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Best Student Paper Award Runner-up, the 8th Conference on Health IT and Analytics, 2017
Best Paper Award Runner-up, Humanitarian Operations & Crisis Management Track, POMS 2016
Young Researcher Award, Workshop on Economics of Healthcare IT, 2012
Bertauche Endowed Fellowship Dissertation Funding, University of Washington, 2010 – 2011
Best Paper Nomination, Conference on Information Systems and Technology, 2011
Best Student Paper Nomination, Conference on Information Systems and Technology, 2011
Best Paper Nomination, Conference on Information Systems and Technology 2010
Global Business Center's Summer Doctoral Fellowship University of Washington, 2010
International Conference on Information Systems Doctoral Consortium Fellow, 2010
Yoo, C., Yoo, E., Yan, L., and Pedraza-Martinez, A. J. (2023). Speak with One Voice? Examining Content Coordination and Social Media Engagement during Disasters. Information Systems Research, accepted.
Tan, X., Yan, L., and Pedraza-Martinez, A. J. (2023). Navigating The Digital Terrain Of Prosocial Announcements And Likability. MIS Quarterly, accepted.
Zhou, T., Wang, Y., Yan, L., and Tan, Y. (2023). Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach. Information Systems Research, in press.
Zhou, T., Yan, L., Wang, Y., and Tan, Y. (2022). Turn Your Online Engagement in Chronic Disease Management from Zero to Hero: A Multi-Dimensional Continuous-Time Evaluation. Management Science, 68(5), 3507–3527.
Yan, L. (2020). The Kindness of Commenters: An Empirical Study of the Effectiveness of Perceived and Received Support for Weight-Loss Outcomes. Production and Operations Management, 29(6), 1448-1466.
Cheng, X., Zhang, J. and Yan, L. (2020). Understanding the Impact of Individual Users’ Rating Characteristics on Predictive Accuracy of Recommender Systems. INFORMS Journal on Computing, 32(2), 303-320.
Wang, L., Yan, L., Zhou, T., Guo, X., and Heim, G. (2020). Understanding Physicians’ Online-Offline Behavior Dynamics: An Empirical Study. Information Systems Research,31(2), 537-555.
Yan, L., Yan, X., Tan, Y., and Sun, S. (2019). Shared Minds: How Patients Use Collaborative Information Sharing via Social Media Platforms. Production and Operations Management, 28(1), 9-26.
Despite our understanding that social media and online healthcare communities can help to eliminate health information asymmetry and improve patients’ self-care engagement, we have yet to understand what happens when patients have access to others’ health data and how patients’ access to these shared experiences and opinions influence their health knowledge and perceived treatment outcome. In this paper, we apply social information processing theory and incorporate (1) uncertainty of a treatment, (2) information exposure, and (3) credibility of the information source into patients’ information evaluation function to assess how patients utilize shared health information and experiences. An empirical model, which combines various aspects of patients’ firsthand experiences about treatments into a single construct, yields empirical evidence that patients’ perceived treatment outcome is prone to social influence from other patients’ shared experiences. By disaggregating the sources of social influence, we find that social influence created by generalized others in the community outweighs that by familiar others of one’s intimate social group. In addition, we find that other factors, such as positive sentiment in comments and patients’ prior experiences, also affect patients’ perceived treatment outcome. Based on our findings, implications for health promotion and health behaviors are presented.
Yan, L., and Pedraza-Martinez, A. J. (2019). Social Media for Disaster Management: Operational Value of the Social Conversation. Production and Operations Management, 28(10), 2514–2532.
Disaster relief organizations increasingly engage in social conversations to inform social media users about activities such as evacuation routes and aid distribution. Concurrently, users share information such as the demand for aid, willingness to donate and availability to volunteer through social conversations with relief organizations. We investigate the effect of this information exchange on social engagement during disaster preparedness, response, and recovery. We propose that the effect of information on social engagement increases from preparedness to response and decreases from response to recovery. Some of the information exchanged in social conversations is actionable as well. We propose, however, that the effect of actionable information reaches its lowest point during disaster response. To test our theory, we use Facebook data from five benchmark organizations that responded to Hurricane Sandy in 2012. We analyze all of the organizations’ posts and users’ comments during a three‐week period before, during and after Hurricane Sandy. Our findings support our theory. Furthermore, we identify an opportunity for relief organizations to improve their use of social media for disaster management. While relief organizations focus on informing disaster victims about aid distribution, most users are asking about how they as individuals can donate or volunteer. Thus, besides posting information directed to victims, organizations should post more information targeting potential donors and volunteers.
Yan, L. (2018). Good Intentions, Bad Outcomes: The Effect of Mismatches in Social Support and Health Outcomes in an Online Weight Loss Community. Production and Operations Management, 27(1), 9-27 (Lead Article).
Although social support has been recognized for its effectiveness in promoting health, that social support may not always lead to good outcomes. By analyzing participants of an online weight‐loss community, Lucy shows that providing and receiving support affects weight‐loss outcomes in different ways. While providing support is positively associated with weight‐loss progress, receiving support could hinder the weight‐loss outcome for a person with high self‐efficacy. She finds evidence that the match between needed and received social support also influences individuals’ performance in the weight‐loss process, and a mismatch of social support could affect weight‐loss outcomes negatively. These findings can help maximize the usefulness of social support for participants, clinicians who refer individuals to online weight‐loss communities, and for the online community designers.
Stauffer, J., Pedraza-Martinez, A. J., Yan, L., and Van Wassenhove, L.N. (2018). Asset Supply Networks in Humanitarian Operations: A Combined Empirical-Simulation Approach. Journal of Operations Management, 63, 44-58.
International humanitarian organizations (IHOs) respond to mega disasters while maintaining development programs in the rest of the world (ROW). This means an IHO's asset supply network must perform the challenging task of supporting a fast disaster response while simultaneously maintaining cost-effective ROW development programs. We study how supply network asset flows are impacted during a mega disaster response and find that resource fluidity, the capability to reallocate resources quickly, impacts both mega disaster and ROW program asset flows within these supply networks. Using data from a large IHO's response to a mega disaster and econometric models, we find a dependency between ROW asset flow and mega disaster asset flow in IHOs with resource fluidity. As mega disaster flow increases, there is a decrease in hub-to-hub ROW asset flows and an increase in other ROW asset flows. This is contrary to most humanitarian operations research, which typically assumes independent asset flows. Because of resource fluidity, the combination of these flows does not compromise ROW operations. We use these empirical results to feed a simulation analysis that extends our research to IHOs without resource fluidity and provides actionable insights for varying types of IHOs in various demand scenarios. Simulation insights illustrate that resource fluidity impacts IHO asset supply network costs and optimal configurations.
Yan, L., and Tan, Y. (2017). The Consensus Effect in Online Healthcare Communities. Journal of Management Information Systems, 34(1), 11-39 (Lead Article).
Online healthcare communities have become increasingly popular among patients, enabling them to connect to a large population of patients who suffer from similar health problems and to access massive amounts of health-related information. We are interested in investigating how other patients’ consensus on treatment experiences affects patients’ perceived treatment effectiveness. In this regard, we use the cue diagnosticity framework to examine patients’ shared treatment reviews. By controlling individual heterogeneity and the inhomogeneous weighting function of social influence on patients, we find that consensus has a positive impact on patients’ perceived treatment effectiveness. This positive effect, however, is negatively moderated by the characteristics of the shared information, including volume and patients’ pre-commitment and social connectedness. Overall, we find that perceived treatment effectiveness is closely related to patients’ perceptions about treatment. These findings can be used to help healthcare practitioners incorporate patients’ experiences into healthcare systems and develop effective interventions to help patients better engage in their disease management.
Yan, L., Peng, J. P., and Tan, Y. (2015). Network Dynamics: How Can We Find Patients Like Us? Information Systems Research, 26(3), 496-512.
Social networks have been shown to affect health. Because online social networking makes it easier for individuals to interact with experientially similar others in regard to health issues and to exchange social support, there has been an increasing effort to understand how networks function. Nevertheless, little attention has been paid to how these networks are formed. In this paper, we examine the driving forces behind patients’ social network formation and evolution. We argue that patients’ health-related traits influence their social connections and that the patients’ network layout is shaped by their cognitive capabilities and their network embeddedness. By studying longitudinal data from 1,322 individuals and their communication ties in an online healthcare social network, we find that firsthand disease experience, which provides knowledge of the disease, increases the probability that patients will find experientially similar others and establish communication ties. Patients’ cognitive abilities, including the information load that they can process and the range of social ties that they can manage, however, limit their network growth. In addition, we find that patients’ efforts to reach out for additional social resources are associated with their embeddedness in the network and the cost of maintaining connections. Practical implications of our findings are discussed.
Yan, L., and Tan, Y. (2014). Feeling Blue? Go Online: An Empirical Study of Online Supports among Patients. Information Systems Research,25(4), 690-709.
In this paper, we investigate whether social support exchanged in an online healthcare community benefits patients’ mental health. We propose a nonhomogeneous Partially Observed Markov Decision Process (POMDP) model to examine the latent health outcomes for online health community members. The transition between different health states is modeled as a probability function that incorporates different forms of social support that patients exchange via discussion board posts. We find that patients benefit from learning from others and that their participation in the online community helps them to improve their health and to better engage in their disease self-management process. Our results also reveal differences in the influence of various forms of social support exchanged on the evolution of patients’ health conditions. We find evidence that informational support is the most prevalent type in the online healthcare community. Nevertheless, emotional support plays the most significant role in helping patients move to a healthier state. Overall, the influence of social support is found to vary depending on patients’ health conditions. Finally, we demonstrate that our proposed POMDP model can provide accurate predictions for patients’ health states and can be used to recover missing or unavailable information on patients’ health conditions.