Legitimacy Disparities in Human-AI Collaborations: How Actor Sequence Shapes Perceptions

1. Investigate Legitimacy Constructs:
- Examine the three dimensions of legitimacy (pragmatic, moral, and
cognitive) in the context of human-AI collaborations.
- Understand how these constructs influence perceptions of legitimacy in
decision-making processes involving both human and AI actors.

2. Analyze Actor Sequence:
- Identify variations in perceived legitimacy based on the order of actors
(human first vs. AI first) in a multi-stage decision-making process.
- Explore how the sequence of human and AI involvement affects
perceptions of fairness, credit attribution, and the quality of the outcome.

3. Examine Credit Attribution and Entitativity:
- Investigate how credit is attributed among human and AI collaborators.
- Assess the perceived entitativity (the perception of the collaboration as a
cohesive unit) of human-AI ensembles.

4. Evaluate Quality of Conjoint Service:
- Measure the quality of the final conjoint service or product resulting from
human-AI collaborations.
- Determine how the order of actors impacts the perceived quality of the
outcome.

5. Moderating Role of AI Receptivity:
- Examine how individuals' receptivity to AI moderates their perceptions of
legitimacy, fairness, and quality in human-AI collaborations.
- Understand the influence of AI receptivity on the acceptance and
effectiveness of AI-enabled tools and features.

6. Practical Implications for Organizations:
- Provide insights for organizations on designing AI-enabled tools and
features.
- Offer recommendations on how and when to introduce nonhuman agents
to reduce user doubt and reluctance.