What Drives Algorithm Use? An Empirical Analysis of Algorithm Use in Type 1 Diabetes Self-Management

42 Pages Posted: 25 Aug 2021 Last revised: 26 Oct 2023

See all articles by Wilson Lin

Wilson Lin

Santa Clara University

Song-Hee Kim

Seoul National University - Business School

Jordan Tong

Wisconsin School of Business

Date Written: October 13, 2023


Problem Definition: Advancements in algorithms hold promise to enhance operations by improving users' decision-making. However, people sometimes fail to use algorithms, which could be a barrier to achieving such improvements. We contribute field analysis to identify drivers of algorithm use.

Methodology/Results: Using the bolus calculator (algorithm) use behavior in over 221,000 bolus insulin decisions from a field experiment on type 1 diabetes self-management (Aleppo et al 2017), we find that 1) previous algorithm use increases future algorithm use, 2) being out-of-target with self-driven decisions increases subsequent algorithm use, whereas being out-of-target with algorithm-driven decisions does not impact algorithm use, 3) exposure to multiple, potentially-conflicting measurements for a single algorithm input decreases algorithm use, 4) increasing one's need for precision increases algorithm use and 5) previous deviations from algorithm recommendations decrease future algorithm use.

Managerial Implications: These field results complement laboratory experiments in the human-algorithm interactions literature by supporting/rejecting natural hypotheses from existing laboratory results, identifying relevant drivers that have yet to receive attention, and providing effect magnitude estimates in setting with highly-invested decision-makers over several months. They also provide insight into how to increase algorithm use: encourage habit formation, strengthen performance feedback, eliminate sources of algorithm input uncertainty, highlight a need for greater precision, and intervene when observing people deviating from algorithmic recommendations.


Funding: None to declare

Declaration of Interest: None to declare

Ethical Approval: We obtained approval from the University of Southern California Institutional Review Board that our research is considered non-human subjects research as it meets the criteria for coded private information (study ID: UP-20-00856). That is, our data uses de-identified (publicly available) data from a published field study (REPLACE-BG).

Keywords: healthcare management, behavioral operations, empirical research, algorithm aversion, Type 1 Diabetes

Suggested Citation

Lin, Wilson and Kim, Song-Hee and Tong, Jordan, What Drives Algorithm Use? An Empirical Analysis of Algorithm Use in Type 1 Diabetes Self-Management (October 13, 2023). Available at SSRN: https://ssrn.com/abstract=3891832 or http://dx.doi.org/10.2139/ssrn.3891832

Wilson Lin (Contact Author)

Santa Clara University ( email )

500 El Camino Real
Santa Clara, CA 95053
United States

Song-Hee Kim

Seoul National University - Business School ( email )

Korea, Republic of (South Korea)

Jordan Tong

Wisconsin School of Business ( email )

975 University Avenue
Madison, WI 53706
United States

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