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型数学模型，允许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现场实验进行评估和测试。对于为客户提供某种服务的大多数组织来说，管理拥堵(排队)是首要考虑的问题。顾客通常对漫长的等待时间感到恼火，因此倾向于不加入长队。最近的实证和实验研究提供了新的证据，表明在服务价值不确定的服务环境中，长队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。如果研究成功，将制定一套原则，指导服务机构更好地理解和利用这一现象。可以在服务环境(例如，在餐馆等待)以及在医疗保健环境(例如，等待选择性手术)中找到应用，在这些环境中，结果的质量有些不确定。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，该研究的结果有望加强行为排队模型的研究领域：(I)通过建立一套先进的排队数学模型，其参数与人的偏见和行为相对应，(Ii)通过创建一系列实验室和现场实验来捕捉服务环境中服务质量不确定的人类行为的本质，以及(Iii)通过建立在线排队模拟器和数据库来设计行为排队实验。

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.