About

Jason Chan is a faculty member associated with the Information & Decision Sciences department at the Carlson School of Management. He holds the position of Curtis L. Carlson Chair Professor in Business Analytics and Information Systems and serves as the Academic Director of the Carlson Analytics Lab. His expertise encompasses data analysis techniques, predictive analytics, programming, data engineering, and machine learning methods. As part of the Analytics for Good Institute, he contributes to initiatives that involve real-world business problem-solving through data science. His work is closely integrated with graduate programs in Business Analytics, where students develop skills in exploratory data visualization, predictive analytics, and other data science techniques, preparing them to become emerging professionals in the field.

Research topics

• Computer Science
• Knowledge management
• Psychology
• Internet privacy
• Engineering
• Social psychology
• Process management
• World Wide Web
• Business
• Human–computer interaction

Selected publications

• Less Is Not Always More: The Impact of Social Media Block Intensity and Immediacy on Work Time

  Information Systems Research · 2026-04-08

  articleSenior author

  Social media use during work hours is a persistent concern for workplace productivity. Many current blocking policies are designed under the assumption that greater restriction will always produce better outcomes. However, our four-week randomized field experiment with 217 U.S. participants challenges this assumption. We compared different blocker designs along two dimensions: the degree of control (partial versus complete blocking) and the immediacy of implementation (gradual versus immediate rollout). The results show that complete blocking can backfire, reducing time spent working, whereas partial blocking increases it. In addition, gradually introducing restrictions proved more effective than imposing them immediately, particularly during periods when users historically engaged more with social media. Assuming a typical eight-hour workday, complete blocking corresponds to an estimated loss of about 20.3 productive workdays per year, whereas partial blocking is associated with a gain of roughly 6.2 productive workdays annually. Partial blocking also increased the duration of uninterrupted work sessions, indicating improved sustained focus. These findings suggest a clear implication for practice and policy; rather than adopting all-or-nothing bans, organizations, app developers, and policymakers should implement calibrated, adaptive, and phased controls that reduce distractions without triggering behavioral backlash.

  PublisherDOI

• Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise

  Management Science · 2024-10-15 · 119 citations

  articleSenior author

  Since the launch of ChatGPT in December 2022, large language models (LLMs) have been rapidly adopted by businesses to assist users in a wide range of open-ended tasks, including creative work. Although the versatility of LLM has unlocked new ways of human-artificial intelligence collaboration, it remains uncertain how LLMs should be used to enhance business outcomes. To examine the effects of human-LLM collaboration on business outcomes, we conducted an experiment where we tasked expert and nonexpert users to write an ad copy with and without the assistance of LLMs. Here, we investigate and compare two ways of working with LLMs: (1) using LLMs as “ghostwriters,” which assume the main role of the content generation task, and (2) using LLMs as “sounding boards” to provide feedback on human-created content. We measure the quality of the ads using the number of clicks generated by the created ads on major social media platforms. Our results show that different collaboration modalities can result in very different outcomes for different user types. Using LLMs as sounding boards enhances the quality of the resultant ad copies for nonexperts. However, using LLMs as ghostwriters did not provide significant benefits and is, in fact, detrimental to expert users. We rely on textual analyses to understand the mechanisms, and we learned that using LLMs as ghostwriters produces an anchoring effect, which leads to lower-quality ads. On the other hand, using LLMs as sounding boards helped nonexperts achieve ad content with low semantic divergence to content produced by experts, thereby closing the gap between the two types of users. This paper was accepted by D. J. Wu, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03014 .

  PublisherDOI

• “Run Forrest Run!”: Measuring the Impact of App-Enabled Performance and Social Feedback on Athletic and Usage Outcomes

  Production and Operations Management · 2024-05-03 · 6 citations

  article

  Exercise-tracking apps are digital tools for delivering personalized behavioral interventions. Despite the growing usage of exercise applications, the efficacy of in-exercise app features in driving usage and athletic outcomes remains poorly understood. To remain competitive, sports organizations now need to leverage tracking tools to efficiently allocate resources and streamline training regimens and interventions for their core assets (i.e., athletes). In response to these operational needs, we examine two specific forms of such in-exercise interventions, namely performance feedback and social feedback. We conducted an 18-month-long field study with 1,037 uniformed group servicemen to assess the effect of these feedback types on running and usage outcomes. Results from the field study provided evidence that these two app features improved the servicemen’s running times and frequency of application usage, on average. Contrary to the common belief that more features are better, the joint usage of two feedback features does not produce additive effects. Tests at more granular levels suggest that users who received both feedback types in exercise episodes exhibit overconfidence behavior by participating in fewer subsequent exercises. The receipt of both feedback may be redundant and can cause user annoyance. Heterogeneity tests revealed that while performance feedback benefited most runners, social features were effective only for already stronger runners. Also, only positive social feedback had a significant impact on running performance. The results further indicate that performance feedback generated a slow but sustained increase in usage frequency, while social feedback spurred quick initial growth in usage but dwindled in effectiveness over time. Implications for theory and practice, as well as directions for further research, are discussed.

  PublisherDOI

• Overcoming Cleaning Challenges in AI Applications

  Wafer-Level Packaging Symposium · 2024-02-01

  article

  ABSTRACT Every technological revolution has had a profound impact on people's lifestyles and the knowledge they acquire in each era. The advent of search engines in the 1990s emphasized the importance of utilizing available information on the Internet and transforming it into valuable outputs, rather than attempting to memorize extensive and intricate information. The current wave of artificial intelligence (AI) applications will bring significant changes and opportunities across various industries, including manufacturing, healthcare, finance, and transportation. With the ability to analyze and process large amounts of data, AI applications enable people to work more efficiently and increase productivity. As Moore's Law reaches its limitations, the semiconductor industry faces challenges in producing smaller and more powerful chips by packing more transistors onto them. To optimize the power, performance, and size of chips, the industry has turned to multi-chip architecture, employing 2.5D/3D IC and SiP packaging technologies. Advanced packaging technologies, such as Intel's Foveros (Embedded Multi-Die Interconnect Bridge; EMIB), TSMC's 3D Fabric (SoIC, InFO, CoWoS), and Samsung's Fan-out panel level packaging (FOPLP), have been developed to overcome these limitations at IDM/Foundry.

  PublisherDOI

• Less is Not Always More: Investigating the Impact of Block Intensity and Immediacy of Social Media Blockers on Work Time

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• The Effects of Federal Law Enforcement Agencies on Wiretap Investigations

  SSRN Electronic Journal · 2023-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise

  SSRN Electronic Journal · 2023 · 45 citations

  Senior authorCorresponding
  • Computer Science
  • Knowledge management
  • Computer Science
  PublisherDOI

• Contextual Targeting in mHealth Apps: Harnessing Weather Information and Message Framing to Increase Physical Activity

  Information Systems Research · 2023-08-08 · 19 citations

  article

  Mobile technologies provide a unique opportunity for practitioners to identify users’ real-time context and provide personalized interventions to influence their behaviors. However, less is known about a way to improve the effectiveness of mobile health intervention by using context information. This study provides design guidelines on how to use weather information with messaging formats to spur exercise. Through a field experiment that each participant experience different weather conditions in two different treatment periods under the gain or loss interventions, we found that the effects of gain or loss interventions under different weather conditions are heterogeneous. Loss intervention leads to higher fulfillment of exercise goals than gain intervention in sunny weather, whereas gain interventions are more effective than loss interventions in cloudy weather. In addition, we found that weather-based intervention can be used repeatedly over time without losing its effectiveness. Furthermore, we reveal that weather-based intervention is effective toward at-risk populations such as inactive individuals or lower income groups, serving as an mhealth solution that closes the health gap between the haves and have nots. Our findings provide useful guidelines for health service providers and health policymakers regarding how to effectively leverage contextual cues into mobile health intervention.

  PublisherDOI

• “Run Forrest Run!”: Measuring the Impact of App-Enabled Performance and Social Feedback on Athletic and Usage Outcomes

  SSRN Electronic Journal · 2023-01-01 · 3 citations

  articleOpen access
  PublisherDOI

• Drivers of Racial and Gender Workplace Inequalities

  Academy of Management Proceedings · 2023-07-24

  article

  This symposium focuses on the drivers of workplace inequality. Racial and gender inequalities are highly persistent in hiring and participation in the workplace. Past research shows that workplace inequalities are driven by two types of mechanisms: demand-side and supply-side mechanisms. In this symposium, we put together five papers that provide insights into how organizations and external stakeholders (e.g., labor market intermediaries) can mitigate or exacerbate these inequality drivers. Each paper investigates inequality mechanisms through either a supply- or a demand-side lens, examining multiple stages in the organization: applying, hiring, and contributing. Considered together, these papers shed light on how organizations can jointly think of supply- and demand-side factors when designing their hiring and knowledge contribution processes. Exploring the Differences in Gender-based Evaluations by Intermediaries versus Hiring Firms Author: Xuege (Cathy) Lu; U. of Minnesota Carlson School of Management Author: Halil Sabanci; Frankfurt School of Finance & Management Author: Elizabeth McClean; Cornell SC Johnson College of Business Race Composition of the Applicant Pool and Employers’ Decision Not To Hire Author: Santiago Campero Molina; U. of Toronto Gender Differences in the Use of Recommendation letters in the Job Search Process Author: Kira Choi; EMLYON Business School The Role of Online Socialization at the Workplace: Impact on Reducing Gender Disparity Author: Jason Chan; - Author: Christina Yong Jeong; U. of Minnesota Author: Yue Guo; southern U. of science and technology How Social Movements Influence Hiring via Networks: Evidence from the Film Industry Author: Daphné Baldassari; U. of Toronto, Rotman School of Management

  PublisherDOI

Frequent coauthors

• Anindya Ghose

  New York University

  16 shared

• Jing Wang

  10 shared

• Zenan Chen

  9 shared

• Alok Gupta

  8 shared

• Gordon Burtch

  Boston University

  7 shared

• Liad Wagman

  Rensselaer Polytechnic Institute

  6 shared

• Ben C. F. Choi

  Nanyang Technological University

  6 shared

• Ravi Bapna

  6 shared

Labs

• Jason Chan's LabPI

Awards & honors

• Management Science's Best Paper Award in 2020
• AIS Best Published Paper 2014
• MISQ Best Paper Award 2014
• Sandy Slaughter Early Career Award in 2021
• Nunamaker-Chen Dissertation Award conferred by INFORMS Infor…