2026-10-13T22:27:19Z https://www.scidb.cn/oai

10.57760/sciencedb.013ll 2026-09-23T17:51:09Z

2026-09-23 Breaking Bureaucratic Ties with Algorithms: How AI-Driven Incentive Decisions Foster Job Well-Being Among Chinese Civil Servants doi:10.57760/sciencedb.013ll en Grounded in Social Information Processing Theory, this study builds a theoretical model to explore how AI-driven incentive decisions shape civil servants’ job well-being and its boundary conditions. This study employs a mixed empirical design based on Social Information Processing Theory. Two independent studies on Chinese civil servants are conducted: a preregistered experiment with 382 participants and a critical incident study with 610 respondents. In the experiment, participants were initially recruited from the Master of Public Administration (MPA) program at a large public university in Chengdu, China, yielding 40 incumbent civil servants who served as seeds for a snowball sampling procedure. This approach ultimately engaged 400 civil servants in the experimental study. Following data screening, 18 responses were excluded for failing the attention and validity checks (employing the embedded bogus items technique; Dunn et al., 2018). The final analytical sample comprised 382 participants with complete and valid data, corresponding to an overall effective response rate of 83.8%. Demographically, the sample included 55.2% females, with a mean age of 33.2 years (SD = 8.10). The vast majority (92.1%) held a bachelor’s degree. Professionally, 59.2% were classified under the comprehensive management track, and 83.8% occupied positions at the section-member rank or higher. Participants reported an average job tenure of 8.9 years (SD = 7.60).In the critical incident study, participants were initially recruited from MPA program at a large public university in Beijing, China, yielding 31 incumbent civil servants who served as seeds for a snowball sampling procedure. This recruitment strategy ultimately engaged 700 civil servants. Of these responses, 90 failed the attention check (“Please select Disagree”). Thus, the final sample was 610. 50.8% were female, with an average age falling within the 31–35 years bracket (SD = 1.25), 21.5% held a postgraduate degree, and 41.8% had accumulated more than five years of tenure in government service. civil servants; AI-driven incentive decisions; perceived organizational fairness; job well-being; public service climate 程进凯 RESTRICTED dataset Science Data Bank