000			01624nam a22001337a 4500
999			_c532805 _d532805
008			260319b ||||| |||| 00| 0 eng d
100			_aWang, Ge Zhang, Zhejun Xie, Shengua and Guo Yue _959786
245			_aProvince of origin, decision-making bias, and responses to bureaucratic versus algorithmic decision-making
260			_aPublic Administration Review
300			_a85(6), Nov-Dec, 2025: p.1738-1756
520			_aAs algorithmic decision-making (ADM) becomes prevalent in certain public sectors, its interaction with traditional bureaucratic decision-making (BDM) evolves, especially in contexts shaped by regional identities and decision-making biases. To explore these dynamics, we conducted two survey experiments within traffic enforcement scenarios, involving 4816 participants across multiple provinces. Results indicate that non-native residents perceived ADM as fairer and more acceptable than BDM when they did not share a province of origin with local bureaucrats. Both native and non-native residents showed a preference for ADM in the presence of bureaucratic and algorithmic biases but preferred BDM when such biases were absent. When bureaucratic and algorithmic biases coexisted, the lack of a shared province of origin further reinforced non-native residents' perception of ADM as fairer and more acceptable than BDM. Our findings reveal the complex interplay among province of origin, decision-making biases, and responses to different decision-making approaches.- Reproduced https://onlinelibrary.wiley.com/doi/10.1111/puar.13928
773			_aPublic Administration Review
942			_cAR