Ken Moon

Ken Moon
  • Assistant Professor of Operations, Information and Decisions

Contact Information

  • office Address:

    3730 Walnut Street
    559 Jon M. Huntsman Hall
    Philadelphia, PA 19104

Research Interests: Empirical operations management, Data analytics, Online marketplaces, Revenue management, Workforce planning, Econometrics and structural estimation

Links: CV

Overview

Ken Moon is an Assistant Professor of Operations, Information and Decisions at the Wharton School.  His research combines empirical methods and analytics with theory to study problems in operations management.  In collaboration with industry partners, his recent work emphasizes challenges in marketplace design and workforce planning faced by online platforms and markets, retailers, and manufacturers.

Ken completed his undergraduate studies in mathematics and economics at Stanford University, his JD at the Harvard Law School, and his PhD at the Stanford Graduate School of Business.

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Research

  • Gad Allon, Georgios Askalidis, Randall Berry, Nicole Immorlica, Ken Moon, Amandeep Singh (Under Revision), When to Be Agile: Ratings and Version Updates in Mobile Apps.

    Abstract: Lean and agile models of product development organize 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, in conjunction with customer ratings. We develop a structural model estimating the dependence of product versioning on (A) market feedback in the form of customer ratings, against (B) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, 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. Agile operations increase firm payoffs by margins of 20-80%, and interestingly partial agility is often sufficient to capture the bulk of these returns. Finally turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 26%).

  • Haim Mendelson, Ken Moon, Yuanyuan Shen (Working), Behavioral and Social Motivations in a Crowdfunding Marketplace.

  • Haim Mendelson and Ken Moon (2018), Modeling Success and Engagement for the App Economy, ACM The Web Conference (WWW'18).

  • Elena Belavina, Karan Girotra, Ken Moon, Jiding Zhang (Working), Matching in Online Marketplaces when Talent is Difficult to Discern.

    Abstract: We study the problem of assigning workers to short-term jobs in online marketplaces. In settings where workers' most relevant skills and attributes are readily observed (e.g., Uber), the marketplace platform should clearly prioritize matches of the workers with the best attributes. However, in many important and growing settings, workers are distinguished in quality by skills and attributes that are difficult to measure at scale. Information about these attributes is perceived by marketplace participants through effort and cost (e.g., interviewing) -- in particular, mounting evidence suggests that reputational systems do not bridge the gap. We expect marketplace to increasingly encounter this challenge as the online gig economy expands from its current niche, at 0.5% of the overall US labor force. We use data covering millions of job postings and transactions on a major online platform for sourcing freelance labor. We structurally estimate employers' demand preferences, including the extent to which they hire based on uncertain information about workers' quality-relevant competencies, in a setting featuring an asymptotically large number of choices (freelancers) sorted into essentially unique consideration sets (rather than each being one of large N instances). We recommend how and when the platform should prioritize matching for compatible skills, matching for repeat relationships, and matching that encourages exploration.

  • Ken Moon, P. Bergemann, D. Brown, A. Chen, J. Chu, E. Eisen, G. Fischer, P. Loyalka, S. Rho, J. Cohen (Under Revision), Manufacturing Productivity with Worker Turnover.

  • Kostas Bimpikis, Wedad J. Elmaghraby, Ken Moon, Wenchang Zhang (2017), Managing Market Thickness in Online B2B Markets, Management Science, Forthcoming.

  • Ken Moon, Kostas Bimpikis, Haim Mendelson (2017), Randomized Markdowns and Online Monitoring, Management Science, 64 (3), pp. 1271-1290.

Teaching

Current Courses

  • OIDD101 - Introduction To Oidd

    OIDD 101 explores a variety of common quantitative modeling problems that arise frequently in business settings, and discusses how they can be formally modeled and solved with a combination of business insight and computer-based tools. The key topics covered include capacity management, service operations, inventory control, structured decision making, constrained optimization and simulation. This course teaches how to model complex business situations and how to master tools to improve business performance. The goal is to provide a set of foundational skills useful for future coursework atWharton as well as providing an overview of problems and techniques that characterize disciplines that comprise Operations and Information Management.

    OIDD101001 ( Syllabus )

    OIDD101201

    OIDD101202

    OIDD101203

    OIDD101204

Past Courses

  • OIDD101 - INTRODUCTION TO OIDD

    OIDD 101 explores a variety of common quantitative modeling problems that arise frequently in business settings, and discusses how they can be formally modeled and solved with a combination of business insight and computer-based tools. The key topics covered include capacity management, service operations, inventory control, structured decision making, constrained optimization and simulation. This course teaches how to model complex business situations and how to master tools to improve business performance. The goal is to provide a set of foundational skills useful for future coursework atWharton as well as providing an overview of problems and techniques that characterize disciplines that comprise Operations and Information Management.

  • OIDD615 - OPERATIONS STRATEGY

    Operations strategy is about organizing people and resources to gain a competitive advantage in the delivery of products (both goods and services) to customers. This course approaches this challenge primarily from two perspectives: 1) how should a firm design their products so that they can be profitably offered; 2) how can a firm best organize and acquire resources to deliver its portfolio of products to customers. To be able to make intelligent decisions regarding these high-level choices, this course also provides a foundation of analytical methods. These methods give students a conceptual framekwork for understanding the linkage between how a firm manages its supply and how well that supply matches the firm's resulting demand. Specific course topics include designing service systems, managing inventory and product variety, capacity planning, approaches to sourcing and supplier management, constructing global supply chains, managing sustainability initiatives, and revenue management. This course emphasizes both quantitative tools and qualitative frameworks. Neither is more important than the other.

In the News

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Activity

Latest Research

Gad Allon, Georgios Askalidis, Randall Berry, Nicole Immorlica, Ken Moon, Amandeep Singh (Under Revision), When to Be Agile: Ratings and Version Updates in Mobile Apps.
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In the News

Exit Line: The Effects of Employee Turnover on Manufacturing

High worker turnover rates in manufacturing can cost companies hundreds of millions of dollars. A new paper co-authored by Wharton’s Ken Moon looks at how firms can keep employees on the job longer.

Knowledge @ Wharton - 2018/12/4
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