Do Customer Emotions Affect Agent Speed? An Empirical Study of Emotional Load in Online Customer Contact Centers

摘要

Problem definition: Research in operations management has focused mainly on system-level load, ignoring the fact that service agents and customers express a variety of emotions that may impact service processes and outcomes. We introduce the concept of emotional load-the emotional demands that customer behaviors impose on service agents-to analyze how customer emotions affect service worker's behavior. Academic/practical relevance: Most theories in organizational behavior literature predict that emotions expressed by customers reduce agent's cognitive abilities and therefore, should reduce the agent's speed (e.g., by increasing the service time required to serve an angry customer). We aim to shed light on the magnitude of that phenomenon while addressing important econometric challenges. We also investigate an important mechanism that drives this relation, namely agent effort. We discuss practical opportunities that arise from measuring emotional load and how it can be used to enhance productivity. Methodology: We measure the emotional load of agents using sentiment analysis tools that quantify positive/negative customer emotion expressions in an online chat-type contact center and link it to agent behavior: response time (RT) and the length and number of messages required to complete a service request. Identifying a causal effect of customer emotion on agent behavior using observational data is challenging because there are confounding factors associated with the complexity of service requests, which are related to both customer emotions and agent behavior. Our identification strategy uses panel data and exploits the variation across messages within a focal request using fixed effects to control for unobserved factors associated with case complexity. Instrumental variables are also used to address issues of measurement error and other endogeneity problems; the instruments are based on exogenous shocks to agent performance indicators that have been studied in the service operations literature. Results: Analyses show that emotional load created by negative customer emotions increases agent RT, the length of the agent messages (a measure of effort), and the required number of messages needed to complete a service request. Emotional load and agent RT reciprocally affect each other, with long agent RTs and a high number of messages producing more negative customer emotion. Managerial implications: We suggest that the emotional content in customer communications should be an important factor to consider when assigning workload to agents in a service system. Our study provides a rigorous methodology to measure the emotional content from customer text messages and objectively evaluate its associated workload. We discuss how this can be used to improve staffing decisions and dynamic workload routing through real-time monitoring of emotional load.