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TRUVACE RECORD VERSION
record: TRV-2026-2677
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T16:59:49.395049Z
status: published
lens: g_space
sector: labor
headline: From AI adaptation to innovation: how leader AI crafting shapes employee innovative behavior
dek: As artificial intelligence reshapes work, organizations face the challenge of helping employees move beyond routine technological adaptation toward innovation. Although prior research has shown that leader AI crafting facilitates employees' adaptation to AI, whether and how it promotes employees' innovative behavior remains unclear. Drawing on social cognitive theory, this study examines how leader AI crafting relates to employees' innovative behavior through AI self-efficacy and when this relationship becomes s…
gain_title: In a two-wave survey of 294 full-time employees who use AI at work, leader AI crafting was associated with higher innovative behavior (b = 0.45), partly through greater AI self-efficacy.
problem_title: (none)
trace_subject: (none)
gain_reading: In a two-wave survey of 294 full-time employees who use AI at work, leader AI crafting was associated with higher innovative behavior (b = 0.45), partly through greater AI self-efficacy.
gain_evidence: leader AI crafting was positively associated with innovative behavior [ b  = 0.45, SE = 0.05, 95% CI (0.36, 0.54)] | the indirect effect of leader AI crafting on innovative behavior through AI self-efficacy was significant
problem_reading: (none)
problem_evidence: (none)
quick_read: A two-wave survey of 294 full-time employees who frequently use AI at work, run two weeks apart, found that people whose supervisors proactively redesign their own work around AI reported more innovative behavior. Leader AI crafting was linked to innovative behavior (b = 0.45) and to employees' confidence in using AI (b = 0.37).

The link ran partly through that confidence. AI self-efficacy was positively associated with innovation (b = 0.30), and the indirect effect of leader AI crafting through it was significant (0.06). The effect on confidence was stronger for employees whose tasks depended heavily on AI (b = 0.61) than for those who depended on it little (b = 0.13).

A second study, a vignette experiment with 382 business-school participants, repeated the confidence finding under controlled conditions. The survey sample came mainly from internet technology firms in three eastern Chinese cities, and innovation was self-reported, so the results may not carry over to other industries.
limitation: AI self-efficacy and innovative behavior were measured in the same wave, innovation was self-reported, and the sample came mainly from internet technology firms in three eastern Chinese cities, limiting causal and general conclusions.
tag: Evidence-backed gain
key_points: Study 1 surveyed 352 employees in wave 1; 326 valid responses remained, and 294 matched responses were analysed after wave 2 (73.5% response rate). | Leader AI crafting predicted AI self-efficacy (b = 0.37) and innovative behavior (b = 0.45); AI self-efficacy predicted innovative behavior (b = 0.30). | The indirect effect through AI self-efficacy was 0.06 (95% CI 0.02 to 0.12). | With high AI task dependence the indirect effect was 0.18; with low dependence it was 0.04, and its interval (-0.01 to 0.09) included zero. | Study 2 randomly assigned 382 valid participants to four vignette conditions; the interaction of leader AI crafting and AI task dependence on self-efficacy was b = 0.10. | In the experiment, leader AI crafting raised AI self-efficacy more under high task dependence (b = 0.50) than low (b = 0.29). | In Study 1, 65.6% of respondents were women, 75.2% had 1-3 years of AI experience and 38.4% were managers.
rundown: Study 1 recruited full-time employees through the Credamo platform who frequently used AI systems in daily work. Wave 1 measured leader AI crafting and AI task dependence; wave 2, two weeks later, measured AI self-efficacy and innovative behavior over the prior two weeks. Analysis used PROCESS Models 4 and 7 with controls including gender, age, education, position, tenure, industry and AI exposure time.

Study 2 recruited faculty and students from business schools at two Chinese universities, with 400 completing and 382 valid after attention checks (average age 22.36, 71% female). Participants imagined being a product operations specialist whose manager, Manager Zhang, either actively redesigned workflows with AI or rarely did, in a 2 x 2 design crossed with high or low AI task dependence. Manipulation checks were significant (leader AI crafting means 4.02 vs 2.44).

The authors recommend that organisations develop leaders' AI crafting skills, build employees' AI confidence through task-specific training and guided experimentation, and tailor efforts to how much each role depends on AI. For roles with low AI dependence, they suggest redesigning workflows to integrate AI more before relying on leader modelling.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1847344 | 2026-07-09
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