703
Views
0
CrossRef citations to date
0
Altmetric
Research Article

News Audiences in the Age of Artificial Intelligence: Perceptions and Behaviors of Optimizers, Mainstreamers, and Skeptics

ORCID Icon, , , , &

References

  • Al-Razgan, M., Alrowily, A., Al-Matham, R. N., Alghamdi, K. M., Shaabi, M., & Alssum, L. (2021). Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women. Technology in Society, 65, 101558. https://doi.org/10.1016/j.techsoc.2021.101558
  • Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2018). Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 28, 167–182. https://doi.org/10.1016/j.jocm.2018.07.002
  • Amato, P. R., King, V., & Thorsen, M. L. (2016). Parent–child relationships in stepfather families and adolescent adjustment: A latent class analysis. Journal of Marriage and Family, 78(2), 482–497. https://doi.org/10.1111/jomf.12267
  • Bellur, S., & Sundar, S. S. (2017). Talking health with a machine: How does message interactivity affect attitudes and cognitions? Human Communication Research, 43(1), 25–53. https://doi.org/10.1111/hcre.12094
  • Bodó, B. (2019). Selling news to audiences – a qualitative inquiry into the emerging logics of algorithmic news personalization in European quality news media. Digital Journalism, 7(8), 1054–1075. https://doi.org/10.1080/21670811.2019.1624185
  • Bodó, B., Helberger, N., Eskens, S., & Möller, J. (2019). Interested in diversity. Digital Journalism, 7(2), 206–229. https://doi.org/10.1080/21670811.2018.1521292
  • Bol, N., Dienlin, T., Kruikemeier, S., Sax, M., Boerman, S. C., Strycharz, J., Helberger, N., & De Vreese, C. H. (2018). Understanding the effects of personalization as a privacy calculus: Analyzing self-disclosure across health, news, and commerce contexts. Journal of Computer-Mediated Communication, 23(6), 370–388. https://doi.org/10.1093/jcmc/zmy020
  • Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87. https://doi.org/10.1007/s11747-015-0441-x
  • Dedehayir, O., Ortt, R. J., Riverola, C., & Miralles, F. (2017). Innovators and early adopters in the diffusion of innovations: A literature review. International Journal of Innovation Management, 21(08), 1740010. https://doi.org/10.1142/s1363919617400102
  • Edwards, D. J., Holt, G. D., & Harris, F. C. (2000). A comparative analysis between the multilayer perceptron “neural network” and multiple regression analysis for predicting construction plant maintenance costs. Journal of Quality in Maintenance Engineering, 6(1), 45–61. https://doi.org/10.1108/13552510010371376
  • Escobar-Rodríguez, T., & Romero-Alonso, M. (2014). The acceptance of information technology innovations in hospitals: Differences between early and late adopters. Behaviour & Information Technology, 33(11), 1231–1243. https://doi.org/10.1080/0144929X.2013.810779
  • Garg, R., Kiwelekar, A. W., Netak, L. D., & Bhate, S. S. (2021). Personalization of news for a logistics organisation by finding relevancy using NLP. In V. K. Gunjan & J. M. Zurada (Eds.), Modern approaches in machine learning and cognitive science: a walkthrough: Latest trends in AI (Vol. 2, pp. 215–226). Springer International Publishing.
  • Jahanmir, S. F., & Cavadas, J. (2018). Factors affecting late adoption of digital innovations. Journal of Business Research, 88, 337–343. https://doi.org/10.1016/j.jbusres.2018.01.058
  • Lam, S. Y., & Shankar, V. (2014). Asymmetries in the effects of drivers of brand loyalty between early and late adopters and across technology generations. Journal of Interactive Marketing, 28(1), 26–42. https://doi.org/10.1016/j.intmar.2013.06.004
  • Laukkanen, T., & Pasanen, M. (2008). Mobile banking innovators and early adopters: How they differ from other online users? Journal of Financial Services Marketing, 13(2), 86–94. https://doi.org/10.1057/palgrave.fsm.4760077
  • Lee, F. L. F., Chan, M.C. -M., Chen, H. -T., Nielsen, R., & Fletcher, R. (2019). Consumptive news feed curation on social media as proactive personalization: A study of six East Asian markets. Journalism Studies, 20(15), 2277–2292. https://doi.org/10.1080/1461670X.2019.1586567
  • Lim, J. S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965
  • Loftus, T. J., Brakenridge, S. C., Croft, C. A., Stephen Smith, R., Efron, P. A., Moore, F. A., Mohr, A. M., & Jordan, J. R. (2017). Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention. The Journal of Surgical Research, 212, 42–47. https://doi.org/10.1016/j.jss.2016.12.032
  • Loy, J. (2019). Neural network projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects. Packt Publishing.
  • Lund, B. D., Omame, I., Tijani, S., & Agbaji, D. (2020). Perceptions toward artificial intelligence among academic library employees and alignment with the diffusion of innovations’ adopter categories. College & Research Libraries, 81(5), 865–882. https://doi.org/10.5860/crl.81.5.865
  • McDonald, H., Corkindale, D., & Sharp, B. (2003). Behavioral versus demographic predictors of early adoption: A critical analysis and comparative test. Journal of Marketing Theory and Practice, 11(3), 84–95. https://doi.org/10.1080/10696679.2003.11658503
  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
  • Oser, J., Hooghe, M., & Marien, S. (2013). Is online participation distinct from offline participation? A latent class analysis of participation types and their stratification. Political Research Quarterly, 66(1), 91–101. https://doi.org/10.1177/1065912912436695
  • Owen, L. H. (2017, December 11). With “my WSJ,” the wall street journal makes a personalized content feed central to its app. NiemanLab. Retrieved from: https://www.niemanlab.org/2017/12/with-my-wsj-the-wall-street-journal-makes-a-personalized-content-feed-central-to-its-app/
  • Park, N. (2010). Adoption and use of computer-based voice over internet protocol phone service: Toward an integrated model. The Journal of Communication, 60(1), 40–72. https://doi.org/10.1111/j.1460-2466.2009.01440.x
  • Raza, S., & Ding, C. (2022). News recommender system: A review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55(1), 749–800. https://doi.org/10.1007/s10462-021-10043-x
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
  • Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of Health Communication, 9(sup1), 13–19. https://doi.org/10.1080/10810730490271449
  • Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021). Mobile data science and intelligent apps: Concepts, AI-based modeling and research directions. Mobile Networks and Applications, 26(1), 285–303. https://doi.org/10.1007/s11036-020-01650-z
  • Shi, H. -Y., Lee, K. -T., Lee, H. -H., Ho, W. -H., Sun, D. -P., Wang, J. -J., & Chiu, C. -C. (2012). Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. Plos One, 7(4), e35781. https://doi.org/10.1371/journal.pone.0035781
  • Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541–565. https://doi.org/10.1080/08838151.2020.1843357
  • Shin, D. (2021). The perception of humanness in conversational journalism: An algorithmic information-processing perspective. New Media & Society, Advance online publication. https://doi.org/10.1177/1461444821993801
  • Shin, D. (2022). Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm. Journal of Information Science, Advance online publication. https://doi.org/10.1177/0165551520985495
  • Sundar, S. S., Bellur, S., Oh, J., Jia, H., & Kim, H. -S. (2016). Theoretical importance of contingency in human-computer interaction: Effects of message interactivity on user engagement. Communication Research, 43(5), 595–625. https://doi.org/10.1177/0093650214534962
  • Thurman, N. (2011). Making ‘the daily me’: Technology, economics and habit in the mainstream assimilation of personalized news. Journalism, 12(4), 395–415. https://doi.org/10.1177/1464884910388228
  • Thurman, N., Moeller, J., Helberger, N., & Trilling, D. (2019). My friends, editors, algorithms, and I. Digital Journalism, 7(4), 447–469. https://doi.org/10.1080/21670811.2018.1493936
  • Upstill, T. (2018). The new google news: AI meets human intelligence. The Keyword [blog] Retrieved from: https://www.blog.google/products/news/new-google-news-ai-meets-human-intelligence/

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.