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Can Employees’ Past Helping Behavior be Used to Improve Shift Scheduling? Evidence from ICU Nurses 2022-12-16

Subject:Can Employees’ Past Helping Behavior be Used to Improve Shift Scheduling? Evidence from ICU Nurses

Guest:Wang Yixin, Assistant Professor, University of Illinois at Urbana-Champaign

Host:Cao Yufeng, Assistant Professor, ACEM of SJTU

Time:Wednesday, December 21, 2022 8:30-10:00am

Venue:Tencent Meeting


Employees routinely make valuable contributions at work that are not part of their formal job description, such as helping a struggling coworker. These contributions, termed organizational citizenship behavior (OCB), have been studied from many angles in the organizational behavior literature. However, whether an employee's past helping behaviors can be used to improve shift scheduling decisions remains an under-explored operations question. We define two measures of past helping behavior for members of a shift - the total past helping of each employee and the past helping between each pair of employees - and hypothesize that they are associated with shift performance. We empirically confirm our hypotheses with detailed scheduling and patient outcome data from six ICUs at a large academic medical center, using the hospital's electronic medical records to identify cases of one nurse helping another. Our empirical results indicate that both measures of past helping are predictive of patient length of stay (LOS), more so than the broadly studied notion of team familiarity. Counterfactual analysis shows that relatively small changes in shift composition can yield significant reduction in total LOS, indicating the managerial significance of the results. Overall, our study suggests the potential value of shift scheduling using data on past helping behaviors, which may have promise far beyond the selected application to ICU nursing.

Guest Bio:

Yixin “Iris” Wang is an Assistant Professor of Operations Management at the Gies College of Business, University of Illinois at Urbana-Champaign. Her main research interests are empirical supply chain management and human-interactions in operations management. She has worked extensively with large-scale data and investigated how supply network, strategic policy reactions, and behavioral business decisions affect company performance. She has worked in collaboration with big automakers in U.S. and conducted experiments with pharmacy retail chains in China. Her research has been recognized by several awards and received honorable mention at paper competitions.