Collaborative Research: The Positive Role of Queues on Customer Value Perception: Mathematical Models and Laboratory Experiments

这个合作研究项目的主要目标是开发基于排队理论的行为增强数学模型，以预测客户服务环境中的人类行为。这些模型将通过实验室和实地实验进行评估和测试。管理拥堵（排队等候）是大多数为客户提供某种服务的组织最关心的问题。顾客通常对长时间的等待感到厌烦，因此往往不会加入排长队的队伍。最近的实证和实验研究提供了新的证据，证明在服务价值不确定的服务环境中，长队列可能会对价值感知产生积极的影响。因此，排长队实际上可能会吸引更多的顾客，当这些顾客看到他们后面的队伍更长时，他们可能会花更多的钱。这项研究如果成功的话，将发展出一套指导服务组织更好地理解和利用这一现象的原则。应用可以在服务环境中找到（例如在餐馆等待），也可以在结果质量有些不确定的卫生保健环境中找到（例如等待选择性手术）。我们期望提高服务质量（使客户受益）和服务系统的利润（使组织或企业受益）。此外，研究结果有望加强行为排队模型领域：(1)建立了一套先进的排队数学模型，其参数与人类的偏见和行为相对应；(2)创建了一系列实验室和现场实验，捕捉了服务质量不确定的服务环境中人类行为的本质；(3)建立了在线排队模拟器和数据库，用于设计行为排队实验。

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.