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Sam Ransbotham

Sam Ransbotham

Visiting Scholar

Visiting Scholar

Sam Ransbotham is a Professor of Analytics at Boston College, Carroll School of Management. Sam earned a bachelor’s degree in chemical engineering, an MBA, and a PhD, all from the Georgia Institute of Technology. He teaches “Analytics in Practice” and “Machine Learning and Artificial Intelligence.” Sam serves as a Senior Editor at Information Systems Research, an Associate Editor at Management Science, and an Academic Contributing Editor at the MIT Sloan Management Review. He co-hosts the “Me, Myself, and AI” podcast, available on all major platforms. During 2022-2023, he is a visiting scholar at Harvard Business School.
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Sam Ransbotham is a Professor of Analytics at Boston College, Carroll School of Management. Sam earned a bachelor’s degree in chemical engineering, an MBA, and a PhD, all from the Georgia Institute of Technology. He teaches “Analytics in Practice” and “Machine Learning and Artificial Intelligence.” Sam serves as a Senior Editor at Information Systems Research, an Associate Editor at Management Science, and an Academic Contributing Editor at the MIT Sloan Management Review. He co-hosts the “Me, Myself, and AI” podcast, available on all major platforms. During 2022-2023, he is a visiting scholar at Harvard Business School.
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Sam Ransbotham
Contact Information
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Featured Work Publications
Achieving Individual — and Organizational — Value With AI
Findings from the 2022 Artificial Intelligence and Business Strategy Global Executive Study and Research Project
New research shows that employees derive individual value from AI when using the technology improves their sense of competency, autonomy, and relatedness. Likewise, organizations are far more likely to obtain value from AI when their workers do. This report offers key insights for leaders on achieving individual and organizational value with artificial intelligence in their organizations.
Electronic Trace Data and Legal Outcomes: The Effect of Electronic Medical Records on Malpractice Claim Resolution Time

Information systems generate copious trace data about what individuals do and when they do it. Trace data may affect the resolution of lawsuits by, for example, changing the time needed for legal discovery. Trace data might speed resolution by clarifying what events happened when, or they might slow resolution by generating volumes of new and potentially irrelevant data that must be analyzed. To investigate this, we analyze the effect of electronic medical records (EMRs) on malpractice claim resolution time. Use of EMRs within hospitals at the time of the alleged malpractice is associated with a four-month (12%) reduction in resolution time. Because unresolved malpractice claims impose sub- stantial costs on the entire healthcare system, our finding that EMRs are associated with faster resolution has broad welfare implications. Furthermore, as we increasingly digitize society, the ramifications of trace data on legal outcomes matter beyond the medical context.

Learning to Successfully Hire in Online Labor Markets
Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions.
Sam Ransbotham is a Professor of Analytics at Boston College, Carroll School of Management. Sam earned a bachelor’s degree in chemical engineering, an MBA, and a PhD, all from the Georgia Institute of Technology. He teaches “Analytics in Practice” and “Machine Learning and Artificial Intelligence.” Sam serves as a Senior Editor at Information Systems Research, an Associate Editor at Management Science, and an Academic Contributing Editor at the MIT Sloan Management Review. He co-hosts the “Me, Myself, and AI” podcast, available on all major platforms. During 2022-2023, he is a visiting scholar at Harvard Business School.
Featured Work
Achieving Individual — and Organizational — Value With AI
Findings from the 2022 Artificial Intelligence and Business Strategy Global Executive Study and Research Project
New research shows that employees derive individual value from AI when using the technology improves their sense of competency, autonomy, and relatedness. Likewise, organizations are far more likely to obtain value from AI when their workers do. This report offers key insights for leaders on achieving individual and organizational value with artificial intelligence in their organizations.
Electronic Trace Data and Legal Outcomes: The Effect of Electronic Medical Records on Malpractice Claim Resolution Time

Information systems generate copious trace data about what individuals do and when they do it. Trace data may affect the resolution of lawsuits by, for example, changing the time needed for legal discovery. Trace data might speed resolution by clarifying what events happened when, or they might slow resolution by generating volumes of new and potentially irrelevant data that must be analyzed. To investigate this, we analyze the effect of electronic medical records (EMRs) on malpractice claim resolution time. Use of EMRs within hospitals at the time of the alleged malpractice is associated with a four-month (12%) reduction in resolution time. Because unresolved malpractice claims impose sub- stantial costs on the entire healthcare system, our finding that EMRs are associated with faster resolution has broad welfare implications. Furthermore, as we increasingly digitize society, the ramifications of trace data on legal outcomes matter beyond the medical context.

Learning to Successfully Hire in Online Labor Markets
Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions.
Academic Publications
  • Kokkodis, Marios, and Sam Ransbotham. "Learning to Successfully Hire in Online Labor Markets." Management Science 69, no. 3 (March 2023): 1597–1614. View Details
  • Ransbotham, Sam, Eric Overby, and Michael C. Jernigan. "Electronic Trace Data and Legal Outcomes: The Effect of Electronic Medical Records on Malpractice Claim Resolution Time." Management Science 67, no. 7 (July 2021): 4341–4361. View Details
  • Subramanian, Hemang, Sabyasachi Mitra, and Sam Ransbotham. "Capturing Value in Platform Business Models that Rely on User-Generated Content." Organization Science 32, no. 3 (May–June 2021): 804–823. View Details
Industry Reports
  • Ransbotham, Sam, David Kiron, François Candelon, Shervin Khodabandeh, and Michael Chu. "Achieving Individual—and Organizational—Value with AI." MIT Sloan Management Review (Artificial Intelligence and Business Strategy) (2022). View Details
  • Ransbotham, Sam, François Candelon, David Kiron, Burt LaFountain, and Shervin Khodabandeh. "The Cultural Benefits of Artificial Intelligence in the Enterprise." MIT Sloan Management Review (Artificial Intelligence and Business Strategy) (2021). View Details
  • Ransbotham, Sam, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu, and Burt LaFountain. "Expanding AI's Impact with Organizational Learning." MIT Sloan Management Review (Artificial Intelligence and Business Strategy) (2020). View Details
Additional Information
  • "Me, Myself, and AI" podcast episodes
  • LinkedIn profile
  • MIT Sloan Management Review profile
  • CV
Areas of Interest
  • artificial intelligence
  • information technology
  • machine learning
  • open source
  • technology strategy
Additional Information
"Me, Myself, and AI" podcast episodes
LinkedIn profile
MIT Sloan Management Review profile
CV

Areas of Interest

artificial intelligence
information technology
machine learning
open source
technology strategy
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