An Assessment of Digitalization Techniques in Contact Centers and Their Impact on Agent Performance and Well-Being

Massimo Pacella (), Paride Vasco, Gabriele Papadia and Vincenzo Giliberti
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Massimo Pacella: Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Paride Vasco: Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Gabriele Papadia: Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Vincenzo Giliberti: IN & OUT S.p.A. a Socio Unico Teleperformance S.E., 74121 Taranto, Italy

Sustainability, 2024, vol. 16, issue 2, 1-20

Abstract: The role of contact centers in improving the operational efficiency of numerous organizations is of utmost importance. Presently, digitalization technology has enabled contact centers to deliver exceptional customer service and support, while minimizing the adverse impact on agent well-being. Artificial intelligence techniques such as topic modeling and sentiment analysis can aid agents in addressing specific queries, providing real-time support and feedback, and helping them build stronger relationships with customers. This study aims to investigate the advantages of integrating these techniques in the analysis of customer–agent conversations within contact centers. This study examines whether there is a discernible advantage in analyzing customer–agent conversations in real-time and whether it is worth using this type of digitization to enhance agent performance and well-being. Furthermore, this study explores the impact of these technologies on European privacy, business, real-time agent support, the value of conversation data, brand reputation, and customer satisfaction. The results of this study demonstrate the significance of incorporating topic modeling and sentiment analysis into the analysis of customer–agent conversations at contact centers.

Keywords: topic modeling; sentiment analysis; contact centers; agent performance; agent well-being (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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