Service industries, which dominate advanced economies, display a productivity paradox: Despite quick adoption of digital and self-service technologies, measured productivity growth remains slow. We reinterpret service productivity using a Holistic Service Productivity (HSP) framework that includes customer surplus and effort in measuring productivity. We define productivity as the ratio of total value created—producer surplus plus consumer surplus—to total input, which includes both firm resources and customer time, effort, and data. Based on service-dominant logic and welfare economics, HSP addresses mismeasurement by capturing co-created value that traditional firm-focused metrics miss. A typology based on service separability and customer involvement shows the framework’s versatility, and examples and simulations demonstrate how HSP reveals hidden efficiency improvements and highlights cost-shifting. We finish with managerial and policy implications, showing how HSP can guide decision-making and enhance official productivity statistics. For managers, HSP helps distinguish real efficiency gains from cost-shifting and directs investments toward mutually beneficial solutions. By highlighting the co-created value of modern services for both firms and customers, HSP offers a new perspective for understanding and boosting productivity and presents a transformative method for measuring service productivity.

This multicity, qualitative study provides a nuanced understanding of how social service providers deploy human-centered service design approaches and the respective intended value creation through a diverse set of social services. This work makes three central contributions. First, it offers an initial typology of social service design approaches based on their intended value: (1) triaging to stabilize, (2) incubating to develop, and (3) empowering to liberate. Second, we demonstrate the imperative to focus on the experiences of both vulnerability and resilience for disadvantaged consumers, allowing sensitivity to consumers’ resource deficits and capabilities. Third, we introduce the concept of humanitarian value, a dynamic, adaptive, and encompassing value that addresses immediate and long-term needs, recognizes human strengths and resource deficits, and serves to enhance the well-being of individuals and communities. The findings provide a guide for social service organizations, which often operate in unstable, precarious environments, to address the idiosyncratic needs of their clients and holistically assess the persistent wicked problems and social needs in their respective communities.

Employees with intellectual disabilities remain markedly underrepresented in frontline service roles, despite the social and economic benefits of inclusion. A key barrier is firms’ uncertainty about consumer reactions. To address this gap, we develop and validate the CAFID scale (Consumer Attitudes toward Frontline Employees with Intellectual Disabilities), designed specifically for the service sector—unlike existing, general disability attitude measures. Across multiple studies, we demonstrate the scale’s reliability, dimensionality, and validity. Results show that more favorable consumer attitudes not only support inclusive hiring but also buffer negative responses to service failures, improving repatronage and word-of-mouth intentions. With this practical tool, firms can assess customers’ readiness and support for inclusive hiring practices; policy makers can develop data-driven incentives to promote workplace equity; and researchers can explore dynamic consumer–employee interactions involving people with intellectual disabilities. Ultimately, the CAFID scale contributes to building more equitable, socially responsible service environments.

Artificial intelligence (AI)-powered chatbots offer a cost-effective solution for customer service, but often fall short in delivering personalized or complex interactions. In response, many merchants invest in AI training and explore human–AI collaboration to leverage the strengths of both automation and human touch. However, this introduces a strategic tradeoff between service quality and operational efficiency. Using a game-theoretic framework, this study examines how merchants can optimally choose service strategies with AI involvement. Our analysis reveals a critical collaboration trap. Contrary to the prevailing belief that increased collaboration consistently enhances service, consumers’ sensitivity to the identity of the service agent erodes profitability, and greater collaboration will worsen this effect by accelerating task delegation to AI, thereby amplifying negative consumer perceptions. Furthermore, the study shows that a moderate-cost collaboration trap emerges: human–AI collaboration underperforms compared to human-only service when labor costs are at intermediate levels. Collaboration yields benefits only when labor costs are either very low or prohibitively high. In competitive markets, merchants can gain a strategic advantage not by enhancing their own service quality, but by capitalizing on rivals’ inefficient AI deployment. These findings challenge the assumption that more human–AI collaboration is always better and provide actionable insights for managing hybrid service strategies and optimizing AI investments.

The conventional wisdom in service and organizational frontline research suggests that if a firm treats employees well, its employees will treat customers well. Combining survey data of current frontline employees (FLEs) with experimental data, our research suggests that, in addition to this “trickle-out effect,” there is also a “trickle-in effect,” wherein the exchange quality of the customer–organization interface may inversely influence that of the employee–organization interface. As boundary-spanners, FLEs may feel betrayed when they perceive organizational injustice toward customers by their firms. This effect is mediated by role conflict. This is because organizational injustice toward customers often prevents FLEs from fulfilling their dual role expectations of serving both organizations and external customers. Other times, these policies may be inherently incompatible with FLEs’ own job expectations. Intensified inter- and intrarole conflict could, in turn, lead to FLEs’ feelings of betrayal and contract violation. In addition, we found that the mediating effect of role conflict is simultaneously moderated by both organizational and customer identification. Only when FLEs hold strong identification with both parties are the negative consequences of organizational injustice toward customers carried through role conflict.

While the sharing economy has been described as a potential pathway to enhancing sustainability and reducing hyper-consumption, scholarly inquiry into this linkage remains limited. In this research, we examined whether and how service providers’ motivations (monetary and social) for resource sharing influence their green consumption both outside and on sharing platforms. We conducted a series of eight studies that involved surveys, lab experiments, and large-scale secondary datasets across various sharing economy platforms. Results showed that service providers who are strongly motivated by the sharing economy’s social benefits are more likely to engage in green consumption, reflected in purchases made outside the platform (e.g., buying green products) and on the platform (e.g., adopting eco-friendly practices). However, service providers’ motivations have no bearing on their regular (non-green) purchases. We identified sense of pride as an underlying process and ruled out household income, perceived financial resource availability, sense of morality, and environmental concerns as alternative accounts. The findings not only contribute to the literature on the sharing economy and pro-environmental behavior but also provide actionable insights for policymakers, sharing platforms, and marketers seeking to promote sustainable lifestyles and environmentally responsible practices among service providers.

To investigate how socially assistive robots shape caring practices and support well-being in dementia care settings, this study focuses on agencement—emergent configurations of humans, nonhuman actors, and discourses. Using practice-based action research, the authors introduced the companion robot Hiro in two Italian nursing homes over an 8-month period, involving 22 cognitively impaired residents, followed by a 1-year follow-up. Drawing on qualitative data from observations, interviews, and field notes, the study advances three key contributions, formalized through five propositions. First, caring constitutes a sociomaterial practice, emerging through relational, embodied engagements in which care recipients actively participate in shaping the care. Second, the operationalization of agencement in human–robot dynamics shows how Hiro mobilizes sensory experiences (touch, sound, and movement) to generate relational entanglements that transform caregiving roles and flows of affect. Third, a proposed framework of affect-based well-being, articulated across three interrelated sensory dimensions—psychological (sensory intimacy), physical (sensory responsiveness), and social (sensory bonding)—extends existing service research by capturing embodied, situated, and relational dimensions of well-being. For managers, the findings provide actionable insights for using companion robots to enhance care practices by supporting affect-based well-being, reinforcing relational ties, and enabling personalized, dignified sensory experiences in resource-constrained settings.

Loyalty programs remain a cornerstone in the relationships between customers and service providers. While most previous research focuses on comparing the effectiveness of loyalty programs by comparing the consumption behaviors of members and non-members, few papers have investigated whether new enrollees are profitable and how such enrollments affect non-members. Results across data from two different firms demonstrate that enrolling new members in a loyalty program can increase profits. However, additional studies provide evidence that increased new member enrollment is associated with decreased non-member spending, with new members seeking more discounts than other customers. We show that non-members may feel that member benefits are undeserved, thus decreasing their own spending. Adopting a service management approach, we argue that non-members should not be neglected as they also form an important asset class. Also, we find that experienced managers and greater clarity about loyalty program rules may mitigate some of the negative effects of higher enrollments. The results suggest that managers of loyalty programs may need to more actively assess the return on investment of established reward members, new enrollees, and even non-members.

Developing responsive dynamic marketing strategies can be challenging in the absence of complete customer information, such as share of wallet, limiting the ability of the firm to target promotions and other marketing efforts with a view to optimizing customer lifetime value (CLV). Furthermore, much of the existing research on CLV treats customers as receivers, rather than co-creators, of services. We address these two key challenges by developing a reinforcement learning (RL)-based promotion optimization model to determine which promotion strategies are most suitable for targeting different customer groups. Specifically, using feedback derived from customers’ real-time transactional responses to promotional campaigns, we present an RL algorithm that (a) continually refines the estimated effectiveness of promotions, aligning them to customers’ preferences to maximize their CLV, and that (b) supports value co-creation by involving customers as active participants to enhance their service experience. We demonstrate the effectiveness of the model through simulation scenarios within the context of a ferry travel agency, providing evidence of its real-world potential.

Services are often segmented into discrete events, allowing customers to self-schedule their own itinerary. We term this behavior as self-scheduling . We theorize that customers self-schedule peak events—those they predict will be their most salient—at predictable points, primarily for the bookends (i.e., at the beginning or the end). This behavior can create demand fluctuations and pose challenges for demand management. To examine this phenomenon, we conducted two exploratory studies. First, a survey using a tour context revealed a preference for self-scheduling the peak event at the beginning. A more balanced distribution between bookends emerged when information promoting the peak event was provided. Second, wait-time data from three major theme parks in the United States was collected during the summer of 2024 and validated that customers predominantly self-schedule peak events for the beginning. Next, we hypothesized how information provision may influence customers’ self-scheduling behavior of a peak event. A scenario-based experiment and a conjoint study in a theme park context found that practices enhancing perceived control (i.e., wayfinding and wait line management information) effectively shifted some of the demand from the beginning to later in a visit. We discuss insights to support demand management in self-scheduling service contexts.