這項研究深入探討了新聞內容中的威脅和應對評估訊息如何影響社群媒體上的使用者參與，特別關注現有研究中存在的有關這些訊息類型影響的研究不足。儘管先前的研究已經研究了內容特徵對使用者分享行為的影響，但威脅和應對評估的具體影響仍然不清楚。通過分析與疫苗相關的新聞內容，並使用次級社群媒體數據。研究發現，威脅評估訊息顯著提升了使用者參與，比應對評估訊息更為明顯，甚至對參與產生了負面影響。研究還揭示了新聞媒體特徵可以積極影響應對效能對參與的影響，特別是在點讚和分享方面，而嚴重性則影響了評論。這項研究突顯了新聞媒體特徵如何平衡易感性的正面影響。這些見解為新聞媒體機構制定社群媒體內容的策略方法提供了相關建議，以有效使用應對評估訊息來增強使用者參與。

This research delves into how threat and coping appraisal messages in news content influence user engagement on social media, particularly focusing on a gap in existing research regarding the impact of these message types. Previous research has investigated how the characteristics of content influence on user behavior, the specific influence of threat and coping appraisals has remained unclear. Analyzing news content related to vaccines and using secondary social media data, the research finds that threat appraisal messages significantly boost user engagement, more so than coping appraisal messages, and even have a negative effect on engagement. It also reveals that news media characteristics can positively influence the impact of response-efficacy on engagement, especially in terms of likes and shares, with severity affecting comments. The study highlights how news media characteristics can balance the positive effects of susceptibility. These insights inform a strategic approach for news media agencies to craft social media content that effectively uses coping appraisal messages to enhance user engagement.

Table of Contents
論文審定書 i
摘要 ii
Abstract iii
Table of Figures v
Table of Tables vi
1 Introduction 1
1.1 Research Questions 3
2 Literature Review and Hypothesis Development 5
2.1 User Engagement 5
2.2 Protection Motivation Theory 7
2.3 Content Characteristics 11
2.4 News Media Characteristics 15
2.5 Hypothesis Development 19
3 Research Methods 25
3.1 Data Collection 25
3.2 Operationalization of Variables 28
4 Results 43
4.1 Descriptive Statistics 43
4.2 Main Effect 45
4.3 Moderating Effect 47
5 Discussion and Conclusion 52
5.1 Theoretical Contribution 56
5.2 Practical Contribution 57
5.3 Limitations 58
References 59
Appendix A: Word Embedding Analysis Results 70

Abbasi, M.-A., & Liu, H. (2013). Measuring user credibility in social media. Social Computing, Behavioral-Cultural Modeling and Prediction: 6th International Conference, SBP 2013, Washington, DC, USA, April 2-5, 2013. Proceedings 6, 441–448.
An, J., Cha, M., Gummadi, K. P., Crowcroft, J., & Quercia, D. (2012). Visualizing media bias through twitter. AAAI Workshop - Technical Report, WS-12-01, 2–5. https://doi.org/10.1609/icwsm.v6i2.14343
Babazadeh, T., Nadrian, H., Banayejeddi, M., & Rezapour, B. (2017). Determinants of Skin Cancer Preventive Behaviors Among Rural Farmers in Iran: an Application of Protection Motivation Theory. Journal of Cancer Education, 32(3), 604–612. https://doi.org/10.1007/s13187-016-1004-7
Bélanger, F., Collignon, S., Enget, K., & Negangard, E. (2017). Determinants of early conformance with information security policies. Information and Management, 54(7), 887–901. https://doi.org/10.1016/j.im.2017.01.003
Benight, C. C., Ironson, G., Klebe, K., Carver, C. S., Wynings, C., Burnett, K., Greenwood, D., Baum, A., & Schneiderman, N. (1999). Conservation of resources and coping self-efficacy predicting distress following a natural disaster: A causal model analysis where the environment meets the mind. Anxiety, Stress and Coping, 12(2), 107–126.
Benjamin, V., & Raghu, T. S. (2023). Augmenting Social Bot Detection with Crowd-Generated Labels. Information Systems Research, 34(2), 487–507. https://doi.org/10.1287/isre.2022.1136
Berger, J. (2012). What makes online content viral? Strategic Direction, 28(8), 90–91. https://doi.org/10.1108/sd.2012.05628haa.014
Bhatia, S. (2019). Predicting risk perception: New insights from data science. Management Science, 65(8), 3800–3823. https://doi.org/10.1287/mnsc.2018.3121
Biggest social media platforms 2023 | Statista. (2023). https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/
Boss, S. R., Galletta, D. F., Lowry, P. B., Moody, G. D., & Polak, P. (2015). What Do Systems Users Have to Fear? Using Fear Appeals to Engender Threats and Fear that Motivate Protective Security Behaviors. MIS Quarterly, 39(4), 837–864. https://doi.org/10.2307/26628654
Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Chen, K.-J., Lin, J.-S., Choi, J. H., & Hahm, J. M. (2015). Would you be my friend? An examination of global marketers’ brand personification strategies in social media. Journal of Interactive Advertising, 15(2), 97–110.
Chen, Y., Galletta, D. F., Lowry, P. B., Luo, X., Moody, G. D., & Willison, R. (2021). Understanding inconsistent employee compliance with information security policies through the lens of the extended parallel process model. Information Systems Research, 32(3), 1043–1065. https://doi.org/10.1287/ISRE.2021.1014
Cheung, C., Zheng, X., & Lee, M. (2012). Consumer engagement behaviors in brand communities of social networking sites.
Cho, H., & Lee, J. S. (2015). The influence of self-efficacy, subjective norms, and risk perception on behavioral intentions related to the H1N1 flu pandemic: A comparison between Korea and the US. Asian Journal of Social Psychology, 18(4), 311–324. https://doi.org/10.1111/AJSP.12104
Claussen, J., Kretschmer, T., & Mayrhofer, P. (2013). The effects of rewarding user engagement: The case of Facebook apps. Information Systems Research, 24(1), 186–200.
Correa, T., Hinsley, A. W., & De Zuniga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253.
Cui, B., Liao, Q., Lam, W. W. T., Liu, Z. P., & Fielding, R. (2017). Avian influenza A/H7N9 risk perception, information trust and adoption of protective behaviours among poultry farmers in Jiangsu Province, China. BMC Public Health, 17(1). https://doi.org/10.1186/s12889-017-4364-y
Davis, C. J., Golding, M., & McKay, R. (2022). Efficacy information influences intention to take COVID-19 vaccine. British Journal of Health Psychology, 27(2), 300–319. https://doi.org/10.1111/bjhp.12546
De Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26(2), 83–91. https://doi.org/10.1016/j.intmar.2012.01.003
Deng, Q., Hine, M. J., Ji, S., & Wang, Y. (2021). Understanding consumer engagement with brand posts on social media: The effects of post linguistic styles. Electronic Commerce Research and Applications, 48(June), 101068. https://doi.org/10.1016/j.elerap.2021.101068
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv:1810.04805.
Dixon, S. (2021). Twitter brand post engagement 2021, by vertical. Statista. https://www.statista.com/statistics/798414/daily-twitter-brand-audience-mentions-by-vertical/
Dixon, S. J. (2023, September 13). Social media post engagement 2020-2022. Https://Www.Statista.Com/Statistics/1274133/Engagement-Rate-per-Post-Social-Media/.
Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2016). Social media engagement behaviour: a uses and gratifications perspective. Journal of Strategic Marketing, 24(3–4), 261–277.
Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of techniques that effectively change health behavior: Evidence from Meta-CART analysis. Health Psychology, 33(12), 1530–1540. https://doi.org/10.1037/hea0000018
Ehrenstein, B. P., Hanses, F., Blaas, S., Mandraka, F., Audebert, F., & Salzberger, B. (2010). Perceived risks of adverse effects and influenza vaccination: a survey of hospital employees. European Journal of Public Health, 20(5), 495–499.
Florini, S. (2014). Tweets, Tweeps, and Signifyin’ Communication and Cultural Performance on “Black Twitter.” Television & New Media, 15(3), 223–237.
Floyd, D. L., Prentice-Dunn, S., & Rogers, R. W. (2000). A meta-analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30(2), 407–429. https://doi.org/10.1111/j.1559-1816.2000.tb02323.x
Gentzkow, M., & Shapiro, J. M. (2010). What Drives Media Slant? Evidence From U.S. Daily Newspapers. Econometrica, 78(1), 35–71. https://doi.org/10.3982/ECTA7195
Goes, P. B., Lin, M., & Au Yeung, C. (2014). “Popularity effect” in user-generated content: Evidence from online product reviews. Information Systems Research, 25(2), 222–238.
Gollust, S. E., Lantz, P. M., & Ubel, P. A. (2009). The polarizing effect of news media messages about the social determinants of health. American Journal of Public Health, 99(12), 2160–2167. https://doi.org/10.2105/AJPH.2009.161414
Guo, L., Zhang, M., Kang, K., & Hu, M. (2017). Transforming followers into fans: a study of Chinese users of the WeChat Official Account. Online Information Review, 41(7), 1029–1045.
Guo, X., Han, X., Zhang, X., Dang, Y., & Chen, C. (2015). Investigating m-health acceptance from a protection motivation theory perspective: Gender and age differences. In Telemedicine and e-Health (Vol. 21, Issue 8, pp. 661–669). Mary Ann Liebert Inc. https://doi.org/10.1089/tmj.2014.0166
Han, Y., Lappas, T., & Sabnis, G. (2020). The Importance of Interactions Between Content Characteristics and Creator Characteristics for Studying Virality in Social Media. Information Systems Research, 31(2), 576–588. https://doi.org/10.1287/isre.2019.0903
Harrison, A., & Windeler, J. B. (2020). Framing communication: How agenda alignment and media capabilities shape partially cooperative communication. MIS Quarterly: Management Information Systems, 44(2), 771–808. https://doi.org/10.25300/MISQ/2020/15062
Heimbach, I., & Hinz, O. (2018). The impact of sharing mechanism design on content sharing in online social networks. Information Systems Research, 29(3), 592–611. https://doi.org/10.1287/isre.2017.0738
Hildebrandt, T., Bode, L., & Ng, J. S. C. (2019). Effect of “lifestyle stigma” on public support for NHS-provisioned pre-exposure prophylaxis (PrEP) and preventative interventions for HPV and type 2 diabetes: A nationwide UK survey. In BMJ Open (Vol. 9, Issue 8). BMJ Publishing Group. https://doi.org/10.1136/bmjopen-2019-029747
Ho, J. Y. C., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9–10), 1000–1006.
Hodgkins, S., & Orbell, S. (1998). Psychology and Health Can protection motivation theory predict behaviour? A longitudinal test exploring the role of previous behaviour. Psychology and Health, 13, 237–250. https://doi.org/10.1080/08870449808406749
Hogan, B. (2010). The presentation of self in the age of social media: Distinguishing performances and exhibitions online. Bulletin of Science, Technology & Society, 30(6), 377–386.
Hollebeek, L. (2011). Exploring customer brand engagement: definition and themes. Journal of Strategic Marketing, 19(7), 555–573.
Hudson, S., Huang, L., Roth, M. S., & Madden, T. J. (2016). The influence of social media interactions on consumer-brand relationships: A three-country study of brand perceptions and marketing behaviors. International Journal of Research in Marketing, 33(1), 27–41. https://doi.org/10.1016/j.ijresmar.2015.06.004
Hugging Face. (2023, September 20). sentence-transformers / all-MiniLM-L6-v2. Https://Huggingface.Co/Sentence-Transformers/All-MiniLM-L6-V2.
Ibrahim, N. F., Wang, X., & Bourne, H. (2017). Exploring the effect of user engagement in online brand communities: Evidence from Twitter. Computers in Human Behavior, 72, 321–338. https://doi.org/10.1016/j.chb.2017.03.005
Johnston, A. C., & Warkentin, M. (2010). Fear Appeals and Information Security Behaviors: An Empirical Study. In Source: MIS Quarterly (Vol. 34, Issue 3).
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of Tricks for Efficient Text Classification. http://arxiv.org/abs/1607.01759
Kane, G. C., Alavi, M., & Labianca, G. (2014). What’s Different About Social Media Networks? A Framework and Research Agenda. Borgatti Source: MIS Quarterly, 38(1), 275–304. https://doi.org/10.2307/26554878
Kim, C., & Yang, S. U. (2017). Like, comment, and share on Facebook: How each behavior differs from the other. Public Relations Review, 43(2), 441–449. https://doi.org/10.1016/j.pubrev.2017.02.006
Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption. MIS Quarterly, 44(4), 1619–1649. https://doi.org/10.25300/MISQ/2020/16371
Kornfield, R., Smith, K. C., Szczypka, G., Vera, L., & Emery, S. (2015). Earned media and public engagement with CDC’s “tips from former smokers” campaign: An analysis of online news and blog coverage. Journal of Medical Internet Research, 17(1), e12. https://doi.org/10.2196/jmir.3645
Kumar, J., & Nayak, J. K. (2018). Brand community relationships transitioning into brand relationships: Mediating and moderating mechanisms. https://doi.org/10.1016/j.jretconser.2018.08.007
Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science, 64(11), 5105–5131. https://doi.org/10.1287/mnsc.2017.2902
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
Leventhal, H. (1970). Findings and theory in the study of fear communications. Advances in Experimental Social Psychology, 5, 119–186.
Li, F., & Du, T. C. (2011). Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs. Decision Support Systems, 51(1), 190–197.
Li, Q., Liu, Q., Chen, X., Tan, X., Zhang, M., Tuo, J., Xiang, Q., Yu, Q., & Zhu, Z. (2020). Protection motivation theory in predicting cervical cancer screening participation: A longitudinal study in rural Chinese women. Psycho-Oncology, 29(3), 564–571. https://doi.org/10.1002/PON.5307
Li, T., Kauffman, R. J., van Heck, E., Vervest, P., & Dellaert, B. G. C. (2014). Consumer informedness and firm information strategy. Information Systems Research, 25(2), 345–363. https://doi.org/10.1287/isre.2014.0521
Liao, G.-Y., Huang, T.-L., Dennis, A. R., & Teng, C.-I. (2023). The Influence of Media Capabilities on Knowledge Contribution in Online Communities. Information Systems Research. https://doi.org/10.1287/isre.2023.1225
Ling, M., Kothe, E. J., & Mullan, B. A. (2019). Predicting intention to receive a seasonal influenza vaccination using Protection Motivation Theory. Social Science and Medicine, 233, 87–92. https://doi.org/10.1016/j.socscimed.2019.06.002
Liu, C., Nicholas, S., & Wang, J. (2020). The association between protection motivation and hepatitis b vaccination intention among migrant workers in Tianjin, China: A cross-sectional study. BMC Public Health, 20(1). https://doi.org/10.1186/s12889-020-09292-2
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. ArXiv Preprint ArXiv:1907.11692.
Liu, Z., Liu, L., & Li, H. (2012). Determinants of information retweeting in microblogging. Internet Research, 22(4), 443–466. https://doi.org/10.1108/10662241211250980
London, J., Li, S., & Sun, H. (2022). Seems Legit: An Investigation of the Assessing and Sharing of Unverifiable Messages on Online Social Networks. Information Systems Research, 33(3), 978–1001. https://doi.org/10.1287/isre.2021.1095
Lu, Y., Wu, J., Tan, Y., & Chen, J. (2022). Microblogging Replies and Opinion Polarization: A Natural Experiment. MIS Quarterly, 46(4), 1901–1936. https://doi.org/10.25300/MISQ/2022/15455
Luarn, P., Lin, Y.-F., & Chiu, Y.-P. (2015). Influence of Facebook brand-page posts on online engagement. Online Information Review, 39(4), 505–519. https://doi.org/10.1108/OIR-01-2015-0029
Ma, L., Ou, W., & Sian Lee, C. (2022). Investigating consumers’ cognitive, emotional, and behavioral engagement in social media brand pages: A natural language processing approach. Electronic Commerce Research and Applications, 54(July), 101179. https://doi.org/10.1016/j.elerap.2022.101179
Maddux, J. E., & Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of Experimental Social Psychology, 19(5), 469–479. https://doi.org/10.1016/0022-1031(83)90023-9
Malhotra, A., Malhotra, C. K., & See, A. (2012). How to create brand engagement on Facebook. MIT Sloan Management Review.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. http://arxiv.org/abs/1301.3781
Milne, S., Sheeran, P., & Orbell, S. (2000). Prediction and intervention in health-related behavior: A meta-analytic review of protection motivation theory. Journal of Applied Social Psychology, 30(1), 106–143. https://doi.org/10.1111/j.1559-1816.2000.tb02308.x
Miranda, S. M., Young, A., & Yetgin, E. (2016). Are Social Media Emancipatory or Hegemonic? Societal Effects of Mass Media Digitization in the Case of the Sopa Discourse. Source: MIS Quarterly, 40(2), 303–329. https://doi.org/10.2307/26628908
Molyneux, L. (2015). What journalists retweet: Opinion, humor, and brand development on Twitter. Journalism, 16(7), 920–935.
Mou, J., Cohen, J., Bhattacherjee, A., & Kim, J. (2022). A Test of Protection Motivation Theory in the Information Security Literature: A Meta-Analytic Structural Equation Modeling Approach. Journal of the Association for Information Systems, 23(1), 196–236. https://doi.org/10.17705/1jais.00723
Mousavi, R., & Gu, B. (2019). The impact of twitter adoption on lawmakers’ voting orientations. Information Systems Research, 30(1), 133–153. https://doi.org/10.1287/isre.2018.0791
Mousavi, R., & Zhao, K. (2022). Examining the Impacts of Airbnb Review Policy Change on Listing Reviews. Journal of the Association for Information Systems, 23(1), 303–328. https://doi.org/10.17705/1jais.00720
Mu, J., Zhang, J., Borah, A., & Qi, J. (2022). Creative Appeals in Firm-Generated Content and Product Performance. Information Systems Research, 33(1), 18–42. https://doi.org/10.1287/isre.2021.1051
Oh, C., Roumani, Y., Nwankpa, J. K., & Hu, H.-F. (2017). Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Information & Management, 54(1), 25–37.
Oh, H., Goh, K. Y., & Phan, T. Q. (2023). Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing. Information Systems Research, 34(1), 111–136. https://doi.org/10.1287/isre.2022.1112
Park, J. H., Konana, P., Gu, B., Kumar, A., & Raghunathan, R. (2013). Information valuation and confirmation bias in virtual communities: Evidence from stock message boards. Information Systems Research, 24(4), 1050–1067. https://doi.org/10.1287/isre.2013.0492
Peng, C. H., Yin, D., & Zhang, H. (2020). More than words in medical question-and-answer sites: A content-context congruence perspective. Information Systems Research, 31(3), 913–928. https://doi.org/10.1287/ISRE.2020.0923
Pennycook, G., Bear, A., Collins, E. T., & Rand, D. G. (2020). The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings. Management Science, 66(11), 4944–4957.
Ray, S., Kim, S. S., & Morris, J. G. (2014). The central role of engagement in online communities. Information Systems Research, 25(3), 528–546. https://doi.org/10.1287/isre.2014.0525
Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. ArXiv Preprint ArXiv:1908.10084.
Rippetoe, P. A., & Rogers, R. W. (1987). Effects of Components of Protection-Motivation Theory on Adaptive and Maladaptive Coping With a Health Threat. In Journal of Personality and Social Psychology (Vol. 52, Issue 3).
Rogers, R. W. (1975). A Protection Motivation Theory of Fear Appeals and Attitude Change. The Journal of Psychology, 91(1), 93–114. https://doi.org/10.1080/00223980.1975.9915803
Ruthig, J. C. (2014). Health Risk Perceptions and Exercise in Older Adulthood. Journal of Applied Gerontology, 35(9), 939–959. https://doi.org/10.1177/0733464814544214
RW, R. (1983). Cognitive and physiological processes in fear appeals and attitude change : A revised theory of protection motivation. Social Psychology : A Source Book, 25(2), 153–176. https://doi.org/10.3388/JSPACI.25.177
Saini, V., Liang, L. L., Yang, Y. C., Le, H. M., & Wu, C. Y. (2022). The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. JMIR Infodemiology, 2(1), 1–15. https://doi.org/10.2196/37077
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. ArXiv Preprint ArXiv:1910.01108.
Schultz, C. D. (2017). Proposing to your fans: Which brand post characteristics drive consumer engagement activities on social media brand pages? Electronic Commerce Research and Applications, 26, 23–34. https://doi.org/10.1016/j.elerap.2017.09.005
Serra-Garcia, M., & Szech, N. (2023). Incentives and defaults can increase COVID-19 vaccine intentions and test demand. Management Science, 69(2), 1037–1049.
Siponen, M., Adam Mahmood, M., & Pahnila, S. (2014). Employees’ adherence to information security policies: An exploratory field study. Information and Management, 51(2), 217–224. https://doi.org/10.1016/j.im.2013.08.006
Stefanov, P., Darwish, K., Atanasov, A., & Nakov, P. (2020). Predicting the Topical Stance and Political Leaning of Media using Tweets. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , 527–537.
Susarla, A., Oh, J. H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from youtube. Information Systems Research, 23(1), 23–41. https://doi.org/10.1287/isre.1100.0339
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models. ArXiv Preprint ArXiv.2302.13971. http://arxiv.org/abs/2302.13971
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. 31st Conference on Neural Information Processing Systems. http://arxiv.org/abs/1706.03762
Verhagen, T., Swen, E., Feldberg, F., & Merikivi, J. (2015). Benefitting from virtual customer environments: An empirical study of customer engagement. Computers in Human Behavior, 48, 340–357.
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of Marketing Theory and Practice, 20(2), 122–146.
Wade, J. T., Roth, P. L., Thatcher, J. B., & Dinger, M. (2020). Social Media and Selection: Political Issue Similarity, Liking, and the Moderating Effect of Social Media Platform. MIS Quarterly, 44(3), 1301–1357. https://doi.org/10.25300/MISQ/2020/14119
Warkentin, M., Johnston, A. C., Shropshire, J., & Barnett, W. D. (2016). Continuance of protective security behavior: A longitudinal study. Decision Support Systems, 92, 25–35. https://doi.org/10.1016/j.dss.2016.09.013
Weng, L., Menczer, F., & Ahn, Y.-Y. (2013). Virality prediction and community structure in social networks. Scientific Reports, 3(1), 1–6.
Witte, K. (1996). Predicting risk behaviors: Development and validation of a diagnostic scale. Journal of Health Communication, 1(4), 317–342.
Wu, J., Fan, S., & Zhao, J. L. (2018). Community engagement and online word of mouth: An empirical investigation. Information & Management, 55(2), 258–270.
Yang, M., Ren, Y., & Adomavicius, G. (2019). Understanding user-generated content and customer engagement on Facebook business pages. Information Systems Research, 30(3), 839–855. https://doi.org/10.1287/isre.2019.0834
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., & Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, 32.
Yoo, C. W., Lee, J., Yoo, C., & Xiao, N. (2021). Coping behaviors in short message service (SMS)-based disaster alert systems: From the lens of protection motivation theory as elaboration likelihood. Information and Management, 58(4). https://doi.org/10.1016/j.im.2021.103454
Yoon, G., Li, C., Ji, Y., North, M., Hong, C., & Liu, J. (2018). Attracting comments: Digital engagement metrics on Facebook and financial performance. Journal of Advertising, 47(1), 24–37.
Zhang, X., Liu, S., Chen, X., Wang, L., Gao, B., & Zhu, Q. (2018). Health information privacy concerns, antecedents, and information disclosure intention in online health communities. Information and Management, 55(4), 482–493. https://doi.org/10.1016/j.im.2017.11.003
Zhao, E., Wu, Q., Crimmins, E. M., & Ailshire, J. A. (2020). Media trust and infection mitigating behaviours during the COVID-19 pandemic in the USA. BMJ Global Health, 5(10). https://doi.org/10.1136/bmjgh-2020-003323
Zhao, H., Fu, S., & Chen, X. (2020). Promoting users’ intention to share online health articles on social media: The role of confirmation bias. Information Processing and Management, 57(6). https://doi.org/10.1016/j.ipm.2020.102354
Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. In International Journal of Information Management (Vol. 43, pp. 342–350). Elsevier Ltd. https://doi.org/10.1016/j.ijinfomgt.2017.08.006
Zou, X., Chen, Q., Zhang, Y., & Evans, R. (2023). Predicting COVID-19 vaccination intentions: the roles of threat appraisal, coping appraisal, subjective norms, and negative affect. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-15169-x