Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention

Abstract

Firms use robots to deliver an ever-expanding range of services. However, as service failures are common, service recovery actions are necessary to prevent user churn. This research further suggests that firms need to know how to design service robots that avoid alienating users in case of service failures. Robust evidence across two experiments demonstrates that users attribute successful service outcomes internally, while robot-induced service failures are blamed on the firm (and not the robot), confirming the well-known self-serving bias. While this external attributional shift occurs regardless of the robot design (i.e., it is the same for warm vs. competent robots), the findings imply that service recovery minimizes the undesirable external shift and that this effect is particularly pronounced for warm robots. For practitioners, this implies prioritizing service robots with a warm design for maximizing user retention for either type of service outcome (i.e., success, failure, and failure with recovery). For theory, this work demonstrates that attribution represents a meaningful mechanism to explain the proposed relationships.

Some agents are more similar than others: customer orientation of frontline robots and employees

“…Although studies have concentrated on customer adoption (e.g. Belanche et al, 2019;Schwede et al, 2022;Schepers et al, 2022;Van Pinxteren et al, 2019;Song and Kim, 2022) and evaluation of FLRs (e.g. Choi et al, 2019;Ivanov and Webster, 2021;Mende et al, 2019), the question of how FLRs affect customer orientation perceptions of the frontline agent remained untapped.…”

Robot service failure and recovery: Literature review and future directions

“…16,[20][21][22]34 Within these investigations, there existed several inquiries that have endeavored to investigate the interplay between various antecedent variables. For example, Meyer et al 42 probed the interplay of service outcome and robot design.…”

“…According to the attribution theory, users attribute less to service failures caused by robots than to service failures caused by human employees or humanoid robots. 33,34,38,42 However, when the attribution agent switched to the service provider (i.e. firm), users attribute more responsibility to the service provider in the robot service failure scenario than in the human employee service failure scenario, as users believed that the service provider modified the programs embedded in robots.…”

“…Twelve studies take the appearance of robots as antecedent variable; that is, the degree of anthropomorphism of robots. 30 –42 Within the academic discourse, several scholars have proposed alternative terminology to describe anthropomorphism; for example, robot cuteness 36 and robot design (warm vs. competence). 42…”

Electronic Markets on AI and standardization

“…From the experiments several recommendations for service robot design are derived since they have different implications on how users attribute service outcomes. For example, robots with warm (i.e., friendly and trustworthy) design features should be applied if service failures are recoverable, whereas robots with competent (i.e., purposeful and intelligent) design features should be used for successful service outcomes as well as for non-recoverable service failures (Meyer et al, 2022).…”