@article{zhao_cheng_lee_shaw_2024, title={Situating deep learning in a relating management approach: Examining the dynamics and outcomes of contingent organization-public relationships (COPRs) in crisis}, volume={50}, ISSN={["1873-4537"]}, DOI={10.1016/j.pubrev.2024.102437}, abstractNote={Existing research in crisis communication and public relations focuses on relationship quality or outcomes, along with their causes and effects, mainly using cross-sectional survey data. This research expands the COPR theoretical framework by employing big data and deep learning to analyze the dynamics and intricacies of corporate and public stances during a long-term organizational crisis. Focusing on Monsanto/Bayer’s Roundup crisis from 2012 to 2022, the study analyzed 232,694 tweets and 334 articles to examine corporate stances, public stances, and various relationship modes formed from both party’s standpoints throughout the crisis. The results show the evolving and interdependent interactions between corporations and their publics, as well as the longitudinal impacts of public stances on stock prices. Our findings highlight the application of computational methods for enhancing strategic decision-making in managing organization-public relationships, particularly during prolonged and complex crisis scenarios.}, number={2}, journal={PUBLIC RELATIONS REVIEW}, author={Zhao, Xinyan and Cheng, Yang and Lee, Jaekuk and Shaw, Jessica}, year={2024}, month={Jun} }
 @article{cheng_wang_lee_2024, title={Using a Chatbot to Combat Misinformation: Exploring Gratifications, Chatbot Satisfaction and Engagement, and Relationship Quality}, volume={4}, ISSN={["1532-7590"]}, url={https://doi.org/10.1080/10447318.2024.2344149}, DOI={10.1080/10447318.2024.2344149}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION}, author={Cheng, Yang and Wang, Yuan and Lee, Jaekuk}, year={2024}, month={Apr} }