Professor Wei Gu of USTB's School of Economics and Management has published research in Production and Operations Management on how algorithmic supervision shapes employee performance at a leading service firm's call center.
The paper, titled "Algorithmic Supervisor and Employee Performance," lists Gu as first author and was co-authored with Meng Li of the University of Houston and others. It appears in Production and Operations Management, a UTD24 journal published by the Production and Operations Management Society (POMS).
An algorithmic supervisor is a data analytics tool that gives employees real-time feedback. The study partnered with a leading service company that shifted its outbound call center from human to algorithmic supervision. Over six months, the researchers collected 220,085 call records covering the three months before and the three months after the system went live.
They found that algorithmic supervision improved service quality but reduced service quantity. After adoption, the number of customers served fell by 15.80 percent, while the number of satisfied customers rose by 14.91 percent.
The effects also differed by gender. Before adoption, male agents outperformed female agents. After adoption, female agents showed an 11.52 percent increase in customers served and a 14.00 percent increase in satisfied customers compared with male peers.
The authors conclude that algorithmic supervision creates a new trade-off between service quality and quantity, while producing differentiated social effects across male and female employees.
