The Reference Effect of Delay Announcements: A Field Experiment

2021 ◽  
Author(s):  
Qiuping Yu ◽  
Gad Allon ◽  
Achal Bassamboo

We explore whether customers are loss averse in time and how delay information may impact such reference-dependent behavior using observational and field experiment data from two call centers of an Israeli bank. We consider settings with no announcements and announcements of different accuracy levels. We face two key challenges: (1) we do not observe the reference points customers use in our data, as any other field studies, and (2) it is difficult to separate the reference-dependent behavior from the potential nonlinear waiting cost of customers. To address these challenges, we develop a dynamic decision model with consumer learning, through which we infer the reference point each customer used during any given call. The reference points may be different across different customers and evolve across different calls of the same customers. We also exclude the alternative explanation by showing that our main reference-dependent models better explain the observed customer abandonment than models where customers have nonlinear waiting cost. Our results indicate that customers are loss averse regardless of the availability or accuracy of the announcements when their waiting time is relatively long (≥ 90s). Although delay announcements do not alter the nature that customers are loss averse, accurate announcements may affect customers’ belief about the offered waiting time and thus, impact the reference points. Through counterfactual studies, we demonstrate that providing delay announcements improves the call center performance given the loss aversion behavior observed in our data. Interestingly, as customers become more loss averse, the value of providing delay announcements decreases. This paper was accepted by Terry Taylor, operations management.

2020 ◽  
Author(s):  
Brett A. Hathaway ◽  
Seyed M. Emadi ◽  
Vinayak Deshpande

Although call centers have recently invested in callback technology, the effects of this innovation on call center performance are not clearly understood. In this paper, we take a data-driven approach to quantify the operational impact of offering callbacks under a variety of callback policies. To achieve this goal, we formulate a structural model of the caller decision-making process under a callback option and impute their underlying preferences from data. Our model estimates shed light on caller preferences under a callback option. We find that callers experience three to six times less discomfort per unit of time while waiting for callbacks than while waiting in queue, suggesting that offering callbacks can increase service quality by channeling callers to an alternative service channel where they experience less discomfort while waiting. However, after controlling for expected waiting times, callers generally prefer waiting in a queue over accepting a callback and waiting offline. This suggests that managers of this call center may want to spend efforts in educating their customers on the benefits of the callback option. Using the callers’ imputed preferences, we are able to conduct counterfactual analyses of how various callback policies affect the performance of this call center. We find that in this call center, offering to hold the callers’ spot in line or to call back within a window (guaranteed timeframe) reduces average online waiting time (the average time callers wait on the phone) by up to 71% and improves service quality by decreasing callers’ average incurred waiting cost by up to 46%. Moreover, we find that offering callbacks as a demand postponement strategy during periods of temporary congestion reduces average online waiting time by up to 86%, increases service quality by up to 54%, and increases system throughput by up to 2.1%. This paper was accepted by Vishal Gaur, operations management.


2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ying Ji ◽  
Ju Wei ◽  
Zhong Wu ◽  
Shaojian Qu ◽  
Baojun Zhang

Taking investor’s perception into account, the optimal decisions about the product quality and platform advertisement are investigated in a dynamic model in the context of crowdfunding. Researches in the literature, however, usually set investor’s perception as a fixed value and rarely consider the important phenomenon that the online information has some influences on investor’s perception. Considering the effects of information about product quality and platform advertisement on the investor’s perception, a dynamic decision model is proposed. Firstly, investment desire and reference price of the investor are introduced in two dynamic settings to describe investor’s perception. Then, the optimal decisions about the product quality and platform advertisement are formulated under two circumstances: the sponsor and the platform make decisions independently and they cooperate as a system. Finally, the influences of reference price and cost-sharing ratio on the optimal results are compared and the data simulation experiment verifies the necessity of the study. Some new insights can be drawn for the operations management of the firm in crowdfunding as follows: (i) it is more profitable for the firm to cooperate with the platform when investors pay more attention to their reference price; (ii) it is optimal for the firm to share a larger proportion of platform cost when the profit-sharing ratio is low.


2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Rudy Santosa Sudirga

<p>The Management of Academic Service continues to be a major challenge for many college, high school and college organizations in providing better services with fewer resources. The allocation of service staffs and response-time in service involve many challenging issues, because the mean and variance of the response-time in service can be increased dramatically with the intensity of heavy traffic. This study discusses how to use simulation models to improve response time in service operation. Performance at the Academic Service as a whole can be considered very good and is still idle due to utilization of Academic Service, which is still equal to an average of 17%, or it can be said that the workload is not too excessive and deemed to be able to serve the students and lecturers. The performance of Academic Sevice University Bunda Mulia can be considered excellent in terms of operations management, as indicated by the average waiting time, which is very short at only 9.10 seconds.<br />Keywords: Queueing System, Waiting Time, and Simulation</p>


2019 ◽  
Vol 9 (3) ◽  
pp. 151
Author(s):  
Hendro Purwadi

Service level in the call center is calculated based on the number of calls answered during the certain time intervals compared to the total number of calls received. The measurement of service level on the call center operator starts when the caller presses the menu to talk to the  operator on interactive voice response (IVR) menu, and is expressed as a percentage. The higher expected percentage of service level will be higher the needs of operator in the services.  Regulation in Indonesia determines service level for the call center of Basic Telephony Services is in the amount of more than or equal to 90% in 30 seconds. The author uses a business approach to the operational of the call center to analyze existing statutory data. Through the comparative method between operator occupancy and the costs required for the operation of call center using supply and demand curve, the optimum service level value at the call center of Basic Telephony Services can be known, which is 85% in 25 seconds. This means that 85% of incoming calls must be answered by the operator with a maximum waiting time of 25 seconds.


2002 ◽  
Vol 12 (3) ◽  
pp. 193-202 ◽  
Author(s):  
Gerrit Antonides ◽  
Peter C. Verhoef ◽  
Marcel van Aalst

2020 ◽  
Vol 10 (13) ◽  
pp. 4653 ◽  
Author(s):  
Milana Bojanić ◽  
Vlado Delić ◽  
Alexey Karpov

Call center operators communicate with callers in different emotional states (anger, anxiety, fear, stress, joy, etc.). Sometimes a number of calls coming in a short period of time have to be answered and processed. In the moments when all call center operators are busy, the system puts that call on hold, regardless of its urgency. This research aims to improve the functionality of call centers by recognition of call urgency and redistribution of calls in a queue. It could be beneficial for call centers giving health care support for elderly people and emergency call centers. The proposed recognition of call urgency and consequent call ranking and redistribution is based on emotion recognition in speech, giving greater priority to calls featuring emotions such as fear, anger and sadness, and less priority to calls featuring neutral speech and happiness. Experimental results, obtained in a simulated call center, show a significant reduction in waiting time for calls estimated as more urgent, especially the calls featuring the emotions of fear and anger.


2004 ◽  
Vol 25 (3) ◽  
pp. 317-337 ◽  
Author(s):  
Michael Workman ◽  
William Bommer

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