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This paper aims to advance understanding of value co-creation and value co-destruction in artificial intelligence (AI)-enabled services by introducing the Refined Interaction Value Framework (R-IVF). The R-IVF explains how interaction value depends not only on technical performance, but also on how users interpret machine initiative, calibrate trust, experience meaningful exploration and evaluate contextual fit.This paper develops the R-IVF conceptually by integrating three AI-era constructs – Agentic Perception (AP), Dynamic Trust Calibration (DTC) and Serendipitous Exploration (SE) – into the original framework and by reconceptualizing Configuration Value as Contextual Intelligence, an integrative mechanism that situates these constructs within the user’s evolving context. The framework is illustrated through two contrasting AI-enabled service contexts and extended through cross-functional managerial implications and an illustrative diagnostic monitoring approach.The paper shows that interaction value in AI-enabled services is shaped by three recurrent tensions: increasing machine agency within interaction, the need for dynamically calibrated trust and the challenge of preserving meaningful exploration under increasingly personalized service conditions. The R-IVF explains how these tensions may either support value co-creation or contribute to value co-destruction, depending on whether AI-enabled interactions are experienced as supportive, trustworthy, contextually appropriate and open to meaningful discovery.The R-IVF offers managers a practical lens for designing, governing and monitoring AI-enabled service interactions. It highlights cross-functional priorities across design, operations and customer communication and supports the use of illustrative diagnostic indicators to identify emerging vulnerabilities such as overreach, trust erosion, constrained exploration and contextual mismatch.This paper extends interaction value theory into the AI era by introducing AP, DTC and SE as AI-specific mechanisms shaping service interaction and by reconceptualizing Configuration Value as Contextual Intelligence. In doing so, it reframes interaction value as an adaptive, context-sensitive process rather than a fixed set of service attributes. It contributes a conceptually grounded framework that clarifies how AI-enabled interactions may sustain, undermine, or reshape value over time.
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Purpose This paper aims to explore the effect of employees’ state mindfulness, a short period of mindful presence, on the quality of the service they provide in a service encounter. Design/methodology/approach Three studies are conducted. A pilot study explores the relationship between state mindfulness and service encounter quality. Experiment 1 examines whether a 15-min mindfulness exercise results in an increase in service employees’ state mindfulness. Experiment 2 tests whether induced state mindfulness produces higher service quality and whether a reminding technique can prolong state mindfulness between service encounters. Findings The results demonstrate the following. First, that more mindful employees provide better service quality. Second, that a short, easily implemented, mindfulness exercise can reliably increase employees’ state mindfulness. Third, induced mindfulness has an impact on subsequent service quality in terms of reliability, assurance, empathy and responsiveness. These effects persist regardless of the service encounter structure (high vs low structure) or the degree of emotional labor involved (high vs low emotionally charged). Finally, the reminding technique developed as part of this research suggests that state mindfulness can be maintained between service encounters. Research limitations/implications As simulated (programmed) customers are used, independent evaluators to assess service quality are used. Service providers in this study are college students; future field studies should consider a wider range of service providers. The research focuses on state mindfulness; exploration of trait mindfulness offers future research opportunities. Originality/value To the best of the authors’ knowledge, this research is the first to empirically examine the link between mindfulness and service quality. It shows that mindfulness can be induced, and through a reminding technique be maintained, and improve service quality across service interactions. This is a powerful finding for marketing managers, for it offers a new method to enhance service provision. Moreover, this research implies that the increase in service quality is likely to be accompanied by reduced job burnout: a double win for employees, employers and customers.
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From the seclusion of monastic life to the noise of Silicon Valley, the ancient practice of mindfulness has ‘come out of the cloister.’ As an antidote to mindless cognition and behavior, the practice of mindfulness—with its principle of grounding attention in the present moment—has been shown to have powerful and positive effects at both the individual and the collective level and in fields as wide-ranging as medicine, schooling, prison programs, law and negotiation, business, and even the army. This installment of Marketing & Technology introduces mindfulness to managers and explores its potential for enhancing the service encounter. We begin by reviewing the two main conceptualizations of mindfulness: the cognitive and the contemplative. We then explore the service encounter from the perspective of emotional labor and show how mindfulness can change surface acting into deep acting, thereby significantly improving the service encounter for both the consumer and provider. We also explore the other benefits of mindfulness and their application to the service encounter: adaptability, flexibility, and creativity. We conclude by sharing resources for managers interested in implementing mindfulness training.
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Despite the ethical concerns over the datafication and surveillance of individuals and groups, companies are making ever greater investments in big data. The assumptions underpinning this movement are: (1) organizations are passive implementers of big data—more data is the inevitable consequence of technology and a competitive necessity for business, (2) more data offers a more objective and accurate picture of reality and (3) more data enables better prediction. We argue that this perspective is strategically unsustainable and abdicates ethical responsibility.
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