This dissertation takes an employee-oriented approach to the within-firm OM decisions and investigates the effects of interventions focusing on employees on the process outcomes. Through a series of three essays, we handle three management tools; rank-based performance feedback, knowledge transfer via the adoption of best practices, and algorithmic real-time feedback and coaching; each has potential adverse effects on employees yet could be very rewarding once successfully implemented. We seek to gain a profound understanding of employee behavior and stimulate engagement, thereby fostering more efficient and productive systems.
In the first chapter, 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. 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 their shame.
The second chapter 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.
The concluding chapter 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.
In summary, through three studies, we offer theoretical and practical implications about the use and challenges associated with various management tools and provide ways to improve employee behavior to stimulate engagement and foster more efficient and productive systems.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46596 |
| Date | 23 August 2023 |
| Creators | Türkoğlu, Aykut |
| Contributors | Carson, Anita L. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nd/4.0/ |
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