We propose an automated triage design for intelligent customer routing in live-chat contact centers and demonstrate its implementation using a realworld data set from an S&P 500 firm. The proposed design emerges as a synthesis of text analytics and predictive machine learning methods. Using numerical experiments based on the simulation of the firm's contact center, we demonstrate the service level, time, and labor cost benefits of the automated design over two other triage designs (i.e., customer choice triage and human expert triage) that are commonly employed in the real world. Through additional analyses, we explore the generalizability of the automated design for creating solutions for different types of communication channels. Our work has implications for managing customer relations under emerging communication technologies (e.g., live-chat, e-mail, and social media) and more broadly for demonstrating the use of text analytics and machine learning to improve Operations Management practice.

Cryptocurrencies such as Bitcoin are breakthrough financial technologies that promise to revolutionize the digital economy. Unfortunately, their long-term adoption in the business world is imperiled by a lack of stability that manifests as dramatic swings in transaction fees and severe participant dissatisfaction. To date, there has been little academic effort to study how system participants react to volatility in fee movements. Our study addresses this research gap by conceptualizing the Bitcoin platform as a data space market and studying how market equilibrium forms between users who demand data space while trying to avoid transaction delays, and miners who supply data space while trying to maximize fee revenues. Our empirical analysis based on past bitcoin transactions reveals the existence of a relatively flat downward-sloping demand curve and a much steeper upward-sloping supply curve. Regarding users, the inelastic nature of demand signals the utility of Bitcoin as a niche platform for transactions that are otherwise difficult to conduct. This result challenges the belief that users may easily abandon Bitcoin technology given rising transaction costs. We also find that the use of bitcoins as a trading asset is associated with higher levels of tolerance to fees. Regarding miners, the comparatively elastic nature of supply indicates that higher fees stimulate mining by a larger magnitude than suppressing demand. This finding implies that, ceteris paribus, the Bitcoin system turns to self-regulate transaction fees in an efficient manner. Our work has implications for the management of congestion in blockchain-based systems and more broadly for the stability of cryptocurrency markets.

Online customer service chats provide new opportunities for firms to interact with their customers and have become increasingly popular in recent years for firms of all sizes. One reason for their popularity is the ability for customer service agents to multitask (i.e., interact with multiple customers at a time) thereby increasing the system “throughput” and agent productivity. Yet little is known about how multitasking impacts customer satisfaction—the ultimate goal of customer engagements. We address this question using a proprietary data set from an S&P 500 service firm that documents agent multitasking activities (unobservable to customers) in the form of server logs, customer service chat transcripts, and postservice customer surveys. We find that agent multitasking leads to longer in-service delays for customers and lower problem resolution rates. Both lead to lower customer satisfaction, although the impact varies for different customers. Our study is among the first to document the link between multitasking and customer satisfaction, and it has implications for the design of agent time allocation in contact centers and more broadly for how firms can best manage customer relations in new service channels enabled by information technology. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2763 . This paper was accepted by Lorin Hitt, information systems.

Many e-commerce firms provide live-chat capability on their Web sites to promote product sales and to offer customer support. With increasing traffic on e-commerce Web sites, providing such live-chat services requires a good allocation of service resources to serve the customers. When resources are limited, firms may consider employing priority-processing and reserving resources for high-value customers. In this article, we model a reserve-based priority-processing policy for e-commerce systems that have imperfect customer classification. Two policy decisions considered in the model are: (1) the number of agents exclusively reserved for highvalue customers, and (2) the configuration of the classification system. We derive explicit expressions for average waiting times of high-value and low-value customer classes and define a total waiting cost function. Through numerical analysis, we study the impact of these two policy decisions on average waiting times and total waiting costs. Our analysis finds that reserving agents for high-value customers may have negative consequences for such customers under imperfect classification. Further, we study the interaction between the two policy decisions and discuss how one decision should be modified with respect to a change in the other one in order to keep the waiting costs minimized.

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