Conceptual papers on AI in service work
| Authors (year) | Purpose | Core theory/lens | Technology focus | Actor focus | Work design | Findings |
|---|---|---|---|---|---|---|
| Bowen (2016) | Examines FLEs' evolving roles in increasingly complex service ecosystems | Service-dominant logic | Broad coverage of AI and robotics | Employees | Partially | The paper presents four service roles, including differentiator, innovator, coordinator and enabler; the integration of AI and automation emphasizes the importance of emotional labor and service quality, encouraging a shift toward empowerment-focused roles where employees are given greater autonomy to handle complex social interactions and adapt to customer needs |
| Buhalis et al. (2019) | Reveals the transformative potential of technology to enable hyper-personalized experiences | Value co-creation; service ecosystems | AI, Internet of Things, virtual reality, augmented reality | Employees and customers | Partially | AI-driven smart environments enable employees to support hyper-personalized and sensory-rich customer experiences by providing real-time data and predictive insights, requiring employees to become more adaptive and responsive to dynamic customer needs within digitally enabled, multi-stakeholder ecosystems |
| De Keyser et al. (2019) | Classifies frontline service technology | Service pyramid framework; service-dominant logic | Broad coverage of technologies, e.g. conversational agents, extended reality, blockchain | FLEs and customers | Partially | The infusion of AI and extended reality in frontline service work leads to a blended human–machine interaction model, where employees become managers of digital interfaces and engage in tasks that require higher cognitive skills, such as interpreting customer data insights, offering nuanced responses and maintaining continuity across AI-driven service touchpoints |
| Huang and Rust (2021) | Presents a strategic framework that aligns AI with service benefits | None | Mechanical, thinking and feeling AI | FLEs and customers | Partially | AI's advancement in handling routine and analytical tasks pushes frontline employees toward roles that emphasize relational engagement and emotional intelligence (e.g. empathy and problem-solving in high-touch contexts), thus requiring human workers to focus on relationship-building and tailored customer interactions as AI assumes transactional functions |
| Huang and Rust (2018) | Explores AI job replacement | None | Mechanical, analytical, intuitive and empathetic AI | Employees | None | A framework for AI job replacement is presented, suggesting that AI progresses through mechanical, analytical, intuitive and empathetic tasks, reshaping the nature of service work and elevating the importance of human intuition and empathy |
| Larivière et al. (2017) | Conceptualizes service encounter 2.0 and examines how technology augments or substitutes traditional roles of employees and customers | None | AI, Internet of Things, robotics, self-service technologies | FLEs and customers | Partially | AI in service encounters transforms traditional FLE roles by positioning employees as differentiators, innovators, coordinators and enablers; FLEs now support or complement automated systems rather than solely delivering services, with responsibilities expanding to supervising and enhancing customer–technology interactions and facilitating networked service experiences |
| Wirtz et al. (2018) | Defines service robots, contrasts their capabilities with humans and explores the impact on service delivery | Role theory; job design theory; service-dominant logic | Service robots with AIs (autonomous and adaptable interfaces) | FLEs and customers | Partially | With service robots handling predictable, repetitive tasks, FLEs are freed up to focus on creative problem-solving, personalized customer interactions and emotional support, which leads to more human-centered roles that leverage uniquely human skills such as empathy, adaptability and nuanced customer understanding |
| Authors (year) | Purpose | Core theory/lens | Technology focus | Actor focus | Work design | Findings |
|---|---|---|---|---|---|---|
| Examines FLEs' evolving roles in increasingly complex service ecosystems | Service-dominant logic | Broad coverage of AI and robotics | Employees | Partially | The paper presents four service roles, including differentiator, innovator, coordinator and enabler; the integration of AI and automation emphasizes the importance of emotional labor and service quality, encouraging a shift toward empowerment-focused roles where employees are given greater autonomy to handle complex social interactions and adapt to customer needs | |
| Reveals the transformative potential of technology to enable hyper-personalized experiences | Value co-creation; service ecosystems | AI, Internet of Things, virtual reality, augmented reality | Employees and customers | Partially | AI-driven smart environments enable employees to support hyper-personalized and sensory-rich customer experiences by providing real-time data and predictive insights, requiring employees to become more adaptive and responsive to dynamic customer needs within digitally enabled, multi-stakeholder ecosystems | |
| Classifies frontline service technology | Service pyramid framework; service-dominant logic | Broad coverage of technologies, e.g. conversational agents, extended reality, blockchain | FLEs and customers | Partially | The infusion of AI and extended reality in frontline service work leads to a blended human–machine interaction model, where employees become managers of digital interfaces and engage in tasks that require higher cognitive skills, such as interpreting customer data insights, offering nuanced responses and maintaining continuity across AI-driven service touchpoints | |
| Presents a strategic framework that aligns AI with service benefits | None | Mechanical, thinking and feeling AI | FLEs and customers | Partially | AI's advancement in handling routine and analytical tasks pushes frontline employees toward roles that emphasize relational engagement and emotional intelligence (e.g. empathy and problem-solving in high-touch contexts), thus requiring human workers to focus on relationship-building and tailored customer interactions as AI assumes transactional functions | |
| Explores AI job replacement | None | Mechanical, analytical, intuitive and empathetic AI | Employees | None | A framework for AI job replacement is presented, suggesting that AI progresses through mechanical, analytical, intuitive and empathetic tasks, reshaping the nature of service work and elevating the importance of human intuition and empathy | |
| Conceptualizes service encounter 2.0 and examines how technology augments or substitutes traditional roles of employees and customers | None | AI, Internet of Things, robotics, self-service technologies | FLEs and customers | Partially | AI in service encounters transforms traditional FLE roles by positioning employees as differentiators, innovators, coordinators and enablers; FLEs now support or complement automated systems rather than solely delivering services, with responsibilities expanding to supervising and enhancing customer–technology interactions and facilitating networked service experiences | |
| Defines service robots, contrasts their capabilities with humans and explores the impact on service delivery | Role theory; job design theory; service-dominant logic | Service robots with AIs (autonomous and adaptable interfaces) | FLEs and customers | Partially | With service robots handling predictable, repetitive tasks, FLEs are freed up to focus on creative problem-solving, personalized customer interactions and emotional support, which leads to more human-centered roles that leverage uniquely human skills such as empathy, adaptability and nuanced customer understanding |
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