Table 2 Propositions for robotic role...

Table 2

Propositions for robotic role theory and future research avenues

Propositions	Future research avenues
P1. Human–robot service interaction can be characterized as role performances. The structure of the interaction is socially defined with associated meanings that guide and direct the behavior of the human interactant and the design of the role interface (i.e. appearance) and role enactment (i.e. role script) of the robotic interactant.	What associations (i.e. mental schemas) do consumers have of different types of social robots in different service roles? What associations for which frontline roles evoke beneficial or unfavorable consumer role behaviors and how can this be managed through robot design? What are the underlying psychological mechanisms that evoke consumer associations with robots in HRSI?
P2. Role behavior/enactment is ritualized, learned, or programmed^a behavior. a. The content of roles is relatively consistent across actors. b. Facility in human role performance in HRSI is a function of experience, communication, and consumers' role expectations. These role expectations affect role behavior and are a function of consumer individual characteristics, the robotic interface, the robot's role enactment, perceived role suitability, and expectations of human service provision. Facility in robot role performance is a function of programming (role scripts), machine learning, appearance, and physical dexterity. c. Service scripts, containing information about the role set, are learned by human consumers and programmed for robotic service providers.	How do consumer role expectations translate from human to robotic service encounter? Are there any additional expectations from service robots (e.g. instantly personalized service according to personal preferences based on consumer data or flawlessness)? What general robot role behaviors (e.g. non-verbal social cues, voice and speech design) are essential for robot role performance and thus generalizable across service contexts? What are appropriate service scripts for robots and how can they best be collaboratively developed? How can service excellence be “learned” by robots in the future?
P3. Role similarity is a potential basis for classifying robotic services.	What are distinct role similarities of service roles that robotic service providers take on?
P4. Role behaviors are interdependent. The appropriateness of behaviors is determined by others (management, co-workers, consumers, programmers/robot designers) and the service context.	How do biases and individual differences of programmers/robot designers and unrepresentative training data of algorithms affect service outcomes? How does a service context with related ethical considerations affect appropriateness of a robot's role enactment?
P5. Congruent role expectations/scripts of consumers and robots^b facilitate social interaction. a. When consumers and robotic service providers read from a common script (high inter-role congruence), the encounter is more satisfying for the consumer. b. When service employees and the organization share common role expectations for robotic service providers, employee role clarity and job satisfaction increase.	How should robots be introduced in service networks to ensure role clarity for employees and consumers alike (e.g. by interaction rules or workshops)? How can robots support the formation of employee role clarity (e.g. by explaining their own use)? How does robot aversion or fear of being replaced affect the emergence of congruent role expectations of employees and the organization?
P6. Discrepant role expectations/scripts of consumers and robots^b decrease service efficiency and effectiveness. a. When robots enact their role based on role scripts that are at odds with consumer role expectations, the encounter becomes inefficient. b. When robots enact their role based on role scripts that are at odds with consumer role expectations, the encounter becomes ineffective and may result in service failure (i.e. a human frontline employee must take over).	How can service firms manage consumer role expectations and consequent behavior to avoid service failure in HRSI? How can service firm and robot designers collaborate to equip robots with role enactment that is aligned with the service firms' service culture? How can service firms train employees to manage service recovery in case of service failure during HRSI to mitigate negative effects for the firm?

Propositions	Future research avenues
P1. Human–robot service interaction can be characterized as role performances.The structure of the interaction is socially defined with associated meanings that guide and direct the behavior of the human interactant and the design of the role interface (i.e. appearance) and role enactment (i.e. role script) of the robotic interactant.	What associations (i.e. mental schemas) do consumers have of different types of social robots in different service roles? What associations for which frontline roles evoke beneficial or unfavorable consumer role behaviors and how can this be managed through robot design? What are the underlying psychological mechanisms that evoke consumer associations with robots in HRSI?
P2. Role behavior/enactment is ritualized, learned, or programmed^a behavior. a. The content of roles is relatively consistent across actors.b. Facility in human role performance in HRSI is a function of experience, communication, and consumers' role expectations. These role expectations affect role behavior and are a function of consumer individual characteristics, the robotic interface, the robot's role enactment, perceived role suitability, and expectations of human service provision. Facility in robot role performance is a function of programming (role scripts), machine learning, appearance, and physical dexterity.c. Service scripts, containing information about the role set, are learned by human consumers and programmed for robotic service providers.	How do consumer role expectations translate from human to robotic service encounter? Are there any additional expectations from service robots (e.g. instantly personalized service according to personal preferences based on consumer data or flawlessness)? What general robot role behaviors (e.g. non-verbal social cues, voice and speech design) are essential for robot role performance and thus generalizable across service contexts? What are appropriate service scripts for robots and how can they best be collaboratively developed? How can service excellence be “learned” by robots in the future?
P3. Role similarity is a potential basis for classifying robotic services.	What are distinct role similarities of service roles that robotic service providers take on?
P4. Role behaviors are interdependent. The appropriateness of behaviors is determined by others (management, co-workers, consumers, programmers/robot designers) and the service context.	How do biases and individual differences of programmers/robot designers and unrepresentative training data of algorithms affect service outcomes? How does a service context with related ethical considerations affect appropriateness of a robot's role enactment?
P5. Congruent role expectations/scripts of consumers and robots^b facilitate social interaction. a. When consumers and robotic service providers read from a common script (high inter-role congruence), the encounter is more satisfying for the consumer.b. When service employees and the organization share common role expectations for robotic service providers, employee role clarity and job satisfaction increase.	How should robots be introduced in service networks to ensure role clarity for employees and consumers alike (e.g. by interaction rules or workshops)? How can robots support the formation of employee role clarity (e.g. by explaining their own use)? How does robot aversion or fear of being replaced affect the emergence of congruent role expectations of employees and the organization?
P6. Discrepant role expectations/scripts of consumers and robots^b decrease service efficiency and effectiveness. a. When robots enact their role based on role scripts that are at odds with consumer role expectations, the encounter becomes inefficient.b. When robots enact their role based on role scripts that are at odds with consumer role expectations, the encounter becomes ineffective and may result in service failure (i.e. a human frontline employee must take over).	How can service firms manage consumer role expectations and consequent behavior to avoid service failure in HRSI? How can service firm and robot designers collaborate to equip robots with role enactment that is aligned with the service firms' service culture? How can service firms train employees to manage service recovery in case of service failure during HRSI to mitigate negative effects for the firm?

Note(s): All propositions are advanced based on Solomon et al.'s (1985) original propositions; for a detailed juxtaposition, see Online Appendix 4. ^aRole enactment and thus service scripts of current off-the-shelf service robots are programmed in a decision tree logic. However, advancements in machine learning will likely soon allow service robots to learn from their own past interactions. ^bWhile consumer role expectations determine their role enactment, robot role expectations and enactment are programmed by robot designers into role scripts

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