To Wait or Not to Wait: The Theory and Practice of Ticket Queues

Abstract

Ticket queues are prevalent in service industries. They enhance customer satisfaction by eliminating physical lines but may compromise efficiency. Existing studies offer mixed results on the cause and magnitude of such inefficiency. These results, however, are based on simplistic customer behaviors. Taking a holistic approach, we examine how realistic customer behaviors drive ticket queue performance. Our empirical studies reveal that (i) customers are capable of adapting their patience to the waiting context and (ii) they use dynamic updating to improve forecasting and decision making over time. Once we model these two behaviors, we find the ticket queue inefficiency is much smaller than the literature predicted. By testing and highlighting the central role of realistic customer behaviors, this paper advances our understanding on ticket queue theory and practice.

Bayesian modeling of abandonments in ticket queues

“…We could also introduce additional covariates to our MPPM model, such as the average waiting times for those customers who abandon in each interval. However, the interval‐censored nature of the TQ data would require that we use a proxy, such as the “offered waiting time” defined in Kuzu et al (2018), for the actual waiting times before abandonment. Finally, the proposed semiparametric Bayesian modeling framework could be implemented on interval‐censored data from other domains, such as call center operations, as well as for reliability and survival analyses.…”

“…This is because increasing the number of servers, s , means the same number of customers face shorter waits before receiving service, resulting in a lower likelihood of abandoning the system. Batt and Terwiesch () analyze an emergency department data set using probit regression models, and Kuzu et al (2018) analyze a TQ data set using logistic regression models. Their findings show that an increase in the number of servers decreases the abandonment probability, thus supporting our results.…”

“…Unlike patience‐related studies in call centers (eg, Zohar, Mandelbaum, & Shimkin, ; Mandelbaum & Zeltyn, , ; Aksin, Ata, Emadi, & Su, , ) and emergency rooms (eg, Batt & Terwiesch ); Bolandifar, DeHoratius, Olsen, & Wiler, ), there is a dearth of literature on modeling and analyzing abandonment data for TQs. In a recent work, Kuzu, Gao, and Xu (2018) study the effects of factors that customers observe during waits (eg, the number of servers operating or the number of tickets ahead) on the probability of abandonment, but do not provide a framework for predicting customer abandonment.…”

Approximate Performance Measures for a Single Station Two-Stage Reneging Queue

“…This constant patience assumption may be reasonable when customers have no information about their position in queue (e.g., in some call centers). However, customers often change their level of patience, for example based on their queue position if they experience a different level of comfort, or when they know they are close to, or far from, the front of the queue [1]. For example, many restaurants provide some seats (capacity) for customers waiting inside (1 st stage queue), but not everybody can sit there; if all seats inside are filled, any remaining waiting customers must wait outside where it may be cold or raining (2 nd stage queue).…”

Dynamic incentives for sustainable contract farming

“…66There is also extensive literature on supply chain contracting and operations (Cachon, 2003). The main themes include, e.g., demand uncertainty (Gao et al., 2012; Gao, Thomas, et al., 2014; Gao et al., 2020; Hwang et al., 2010; Kuzu et al., 2019; Xu et al., 2007; Yan & Zhao, 2011; H. Zhang et al., 2010), channel coordination (Cakanyıldırım et al., 2012; Gao, 2015a; Gao & Mishra, 2019; Gao et al., 2021; Z. Li & Gao, 2008), supply risk (Akcay & Gao, 2020; Chen & Lee, 2017; Gao, Li, et al., 2014; Gao, 2015b; Gao et al., 2017; Luo et al., 2016; Z. Yang et al., 2009), collaborative investment (Kim & Netessine, 2013), assembly procurement (Fang et al., 2014; B. Hu & Qi, 2018), service requirement (F. Zhang, 2010), contract simplicity (Bolandifar et al., 2017), and contract rigidity (Corbett et al., 2004; Chen et al., 2017). This literature mainly relies on the static principal–agent framework, which cannot capture sequential learning and progressive information revelation—the heart of our problem.…”