Managing congestion (e.g., waiting lines) is of primary concern of most of service firms. Advances in Information Technology (e.g., smartphone applications) make congestion information more available to consumers. It is important to understand how the consumers react to such information. Queuing theory, a discipline in Operations Research, has been the dominant theory helping service firms to manage waiting lines. In the past 40 years of research, waiting lines have been predominantly considered as having negative externalities on consumers because waiting in line makes them waste their time. Waiting lines are not always bad for a service firm. Long lines may arouse potential consumersâ€™ interest in the service or product for which the waiting line is formed, especially, when there is uncertainty about the product or service for which the line is being formed. When traveling, some of us may have selected a restaurant because it has a long line, thinking that a restaurant with a long waiting line must be a good one. Appleâ€™s earlier product launches, when the innovation of Appleâ€™s smartphone was not as obvious as it is nowadays, created long waiting lines that have been commented worldwide. Without a doubt, Apple benefited from this â€œbuzz,â€ which are positive externalities. The objective of this proposal is to develop models, based on queuing theory, that allow predicting â€œhumanâ€ queue joining behavior when both positive and negative externalities determine the queue joining decision. We identify human decision maker biases from rational queue joining behavior in a controlled laboratory environment and we incorporate these biases in â€œbehaviorally enhancedâ€ queue joining models. Our models of human queue joining behavior can then be used by a service firm to influence the extent of creation of â€œbuzzâ€ via queues.
|Effective start/end date||4/1/13 → 3/31/17|
- National Science Foundation (CMMI-1301090)
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