frontline interactions. The current study conceptualizes (1) smart technology use in frontline employee-customer interactions, (2) smart technology–mediated learning mechanisms that elevate service effectiveness and efficiency performance to empower frontline interactions, and (3) stakeholder interaction goals as antecedents of smart technology–mediated learning. We propose that emerging smart technologies, which can substitute for or complement frontline employees' (FLEs) efforts to deliver customized service over time, may help resolve the long-standing tension between service efficiency and effectiveness because they can learn or enable learning from and across customers, FLEs, and interactions. Drawing from pragmatic and deliberate learning theories, the authors conceptualize stakeholder learning mechanisms that mediate the effects of frontline interaction goals on FLEs' and customers' effectiveness and efficiency outcomes. This study concludes with implications for research and practice.

This article contains a set of six invited commentaries written by leading scholars, expressing varied perspectives on the future of frontline research and on the frontline domain itself. The article accompanies the Journal of Service Research special issue on organizational frontlines. In their commentaries, the authors share insightful views on areas of personal interest ranging from employee emotion and customer relationship building to the effect of technology and its implementation at the organizational frontline. Included within each commentary are managerial insights and suggestions for needed research in the highlighted area.

This article utilizes input from service scholars, practitioners, reviews of published literature, and influential policy documents to identify service research priorities that push the boundaries of extant research. In a companion piece, we focused on four service research priorities related to managing and delivering service in turbulent times. Further, we identified a set of stakeholder-wants from the literature and included research questions that tie key stakeholder-wants to each of the three priorities in this article and the four priorities in the companion article. Here, we highlight the critical importance of scholarship and practice related to the design of sustainable service ecosystems and discuss three key service research priorities: large-scale and complex service ecosystems for transformative impact (SRP5), platform ecosystems and marketplaces (SRP6), and services for disadvantaged consumers and communities (SRP7). We call for an engaged service scholarship that considers the interrelationships among consumers, organizations, employees, platforms, and societal institutions and pursues transformative goals.

Transformative changes in the societal and service context call out for the service discipline to develop a coherent set of priorities for research and practice. To this end, we utilized multiple data sources: surveys of service scholars and practitioners, web scraping of online documents, a review of published service scholarship, and roundtable discussions conducted at the world's foremost service research centers. We incorporated innovative methodologies, including machine learning, natural language processing, and qualitative analyses, to identify key service research priorities that are critical to address during these turbulent times. The first two priorities— technology and the changing nature of work and technology and the customer experience—focus on leveraging technology for service provision and consumption. The next two priorities— resource and capability constraints and customer proactivity for well-being—focus on responding to the changing needs of multiple stakeholders. Further, we identified a set of stakeholder-wants from the literature and include research questions that tie key stakeholder-wants to each of the four priorities. We believe the set of research priorities in the present article offer actionable ideas for service research directions in this challenging environment.