意外后果
官僚主义
预测(人工智能)
模棱两可
政府(语言学)
政治学
公共行政
公共关系
计算机科学
法学
政治
语言学
哲学
人工智能
程序设计语言
作者
Yi Lu,Kaifeng Yang,M. Blair Thomas
标识
DOI:10.1177/0095399720976532
摘要
Scholars have long documented the unintended consequences of performance systems, but insufficient attention is paid to the drivers of such consequences and the ways of mitigating them. This article contributes to this line of inquiry by analyzing New York Police Department’s (NYPD) experience with its signature performance system and its affiliated policies. With multiple data sources, it finds three main drivers of the unintended consequences, including the excessive performance pressure, the bureaucracy-public expectation mismatch, and the ambiguity concerning government performance. The article makes six suggestions regarding the design of performance management systems so that their potential unintended consequences can be better identified and mitigated.
科研通智能强力驱动
Strongly Powered by AbleSci AI