Does Customers’ Emotion toward Voice-based Service AI Cause Negative Reactions? Empirical Evidence from a Call Center

Many companies are introducing voice-based artificial intelligence (AI) into their call centers. Little is known about the relationship between customers’ emotions to voice-based AI service and customers’ negative reactions. This study investigates the link between customers’ emotions toward voice-based AI service and customers’ negative reactions. Our results reveal that customers’ emotion toward voice-based AI service could significantly affect their complaint behavior, and customers’ complaints differ among emotion types. Customers’ negative and positive emotions toward voice-based AI services have a significantly negative and positive effect, respectively, on customer complaint behavior than neutral emotions. We also find that the exchange round of human-computer interaction moderates the effect of the customer emotion by attenuating its effect on customer complaints. This study is the first to empirically test the impact of customers’ emotions toward voice-based AI service on customers’ complaint behavior in the service industry

Welfare Implications of Congestion Pricing: Evidence from SFpark

Congestion pricing offers an appealing solution to urban parking problems. Charging varying rates across time and space as a function of congestion levels may shift demand and improve allocation of limited resources. It aims to increase the accessibility of highly desired public goods to consumers who value them and to reduce traffic caused by drivers searching for available parking spaces. Using data from the City of San Francisco, both before and after the implementation of a congestion pricing parking program, we estimate the welfare implications of the policy. We use a two-stage dynamic search model to estimate consumers' search costs, distance disutilities, price sensitivities and trip valuations. We find that congestion pricing increases consumer and social welfare in congested regions but may hurt welfare in uncongested regions. Interestingly, despite the improved availability, congestion pricing may not necessarily reduce search traffic, because highly dispersed prices also induce consumers to search for more affordable spaces. In such cases, a simpler pricing policy may actually achieve higher welfare than a complex one. Lastly, compared to capacity rationing that imposes limits on parking durations, congestion pricing increases social welfare and has an ambiguous effect on consumer welfare. The insights from SFpark offer important implications for local governments considering alternatives for managing parking and congestion, and for public sector managers to evaluate the tradeoffs between regulation vs. market-based approaches to manage public resources.http://deepblue.lib.umich.edu/bitstream/2027.42/133523/1/1330_Li.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/133523/4/1330_Li_May2017.pdfDescription of 1330_Li_May2017.pdf : May 2017 revisio

Reducing Wait Time Prediction In Hospital Emergency Room: Lean Analysis Using a Random Forest Model

Most of the patients visiting emergency departments face long waiting times due to overcrowding which is a major concern across the hospital in the United States. Emergency Department (ED) overcrowding is a common phenomenon across hospitals, which leads to issues for the hospital management, such as increased patient s dissatisfaction and an increase in the number of patients choosing to terminate their ED visit without being attended to by a medical healthcare professional. Patients who have to Leave Without Being Seen (LWBS) by doctors often leads to loss of revenue to hospitals encouraging healthcare professionals to analyze ways to improve operational efficiency and reduce the operational expenses of an emergency department. To keep patients informed of the conditions in the emergency room, recently hospitals have started publishing wait times online. Posted wait times help patients to choose the ED which is least overcrowded thus benefiting patients with shortest waiting time and allowing hospitals to allocate and plan resources appropriately. This requires an accurate and efficient method to model the experienced waiting time for patients visiting an emergency medical services unit. In this thesis, the author seeks to estimate the waiting time for low acuity patients within an ED setting; using regularized regression methods such as Lasso, Ridge, Elastic Net, SCAD and MCP; along with tree-based regression (Random Forest). For accurately capturing the dynamic state of emergency rooms, queues of patients at various stage of ED is used as candidate predictor variables along with time patient s arrival time to account for diurnal variation. Best waiting time prediction model is selected based on the analysis of historical data from the hospital. Tree-based regression model predicts wait time of low acuity patients in ED with more accuracy when compared with regularized regression, conventional rolling average, and quantile regression methods. Finally, most influential predictors for predictability of patient wait time are identified for the best performing model

Engineering Better Decision Making. Improving Decisions Through Behavioral Economic Engineering

Die vorliegende Arbeit befasst sich im Rahmen des Behavioral Operations Management mit der begrenzten Rationalität und den kognitiven Verzerrungen menschlicher Entscheider. In der Arbeit wird mittels experimenteller Studien untersucht, wie man menschliches Verhalten auf vorhersagbare Weise in eine "optimale" Richtung beeinflussen kann. Die Arbeit liefert dabei Antworten auf die Fragen, auf Basis welcher Kennzahlen Menschen klügere Entscheidungen treffen, wie Menschen auf bestimmte Lieferverträge reagieren oder wie man effektiv Prognosen kommuniziert

QUEUING SYSTEMS WITH STRATEGIC AND LEARNING CUSTOMERS

In many service systems customers are strategic and can make their own decisions. In particular, customers can be delay-sensitive and they will leave the system if they think the waiting time is too long. For the service provider, it is important to understand customers’ behaviors and choose the appropriate system design. This dissertation consists of two research projects. The first project studies the pooling decision when customers are strategic. It is generally accepted that operating with a combined (i.e., pooled) queue rather than separate (i.e., dedicated) queues is beneficial mainly because pooling queues reduces long-run average sojourn time. In fact, this is a well-established result in the literature when jobs cannot make decisions and servers and jobs are identical. An important corollary of this finding is that pooling queues improves social welfare in the aforementioned setting. We consider an observable multi-server queueing system which can be operated with either dedicated queues or a pooled one. Customers are delay-sensitive and they decide to join or balk based on queue length information upon arrival. In this setting, we prove that, contrary to the common understanding, pooling queues can considerably increase the long-run average sojourn time so that the pooled system results in strictly smaller social welfare (and strictly smaller consumer surplus) than the dedicated system under certain conditions. Specifically, pooling queues leads to performance loss when the arrival-rate-to-service-rate ratio and the relative benefit of service are both large. We also prove that performance loss due to pooling queues can be significant. Our numerical studies demonstrate that pooling queues can decrease the social welfare (and the consumer surplus) by more than 95%. The benefit of pooling is commonly believed to increase with the system size. In contrast to this belief, our analysis shows that when delay-sensitive customers make rational joining decisions, the magnitude of the performance loss due to pooling can strictly increase with the system size. The second project studies the learning behavior when customers don’t have full information of the service speed. We consider a single-server queueing system where customers make join- ing and abandonment decisions when the service rate is unknown. We study different ways in which customers process service-related information, and how these impact the performance of a service provider. Specifically, we analyze forward-looking, myopic and naive information process- ing behaviors by customers. Forward-looking customers learn about the service rate in a Bayesian framework by using their observations while waiting in the queue. Moreover, they make their abandonment decisions considering both belief and potential future payoffs. On the other hand, naive customers ignore the available information when they make their decisions. We prove that regardless of the way in which the information is processed by customers, a customer’s optimal joining and abandonment policy is of threshold-type. There is a rich literature that establishes that forward-looking customers are detrimental to a firm in settings different than queueing. In contrast to this common understanding, we prove that for service systems, forward-looking customers are beneficial to the firm under certain conditions.Doctor of Philosoph