Literature review of relevant studies
| Author(s)/Year | Context | Nature of research | Key findings |
|---|---|---|---|
| Burton et al. (2024) | AI robot bosses | Conceptual | The authors propose four types of AI robot bosses: manager, producer, maestro and leader. The human only traditional organization chart becomes an incomplete and misleading representation of the organization and how it works. Furthermore, trust is a central issue for the acceptance of AI robots as a boss |
| Huang and Dootson (2022) | Customers’ response to chatbot service failures in retail settings | Empirical (experiment) | Studies tension between human and non-human employees (chatbots). Disclosing the availability of a human employee late in the service interaction results in higher customer aggression, as they feel deprived of the opportunity to interact with a human |
| Jeon (2022) | AI agents and the impact of manager-level job titles | Empirical (experimental studies) | The job titles given to the AI agents are found to have favorable downstream effects on customer satisfaction, brand attitude and the customers’ intentions to buy the products recommended during the chat by the AI manager |
| Kim et al. (2016) | Leader-member exchanges and spillover effect on marketing outcomes | Empirical (survey among employees and customers) | Leader behavior does not directly impact customers’ perceptions of service. However, leader-member exchanges have an indirect influence via job satisfaction. The authors link leader-member exchange theory to the services marketing field |
| Novak and Hoffman (2019) | Relationship with smart objects | Conceptual | Consumer relationships with smart objects can be classified as two types of master–servant relationships, partner relationships and unstable relationships. Complementary master–servant relationships that are high in communality reflect trusting master–servant relationship styles |
| Longoni et al. (2019) | Studies consumer resistance to medical AI, when AI and human providers coexist | Empirical (series of experiments) testing consumer preferences between AI and human healthcare providers | Customers resistance to use AI healthcare providers is stronger compared to human ones because their inability to account for customers’ unique characteristics. Specifically, consumer resistance to AI is due to “uniqueness neglect” where patients fear that AI lacks the ability to consider their individual circumstances. Furthermore, consumers are more willing to accept AI when there is clear human oversight. This suggests the importance of human supervision for customer trust in AI-based services |
| Schweitzer et al. (2019) | Relationship with voice-controlled smart assistants (VCSA) | Exploratory (user journey and interviews) | Users who perceived the VCSA as a master tended to have more negative experiences with the VCSA interaction than those who considered the devices as servant or partner. Furthermore, participants who regarded the VCSA a master considered themselves as slaves who had to obey to the VCSA’s rules and regarded the human-object relationship as perverted or reversed |
| Shanks et al. (2024) | Collaborative robotic teams in service environments | Empirical (6 studies in different healthcare settings) | Consumers respond less favorable when robots are in subordinate or leadership roles (lower behavioral intentions). This negative effect can be reduced by providing customers with a sense of control over the robot |
| Van Doorn et al. (2023) | Relationship consumer-facing and worker-facing automation technologies | Empirical (interviews with workers and customers in hospitality settings) | Workers see robots as tools to free them to focus on more relational and emotionally driven interactions with customers. When robots are perceived to take a leadership role, it undermines the relationship between workers and consumers The authors conclude that while robots handle routine tasks efficiently, consumers prefer human workers for emotionally driven interactions. Thus, having human employees alongside robots increases customer satisfaction by combining efficiency with personal attention |
| Author(s)/Year | Context | Nature of research | Key findings |
|---|---|---|---|
| AI robot bosses | Conceptual | The authors propose four types of AI robot bosses: manager, producer, maestro and leader. The human only traditional organization chart becomes an incomplete and misleading representation of the organization and how it works. Furthermore, trust is a central issue for the acceptance of AI robots as a boss | |
| Customers’ response to chatbot service failures in retail settings | Empirical (experiment) | Studies tension between human and non-human employees (chatbots). Disclosing the availability of a human employee late in the service interaction results in higher customer aggression, as they feel deprived of the opportunity to interact with a human | |
| AI agents and the impact of manager-level job titles | Empirical (experimental studies) | The job titles given to the AI agents are found to have favorable downstream effects on customer satisfaction, brand attitude and the customers’ intentions to buy the products recommended during the chat by the AI manager | |
| Leader-member exchanges and spillover effect on marketing outcomes | Empirical (survey among employees and customers) | Leader behavior does not directly impact customers’ perceptions of service. However, leader-member exchanges have an indirect influence via job satisfaction. The authors link leader-member exchange theory to the services marketing field | |
| Relationship with smart objects | Conceptual | Consumer relationships with smart objects can be classified as two types of master–servant relationships, partner relationships and unstable relationships. Complementary master–servant relationships that are high in communality reflect trusting master–servant relationship styles | |
| Studies consumer resistance to medical AI, when AI and human providers coexist | Empirical (series of experiments) testing consumer preferences between AI and human healthcare providers | Customers resistance to use AI healthcare providers is stronger compared to human ones because their inability to account for customers’ unique characteristics. Specifically, consumer resistance to AI is due to “uniqueness neglect” where patients fear that AI lacks the ability to consider their individual circumstances. Furthermore, consumers are more willing to accept AI when there is clear human oversight. This suggests the importance of human supervision for customer trust in AI-based services | |
| Relationship with voice-controlled smart assistants (VCSA) | Exploratory (user journey and interviews) | Users who perceived the VCSA as a master tended to have more negative experiences with the VCSA interaction than those who considered the devices as servant or partner. Furthermore, participants who regarded the VCSA a master considered themselves as slaves who had to obey to the VCSA’s rules and regarded the human-object relationship as perverted or reversed | |
| Collaborative robotic teams in service environments | Empirical (6 studies in different healthcare settings) | Consumers respond less favorable when robots are in subordinate or leadership roles (lower behavioral intentions). This negative effect can be reduced by providing customers with a sense of control over the robot | |
| Relationship consumer-facing and worker-facing automation technologies | Empirical (interviews with workers and customers in hospitality settings) | Workers see robots as tools to free them to focus on more relational and emotionally driven interactions with customers. When robots are perceived to take a leadership role, it undermines the relationship between workers and consumers |
Source(s): Authors’ own work
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