Ken Moon

Ken Moon

Assistant Professor @The Wharton School, interested in technology- and data-driven innovations for worker management, service platforms, and supply chains

Publications and Working Papers

The Hidden Cost of Worker Turnover: Attributing Product Reliability to the Turnover of Factory Workers

Published in Management Science, 2021

Product reliability is a key concern for manufacturers. We examine worker turnover as a significant but under-recognized determinant of product reliability. Our study collects and integrates (1) data reporting factory worker staffing and turnover from within a major consumer electronics producer’s supply chain and (2) traceable data reporting the component quality and field failures—i.e., replacements and repairs—of nearly 50M consumer mobile devices over four years of customer usage.

Recommended citation: Moon, K., Bergemann, P., Loyalka, P., and J. Cohen. (2021). "The Hidden Cost of Worker Turnover: Attributing Product Reliability to the Turnover of Factory Workers" Management Science. Forthcoming https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3568792

Searching for the Best Yardstick: Cost of Quality Improvements in the US Hospital Industry

Published in Working paper, 2021

Medicare’s Hospital Value-Based Purchasing (VBP) Program incentivizes hospitals to improve quality by withholding and redistributing 2% of Medicare payments based on relative performances in quality-related metrics. We address the policymaker’s problem of designing the VBP program to efficiently raise the quality of care. We study whether the program benefits by changing the scale of the incentives and/or tailoring the scope of peer groups by hospital type.

Recommended citation: Lim, J., Moon, K., and S. Savin. (2021). "Searching for the Best Yardstick: Cost of Quality Improvements in the US Hospital Industry" Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3885132

Strategic Choices and Routing Within Service Networks: Modeling and Estimation Using Machine Learning

Published in Working paper, 2021

Service networks with self-routing by customers have drawn attention in the theoretical literature (Arlotto et al. 2019, Parlakturk and Kumar 2004). We develop structural empirical methods to recover customer demand preferences and congestion sensitivities from diverse trajectory patterns using machine learning. Specifically, we employ adversarial neural networks to handle the high-dimensional space of (combinatorially many) trajectory types. Key innovations collapse the dynamics of customer trajectory choices into static trajectory market shares and derive theoretically efficient incentive-compatibility bounds on customers’ preferences.

Recommended citation: Moon, K. (2021). "Strategic Choices and Routing Within Service Networks: Modeling and Estimation Using Machine Learning" Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3819117

Manufacturing Productivity with Worker Turnover

Published in Working paper, 2020

Firms that responsively match supply to demand may pay a steep price for internally generated disruptions to productivity. In the case of responsive manufacturers, their performance depends on variable workloads of tasks being completed by coordinated groups of workers—and thus may suffer when workers leave. By tracking the staffing and productivity of tens of thousands of assembly line workers, we show that worker turnover significantly affects productivity by severing knowledge-sharing and relationships between assembly line co-workers. We show considerable value from a less turnover-prone workforce and that workers’ incentives can be designed to effectively control operationally disruptive worker turnover.

Recommended citation: Moon, K., Bergemann, P., Brown, D., Chen, A., Chu, J., Eisen, E., Fischer, G., Loyalka, P., Rho, S., and J. Cohen. (2020). "Manufacturing Productivity with Worker Turnover" Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3248075

Matching in Labor Marketplaces: The Role of Experiential Information

Published in Working paper, 2020

A discussion of online labor marketplaces for services where quality in the form of skills, adaptability, and reliability are highly valued. Studies have shown that employers/clients in such online marketplaces fail to share quality information about workers (e.g., via ratings and reviews) that is known to them and valuable to the platform and other prospective employers. We first propose new empirical methods to measure the value of such information and then explore the implications for matching.

Recommended citation: Belavina, E., Girotra, K., Moon, K., and J. Zhang. (2020). "Matching in Labor Marketplaces: The Role of Experiential Information." Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3543906

When to Be Agile: Ratings and Version Updates in Mobile Apps

Published in Management Science, 2020

Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. While agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps’ customer ratings. Firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when able to exploit scale economies from coordinating development over a portfolio of apps. We find significant returns to agile operations, and interestingly partial agility is often sufficient to capture the bulk of these returns. Finally, we study the mobile app marketplace’s design of the display of ratings to incentivize app quality.

Recommended citation: Allon, G., Askalidis, G., Berry, R., Immorlica, N., Moon, K., and A. Singh. (2021). "When to Be Agile: Ratings and Version Updates in Mobile Apps" Management Science. Forthcoming https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3476286

Responsible Sourcing: The First Step Is the Hardest

Published in Working paper, 2020

Responsible sourcing is a priority for companies and consumers concerned with corporate social responsibility (CSR) in global supply chains. Most brands’ product lines contain just a few products certified by third parties—which suggests that brands limit their efforts at ensuring that suppliers behave responsibly. In this paper, we examine a previously under-appreciated role of certifications: that certifications enable brands to learn about how to source responsibly. By successfully certifying even a single product, the certifying brand may enjoy positive, knowledge-based spillovers encouraging responsible sourcing throughout its product line. Using data from the USD 48B US consumer coffee market, our work novelly suggests that prevalent dual-sourcing may surprisingly amplify, rather than limit, responsible sourcing in supply chains, and that certified sourcing valuably develops the pool of responsible suppliers in high-risk countries.

Recommended citation: Ramchandani, P., Bastani, H., and K. Moon. (2020). "Responsible Sourcing: The First Step Is the Hardest" Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3656497

Managing Market Thickness in Online Business-to-Business Markets

Published in Management Science, 2020

We explore online marketplace design in the context of business-to-business auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, the platform’s ability to manage the availability of supply over time (i.e., market thickness) yields significant value. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. A second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. We estimate and optimize these levers to highlight a novel trade-off.

Recommended citation: Bimpikis, K., Elmaghraby, W., Moon, K., and W. Zhang. (2020). "Managing Market Thickness in Online Business-to-Business Markets" Management Science. 66(12): 5485--6064 https://pubsonline.informs.org/doi/10.1287/mnsc.2019.3497

Modeling Success and Engagement for the App Economy

Published in Proceedings of ACM The Web Conference (WWW18), 2018

While apps increasingly drive and shape present-day Web usage, they individually thrive and fall based on rich patterns of user adoption and long-term engagement. In studying these patterns, researchers are typically unable to access consistently detailed data on representative cross-sections of apps. We propose an empirical framework to glean patterns of adoption and engagement from apps’ widely available metrics. We show that Facebook apps over time became less viral and more engaging. Yet they counter-intuitively still boosted their new user adoptions by raising user retention, because engaged users contribute to word of mouth over a longer period of time. Finally, developers that learn to successfully retain their users carry this valuable experience over into their new apps.

Recommended citation: Mendelson, H., and K. Moon. (2018). "Modeling Success and Engagement for the App Economy" Proceedings of ACM The Web Conference (WWW18), Research track: Social Network Analysis and Graph Algorithms for the Web, 569-578. https://dl.acm.org/doi/10.1145/3178876.3186123

Randomized Markdowns and Online Monitoring

Published in Management Science, 2018

We study dynamic pricing in online retail, where customers may strategically monitor prices and time their purchases. However, firms can observe and exploit customers’ monitoring behavior. We find that consumers’ opportunity costs for online visits vary significantly in inverse relation to their price elasticities. We discuss randomized markdown practices that nonetheless commit to price levels and targeted promotions personalized using customers’ online histories. Such dynamic pricing not only increases retailer profits, but also improves overall customer welfare by allowing retailers to expand inventories.

Recommended citation: Moon, K., Bimpikis, K., and H. Mendelson. (2018). "Randomized Markdowns and Online Monitoring" Management Science. 64(3): 1271--1290 https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2016.2661