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The effect of source disclosure on evaluation of AI-generated messages
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“Correspondingly, well-known communication theories, such as the elaboration likelihood model (Petty and Cacioppo 1984) and the heuristic-systematic model (Chen and Chaiken 1999), have examined the cognitive processes underlying source effects, demonstrating that people's perceptions of a source significantly shape how they evaluate message content (Lim and Schmälzle 2024).”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Lim and Schmälzle 2024
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“Advancements in AI, such as ChatGPT and other large language models (LLMs), have demonstrated a capacity to produce high-quality, tailored communication, offering PR professionals opportunities to enhance efficiency and creativity in their work (Lim and Schmälzle 2024).”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Lim and Schmälzle 2024
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Cites
lmerTest Package: Tests in Linear Mixed Effects Models
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778
Slovic, P. (1987) · Science · doi:10.1126/science.3563507 · Cited by 9,271
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
Prolific.ac—A subject pool for online experiments
Palan, S., & Schitter, C. (2018) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2017.12.004 · Cited by 3,450 · cited 2×
“A total of 151 young adults (18-24 years of age) were recruited from two study pools and either received course credit (University study pool) or $2.80 (Prolific; Palan and Schitter (2018)) as compensation for participating in the study.”
BackgroundMethodInfluentialThe Elaboration Likelihood Model of Persuasion
Petty, R. E., & Cacioppo, J. T. (1986) · Communication and Persuasion · doi:10.1007/978-1-4612-4964-1_1 · Cited by 3,202
The Persuasiveness of Source Credibility: A Critical Review of Five Decades' Evidence
Pornpitakpan, C. (2004) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.2004.tb02547.x · Cited by 2,205 · cited 1×
“For instance, a plethora of literature has examined the influence of various aspects of the source, such as credibility, trustworthiness, and similarity, on people’s attitudes and behavior (O’Keefe, 2015; Pornpitakpan, 2004; Wilson and Sherrell, 1993).”
BackgroundResistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712
Research works printed but not linked: 59
- Abalo, 2007, Importance values for importance–performance analysis: A formula for spreading out values derived from preference rankings, Journal of Business Research, 60, 115, 10.1016/j.jbusres.2006.10.009 · DOI · Paper page
- Ali, 2012, Ordinal preference elicitation methods in health economics and health services research: Using discrete choice experiments and ranking methods, British Medical Bulletin, 103, 21, 10.1093/bmb/lds020 · DOI
- Allem, 2017, Campaigns and counter campaigns: Reactions on twitter to e-cigarette education, Tobacco Control, 26, 226, 10.1136/tobaccocontrol-2015-052757 · DOI
- Alwin, 1985, The measurement of values in surveys: A comparison of ratings and rankings, Public Opinion Quarterly, 49, 535, 10.1086/268949 · DOI · Paper page
- Arendt, 2019, Selective exposure and news media brands: Implicit and explicit attitudes as predictors of news choice, Media Psychology, 22, 526, 10.1080/15213269.2017.1338963 · DOI
- Asscher, 2023, Human evaluations of machine translation in an ethically charged situation, New Media & Society, 25, 1087, 10.1177/14614448211018833 · DOI
- Atkin, 1983, Effectiveness of celebrity endorsers, Journal of Advertising Research, 23, 57, 10.1080/00218499.1983.12468060 · DOI · Paper page
- Baig, 2019, UNC perceived message effectiveness: Validation of a brief scale, Annals of Behavioral Medicine, 53, 732, 10.1093/abm/kay080 · DOI
- Bates, 2014, Fitting linear mixed-effects models using lme4, arXiv
- Bettinghaus, 1970, Source evaluation, syllogistic content, and judgments of logical validity by high- and low-dogmatic persons, Journal of Personality and Social Psychology, 4, 614
- Boakye, 2022, Assessment of patterns in e-cigarette use among adults in the US, 2017-2020, JAMA Network Open, 5, 10.1001/jamanetworkopen.2022.23266 · DOI
- Boynton, 2023, Perceived effectiveness of objective elements of vaping prevention messages among adolescents, Tobacco Control, 32, e228, 10.1136/tobaccocontrol-2021-057151 · DOI
- Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. https://doi.org/10.48550/arXiv.2303.12712. · DOI · Paper page
- Buda, 2000, Consumer product evaluation: The interactive effect of message framing, presentation order, and source credibility, Journal of Product and Brand Management, 9, 229, 10.1108/10610420010344022 · DOI
- Castelo, 2019, Task-dependent algorithm aversion, Journal of Marketing Research, 56, 809, 10.1177/0022243719851788 · DOI · Paper page
- Castelo, 2021, Conservatism predicts aversion to consequential Artificial Intelligence, PLoS One, 16, 10.1371/journal.pone.0261467 · DOI
- Chaiken, 1990, Heuristic versus systematic information processing and the use of source versus message cues in persuasion, Journal of Personality and Social Psychology, 39, 752, 10.1037/0022-3514.39.5.752 · doi:10.1037/0022-3514.39.5.752 · Paper page
- Clark, 2016, Vaporous marketing: Uncovering pervasive electronic cigarette advertisements on Twitter, PLoS One, 11, 10.1371/journal.pone.0157304 · DOI
- Clark, 2012, Source expertise and persuasion: The effects of perceived opposition or support on message scrutiny, Personality and Social Psychology Bulletin, 38, 90, 10.1177/0146167211420733 · DOI
- Claudy, 2022, Artificial intelligence can’t be charmed: The effects of impartiality on laypeople’s algorithmic preferences, Frontiers in Psychology, 13, 898027, 10.3389/fpsyg.2022.898027 · DOI · Paper page
- Collins, 2019, E-cigarette marketing and communication: How e-cigarette companies market e-cigarettes and the public engages with e-cigarette information, Nicotine & Tobacco Research, 21, 14, 10.1093/ntr/ntx284 · DOI · Paper page
- Grummon, 2022, Reactions to messages about smoking, vaping and COVID-19: Two national experiments, Tobacco Control, 31, 402, 10.1136/tobaccocontrol-2020-055956 · DOI
- Ismagilova, 2020, The effect of characteristics of source credibility on consumer behaviour: A meta-analysis, Journal of Retailing and Consumer Services, 53, 101736, 10.1016/j.jretconser.2019.01.005 · DOI · Paper page
- Jussupow, 2020, Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion, ECIS 2020 Proceedings
- Lagerkvist, 2013, Consumer preferences for food labelling attributes: Comparing direct ranking and best–worst scaling for measurement of attribute importance, preference intensity and attribute dominance, Food Quality and Preference, 29, 77, 10.1016/j.foodqual.2013.02.005 · DOI
- Lim, 2023, Artificial intelligence for health message generation: An empirical study using a large language model (LLM) and prompt engineering, Frontiers in Communication, 8
- Liu, 2020, Incorporating message framing into narrative persuasion to curb e‐cigarette use among college students, Risk Analysis, 40, 1677, 10.1111/risa.13502 · DOI
- Luo, 2013, Impact of informational factors on online recommendation credibility: The moderating role of source credibility, Decision Support Systems, 56, 10.1016/j.dss.2013.05.005 · DOI
- Lyu, 2021, Vaping discussion in the COVID-19 pandemic: An observational study using Twitter data, PLoS One, 16, 10.1371/journal.pone.0260290 · DOI
- Ma, 2017, User generated content and credibility evaluation of online health information: A meta analytic study, Telematics and Informatics, 34, 472, 10.1016/j.tele.2016.09.009 · DOI
- Marcus, 2020, The next decade in AI: Four steps towards robust artificial intelligence, arXiv
- Miles, 2021, Health chatbots acceptability moderated by perceived stigma and severity: A cross-sectional survey, Digital Health, 7, 10.1177/20552076211063012 · DOI
- Noar, 2020, Evaluating the actual and perceived effectiveness of e-cigarette prevention advertisements among adolescents, Addictive Behaviors, 109, 106473, 10.1016/j.addbeh.2020.106473 · DOI
- O’Keefe, 2003, Message properties, mediating states, and manipulation checks: Claims, evidence, and data analysis in experimental persuasive message effects research, Communication Theory, 13, 251, 10.1111/j.1468-2885.2003.tb00292.x · doi:10.1111/j.1468-2885.2003.tb00292.x · Paper page
- Ozawa, 2017, Using best-worst scaling to rank factors affecting vaccination demand in northern Nigeria, Vaccine, 35, 6429, 10.1016/j.vaccine.2017.09.079 · DOI
- Prasetyo, 2021, The evaluation of preference and perceived auality of health communication icons associated with COVID-19 prevention measures, Healthcare, 9, 10.3390/healthcare9091115 · DOI
- Priester, 1995, Source attribution and persuasion: Perceived honesty as a determinant of message scrutiny, Personality and Social Psychology Bulletin, 21, 637, 10.1177/0146167295216010 · DOI · Paper page
- Priester, 2003, The influence of spokesperson trustworthiness on message elaboration, attitude strength, and advertising effectiveness, Journal of Consumer Psychology, 13, 408, 10.1207/S15327663JCP1304_08 · DOI · Paper page
- Ratneshwar, 1991, Comprehension’s role in persuasion: The case of its moderating effect on the persuasive impact of source cues, Journal of Consumer Research, 18, 52, 10.1086/209240 · DOI · Paper page
- Rohde, 2021, Comparison of message and effects perceptions for the Real Cost e-cigarette prevention ads, Health Communication, 36, 1222, 10.1080/10410236.2020.1749353 · DOI
- Rollins, 2021, Effects of endorser type and testimonials in direct-to-consumer prescription drug advertising (DTCA), International Journal of Pharmaceutical and Healthcare Marketing, 15, 1, 10.1108/IJPHM-06-2019-0042 · DOI
- Roy, 2013, The moderating role of consumer personality and source credibility in celebrity endorsements, Asia-Pacific Journal of Business Administration, 5, 72, 10.1108/17574321311304549 · DOI
- Scao, T.L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., & Manica, M. (2022). Bloom: A 176b-parameter open-access multilingual language model. arXiv. https://doi.org/10.48550/arXiv.2211.05100. · DOI · Paper page
- Schepman, 2023, The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust, International Journal of Human-Computer Interaction, 39, 2724, 10.1080/10447318.2022.2085400 · DOI · Paper page
- Shank, 2023, AI composer bias: Listeners like music less when they think it was composed by an AI, Journal of Experimental Psychology: Applied, 29, 676
- Shannon, 1948, A mathematical theory of communication, Bell Systems Technical Journal, 27, 379, 10.1002/j.1538-7305.1948.tb01338.x · DOI · Paper page
- Smith, 2013, Consider the source: Persuasion of implicit evaluations is moderated by source credibility, Personality and Social Psychology Bulletin, 39, 193, 10.1177/0146167212472374 · DOI
- Sülflow, 2019, Selective attention in the news feed: An eye-tracking study on the perception and selection of political news posts on Facebook, New Media & Society, 21, 168, 10.1177/1461444818791520 · DOI
- Tannenbaum, 1956, Initial attitude toward source and concept as factors in attitude change through communication, Public Opinion Quarterly, 20, 413, 10.1086/266638 · DOI
- Thurstone, 1931, The measurement of social attitudes, The Journal of Abnormal and Social Psychology, 26, 249, 10.1037/h0070363 · DOI · Paper page
- Tubaishat, 2014, An investigation into the attitudes of nursing students toward technology, Journal of Nursing Research, 22, 119, 10.1097/jnr.0000000000000029 · DOI · Paper page
- Van Der Heide, 2016, On the conditional cueing of credibility heuristics: The case of online influence, Communication Research, 43, 672, 10.1177/0093650214565915 · DOI
- Villanti, 2021, Identifying message content to reduce vaping: Results from online message testing trials in young adult tobacco users, Addictive Behaviors, 115, 106778, 10.1016/j.addbeh.2020.106778 · DOI
- von Eschenbach, 2021, Transparency and the black box problem: Why we do not trust AI, Philosophy & Technology, 34, 1607, 10.1007/s13347-021-00477-0 · DOI · Paper page
- Wang, 2021, Characteristics of e-cigarette use behaviors among US youth, 2020, JAMA Network Open, 4, 10.1001/jamanetworkopen.2021.11336 · DOI
- Wang, 2023, Moralization of e-cigarette use and regulation: A mixed-method computational analysis of opinion polarization, Health Communication, 38, 1666, 10.1080/10410236.2022.2027640 · DOI
- Wilson, 1993, Source effects in communication and persuasion research: A meta-analysis of effect size, Journal of the Academy of Marketing Science, 21, 101, 10.1007/BF02894421 · DOI · Paper page
- Winter, 2012, Selecting science information in Web 2.0: How source cues, message sidedness, and need for cognition influence users’ exposure to blog posts, Journal of Computer-Mediated Communication, 18, 80, 10.1111/j.1083-6101.2012.01596.x · DOI
- Zhou, 2023, An artificially intelligent, natural language processing chatbot designed to promote COVID-19 vaccination: A proof-of-concept pilot study, Digital Health, 9, 10.1177/20552076231155679 · DOI
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Makhortykh, M., Sydorova, M., Baghumyan, A., Vziatysheva, V., & Kuznetsova, E. (2024) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-154 · Cited by 19
Generating Credibility in Crisis: Will an AI-Scripted Response Be Accepted?
Ray, E. C., Merle, P. F., & Lane, K. (2025) · International Journal of Strategic Communication · doi:10.1080/1553118x.2024.2435494 · Cited by 16
Generative AI and Perceptual Harms: Who's Suspected of using LLMs?
Kadoma, K., Metaxa, D., & Naaman, M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713897 · Cited by 14
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Metadata
- Authors
- Lim, S., & Schmälzle, R.
- Venue
- Computers in Human Behavior: Artificial Humans
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 2
- Issue
- 1
- Pages
- 100058
- ISSN
- 2949-8821
- Cited by
- 81
- References
- 20
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor, 50 d)
- Funders
- Michigan State University College of Communication Arts and Sciences
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