| Autor: |
Naveh, Eitan, Nissinboim, Noa, Leib, Ryan, Cleveland, Jessica, Das, Partha, Frank, David, Bunnell, Craig, Singer, Sara |
| Zdroj: |
Academy of Management Annual Meeting Proceedings; 2023, Vol. 2023 Issue 1, p3879-3879, 1p |
| Abstrakt: |
Errors, an everyday concern in virtually all organizations, are occasionally attributed to heavy workload. In this paper we re-examined the relationship between workload and errors. While it is usually assumed that heavy workloads lead to increased frequency of errors, we explore the counterintuitive experience of diminished error rates in such work conditions. In the absence of an acceptable theoretical explanation, this is commonly attributed to a measurement flaw in the form of incomplete or diminished error reporting. We draw on a new and advanced sensor-based data collection method, usually employed for inventory purposes, to locate individuals in order to test an alternative explanation. The results are based on real-time location data of 53 staff members and a daily average of 321 cancer patients, collected by 1,000 sensors every three seconds throughout a two-year period in six infusion units in one outpatient cancer facility. The resulting data are integrated with a second data set of patients' scheduled appointments and a third data set of error reports for the same period of time. This allows us to show that the nurses' positive adaptive behavior during periods of heavy workloads leads to a valid process improvement that diminishes the error rate in such situations. In addition to contributing to our understanding of the workload-error relationship, this paper demonstrates an empirical example, so far rare, of the use of powerful new data collection technologies in operations management. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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