Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and whether they experience respectful treatment (i.e., interactional justice). In this experimental survey study with open-ended qualitative questions, we examine decision making in six HRM functions and manipulate the decision maker (AI or human) and decision valence (positive or negative) to determine their impact on individuals’ experiences of interactional justice, trust, dehumanization, and perceptions of decision-maker role appropriate- (...) ness. In terms of decision makers, the use of human decision makers over AIs generally resulted in better perceptions of respectful treatment. In terms of decision valence, people experiencing positive over negative decisions generally resulted in better perceptions of respectful treatment. In instances where these cases conflict, on some indicators people preferred positive AI decisions over negative human decisions. Qualitative responses show how people identify justice concerns with both AI and human decision making. We outline implications for theory, practice, and future research. (shrink)

Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...) treated in a dignified and respectful way in various healthcare decision contexts. Participants were subject to a 2 (human or AI decision maker) x 2 (positive or negative decision outcome) x 2 (diagnostic or resource allocation healthcare scenario) factorial design. We found evidence of a “human bias” (i.e., a preference for human over AI decision makers) and an “outcome bias” (i.e., a preference for positive over negative outcomes). However, we found that for perceptions of respectful and dignified interpersonal treatment, it matters more who makes the decisions in diagnostic cases and it matters more what the outcomes are for resource allocation cases. We also found that humans were consistently viewed as appropriate decision makers and AI was viewed as dehumanizing, and that participants perceived they were treated better when subject to diagnostic as opposed to resource allocation decisions. Thematic coding of open-ended text responses supported these results. We also outline the theoretical and practical implications of these findings. (shrink)

With the knowledge that team work is not always associated with high(er) performance, we draw from the Multi-Level Theory of Psychological Contracts, Person-Environment Fit Theory, and Optimal Distinctiveness Theory to study shared perceptions of psychological contract (PC) breach in relation to shared perceptions of complementary and supplementary fit to explain why some teams perform better than other teams. We collected three repeated survey measures in a sample of 128 respondents across 46 teams. After having made sure that we met all (...) statistical criteria, we aggregated our focal variables to the team-level and analysed our data by means of a longitudinal three-wave autoregressive moderated-mediation model in which each relationship was one-time lag apart. We found that shared perceptions of PC breach were directly negatively related to team output and negatively related to perceived team member effectiveness through a decrease in shared perceptions of supplementary fit. However, we also demonstrated a beneficial process in that shared perceptions of PC breach were positively related to shared perceptions of complementary fit, which in turn were positively related to team output. Moreover, best team output appeared in teams that could combine high shared perceptions of complementary fit with modest to high shared perceptions of supplementary fit. Overall, our findings seem to indicate that in terms of team output there may be a bright side to perceptions of PC breach and that perceived person-team fit may play an important role in this process. (shrink)