When dual motivation shapes performance
Apparently, using AI improves job performance by increasing autonomous motivation and proactive task adaptation, but...it hurts it by increasing controlled motivation and defensive rigidity. These effects are amplified or dampened depending on each person's level of self-evaluation.
Using AI improves job performance by increasing autonomous motivation and proactive task adaptation, but hurts it by increasing controlled motivation and defensive rigidity. These effects are amplified or dampened depending on the employee's level of core self-evaluation.
In the race to digitize processes, department heads receive contradictory reports. Some teams report productivity gains from AI tools, while others experience tension and a drop in the quality of work. That gap is no accident. The study, published in *Humanities and Social Sciences Communications*, shows that AI is a double-edged sword, shaped by how employees perceive it and what motivates them.
The authors ran a three-wave longitudinal field study with 409 employee-supervisor pairs from AI-intensive industries in China. Using paired questionnaires and validated scales, they measured AI use, types of motivation (autonomous vs. controlled), proactive work configuration (promotion-oriented or prevention-oriented) and task performance, as well as each worker's core self-evaluation (CSE). The analyses combined hierarchical regression, bootstrapping and moderated mediation to untangle the causal paths.
The results reveal two parallel mediation chains. On one side, AI use raises autonomous motivation, which drives a promotion-oriented way of configuring work and, in turn, improves performance. On the other, the same use increases controlled motivation, favoring a preventive configuration that degrades performance. Core self-evaluation acts as a regulator: employees with high CSE amplify the positive effect and cushion the negative one, while those with low CSE get fewer benefits and more harm.
In practice, managers need to go beyond simply rolling out the technology. Fostering environments that strengthen autonomous motivation, for example by giving people a say in choosing their tools and highlighting the personal value of their work, maximizes the returns on AI. At the same time, identifying and supporting people with low self-evaluation helps contain the adverse effects, so AI becomes an ally rather than a source of rigidity.
Conclusions
- AI has a dual impact on performance: it boosts internal resources but also creates dependence and stress.
- People with high self-esteem and confidence in their skills get the most out of AI.
- Organizations need to pair technology rollout with building resilience and autonomy in their teams.