Media & Publications

Relative performance feedback: what is it and how does it affect employee performance? During this Expert Take, Aykut Turkoglu, Lecturer of Operations and Technology Management, and Anita Carson, Larz Anderson Professor and Professor of Operations and Technology Management, share their findings from a series of experiments analyzing how relative performance feedback can demotivate middle-ranked workers.

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Click the image below to watch the interview:

Shell, M., and Turkoglu, A. Artificial Intelligence in Customer Service Operations, Working Paper

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Overview: This research focuses on the effect of using algorithmic feedback and coaching as management tools in service operations within call center environments. Companies are deploying artificial intelligence applications into service settings in a variety of ways, from automating agent tasks to replacing human servers altogether. This study examines how artificial intelligence-based feedback (AI) impacts customer service agent employee productivity as measured by three key performance indicators: call-handle time, customer satisfaction, and call service quality. Our field partner, a North American outsourced call center deployed the AI software to monitor calls during a bill collection campaign and provide visible cues to remind agents of their service script requirements. In this way, the AI acts as a real-time supervisor, assessing agent performance and offering real-time feedback during and after the call. Using international call center data, we provide evidence that agents with access to the AI feedback are indeed more likely to comply with scripts and in so doing, deliver increased operational efficiency with lower call handle time. Moreover, calls conducted with AI feedback show an increase in two service quality metrics not commonly associated with technology-assisted communication: respect and rapport.

Turkoglu, A., and Carson, L. A. Effect of RPF on Adoption of Best Practices and Worker Performance, Working Paper

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Overview: This research concentrates on enhancing performance through fostering internal knowledge transfer and promoting the adoption of best practices. Through a series of experiments, we assess the effects of providing performance feedback in conjunction with best practices on knowledge-seeking behavior, best practice adaptions, and operational performance. Our study poses an exciting finding by showing that RPF's previously documented negative effect on middle-ranked workers could be mitigated, and performance improvement could be attained when combined with best practices.

Turkoglu, A., and Carson, L. A, (2022), The Demotivating Effects of Relative Performance Feedback on Middle-Ranked Workers’ Performance

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Abstract: We conduct a series of experiments to study the impact of three different types of relative performance feedback (RPF) on middle-ranked workers’ output on a skill-based task. Few studies investigate the impact of RPF on middle-ranked workers, which is a substantial omission given that they form the majority of the workforce. In our study, participants do not receive any information to help them improve at the task, and there are no financial incentives. We find that receiving any type of feedback reduces performance compared to no feedback. We conduct mediation analysis and show that receiving feedback changes employees’ feelings associated with general performance, which explains the performance reduction. Aligned with theory, delivering feedback increases the focal employee’s social comparison involvement (SCI), which measures the focal individual’s tendency to compare themselves to others while performing the task and also their shame. Our results imply that making individuals care more about social comparisons and creating feelings of shame decreases performance on skill-based tasks for middle-ranked workers in the absence of financial incentives and information on how to improve. An implication of our study is that any form of rank-based performance feedback should be implemented with caution as it may harm the performance of the majority of workers.

Eriksen, R., Turkoglu, A., Bernard, A., Joglekar, N., Horenstein, M. and Mazumder, M., (2018), Water and Cost Reduction from the Application of EDS to Facilitate Water Free Cleaning in Concentrated Solar Power, MRS Advances

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Overview: Soiling in solar power generation will be a significant obstacle to its growth if a water free method cannot be found. Demand for water in arid regions will increase as more solar power generation is built, requiring more water to clean the optical surface, in turn increasing the price of water. This will lead to increased operating costs for solar power generation, and potentially disputes in locations where water is scarce. The electrodynamic screen (EDS) can reduce soiling and contribute to restoring the optical surface without the use of water. Periodic cleaning will still be required, but at reduced frequency, leading to a significant reduction in the consumption of water. In this model, it was found that a 250 MW concentrated solar power plant would have a 74% reduction in water given current laboratory production uncertainties. This indicates that EDS technology could decrease both the operating cost and the water use for solar generation plants.

Altay, A. Turkoglu, A., (2015) An Intelligent Prediction of Self-Produced Energy, In Sustainable Future Energy Technology and Supply Chains Working Paper, Springer, Cham.

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Overview: The need for energy has been aggressively increasing since the industrial revolution. An exponential growth of industrial and residential power use is encountered with the technological revolution. Cogenerated and self-produced energy is a solution that allows the reuse of heat produced, decreases transmission investments, and reduces carbon emissions and decreases dependency on energy resource owners. The mass production sites, health centers, big residential sites and more can use the system. In this chapter, the focus is given to industrial auto-producers. Power market balance is based on the day-ahead declarations; Therefore, the production is to be planned in detail to avoid penalties. A recurrent Artificial Neural Network model is constructed in order to predict the day ahead energy supply. The model considers energy resource price, demand from multiple sites, production cost, the amount of energy imported from the grid and the amount of energy exported to the grid. In order to achieve the energy production rate with the least error rate possible, an energy demand forecasting model is constructed for a paper producing company, using a Nonlinear Autoregressive Exogenous Model (NARX) network implemented in Matlab. Three parameters of the forecasting model are tuned using the Particle Swarm Optimization (PSO) algorithm: the number of layers, the number of nodes in hidden layers and the number of delays in the network. Error level is measured using the Minimum Absolute Percentage Error between the predictions and the actual output.