The main goal of this collaborative research project is to develop behaviorally enhanced mathematical models grounded in queuing theory that allow predicting human behavior in customer service environments. These models will be evaluated and tested through laboratory and field experiments. Managing congestion (waiting lines) is of primary concern for most organizations providing some sort of service to customers. Customers are typically annoyed by long waiting times and therefore tend not to join long queues. Recent empirical and experimental studies provide new evidence that, in service environments in which the service value is uncertain, long queues may impact the value perception in a positive way. As a consequence, long queues may actually attract more customers and these customers may spend more when they see that the queue behind them is longer.
The research, if successful, will develop a set of principles that guides service organizations to better understand and utilize this phenomenon. Applications can be found in service settings (e.g. waiting in a restaurant), as well as in health care settings (e.g. waiting for elective surgery) in which the quality of the outcome is somewhat uncertain. We expect to improve the quality of service (benefiting customers) and profit of service systems (benefiting organizations or businesses). Furthermore, the results of the research are expected to strengthen the field of behavioral queueing models: (i) by developing a set of advanced mathematical models of queues with parameters corresponding to human biases and behavior, (ii) by creating a series of lab and field experiments that captures the essence of human behavior in service environment with uncertainty in service quality, and (iii) by establishing an online queueing simulator and data base for designing behavioral queueing experiments.
