JOURNAL ARTICLE
The Performance Effects of Giving Front-Line Employees Direct Access to Performance Data and Thereby Limiting the Supervisor's Feedback-Intermediation Role: Evidence from a Field Experiment.
Published In: Management Science (INFORMS), 2026, v. 72, n. 2. P. 805 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Bernstein, Ethan; Li, Shelley Xin 3 of 3
Abstract
This article investigates the impact of providing front-line employees direct access to their own performance data—previously accessible only to supervisors—on employee performance in a large U.S. natural gas service organization. Using a randomized field experiment, the study finds that transparent access to daily time-use analytics led to an 11% reduction in nonproductive time but did not significantly increase productive (revenue-generating) time, indicating employees focused more on avoiding negative behaviors than on enhancing positive ones. The performance improvements were more pronounced among employees who perceived their supervisors as less supportive and those with low intrinsic or high extrinsic motivation, while social comparison orientation showed no significant moderating effect. The findings highlight the evolving role of supervisors from intermediaries to "apomediaries" who facilitate understanding without gatekeeping data, and suggest that the benefits of data transparency depend on employee motivation and supervisor quality.
Additional Information
- Source:Management Science (INFORMS). 2026/02, Vol. 72, Issue 2, p805
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2026
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.02395
- Accession Number:191433134
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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