{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:02:57Z","timestamp":1790139777469,"version":"4.0.1"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Behav. Sci.","Behavioral Sciences"],"abstract":"<jats:p>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\u2019 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 locations: the formation of appraisals and the translation of appraisals into behavior. Using a four-wave panel design among employees from multiple industries, we find that AI application is positively associated with proactive learning behavior overall. This association operates through two opposing pathways: opportunity perception strengthens proactive learning, whereas threat perception suppresses it. Additional analyses indicate that the opportunity pathway is the more robust of the two across longitudinal specifications. The two appraisals are statistically separable and jointly elevated in a subgroup of employees, indicating parallel rather than strictly bipolar appraisal. Proactive personality is positively associated with opportunity perception and proactive learning, negatively associated with threat perception, and moderates the appraisal-formation stage rather than the appraisal-to-behavior stage. These findings extend the cognitive appraisal theory of stress to the AI context and suggest that dispositional proactivity operates mainly by shaping how employees read AI, rather than by changing how they act on an appraisal once it has formed.<\/jats:p>","DOI":"10.3390\/bs16091713","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T16:06:51Z","timestamp":1790006811000},"page":"1713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Opportunity and Threat in Parallel: How Artificial Intelligence Application Predicts Employees\u2019 Proactive Learning Behavior Through Parallel Primary Appraisals"],"prefix":"10.3390","volume":"16","author":[{"given":"Yixun","family":"Lu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojun","family":"Shen","sequence":"additional","affiliation":[{"name":"China Transportation Education Research Association, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"803","DOI":"10.2307\/256569","article-title":"Content, causes, and consequences of job insecurity: A theory-based measure and substantive test","volume":"32","author":"Ashford","year":"1989","journal-title":"Academy of Management Journal"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1002\/job.4030140202","article-title":"The proactive component of organizational behavior: A measure and correlates","volume":"14","author":"Bateman","year":"1993","journal-title":"Journal of Organizational Behavior"},{"key":"ref_3","first-page":"389","article-title":"Translation and content analysis of oral and written material","volume":"Vol. 2","author":"Triandis","year":"1980","journal-title":"Handbook of cross-cultural psychology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1017\/jmo.2016.55","article-title":"Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees\u2019 perceptions of our future workplace","volume":"24","author":"Brougham","year":"2018","journal-title":"Journal of Management & Organization"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1037\/0021-9010.85.1.65","article-title":"An empirical examination of self-reported work stress among U.S. managers","volume":"85","author":"Cavanaugh","year":"2000","journal-title":"Journal of Applied Psychology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1207\/S15328007SEM0902_5","article-title":"Evaluating goodness-of-fit indexes for testing measurement invariance","volume":"9","author":"Cheung","year":"2002","journal-title":"Structural Equation Modeling"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1037\/0021-843X.112.4.558","article-title":"Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling","volume":"112","author":"Cole","year":"2003","journal-title":"Journal of Abnormal Psychology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1177\/014920630002600304","article-title":"Proactive behavior in organizations","volume":"26","author":"Crant","year":"2000","journal-title":"Journal of Management"},{"key":"ref_9","unstructured":"Deloitte (2026). 2026 global human capital trends, Deloitte Insights. 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