The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
The impact of generative AI search result design on user perceptions and information adoption
Open accessAbstract
Artificial intelligence (AI) summaries appear in many Web searches, but there have been errors and questionable quality in the AI responses and source attributions. This dissertation project consists of two studies that explore the impact of generative AI summary features on user perceptions and decision-making. The first experimental study examines the effects of two designs of generative AI search responses (narrative point of view, source authority) on user perceptions and information adoption about consumer health topics. AI summaries were collected, modified, and used in a randomised, within-subjects experiment with crowdsourced workers (N=234). The Information Adoption Model (IAM) informed the conceptual model of the experiment, and the Comprehensive Model of Information Seeking (CMIS) guided the health task design. User perceptions are assessed through self-reported measures of persuasive communication (argument quality, source credibility), information usefulness, domain knowledge, and involvement. The second qualitative study aims to explore how AI summaries and source attributions are utilised alongside traditional Web search results for tasks of personal interest. This is an in-person study involving search sessions, retrospective think-alouds, and semi-structured interviews with graduate students (N=16).
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Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 1×
“The IAM builds upon the prominent Elaboration-Likelihood Model [46] and Technology Acceptance Model [19].”
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615
Saturation in qualitative research: exploring its conceptualization and operationalization
Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018) · Quality & Quantity · doi:10.1007/s11135-017-0574-8 · Cited by 12,033 · cited 1×
“Recruitment will stop once there are no new codes emerging from data analysis [50].”
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom Companion: Business · doi:10.1007/978-3-030-80519-7 · Cited by 10,343
Petty, R. E., & Cacioppo, J. T. (1986) · doi:10.1007/978-1-4612-4964-1 · Cited by 5,219
AIMQ: a methodology for information quality assessment
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002) · Information & Management · doi:10.1016/s0378-7206(02)00043-5 · Cited by 1,611
Informational influence in organizations: An integrated approach to knowledge adoption
Sussman, S. W., & Siegal, W. S. (2003) · Information Systems Research · doi:10.1287/isre.14.1.47.14767 · Cited by 1,250
MTurk Research: Review and Recommendations
Aguinis, H., Villamor, I., & Ramani, R. S. (2020) · Journal of Management · doi:10.1177/0149206320969787 · Cited by 1,098
O’Brien, H. L., Cairns, P., & Hall, M. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.01.004 · Cited by 1,014
Research works printed but not linked: 8
- Pia Borlund and Nils Pharo. 2019. A need for information on information needs, In Proceedings of the Tenth International Conference on Conceptions of Library and Information Science (CoLIS). Information Research 24, 4. https://informationr.net/ir/24-4/colis/colis1908.html
- 10.5555/aai30281871 · DOI
- John W Creswell and J David Creswell. 2018. Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications, Thousand Oaks, CA, USA.
- W Bruce Croft, Donald Metzler, Trevor Strohman, et al. 2010. Search engines: Information retrieval in practice. Addison-Wesley Reading, Boston, Massachusetts.
- J David Johnson. 1997. Cancer-related information seeking. Hampton Press, Cresskill, NJ, USA.
- Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, and Marko Sarstedt. 2022. A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications, Los Angeles, CA, USA.
- Sanghee Oh and Barbara M. Wildemuth. 2016. Think-aloud protocols. In Applications of social research methods to questions in information and library science (2nd ed.), Barbara M. Wildemuth (Ed.). Libraries Unlimited, Santa Barbara, CA, 178–188.
- Soo Young Rieh. 2000. Information quality and cognitive authority in the World Wide Web. Ph. D. Dissertation. Rutgers The State University of New Jersey. https://www.proquest.com/openview/e0f092d68f429553106175636d0bbb1c
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Metadata
- Authors
- Li, A.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Li, Alice
- ORCID
- Li, Alice
- Venue
- Proceedings of the 2026 Conference on Human Information Interaction and Retrieval
- Publisher
- ACM
- Published
- 2026-03-22
- Pages
- 399-404
- OpenAlex
- W7132843950
- Cited by
- 0
- References
- 43
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Conference on Human Information Interaction and Retrieval (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- source attribution, narrative perspective, source authority, information adoption, consumer health information, persuasive communication, source credibility, information usefulness, user perception, think-aloud
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