Behavioral Drivers of Process Deviations and the Effects on Productivity and Quality: Evidence From the Field
Ibanez, Maria R.
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CitationIbanez, Maria R. 2018. Behavioral Drivers of Process Deviations and the Effects on Productivity and Quality: Evidence From the Field. Doctoral dissertation, Harvard Business School.
AbstractThis dissertation provides empirical evidence from high-stakes field settings of how productivity and quality are affected by workers' deviations from prescribed processes. The first essay of the dissertation explores the role of experimentation in field settings to investigate the drivers of performance and how to implement this methodology to answer relevant operational questions rigorously. This dissertation then uses field data from proprietary sources to investigate the behavioral drivers of process variation and their effects on productivity and quality. In particular, the next two essays of this dissertation consider the effects of (1) how workers' decisions are influenced by task schedules and (2) how workers' decisions exert influence on task schedules.
Many tasks are decisions, which are thus subject to human decision errors. How does scheduling affect how humans-in contrast to machines-perform these tasks? To explore this question, the second essay focuses on one critical task: quality evaluations. The accuracy of quality evaluations is critical to their being a useful input to key managerial decisions, to penalize compliance failures, and to motivate quality improvements. Yet, task-scheduling factors that are related to the workers' work structure (but unrelated to the task itself) could shape workers' predisposition toward the task and subsequent performance. We explore how inspection scheduling can affect inspection quality by influencing bias. Analyzing thousands of food safety inspections, we find that inspection results are affected by when the inspection occurs within an inspector's daily schedule and by inspectors' experience at their prior inspection of a different establishment. For example, the more compliance deterioration found in an inspector's prior inspected establishment, the more violations cited in the inspector's next inspection (of a different establishment). Consistent with negativity bias, this effect is asymmetric, applying when compliance at the inspector's prior establishment deteriorates but not when it improves. Overall, by identifying factors that bias inspections, our work contributes to the literature on monitoring, quality improvement, and scheduling. Our work also suggests a cost-effective lever: exploiting the behavioral effects of the organization of work.
Task scheduling is not always a managerial decision. Those who execute tasks often have discretion over the order in which to perform them. How do these choices affect productivity and quality? The third essay of this dissertation focuses on the drivers and consequences of exercising discretion to "deviate" from a prescribed task sequence. Analyzing 2.4 million decisions, we find that radiologists prioritize similar tasks (grouping tasks into batches) and those tasks they expect to complete faster (shortest expected processing time). Exploiting random assignment of tasks to doctors' queues, instrumental variable estimates reveal that both of these types of deviations erode productivity. Actively grouping similar tasks reduces productivity, in stark contrast to productivity gains from exogenous grouping, indicating deviation costs outweigh benefits from repetition. We also find learning-by-doing in exercising discretion, with doctors deviating more often and more productively over time. Our results highlight the tradeoffs between the time required to exercise discretion and the potential gains from doing so, which has implications for managers deciding task sequence assignments and system design.
Together, these essays generate new scholarly insights regarding the connections between operational factors, decision-making, and performance by analyzing data from high-stakes field settings. In doing so, this research seeks to contribute to theory while also improving management practice.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41940977