About

Santiago Gallino is an Associate Professor of Operations, Information and Decisions and of Marketing at the Wharton School, University of Pennsylvania. His research focuses on digital transformation and store execution issues in retail, exploring how algorithmic tools and innovative practices influence marketplace dynamics, consumer behavior, and operational performance. Gallino has studied the impact of algorithms such as the buybox in online marketplaces, demonstrating how these tools reduce search frictions, increase marketplace orders, and affect seller competition and market concentration. His work also examines real-time pricing strategies that incorporate competitor prices, product availability, and customer behavior, providing insights into dynamic pricing models in digital retail environments. Gallino has collaborated with and consulted for numerous organizations, and his research has been published in leading journals including Management Science, Manufacturing & Service Operations Management, Operations Research, the Journal of Marketing, Sloan Management Review, and Harvard Business Review. His contributions extend to understanding the future of retail labor, omnichannel retailing, and operational efficiencies in emerging markets. With a background that includes a PhD in Operations and Information Management, a Master’s in Statistics from the University of Pennsylvania, an MBA from IAE Business School, and a degree in Electrical Engineering from Universidad de Buenos Aires, Gallino brings a comprehensive perspective to the study of retail operations and digital transformation.

Research topics

• Computer Science
• Business
• Marketing
• Economics
• Mathematics
• Finance
• Statistics
• Engineering
• Data science
• Operations research
• Microeconomics
• Econometrics
• Management
• World Wide Web
• Operations management
• Advertising

Selected publications

• Fixed Pay for Output or Time? Implications for Work Speed and Quality

  Journal of Accounting Research · 2026-04-02

  articleOpen accessSenior author

  ABSTRACT This paper explores the influence of two fixed payment arrangements—time‐based and output‐based wages—on worker behavior and performance in a multidimensional task setting. We examine how these wages affect the time workers spend on individual units of a task and their work quality. We contend that fixed compensation schemes can implicitly communicate standards of acceptable work. Our empirical evidence from MTurk experiments and a laboratory experiment indicates that workers on output‐based wages deliver higher quality and spend more time on individual units than their time‐based counterparts. These findings are consistent with output‐based wages, implying a standard of acceptable quality—without a conflicting standard of speed—to which workers respond. Our results emphasize the power of implicit cues from fixed compensation schemes and offer insights for employers, suggesting the choice between output‐ and time‐based wages should be informed by whether quality or turnaround time is valued more.

  PublisherDOI

• The Allure of Free Shipping: How to Choose the Best Policy for Online Retail

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Rented Today, Bought Tomorrow: Buyout Pricing in the Circular Economy

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Uncovering Waste: How Store Characteristics Impact Food Waste in Grocery Retail

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen access
  PublisherDOI

• Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces

  Manufacturing & Service Operations Management · 2025-04-08 · 1 citations

  article1st authorCorresponding

  Problem definition: Online marketplaces have revolutionized online sales by creating platforms that connect millions of buyers and sellers. Although the presence of numerous third-party sellers attracts customers, it also results in a proliferation of listings for each product, making it difficult for customers to choose between the available options. To address this issue, online marketplaces employ algorithmic tools to curate and present different product listings to customers. Although tools that assist customers in choosing between different products, such as recommender systems and reviews, have been studied extensively, there is limited evidence regarding tools that help customers choose between different listings of the same product. This paper focuses on the buybox algorithm, an algorithmic tool that prominently presents one option as the default choice to customers. Methodology/results: We assess the influence of the buybox on marketplace dynamics by examining its staggered introduction within a major product category in a leading online marketplace. Our results show that the implementation of buybox increases the number of orders and enhances the efficiency of the customer journey. This is evidenced by an increase in conversion rates and a more pronounced buybox effect on the mobile channel, where search frictions are higher compared with the desktop channel. The introduction of buybox simplifies the process of posting new products on the marketplace, potentially reducing friction for sellers. We find supporting evidence for this hypothesis, because the number of sellers offering a product increases after the introduction of buybox. Managerial implications: Our analysis reveals that a buybox is an effective tool for reducing search frictions and stimulating competition among sellers. Customers benefit from lower prices and higher average quality levels when competition in a buybox is intense. However, the marketplace becomes more concentrated following the introduction of the buybox, representing an unintended consequence that platforms and vendors should manage. Our study contributes to the growing literature on algorithms in platforms by examining how algorithmic curation affects marketplace participants and overall marketplace dynamics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0254 .

  PublisherDOI

• What Makes Scheduling "Responsible"? Evidence from 280 Million Shifts Across 20 Retailers

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• The Operational Impact of Customer Location in On-Demand Services

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  PublisherDOI

• Multitasking over Time: The Time-dependent Effects of Multitasking

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Pay for Quantity or Time? Implications for Work Speed and Quality

  SSRN Electronic Journal · 2022-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Order-Based Trade Credits and Operational Performance in the Nanostore Retail Channel

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Antonio Moreno

  Harvard University

  25 shared

• Marshall L. Fisher

  University of Pennsylvania

  9 shared

• Nil Karacaoglu

  Fisher College

  8 shared

• David Bell

  6 shared

• Gérard P. Cachon

  William P. Wharton Trust

  4 shared

• Serguei Netessine

  4 shared

• Joseph Xu

  4 shared

• Jun Li

  Ross School

  4 shared

Labs

• Operations, Information and Decisions DepartmentPI

Education

• PhD, The Wharton School

  University of Pennsylvania

  2013

• MSc, The Wharton School

  University of Pennsylvania

  2011

• EE, Facultad de Ingenieria

  Universidad de Buenos Aires

  1999

Awards & honors

• Charles W. Evans Distinguished Faculty Scholar