About

I am an assistant professor of operations management at the Fisher College of Business. My primary research interests are in service operations in new technologies, on-demand services, retail, and data-driven decision-making. My research is inspired by real-world problems. I aim to explore new operational challenges and opportunities facing firms in today’s digital economy, such as the effects of digitalization on customer behavior, and develop implementable solutions for operational improvements. I analyze novel datasets obtained through collaborations with companies and leverage empirical tools to study these problems.

Research topics

• Computer Science
• Business
• Statistics
• Marketing
• Mathematics
• Data science
• World Wide Web

Selected publications

• Algorithmic Nudging for Energy Savings and Environment: Evidence from an IoT Platform

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

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

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

  article

  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

• WeStore or AppStore: How Customers Shop Differently in Mobile Apps vs. Social Commerce

  Production and Operations Management · 2024-02-06 · 7 citations

  articleOpen accessSenior author

  In the dynamic e-commerce environment, social commerce has emerged as a revolutionary force, transforming how consumers interact and transact online. This paper investigates the differences in customers’ search and purchase patterns between a prominent online retailer’s burgeoning social commerce channel, the WeChat mini-program, and its native mobile app. We analyze the customers’ entire journey through a sequential search model that encapsulates decisions from channel selection to product search, search termination, and the final purchase. This study contributes to the search model literature by being the first to estimate both fixed and marginal search costs in a sequential search model in an omnichannel retail environment. We calculate fixed search costs, marginal search costs, and preferences for each channel, revealing differences in customers’ behaviors across channels. Our analysis shows that customers’ fixed search costs are higher, but marginal costs are lower on WeChat channel compared to the App channel. Also, customer characteristics like historical spending levels and search timing influence their search costs. From these insights, we suggest strategies tailored to each channel capitalizing on the differences in customers’ search costs. The first strategy encourages search initiation by lowering fixed search costs through peer-to-peer link sharing in the WeChat channel. The second strategy aims to minimize marginal search costs using search-triggering coupons in the App channel. Implementing these strategies significantly boosts conversion rates and profits for the online retailer. This research is one of the first to explore the differences between traditional retail channels and emerging social commerce channels.

  PublisherOA PDFDOI

• Disintermediation Evidence From a Cleaning Platform

  SSRN Electronic Journal · 2022-01-01 · 6 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail

  Operations Research · 2022 · 39 citations

  • Computer Science
  • Business
  • Marketing

  Online retail has become more prominent around the world in the last decade. As a result, online retailers' website performance is increasingly important. Previous literature has extensively studied customer sensitivity to service speed and wait times in offline services. In “Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail,” Gallino, Karacaoglu, and Moreno extend this literature to online retail. They study the impact of delays in online retail on customer behavior. They estimate sizable negative effects of website slowdowns on online sales and conversion rates. Moreover, they explore how customer sensitivity to online delays varies throughout customers' shopping journeys. They find that the impact of waiting times varies along the different stages of the shopping journey, with customers becoming more sensitive to slowdowns at the checkout stage. Their findings have implications for website design decisions. This research is especially relevant in the current regulatory environment with ongoing policy debates about net neutrality.

  PublisherDOI

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

  SSRN Electronic Journal · 2022 · 6 citations

  • Computer Science
  • Computer Science
  • Data science
  PublisherDOI

• WeStore or AppStore: How we shop differently in Mobile Apps vs. Social Commerce

  SSRN Electronic Journal · 2021-01-01 · 3 citations

  articleOpen accessSenior author
  PublisherDOI

• WeStore or AppStore: Customer Behavior Differences in Mobile Apps and Social Commerce

  SSRN Electronic Journal · 2020-10-02

  articleOpen accessSenior author

  Problem Definition: Social commerce is rapidly growing and attracting new customer segments. This channel is different from traditional retail channels in that it relies on peer-to-peer communication for product discovery in a social media platform. We examine customer behavior and search patterns in an emerging social commerce channel, namely the WeChat mini-programs, and in retailers’ native apps. We show that customers’ fixed and marginal search costs and preferences differ across these two channels. Academic/Practical relevance: Our research constitutes the first attempt to study customer behavior differences between traditional mobile apps and social-media based channels. Rather than focusing on customers’ final purchases, we analyze customers’ entire shopping journey using structural search models to quantify their search costs and price sensitivity across the different channels. Methodology: Previous literature in operations management has estimated traditional discrete choice models to recover customers’ cost parameters based on customers’ purchase decisions. Instead, we use a sequential search model that characterizes customers’ entire shopping journey. We estimate fixed search costs, marginal search costs, and preferences in each channel and uncover differences in customers’ search and purchase behavior across channels. Results: We find that WeChat customers have higher fixed search costs yet smaller marginal search costs compared to App customers. Moreover, customer characteristics such as their user level and time of search impact search costs. Fixed and marginal costs are higher for customers with higher user levels on the WeChat channel. Marginal search costs are higher for customers with higher user levels in the App channel. Managerial implications: We propose two channel-specific strategies that leverage customers’ search costs differences across channels. The first strategy is to send friend referral discounts to lower fixed search costs in the WeChat channel. The second is to send search-triggering coupons to induce app customers to continue their search. Both strategies can significantly increase conversion rate and profit for JD.

  Publisher

• Whether Weather Matters: Impact of Exogenous Factors on Customers Channel Choice

  Springer series in supply chain management · 2019-01-01 · 7 citations

  book-chapter
  PublisherDOI

• Why Retailers Should Care about Net Neutrality: The Impact of Website Performance on Online Retail

  SSRN Electronic Journal · 2018-01-01 · 5 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Antonio Moreno

  Harvard University

  11 shared

• Santiago Gallino

  University of Pennsylvania

  8 shared

• Kejia Hu

  University of Oxford

  8 shared

• Can Özkan

  Kellogg's (Canada)

  3 shared

• Ioannis Stamatopoulos

  The University of Texas at Austin

  2 shared

• Simin Li

  2 shared

Education

• PhD in Operations Management, Operations Management

  Northwestern University Kellogg School of Management

  2019

• M.A. in Economics, Economics

  Northwestern University

  2019

• M.S. in Industrial Engineering, Industrial Engineering

  Bilkent University

  2014

• B.S. in Industrial Engineering, Industrial Engineering

  Bilkent University

  2012

Awards & honors

• Finalist for the IBM Service Science Best Student Paper Awar…
• Finalist for the POMS CBOM Junior Scholar Paper Competition…
• Academic Excellence in M.S. Studies award at Bilkent Univers…