How Do Public Managers Learn from Performance Information? Experimental Evidence on Problem Focus, Innovative Search, and Change

• 成果类型: Article
• 署名作者: Hansen, Jesper Asring; Nielsen, Poul Aaes
• 署名单位: Aarhus University; Aarhus University
• 刊物名称: PUBLIC ADMINISTRATION REVIEW
• ISSN/ISSBN: 0033-3352
• DOI: 10.1111/puar.13533
• 发表日期: 2022
• 页码: 946-957
• 关键词: aspiration levels; reference points; feedback; sector

摘要

Management decisions are considered important for the performance of public organizations, but how does information about performance influence management decision-making? Developed from Simon's notion of bounded rationality, the behavioral model of performance-based learning suggests that managers adjust their understanding of organizational problems, search for information, and consider initiating change when their organization performs below aspirations. In this paper, we offer experimental evidence of how performance information affects the attitudes and decision-making of public managers. We leverage two experiments conducted among managers in public education. Study 1 uses a question-order experiment to prime only treated respondents to consider performance, whereas study 2 uses treatments with high- and low-performance signals. We find that low performance affects problem focus but not managers' intention to conduct innovative search. We also find that low-performance signals increase preferences for immediate performance-oriented changes but not for other types of changes. We discuss the implications for management decision-making. Evidence for Practice Performance information holds the potential to generate organizational learning and inform public managers' decisions about important problems, priorities, and organizational change. Public managers become more focused on direct performance-oriented changes when performance is low, whereas high performance lets them engage in other types of changes. Performance information alone does not ensure that public managers are focused on generating innovative solutions to performance problems.