When Does the Devil Make Work? An Empirical Study of the Impact of Workload on Worker Productivity

Tan, Tom Fangyun; Netessine, Serguei

doi:10.2139/ssrn.2071113

Cited by 69 publications

(128 citation statements)

References 66 publications

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“…This indicates that caregivers' hand hygiene compliance increased at first, post-activation, before subsequently decreasing. To confirm that there was an inverted U-shaped relationship between months since activation and compliance, we conducted further analyses (Kesavan, Staats and Gilland 2014;Tan and Netessine 2014). First, we determined that the stationary point occurred 21.6 months after the activation of monitoring, which was well within the observation period.…”

Section: The Long-term Effects Of Individual Monitoring On Compliancementioning

confidence: 98%

Motivating Process Compliance Through Individual Electronic Monitoring: An Empirical Examination of Hand Hygiene in Healthcare

et al. 2017

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The design and use of standard processes are foundational recommendations in many operations practices. Yet, given the demonstrated performance benefits of standardized processes, it is surprising that they are often not followed consistently. One way to ensure greater compliance is by electronically monitoring the activities of individuals, although such aggressive monitoring poses the risk of inducing backlash. In the setting of hand hygiene in healthcare, a context where compliance with standard processes is frequently less than 50% and where this lack of compliance can result in negative consequences, we investigated the effectiveness of electronic monitoring. We did so using a unique, radio frequency identification (RFID)-based system deployed in 71 hospital units. We found that electronically monitoring individual compliance resulted in a large, positive increase in compliance. We also found that there was substantial variability in the effect across units and that units with higher levels of preactivation compliance experienced increased benefits from monitoring relative to units with lower levels of prepreactivation compliance. By observing compliance rates over three and a half years, we investigated the persistent effects of individual monitoring and found that compliance rates initially increased before they gradually declined. Additionally, in multiple units, individual monitoring was discontinued, allowing for an investigation of the impact of removing the intervention on compliance. Surprisingly, we found that, after removal, compliance rates declined to below prepreactivation levels. Our findings suggest that, although individual electronic monitoring can dramatically improve process compliance, it requires sustained managerial commitment. AcknowledgmentsWe are grateful to many individuals at Proventix, including David Sellers, Harvey Nix, and Elizabeth Pembleton, whose assistance made this project possible. We thank Christian Terwiesch for his helpful comments as we developed the manuscript. We acknowledge the developmental assistance of the DE, AE, and reviewers in improving this paper throughout the review process. Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number P30AG034532. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Any errors are the responsibility of the authors. COMPLIANCE AND MONITORING 1 AbstractThe design and use of standard processes are foundational recommendations in many operations practices. Yet, given the demonstrated performance benefits of standardized processes, it is surprising that they are often not followed consistently. One way to ensure greater compliance is by electronically monitoring the activities of individuals, although such aggressive monitoring poses the risk of inducing backlash. In the setting of hand hygiene in healthcare -a context where compliance with st...

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Section: The Long-term Effects Of Individual Monitoring On Compliancementioning

confidence: 98%

Motivating Process Compliance Through Individual Electronic Monitoring: An Empirical Examination of Hand Hygiene in Healthcare

et al. 2017

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“…Strategic servers can manipulate customer throughput time with these task management decisions when it benefits them to do so (Hopp et al 2007, Link and Naveh 2006, Tan and Netessine 2013. For example, in the restaurant industry, Tan and Netessine (2013) find that wait staff adjust the services offered to customers so that customers spend less time in the restaurant when the workload is high.…”

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

“…For example, in the restaurant industry, Tan and Netessine (2013) find that wait staff adjust the services offered to customers so that customers spend less time in the restaurant when the workload is high.…”

Section: Prior Research On Queue Management and Throughput Timesmentioning

confidence: 99%

“…Similar to workers in other service settings (Debo et al 2008, Hasija et al 2010, Tan and Netessine 2013, physicians in the dedicated queuing system had both the incentive and ability to make sure that their patients' care progressed efficiently, so that their patients in the waiting room could receive care sooner than they otherwise would have (Cachon and Zhang 2007, Gilbert and Weng 1998, Hopp et al 2007). Thus, we find that the advantages of having visibility into one's own workload, in combination with both the ability to manage patient flow and an incentive to efficiently manage one's full workload, outweighed the variability-buffering benefit of a pooled queuing system.…”

Section: Introductionmentioning

confidence: 99%

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The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

2015

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We conduct an empirical investigation of the impact of two different queue management systems on throughput times. Using an Emergency Department's (ED) patient-level data (N = 231,081) from 2007 to 2010, we find that patients' lengths of stay (LOS) were longer when physicians were assigned patients under a pooled queuing system, compared to when each physician operated under a dedicated queuing system. The dedicated queuing system resulted in a 10 percent decrease in LOS-a 32-minute reduction in LOS for an average patient of medium severity in this ED. We propose that the dedicated queuing system yielded shorter throughput times because it provided physicians with greater ability and incentive to manage their patients' flow through the ED from arrival to discharge. Consistent with social loafing theory, our analysis shows that patients were treated and discharged at a faster rate in the dedicated queuing system than in the pooled queuing system. We conduct additional analyses to rule out alternate explanations, such as stinting on care and decreased quality of care. Our paper has implications for health care organizations and others seeking to reduce throughput time, resource utilization, and costs.Key words: pooling, queue management, strategic servers, social loafing, empirical operations, health care 3 IntroductionImproving efficiency and customer experience are key objectives for managers of service organizations.Skillful application of operations management principles may prove helpful for achieving these goals. In this paper, we investigate queue management, a key operational decision. More specifically, we explore the impact on throughput time of using a pooled queuing system or a dedicated queuing system.Prior work on queue management has demonstrated through analytical models that pooling separate streams of identical customers into a single queue served by a bank of identical servers leads to a reduction in waiting time and an increase in server utilization (Eppen 1979, Kleinrock 1976. This occurs because pooling reduces the negative impact of variability in arrival times and processing times. Pooling enables incoming work to be processed by any one server from a bank of servers, which decreases the odds that an incoming unit of work will have to wait for service. This situation compares to the one where the unit of work can only be processed by a single dedicated server.Queuing theorists have advanced this stream of research by identifying conditions under which queue pooling may not yield the expected performance improvements (Debo et al. 2008, van Dijk and van der Sluis 2009, Hopp et al. 2007, Jouini et al. 2008, Loch 1998, Mandelbaum and Reiman 1998. However, few empirical studies have examined the performance of pooled versus dedicated queuing systems. This is an important omission because, in practice, employees can often make adjustments to how they manage their work system to achieve a goal, such as increasing their productivity (Hopp et al. 2009). Operations management scholars advocat...

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“…As such, decision makers observe the consequences of a particular action and then must use this information to determine their next action. Illustrative examples can be seen in a wide variety of contexts, including labor scheduling (Kesavan, Staats and Gilland 2014;Tan and Netessine 2014), production tool choice (Upton 1997;Ramdas et al 2010), job shop scheduling (Fryer 1975), local capacity decisions (Campbell and Frei 2011), inventory ordering (van Donselaar et al 2010), pricing (Phillips, Şimşek and Ryzin 2015), research and development investment (Chao, Kavadias and Gaimon 2009), and Bayesian models (Brown, Lu and Wolfson 1964) more generally. In these and other models, individuals are assumed to use Bayesian updating to generate their beliefs, which ultimately determine their next action.…”

Section: Introductionmentioning

confidence: 99%

Blinded by Experience: Prior Experience, Negative News and Belief Updating

Staats

Gino³

2015

SSRN Journal

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Traditional models of operations management involve dynamic decision-making assuming optimal (Bayesian) updating. However, behavioral theory suggests that individuals exhibit bias in their beliefs and decisions. We conduct both a field study and two laboratory studies to examine the phenomena in the context of health. In particular, we examine how an individual's prior experiences and the experiences of those around them alter the operational decisions that the individual makes. We draw on an exogenous announcement of negative news by the Food and Drug Administration (FDA) and explore how this affects an operational decision -production tool choice -of interventional cardiologists deciding between two types of cardiac stents. Analyzing 147,000 choices over 6 years, we find that individuals do respond to negative news by using the focal production tool less often. However, we find that both individual's own experience and others' experience alter their responses in predictable ways. Moreover, although individual and other experience act as substitutes prior to negative news, the two types of experience act as complements following the negative announcement -leading to even greater use of the same production tool. Two controlled lab studies replicate our main findings and show that behavioral biases, not rational expectations, drive the effect. Our research contributes not only to operations management research, but also to the practice of healthcare and operations more generally.

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When Does the Devil Make Work? An Empirical Study of the Impact of Workload on Worker Productivity

Cited by 69 publications

References 66 publications

Motivating Process Compliance Through Individual Electronic Monitoring: An Empirical Examination of Hand Hygiene in Healthcare

Motivating Process Compliance Through Individual Electronic Monitoring: An Empirical Examination of Hand Hygiene in Healthcare

The Diseconomies of Queue Pooling: An Empirical Investigation of Emergency Department Length of Stay

Blinded by Experience: Prior Experience, Negative News and Belief Updating

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