TY - GEN
T1 - Optimizing Pregnancy Online Health Communities for Decision-Making with Information Quality Heuristics
AU - Bhagat, Sarbottam
AU - Williams, Jason A.
AU - Jozani, Mohsen
AU - Aleroud, Ahmed
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/5/29
Y1 - 2024/5/29
N2 - This research investigates the intricate dynamics of pregnancy online health communities (OHCs) and their profound influence on users' adaptive behaviors and decision-making processes. Guided by the heuristics systematic model (HSM), our study aims to employ cutting-edge machine learning techniques, including causal ML, to dissect the impact of OHC information on user behaviors. By identifying influential message features and validating theoretical models, our approach aims to enhance the efficacy of message strategies and interfaces within pregnancy OHCs, potentially leading to improved health outcomes. This study attempts to bridge the gap between theory and practice, offering nuanced insights into the complexities of user behavior in online health environments. The findings will contribute to both OHC research and the broader field of information systems, providing actionable insights for designing more effective online health platforms.
AB - This research investigates the intricate dynamics of pregnancy online health communities (OHCs) and their profound influence on users' adaptive behaviors and decision-making processes. Guided by the heuristics systematic model (HSM), our study aims to employ cutting-edge machine learning techniques, including causal ML, to dissect the impact of OHC information on user behaviors. By identifying influential message features and validating theoretical models, our approach aims to enhance the efficacy of message strategies and interfaces within pregnancy OHCs, potentially leading to improved health outcomes. This study attempts to bridge the gap between theory and practice, offering nuanced insights into the complexities of user behavior in online health environments. The findings will contribute to both OHC research and the broader field of information systems, providing actionable insights for designing more effective online health platforms.
UR - http://www.scopus.com/inward/record.url?scp=85195965546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85195965546&partnerID=8YFLogxK
U2 - 10.1145/3632634.3655859
DO - 10.1145/3632634.3655859
M3 - Conference contribution
AN - SCOPUS:85195965546
T3 - SIGMIS-CPR 2024 - Proceedings of the Computers and People Research Conference: Trust and Legitimacy in Emerging Technologies: Organizational and Societal Implications for People, Places and Power
BT - SIGMIS-CPR 2024 - Proceedings of the Computers and People Research Conference
PB - Association for Computing Machinery, Inc
T2 - 62nd Computers and People Research Conference: Trust and Legitimacy in Emerging Technologies: Organizational and Societal Implications for People, Places and Power, SIGMIS-CPR 2024
Y2 - 29 May 2024 through 1 June 2024
ER -