Algorithm as Recommending Source and Persuasive Health Communication: Effects of Source Cues, Language Intensity, and Perceived Issue Involvement

References

• Bleakley, A., Jordan, A. B., Hennessy, M., Glanz, K., Strasser, A., & Vaala, S. (2015). Do emotional appeals in public service advertisements influence adolescents’ intention to reduce consumption of sugar-sweetened beverages? Journal of Health Communication, 20(8), 938–948. https://doi.org/10.1080/10810730.2015.1018593
   PubMed Web of Science ®Google Scholar
• Bode, L., & Vraga, E. K. (2018). See something, say something: Correction of global health misinformation on social media. Health Communication, 33(9), 1131–1140. https://doi.org/10.1080/10410236.2017.1331312
   PubMed Web of Science ®Google Scholar
• Brehm, S. S., & Brehm, J. W. (1981). Psychological reactance: A theory of freedom and control. Academic Press.
   Google Scholar
• Burgoon, M., Dillard, J. P., & Doran, N. E. (1983). Friendly or unfriendly persuasion: The effects of violations of expectations by males and females. Human Communication Research, 10(2), 283–294. https://doi.org/10.1111/j.1468-2958.1983.tb00018.x
   Web of Science ®Google Scholar
• Burgoon, M., & Miller, G. R. (1985). An expectancy interpretation of language and persuasion. In H. Giles & R. N. Clair ST (Eds.), Recent advances in language, communication, and social psychology (pp. 199–229). Routledge.
   Google Scholar
• Cappella, J. N., Yang, S., & Lee, S. (2015). Constructing recommendation systems for effective health messages using content, collaborative, and hybrid Algorithms. The ANNALS of the American Academy of Political and Social Science, 659(1), 290–306. https://doi.org/10.1177/0002716215570573
   Web of Science ®Google Scholar
• Centers for Disease Control and Prevention. (n.d.). Get the Facts: Sugar-Sweetened Beverages and Consumption. https://www.cdc.gov/nutrition/data-statistics/sugar-sweetened-beverages-intake.html
   Google Scholar
• Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic processing within and beyond the persuasion context. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought: Limits of awareness, intention, and control (pp. 212–252). Guilford Press.
   Google Scholar
• Chen, L., Tang, H., Liao, S., & Hu, Y. (2021). E-Health campaigns for promoting influenza vaccination: Examining effectiveness of fear appeal messages from different sources. Telemedicine and e-Health, 27(7), 763–770. https://doi.org/10.1089/tmj.2020.0263
   PubMed Web of Science ®Google Scholar
• Chou, W. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009). Social media use in the United States: Implications for health communication. Journal of Medical Internet Research, 11(4), Article e48. https://doi.org/10.2196/jmir.1249
   PubMed Web of Science ®Google Scholar
• Clayton, R. B. (2022). On the psychophysiological and defensive nature of psychological reactance theory. Journal of Communication, 72(4), 461–475. https://doi.org/10.1093/joc/jqac016
   Web of Science ®Google Scholar
• Clore, G. L., Gasper, K., & Garvin, E. (2001). Affect as information. In J. P. Forgas (Ed.), Handbook of affect and social cognition (pp. 121–144). Lawrence Erlbaum Associates.
   Google Scholar
• Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta S., He Y., Lambert M., Livingston B., & Sampath, D. (2010). The YouTube video recommendation system. In Proceedings of the Fourth ACM Conference on Recommender Systems (pp. 293–296). ACM. https://doi.org/10.1145/1864708.1864770
   Google Scholar
• Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
   PubMed Web of Science ®Google Scholar
• Dillard, J. P., & Nabi, R. L. (2006). The persuasive influence of emotion in cancer prevention and detection messages. Journal of Communication, 56(S1), S123–S139. https://doi.org/10.1111/j.1460-2466.2006.00286.x
   Google Scholar
• Dillard, J. P., Plotnick, C. A., Godbold, L. C., Freimuth, V. S., & Edgar, T. (1996). The multiple affective outcomes of AIDS PSAs: Fear appeals do more than scare people. Communication Research, 23(1), 44–72. https://doi.org/10.1177/009365096023001002
   Web of Science ®Google Scholar
• Dillard, J. P., & Shen, L. (2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs, 72(2), 144–168. https://doi.org/10.1080/03637750500111815
   Web of Science ®Google Scholar
• Eastin, M. S. (2001). Credibility assessments of online health information: The effects of source expertise and knowledge of content. Journal of Computer-Mediated Communication, 6(4), Article JCMC643. https://doi.org/10.1111/j.1083-6101.2001.tb00126.x
   Google Scholar
• Fishbein, M., & Azjen, I. (2010). Predicting and changing behavior: A reasoned action approach. Taylor and Francis. https://doi.org/10.4324/9780203838020
   Google Scholar
• Hamilton, M. A., & Stewart, B. L. (1993). Extending an information processing model of language intensity effects. Communication Quarterly, 41(2), 231–246. https://doi.org/10.1080/01463379309369882
   Google Scholar
• Hannon, J., Bennett, M., & Smyth, B. (2010). Recommending Twitter users to follow using content and collaborative filtering approaches. In Proceedings of the Fourth ACM Conference on Recommender Systems (pp. 199–206). ACM. https://doi.org/10.1145/1864708.1864746
   Google Scholar
• Harkins, S. G., & Petty, R. E. (1981). The multiple source effect in persuasion: The effects of distraction. Personality and Social Psychology Bulletin, 7(4), 627–635. https://doi.org/10.1177/014616728174019
   Web of Science ®Google Scholar
• Harkins, S. G., & Petty, R. E. (1987). Information utility and the multiple source effect. Journal of Personality and Social Psychology, 52(2), 260–268. https://doi.org/10.1037/0022-3514.52.2.260
   Web of Science ®Google Scholar
• Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4–40. https://doi.org/10.1080/03637751.2017.1352100
   Web of Science ®Google Scholar
• Holtgraves, T., & Lasky, B. (1999). Linguistic power and persuasion. Journal of Language and Social Psychology, 18(2), 196–205. https://doi.org/10.1177/0261927x99018002004
   Web of Science ®Google Scholar
• Hu, Y., & Sundar, S. S. (2010). Effects of online health sources on credibility and behavioral intentions. Communication Research, 37(1), 105–132. https://doi.org/10.1177/0093650209351512
   Web of Science ®Google Scholar
• Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education Quarterly, 11(1), 1–47. https://doi.org/10.1177/109019818401100101
   PubMedGoogle Scholar
• Jensen, M. L., Averbeck, J. M., Zhang, Z., & Wright, K. B. (2013). Credibility of anonymous online product reviews: A language expectancy perspective. Journal of Management Information Systems, 30(1), 293–324. https://doi.org/10.2753/MIS0742-1222300109
   Web of Science ®Google Scholar
• Kang, H., Bae, K., Zhang, S., & Sundar, S. S. (2011). Source cues in online news: Is the proximate source more powerful than distal sources? Journalism & Mass Communication Quarterly, 88(4), 719–736. https://doi.org/10.1177/107769901108800403
   Web of Science ®Google Scholar
• Kareklas, I., Muehling, D. D., & Weber, T. J. (2015). Reexamining health messages in the digital age: A fresh look at source credibility effects. Journal of Advertising, 44(2), 88–104. https://doi.org/10.1080/00913367.2015.1018461
   Web of Science ®Google Scholar
• Kim, H. J., & Shin, W. (2018). The effects of message source and fear appeal on young adults’ response to Sexually Transmitted Disease (STD) messages in Singapore. Asian Journal of Communication, 28(2), 185–204. https://doi.org/10.1080/01292986.2017.1384030
   Web of Science ®Google Scholar
• Kong, S. (2023). How gender-targeted body image concerns influence people’s sugar-sweetened beverages consumption. Health Communication. Advance online publication. https://doi.org/10.1080/10410236.2023.2210378
   PubMed Web of Science ®Google Scholar
• Laranjo, L., Arguel, A., Neves, A. L., Gallagher, A. M., Kaplan, R., Mortimer, N., Mendes G. A., & Lau, A. Y. S. (2015). The influence of social networking sites on health behavior change: A systematic review and meta-analysis. Journal of the American Medical Informatics Association, 22(1), 243–256. https://doi.org/10.1136/amiajnl-2014-002841
   PubMed Web of Science ®Google Scholar
• Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
   Google Scholar
• Lee, J. Y., & Sundar, S. S. (2013). To tweet or to retweet? That is the question for health professionals on Twitter. Health Communication, 28(5), 509–524. https://doi.org/10.1080/10410236.2012.700391
   PubMed Web of Science ®Google Scholar
• Maher, C. A., Lewis, L. K., Ferrar, K., Marshall, S., De Bourdeaudhuij, I., & Vandelanotte, C. (2014). Are health behavior change interventions that use online social networks effective? A systematic review. Journal of Medical Internet Research, 16(2), Article e40. https://doi.org/10.2196/jmir.2952
   PubMed Web of Science ®Google Scholar
• Miller, C. H., Lane, L. T., Deatrick, L. M., Young, A. M., & Potts, K. A. (2007). Psychological reactance and promotional health messages: The effects of controlling language, lexical concreteness, and the restoration of freedom. Human Communication Research, 33(2), 219–240. https://doi.org/10.1111/j.1468-2958.2007.00297.x
   Web of Science ®Google Scholar
• Mittal, B. (1995). A comparative analysis of four scales of consumer involvement. Psychology & Marketing, 12(7), 663–682. https://doi.org/10.1002/mar.4220120708
   Web of Science ®Google Scholar
• Newhagen, J., & Nass, C. (1989). Differential criteria for evaluating credibility of newspapers and TV news. Journalism Quarterly, 66(2), 277–284. https://doi.org/10.1177/107769908906600202
   Web of Science ®Google Scholar
• Niu, Z., Jeong, D. C., & Willoughby, J. F. (2021). Friends over doctors? The influences of source and perceived customization on college drinking. Health Communication, 36(6), 671–681. https://doi.org/10.1080/10410236.2020.1712034
   PubMed Web of Science ®Google Scholar
• Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39–52. https://doi.org/10.1080/00913367.1990.10673191
   Web of Science ®Google Scholar
• Petty, R. E., & Cacioppo, J. T. (1984). Source factors and the elaboration likelihood model of persuasion. Advances in Consumer Research, 11, 668–672. https://www.acrwebsite.org/volumes/6328/
   Google Scholar
• Pogacar, R., Shrum, L. J., Lowrey, T. M., & John, D. R. (2018). The effects of linguistic devices on consumer information processing and persuasion: A language complexity× processing mode framework. Journal of Consumer Psychology, 28(4), 689–711. https://doi.org/10.1002/jcpy.1052
   Web of Science ®Google Scholar
• Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243–281. https://doi.org/10.1111/j.1559-1816.2004.tb02547.x
   Web of Science ®Google Scholar
• Quick, B. L., & Considine, J. R. (2008). Examining the use of forceful language when designing exercise persuasive messages for adults: A test of conceptualizing reactance arousal as a two-step process. Health Communication, 23(5), 483–491. https://doi.org/10.1080/10410230802342150
   PubMed Web of Science ®Google Scholar
• Quick, B. L., Scott, A. M., & Ledbetter, A. M. (2011). A close examination of trait reactance and issue involvement as moderators of psychological reactance theory. Journal of Health Communication, 16(6), 660–679. https://doi.org/10.1080/10810730.2011.551989
   PubMed Web of Science ®Google Scholar
• Reedy, J., & Krebs-Smith, S. M. (2010). Dietary sources of energy, solid fats, and added sugars among children and adolescents in the United States. Journal of the American Dietetic Association, 110(10), 1477–1484. https://doi.org/10.1016/j.jada.2010.07.010
   PubMed Web of Science ®Google Scholar
• Rivard, C., Smith, D., McCann, S. E., & Hyland, A. (2012). Taxing sugar-sweetened beverages: A survey of knowledge, attitudes and behaviours. Public Health Nutrition, 15(8), 1355–1361. https://doi.org/10.1017/s1368980011002898
   PubMed Web of Science ®Google Scholar
• Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4), 305–335. https://doi.org/10.1016/s0167-8116(02)00097-6
   Web of Science ®Google Scholar
• Rucker, D. D., McShane, B. B., & Preacher, K. J. (2015). A researcher’s guide to regression, discretization, and median splits of continuous variables. Journal of Consumer Psychology, 25(4), 666–678. https://doi.org/10.1016/j.jcps.2015.04.004
   Web of Science ®Google Scholar
• Schmidt, A. M., Ranney, L. M., Pepper, J. K., & Goldstein, A. O. (2016). Source credibility in tobacco control messaging. Tobacco Regulatory Science, 2(1), 31–37. https://doi.org/10.18001/TRS.2.1.3
   PubMedGoogle Scholar
• Schwarz, N., & Clore, G. L. (1988). How do I feel about it? Informative functions of affective states. In K. Fiedler & J. Forgas (Eds.), Affect, cognition, and social behavior (pp. 44–62). Hogrefe.
   Google Scholar
• Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.), Digital media, youth, and credibility (pp. 73–100). The MIT Press.
   Google Scholar
• Sundar, S. S., & Kim, J. (2019). Machine heuristic: When we trust computers more than humans with our personal information. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper No. 538). ACM. https://doi.org/10.1145/3290605.3300768
   Google Scholar
• Tannenbaum, M. B., Hepler, J., Zimmerman, R. S., Saul, L., Jacobs, S., Wilson, K., & Albarracín, D. (2015). Appealing to fear: A meta-analysis of fear appeal effectiveness and theories. Psychological Bulletin, 141(6), 1178–1204. https://doi.org/10.1037/a0039729
   PubMed Web of Science ®Google Scholar
• Willson, M. (2017). Algorithms (and the) everyday. Information, Communication & Society, 20(1), 137–150. https://doi.org/10.1080/1369118X.2016.1200645
   Web of Science ®Google Scholar
• Wilson, E. J., & Sherrell, D. L. (1993). Source effects in communication and persuasion research: A meta-analysis of effect size. Journal of the Academy of Marketing Science, 21(2), 101–112. https://doi.org/10.1007/bf02894421
   Google Scholar
• Witte, K., & Allen, M. (2000). A meta-analysis of fear appeals: Implications for effective public health campaigns. Health Education & Behavior, 27(5), 591–615. https://doi.org/10.1177/109019810002700506
   PubMed Web of Science ®Google Scholar