How Network Embeddedness Affects Real-Time Performance Feedback: An Empirical Investigation

• 成果类型: Article
• 署名作者: Petryk, Mariia; Rivera, Michael; Bhattacharya, Siddharth; Qiu, Liangfei; Kumarb, Subodha
• 署名单位: George Mason University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; State University System of Florida; University of Florida
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.1110
• 发表日期: 2022
• 页码: 1467-1489
• 关键词: social network; information-technology; prediction markets; STRUCTURAL EMBEDDEDNESS; JOB-PERFORMANCE; centrality; location; IMPACT; POWER; TIES

摘要

Firms and organizations are increasingly using real-time performance feedback mechanisms to evaluate employees, where any employee (rather than just the supervisor) can rate other employees. Hence, a need arises to better understand how network positions of employees in such a system impact their performance. Analyzing nearly 4,000 feedback instances from employees at five major organizations that use such a real-time performance feedback application called DevelapMe, we explore the effects of network embeddednessor the nature of relationships among employees-on performance rating scores according to two dimensions of embeddedness: (i) positional, the position of an individual in the emerging network of performance ratings, and (ii) structural, the extent to which a person is entrenched in a network of relationships. We visualize rating networks within organizations: Employees are nodes, and connections between nodes exist if an evaluation between the pair occurs. We find that specific aspects of network embeddedness affect performance rating scores differently. In particular, a rater's positional embeddedness (measured by eigenvector centrality) is positively associated with the rating score he or she gives others. However, a rater's structural embeddedness (measured by outdegree centrality) is negatively associated with the rating score he or she gives. We also uncover the moderating effects of anonymity and hierarchy on the role of network embeddedness, aswell as the sentiment of the textual comments provided with feedback. Our findings have important implications for the design of performancemanagement systems using network analysis.