Webcare requires service providers to publicly respond to negative online customer reviews, creating visible complaint–response exchanges that also influence observers. Although research shows that responding to reviews benefits providers, it offers limited guidance on how the content and pattern of responses are related to subsequent customer behavior. Drawing on signaling and justice conceptualizations, this research explores how three response characteristics, namely, attentiveness, justice cues, and response patterns, are associated with subsequent positive review volume. Using text-mining on 371,878 customer review complaint–response exchanges for hotels and restaurants on Tripadvisor and Yelp, results show providers’ attentiveness to service aspects raised in complaints is positively associated with higher subsequent positive review volume. This association is strengthened when responses also include procedural or distributive justice cues. Moreover, selective responses to negative reviews correspond to greater subsequent positive review volume than blanket responses to all negative reviews. These findings provide empirically grounded insights into webcare and help guide service providers on what to say and how broadly to respond to online complaints.
On digital platforms such as YouTube, TikTok, Twitch, and X, customer engagement occurs not only through traditional employees and self-service systems, but also through influencers. Influencers, particularly those with significant reach, play boundary-spanning roles when they facilitate service delivery and shape the user experience, yet they do so under conditions of informality and limited organizational control. We apply an organizational frontlines lens to discuss the implications of these boundary-spanning roles and how they parallel, and diverge from, those of traditional frontline employees. The proposed framework specifies interdependencies between influencers, platform operators, users, and advertisers and theorizes how these relationships are shaped by four platform governance mechanisms: policy stability, platform transparency, interface capability, and incentive alignment. We develop a set of propositions and offer a comprehensive research agenda for the role of influencers in the frontlines of digital platforms. This work extends organizational frontlines literature by conceptualizing influencers’ roles within the boundary tier and proposing a theory of digital platform frontlines, offering new directions for understanding customer engagement in digitally mediated environments.
Although artificial intelligence (AI) has become deeply integrated into services, many customers exhibit low acceptance of AI-driven offerings. Identifying effective and efficient ways to enhance AI acceptance is both theoretically significant and practically valuable. Drawing on compensatory control theory, we propose that embedded nostalgic cues in AI services can increase customers’ acceptance of these services. The underlying mechanism is an enhanced, agent-specific sense of control: nostalgic cues reduce uncertainty associated with AI-mediated interactions and restore customers’ agent-specific sense of control over the AI service, thereby increasing their willingness to accept AI services. Moreover, the positive effect of nostalgic cues on AI acceptance is contingent upon moderators—AI role positioning and identity threat. We tested our proposed effect using field and online experiments. Our findings contribute theoretical insights into AI services and nostalgia while offering practical solutions for the service industry.
Online customers’ journeys span several touchpoints, which typically do not involve any interpersonal interactions with online retailers—except at the moment of delivery (MoD). When retailers use third-party courier services to fulfill orders, they relinquish control over this critical, last-mile touchpoint, possibly to their detriment. An analysis of over 35,000 reviews shows that a negative MoD experience can trigger ripple effects through detrimental word of mouth (WOM). To regain control, retailers might adopt vertical integration or inoculation. Across four experimental studies, the current research examines how courier type (proprietary vs. third-party) affects consumers’ WOM in response to positive and negative MoD experiences and how inoculation messages can reduce customers’ susceptibility to adverse effects of negative MoD experiences. A single-paper meta-analysis affirms that third-party couriers buffer the detrimental effects of negative MoD experiences, but positive MoD experiences benefit only established retailers handling last-mile delivery themselves. Inoculation messages can also increase customer resilience to last-mile failures by mitigating the adverse effects of negative MoD experiences on WOM. This research highlights couriers’ crucial role as key actors in the MoD and clarifies the effects of MoD experiences for online retailers, emphasizing the need to consider both couriers and preemptive measures.
Past studies on service waits have primarily applied two theoretical perspectives-the expectancy-disconfirmation model and the psychological-cost model-leading to conflicting explanations of how customers react to waits. To reconcile these perspectives, we propose a conceptual framework that integrates the dual perspectives of customer wait perceptions and identify four key moderators that shape which mechanism becomes more influential: wait stage, wait knowledge, wait regret, and wait importance. We validate our conceptual framework through a structural-equation-modeling meta-analysis of 129 studies from 103 articles (672 effect sizes; N = 38,967). Our conceptual integration highlights a coherent system of interdependent mechanisms that shape how customers experience, evaluate, and emotionally respond to waiting. Its validity, however, depends on the efficacy of the moderators. During the wait, subjective time primarily drives psychological cost, heightening anxiety and anger-especially when regret is present. After the wait, subjective time instead shapes how acceptable the wait feels. Disconfirmation becomes more influential when customers have clearer information that strengthens their expectations. When the wait is important, wait acceptability becomes the dominant predictor of service satisfaction. These findings underscore the need to balance strategies that reduce psychological cost and manage expectations, particularly as technology transforms waiting experiences.
This article presents the 2026 Service Research Priorities (SRPs), developed via a hybrid AI-human agenda-setting approach. We construct large-scale concept networks from more than 10,500 service-related articles in core service journals and Financial Times (FT) 50 outlets, and apply machine-learning link prediction to identify high-probability, but underexplored, concept pairs. Fifteen forward-looking themes emerge that fall into four topical clusters: (a) Human-AI Agency, Interactions, and Service Reconfiguration; (b) AI-Enabled Omnichannel Engagement and Ecosystem Orchestration; (c) Responsible, Inclusive, and Resilient AI Service Systems; and (d) Psychological Dynamics of Service Experiences. Comparing the themes' relevance in the service outlets vis-& agrave;-vis the FT50 journals helps identify where service research currently leads, where it imports from adjacent disciplines, and where cross-fertilization is most promising. Furthermore, we reflect our themes against the 2021 SRPs, distinguishing "emerging," "evolved," and "continuing" priorities. Importantly, we also survey the Journal of Service Research leadership to further calibrate and enrich the AI-derived SRPs. Next, we introduce a new descriptive statistic, the "Interdisciplinary Influence Index," to map which disciplines "drive" a particular service theme within the literature broadly defined. Finally, we provide the "Interactive Service Scholarship Incubator" app that enables scholars to explore the underlying concept network, predicted links, and themes, in light of their own interests and skill sets. Together, these SRPs offer a scalable roadmap for high-impact future service scholarship and practice.
Empowered by advances in artificial intelligence (AI) and data technologies, service personnel can deliver highly personalized communication using automatically acquired consumer data. However, as firms increasingly rely on such data to enhance service efficiency, consumers’ privacy concerns have become more salient. Drawing on the persuasion knowledge model, this research integrates field data with four controlled experiments to examine how response speed following data acquisition influences consumer reactions. We find that faster (vs. slower) responses, defined as those delivered moderately earlier (vs. later) than consumers’ normative expectations in comparable service contexts, attenuate consumers’ positive reactions to marketing communications. This effect is driven by a serial mediation involving perceived firm eagerness to use information and privacy concerns, highlighting privacy concerns as one of the key psychological mechanisms shaping consumer reactions. Importantly, this negative effect is attenuated when firms provide prewarning messages about upcoming data-based communication, when service involvement is low, or in service recovery contexts. These findings suggest that response speed should be managed strategically. While overly fast responses may backfire in high-involvement or routine services, combining fast responses with prewarning strategies can help firms balance efficiency and privacy perceptions in digital service environments.
Consumers experiencing vulnerability are prevalent in healthcare contexts across the globe, so it is surprising that such groups do not typically feature in the extant literature on service separation. Telehealth as a spatially separated service offering requires a combined perspective that captures the experiences of both users’ experiencing vulnerability and providers’ delivering the separated service. The inclusion of these actor perspectives with health-related vulnerabilities is essential to fully understand the impacts, dynamics, and effects of a spatially separated service. We address this need by conducting a twelve-month longitudinal qualitative study of an online mental health service in Brazil, drawing on qualitative data from patients experiencing vulnerability and psychologists. Phenomenographic interviews ( n = 67) reveal the consequences of spatial separation on service encounter actors, how spatial separation impacts relational dynamics and roles between users experiencing vulnerability and providers over time, and how these service actors overcome challenges from spatial separation for value creation.
The current research investigates how frontline employee (FLE) creativity contributes to sales performance through value-based selling and how contextual conditions shape this process. Drawing on Amabile’s Componential Theory of Creativity and the Motivation-Opportunity-Ability framework, we test a multilevel moderated mediation model using data from 252 employee-manager dyads in a Canadian art supply retail chain. Findings reveal that creativity influences sales performance indirectly through value-based selling, supporting a full mediation. In terms of moderation effects, the positive impact of creativity on value-based selling is even stronger when managerial job engagement is high. We note a different moderation pattern for organizational commitment. Indeed, when organizational commitment is high, FLEs sustain strong value-based selling regardless of their level of creativity. However, when organizational commitment is low, creativity becomes a key differentiating factor, enhancing value-based selling. Overall, these findings position value-based selling as the behavioral mechanism through which creativity affects sales performance and clarify the boundary conditions under which creativity has more or less effect on this central mediator. We further discuss the theoretical and managerial implications of these findings for frontline service employees.
Service research has typically treated technology as a tool directed by human actors to influence service systems; however, the rapid rise of generative artificial intelligence (GenAI) challenges this assumption and our understanding of service system transformations. Drawing on information systems (IS) research, we examine how GenAI's unique capabilities influence service system transformation mechanisms-particularly emergence and phase transitions-yielding three contributions to service research. First, we theorize how human-GenAI hybrids give rise to hybrid intelligence and influence transformation through unplanned, stabilized, and intelligent forms of delegation in service exchange, and through GenAI-enabled capacities of autoreflexivity and autoreformation. Second, we refine the understanding of service system transformation mechanisms by showing how the compound effects of first-order and fourth-order emergence explain the frequency of phase transitions, while heightened reflexivity and reformation influence their speed. Third, we extend service system design research by demonstrating how GenAI can enhance human reflexivity and reformation, challenging human-centric assumptions by positioning human-GenAI hybrids as contributors to service system design. We discuss how managers can strategically mobilize GenAI to influence markets and institutional arrangements. We conclude with future research directions addressing empirical, ethical, and design-oriented questions related to human-GenAI hybrids in service system transformation.
Growing cost pressures and customer demands toward digital engagement at the organizational frontline have accelerated business-to-business (B2B) salespeople’s adoption of hybrid selling, in which the same frontline salesperson combines face-to-face and remote (video or phone) sales calls to service the same customer. However, many salespeople fear that efficiency and potential interaction quantity gains come at the cost of interaction quality losses, potentially harming their customer relationships. We conceptualize hybrid selling and establish its financial and relational consequences based on two field studies with a leading European B2B manufacturer. Study 1 establishes an inverted U-shaped sales performance effect using customer relationship management (CRM) data from 3,565 customers (15,172 interactions). Study 2 matches customer survey and CRM data for additional 651 customers (5,971 interactions) to extend sales performance findings to relationship quality and show that product complexity, customer channel preferences, and salesperson interaction knowledge shape its effectiveness. We find that, in comparison to pure face-to-face selling, the optimal hybrid mix can increase performance by up to 27 percent in some interaction contexts, while in others, a pure remote approach can harm performance by up to 32 percent. Our results provide guidelines for salespeople and their executives when developing effective hybrid selling strategies to improve B2B performance.
Shopper artificial intelligence (AI) presents a striking paradox: while massive investments drive rapid expansion and increasingly sophisticated AI solutions, two-thirds of consumers express dissatisfaction with AI shopping assistants, citing frustrations with pushy upselling, poor understanding, and inaccurate recommendations. This disconnect motivates our development of the Shopper AI taxonomy. To develop our taxonomy, we synthesized insights from multiple disciplines through a design science research process with empirical validation. Grounded in customer experience management (CEM) theory, our taxonomy identifies 14 dimensions within two meta-characteristics: AI capabilities (knowledge, intelligence, autonomy, breadth of use, quality of work, data privacy) and AI parasocial skills (personalization, anthropomorphism, communications mode, emotion recognition, emotion expression, empathy, influence, engagement). The taxonomy advances service research theory in three ways. First, we extend CEM theory by revealing how AI creates value through interrelated but discrete capabilities and parasocial dimensions. Second, we identify how AI capabilities enable autonomous value creation without active customer participation, representing a new form of value pre-creation. Third, we reveal complex dimensional interactions, where improvements in one dimension can enhance or diminish others. This multidimensional taxonomy provides managers with actionable guidance for navigating dimensional trade-offs, designing efficient, balanced AI systems, identifying context-specific investment priorities, and avoiding common pitfalls.
From retail and transport to hospitality and live events, customer-to-customer (C2C) interactions are integral to many service experiences. However, C2C interactions are not always positive, and customers regularly become targets of other customers' misbehavior. Although such C2C misbehavior has always been a challenge for firms, it has escalated since the COVID-19 pandemic, with incidents becoming not only more frequent but also more intense and severe. However, it remains unknown how customers assess the magnitude of C2C misbehavior. Four studies, including 62 repertory grid interviews using scenario-based and diary-based approaches with international participants from Germany and the United Kingdom, reveal six dimensions that underlie customers' magnitude perceptions: three intensity-related dimensions that assess the magnitude of the misbehavior itself and three severity-related dimensions that examine the magnitude of the misbehavior's impact on other customers. Several sorting studies with 99 U.S. participants support the robustness, generalizability, distinctiveness, and hierarchical structure of our identified magnitude dimensions. By uncovering the facets that underlie customers' evaluations of an incident's perceived magnitude, our study provides an empirically grounded conceptualization and typology of C2C misbehavior. Managerially, this research enables firms, communities, and governments to monitor and categorize misbehavior incidents more effectively and to develop more targeted mitigation strategies.
The rapid growth of artificial intelligence (AI) reflects its widespread adoption across various industries and occupations. Yet, much of the discourse still conceptualizes AI narrowly as a property of computational machinery. Within marketing and service scholarship, such mechanistic framings obscure the relational and systemic dimensions of intelligence that become salient in AI-enabled service interactions. Drawing on service-dominant (S-D) logic, this paper reconceptualizes intelligence as an emergent property of adaptive, self-organizing service ecosystems, foregrounding the systemic processes and relational configurations through which actors and their capacities take form. As intelligent technologies become increasingly entangled in service exchange, they render more visible and consequential the ongoing interactions through which human and non-human actors are co-constituted, underscoring the need for a clearer relational-ontological grounding of S-D logic. We conceptualize adaptation and intelligence as a generative duality: adaptation captures the adjustments enacted within a service ecosystem, whereas intelligence denotes the systemic capacity enabling those adjustments through feedback, learning, and institutional co-evolution. This perspective strengthens S-D logic's relational foundations by showing that AI exemplifies-rather than disrupts-the emergent, relational processes through which service ecosystems coordinate, evolve, and create value.
Frontline service employees are integral to the successful implementation of firms' customer relationship management strategies. Conventional methods typically utilize explicit incentive contracts to motivate employees to align with organizational objectives. Nevertheless, the effects of new organizational goals without specified rewards (NOG-WSR) on employees' performance are not well documented in the literature. Leveraging a quasi-experimental design, this study analyzed the impact of a new organizational goal on employees' responses, introduced by a large service firm that did not specify explicit incentives to its branches. Empirical findings from 48,464 individual-month observations reveal that NOG-WSR has a positive short-term effect on employees' goal-related performance. More importantly, we identified employees who are more likely to respond to the NOG-WSR. This study extends the expectancy theory and reveals that NOG-WSR is effective in motivating employees who have a higher expectancy of being able to attain the new goal and a higher expectancy of reward tied to the new goal. Contributions and implications are discussed.
While service industries like healthcare and elder-care increasingly use technology to address staff shortages, its potential to support consumers experiencing vulnerability remains under-explored and under-utilized. For instance, elderly people who are experiencing vulnerability due to language loss or reduced proficiency in communicating in their national language can be supported by voice-based interfaces, such as robots, that can communicate with them in their native regional language. However, responses to such regional language adaptations, as well as the potential managerial benefits of tailoring service offerings to consumers with specific linguistic needs, remain unclear. This study begins to explore this area and reveals that elderly consumers prefer to interact with robots in their regional language rather than in the national language. We also find that caregivers are willing to pay a 30% premium for a companion robot that can use a regional language, and that regional language capabilities foster trust in robots in the general population, too. The theoretical contribution of the study draws from linguistics to show that these positive effects arise because regional synthetic speech is processed more fluently. The study also highlights human-likeness as a crucial boundary condition, as regional language adaptations unexpectedly backfire when delivered with machine-like voices. For service organizations, regional adaptations in voice-based interfaces present a unique opportunity to better serve linguistically vulnerable consumers while also fostering more positive attitudes in the general population, provided there is a high degree of human-likeness.
Telemedicine services leverage information and communication technologies toward innovating and enabling the delivery of healthcare. We propose and empirically analyze a framework for understanding the impact of telemedicine services on health outcomes at scale across communities in the United States. Our analysis of county-level panel data reveals that increased availability of telemedicine services improves community health that is central to the well-being of a community. We evaluate the benefits of telemedicine services in aggregate-that is, overall health outcomes-and along specific dimensions of reductions in premature death rate, low birthweight rate, preventable hospital stays, smoking rate, and the need for diabetes monitoring. In addition, we find that greater availability of telemedicine services is associated with decreased COVID-19-related mortality. Heterogeneity analyses further show that lower socioeconomic status reduces the effectiveness of telemedicine services while superior digital infrastructure and greater innovation capacity enhance its effectiveness. Demographics by way of higher proportions of Black and female community members enhance the effectiveness of telemedicine services, whereas a higher proportion of older adults reduces its effectiveness. Taken together, the proposed framework and the findings underscore the potential of telemedicine services to improve community health while recognizing the moderating effects of contextual factors. This study makes a significant contribution toward advancing the literature on seamless coordination of digital service innovations to drive community-wide health benefits.
The infusion of generative AI (GenAI) is already disrupting established services. This technology's generative and agentic nature challenges the design and management of service routines, which have been previously handled primarily by frontline service employees. Guided by organizational routines theory, our longitudinal study (2020-2024) examines how the infusion of GenAI changes routines in customer support services. We gathered interview data from 41 employees, managers, and AI experts in two phases, pre- and post-GenAI. Based on the analysis of the qualitative data, we revealed seven recurring micro-level augmentation patterns, illustrating how GenAI-infused service routines function. The results show that GenAI is primarily embedded in the backstage of knowledge-intensive services, from which it then permeates the frontstage. We contribute to the literature on hybrid human-AI service delivery by identifying augmentation patterns and conceptualizing service permeation via two mechanisms: (1) simultaneous service permeation, which unfolds as employees leverage GenAI in real-time and integrate GenAI's responses, recommendations, and adaptations into the frontstage; (2) sequential service permeation, which emerges as employees perform new routines of documentation and AI feeding to facilitate GenAI's adaptability in frontstage and backstage operations. The MAPs and service permeation mechanisms guide practitioners in integrating GenAI into service routines and managing novel employee-GenAI collaborations.
This paper conceptualizes immersive experience as a service phenomenon that arises from the alignment of service design (concept, spatial, and interactive design) and psychological engagement and is manifested in contemporary entertainment settings, sports events, retail, and hospitality spaces. Building on this conceptualization, the paper develops a theoretical framework showing how continuous alignment between service design and participants' psychological processes is dynamically achieved at service delivery in these environments. From the provider side, immersive experiences are curated, technology-mediated, and entertainment-oriented; from the participant side, they elicit patterns of sensory arousal, emotional resonance, and imaginative involvement that evolve in real time as the experience unfolds. At their intersection, immersion emerges as dynamically co-created and characterized by personalized journeys, collaborative sense-making, temporal integration, and value-in-context. Theoretically, the paper advances service research by linking design affordances, alignment, and psychological engagement as mechanisms that explain how immersive experiences emerge and create value. Managerially, the framework suggests service-delivery levers and individual regulatory processes for balancing sensory, affective, and cognitive tensions.
While organizational scholars have established a strong base of research on workplace incivility, one understudied aspect is how witnessing incivility directed toward employees affects the witness' actions. This research seeks to close this gap by examining how consumer incivility targeting a service employee impacts emotions, intentions, and financial compensation provided by the witness. Drawing from social exchange theory, we propose witnessing incivility leads to feelings of pity, followed by increased emotional support and, ultimately, an increased tip for the target employee. We find support for our proposed relationships across four studies. Additionally, we demonstrate that this process is robust to influences from bystander effects but is conditional on service quality and group membership of the uncivil consumer in relation to the witnessing consumer. The findings provide theoretical advancement to the growing literature on behavioral responses of witnesses of incivility.
