The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2520
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:47:14.871267Z
status: published
lens: trace
sector: labor
headline: Opportunity and Threat in Parallel: How Artificial Intelligence Application Predicts Employees’ Proactive Learning Behavior Through Parallel Primary Appraisals
dek: As organizations embed artificial intelligence (AI) in everyday work, understanding how employees respond has become a pressing question for human resource management. Drawing on the cognitive appraisal theory of stress, this study examines how AI application relates to employees' proactive learning behavior through two parallel primary appraisals: opportunity perception and threat perception. It also tests proactive personality both as a direct predictor and as a moderator at two theoretically distinct location…
gain_title: In a four-wave study of 420 Chinese employees, heavier AI use at work was linked to more self-initiated skill learning (total association 0.235), mainly among workers who saw AI as an opportunity.
problem_title: In the same study, employees who saw AI as a threat to their jobs did less proactive learning (indirect association -0.140), though this pathway weakened under the strictest statistical adjustment.
trace_subject: Employees' proactive learning in response to workplace AI use
gain_reading: In a four-wave study of 420 Chinese employees, heavier AI use at work was linked to more self-initiated skill learning (total association 0.235), mainly among workers who saw AI as an opportunity.
gain_evidence: The indirect association via opportunity perception was positive (0.193, 95% CI [0.143, 0.248]
problem_reading: In the same study, employees who saw AI as a threat to their jobs did less proactive learning (indirect association -0.140), though this pathway weakened under the strictest statistical adjustment.
problem_evidence: via threat perception negative (−0.140, 95% CI [−0.183, −0.100]
quick_read: A four-wave panel study of 420 employees in eight industries in China, surveyed monthly from March to June 2025, found that more AI use at work was associated with more self-initiated learning of new skills. After adjusting for each person's earlier learning level, the total association was 0.235.

That net figure combines two opposing paths. Employees who saw AI as an opportunity learned more (indirect association 0.193), while those who saw it as a threat to their jobs learned less (−0.140). The two views were statistically independent, and 18.8% of respondents scored above the midpoint on both.

The threat path was less solid than the opportunity path. It disappeared under the most stringent adjustment, which treated threat perception as change from its earlier level, but was stronger over a three-month window (−0.164 indirect). The authors describe the findings as association, not cause.
limitation: All measures were employee self-reports, the design cannot establish causation or direction of influence, the sample came from a single cultural context, and the AI measure could not separate generative AI from other AI types.
tag: Dual reading
key_points: The sample was 420 matched employees (from 510 responses) across services, manufacturing, public institutions, high technology, construction, education and government; 53.1% were frontline staff and 46.9% managers. | Opportunity perception predicted more proactive learning (β = 0.476) and threat perception predicted less (β = −0.319) in the primary baseline-adjusted model. | Without baseline adjustment the total association was larger (0.328); adjustment cut the opportunity path by roughly 30% and left the threat path essentially unchanged. | Under full autoregressive adjustment the opportunity indirect path stayed significant but small (0.046), while the threat indirect path was not significant (−0.003). | Among respondents, 18.8% scored above the scale midpoint on both opportunity and threat, 22.9% on opportunity only, 24.8% on threat only and 33.6% on neither. | Proactive personality shaped how AI was read: the AI-to-opportunity slope rose from 0.144 to 0.727 from low to high proactive personality, and the AI-to-threat slope fell from 0.711 to 0.105. | Proactive personality did not change how strongly a formed appraisal turned into learning (second-stage interactions not significant). | The threat-to-learning path ranged from −0.035 in construction to −0.529 in public institutions, in subsamples of 36 to 104 respondents.
rundown: Participants were recruited from organizations where AI is used in daily tasks. AI use was measured in March 2025, baseline learning in April, opportunity and threat perception in May, and proactive learning in June. Attention checks, response-time filters and straight-lining screens removed 90 responses, and retained and excluded participants did not differ on the main Wave 1 variables.

The primary model adjusted the June learning outcome for its April baseline. AI application predicted opportunity perception (β = 0.406) and threat perception (β = 0.439). The decomposition was exact: 0.193 − 0.140 + 0.182 = 0.235.

The researchers say the opportunity pathway survived every specification, while the threat pathway depended on how baseline adjustment was done. AI use predicted the level of threat perception but not month-to-month change in it. Threat perception was highly stable over time (r = 0.858).

Proactive personality was positively linked to opportunity perception (β = 0.202), negatively to threat perception (β = −0.209) and positively to learning (β = 0.127). Employees who already learned proactively also read AI more favorably, so the relationship may run in both directions.

The authors suggest employers frame AI as a complement to workers during onboarding and pair benefit messaging with reskilling and job-scope clarity, but say their design did not test any intervention.
sources:
- peer_reviewed | Behavioral Sciences | https://doi.org/10.3390/bs16091713 | 2026-09-21
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

8133c082b9af971ce8853eca0fa5363269861dbc2db9a1e54dec25f51c40cea4

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000