Approach or avoidance? Relationship between perceived AI explainability and employee job crafting

Area of Science:

• Organizational Behavior
• Artificial Intelligence Ethics
• Human Resource Management

Background:

• Growing concerns regarding algorithmic transparency necessitate research into artificial intelligence (AI) explainability in organizational decision-making.
• Employee job crafting, the proactive modification of job roles, is a key area influenced by workplace technologies.
• Understanding the interplay between AI explainability and job crafting is crucial for effective human-AI collaboration.

Purpose of the Study:

• To investigate the impact of perceived artificial intelligence (AI) explainability on employee approach and avoidance job crafting.
• To examine the mediating roles of AI-oriented benefit and threat perceptions in the relationship between AI explainability and job crafting.
• To explore the moderating effect of organizational ethical climate on these relationships.

Main Methods:

• Analysis of multi-wave survey data from 278 medical staff.
• Application of a dual-pathway model to assess the effects of perceived AI explainability.
• Statistical examination of mediation by benefit/threat perceptions and moderation by ethical climate.

Main Results:

• Perceived AI explainability positively influenced both approach and avoidance job crafting.
• AI explainability enhanced the perception of AI-related benefits and reduced the perception of AI-related threats.
• An ethical climate significantly strengthened the effects of AI explainability on benefit and threat perceptions.

Conclusions:

• Perceived AI explainability is a significant driver of employee job crafting through altered perceptions of AI.
• Organizational ethical climate plays a vital role in shaping the impact of AI explainability on employees.
• Findings offer theoretical contributions to AI, job crafting, and organizational behavior research, with practical implications for managing AI in the workplace.