This study aims to investigate consumers’ modification of retrieval platform switch paths across health-related search tasks and learning characteristics via such a switch. Mixed research methods were used in this study. A lab user experiment was designed to obtain data on consumers’ health information search behaviour. Screen recordings and interview data were both coded and analysed. Research results show that health consumers acquired different kinds of health knowledge units from different retrieval platforms, and there are five change patterns of retrieval platform switch paths which reveal three types of learning. The results suggest that health consumers learn not only task-related knowledge but also retrieval skills during the switch of retrieval platforms. The research findings further develop the search as learning process research framework from the dimension of retrieval platform switch patterns and contribute to the enhancement of consumers’ health information retrieval abilities.
ZhangYLiuC. Users’ knowledge use and change during information searching process: a perspective of vocabulary usage. In: Proceedings of the ACM/IEEE joint conference on digital libraries in 2020, Virtual Event, China, 1–5 August 2020, pp. 47–56. New York: Association for Computing Machinery.
GüldMOThiesCJFischerB, et al. Platform for distributed image processing and image retrieval. Proc SPIE Int Soc Opt Eng2003; 5150(2): 1109–1120.
4.
MarchioniniG. Exploratory search: from finding to understanding. Commun ACM2006; 49(4): 41–46.
5.
ShahCHendahewaCGonzález-IbánezR. Two’s company, but there’s no crowd. Aslib J Inf Manag2015; 67(6): 636–662.
6.
KanthawalaSVermeeschAGivenB, et al. Answers to health questions: internet search results versus online health community responses. J Med Internet Res2016; 18(4): e95.
7.
ChiYHeDJengW. Laypeople’s source selection in online health information-seeking process. J Assoc Inf Sci Technol2020; 71(12): 1484–1499.
8.
ZhangY. Consumers’ selection and use of sources for health information. In: BianJGuoYHeZ, et al. (eds) Social web and health research. Cham: Springer, 2019, pp. 83–101.
9.
XiaoHWeiX. Analysis and evaluation on the Chinese web patent retrieval platforms. Sci Technol Manag Res2010; 14: 82–84.
10.
ChenYZhaoYWangZ. Understanding online health information consumers’ search as a learning process: search platform sequences, query reformulation and conceptual changes. Libr Hi Tech2020; 38(4): 859–881.
11.
SongXLiuCZhangY. Chinese college students’ source selection and use in searching for health-related information online. Inf Process Manag2021; 58(3).
12.
TeevanJAlvaradoCDavidR. The perfect search engine is not enough: a study of orienteering behavior in directed search. In: Proceedings of the SIGCHI conference on human factors in computing systems, Vienna, 24–29 April 2004, pp. 415–422. New York: Association for Computing Machinery.
13.
MukhopadhyayTRajanUTelangR. Competition between internet search engines. In: Proceedings of the 37th annual Hawaii international conference on system sciences, Washington, DC, 5–8 January 2004, pp. 10–19. New York: Association for Computing Machinery.
14.
WhiteRWKapoorADumaisST. Modeling long-term search engine usage. In: Proceedings of the 18th international conference on user modeling, adaptation, and personalization. UMAP 2010, Big Island, HI, 20–24 June 2010, pp. 28–39. New York: Association for Computing Machinery.
15.
GuoQWhiteRZhangY, et al. Why searchers switch: understanding and predicting engine switching rationales. In: Proceedings of the of SIGIR’ 11: the 34th international ACM SIGIR conference on research and development in information retrieval, Beijing, China, 24–28 July 2011, pp. 335–344. New York: Association for Computing Machinery.
16.
WhiteRDumainsS. Characterizing and predicting search engine switching behavior. In: Proceedings of the 18th ACM conference on information and knowledge management, Hong Kong, China, 2–6 November 2009, pp. 87–96. New York: Association for Computing Machinery.
17.
SavenkovDLagunDLiuQ. Search engine switching detection based on user personal preferences and behavior patterns. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval, Dublin, 28 July–1 August 2013, pp. 33–44. New York: Association for Computing Machinery.
18.
FoxS. The social life of health information. Pew Internet and American Life Project, Washington, DC, 12March2011.
19.
RowleyJJohnsonFSbaffiL. Gender as an influencer of online health information seeking and evaluation behavior. J Assoc Inf Sci Technol2017; 68(1): 36–47.
20.
LinTCHwangLLLaiYJ. Effects of argument quality, source credibility and self-reported diabetes knowledge on message attitudes: an experiment using diabetes related messages. Health Info Libr J2017; 34(3): 225–235.
21.
JohnsonJD. Cancer-related information seeking. New York: Hampton Press, 1997.
22.
ZhangY. Beyond quality and accessibility: source selection in consumer health information searching. J Assoc Inf Sci Technol2014; 65(5): 911–927.
23.
ZhangY. College students’ uses and perceptions of social networking sites for health and wellness information. Inf Res Int Electron J2012; 17(3): n3.
24.
TaoDLerougeCSmithKJ, et al. Defining information quality into health websites: a conceptual framework of health website information quality for educated young adults. JMIR Hum Factors2017; 4(4): e25.
25.
ZhangYSunYXieB. Quality of health information for consumers on the web: a systematic review of indicators, criteria, tools, and evaluation results. J Assoc Inf Sci Technol2015; 66(10): 2071–2084.
26.
WalkerLOMackertMSAhnJ, et al. E-health and new moms: contextual factors associated with sources of health information. Public Health Nurs2017; 34(6): 561–568.
27.
GivenLMWillsonRAlbrechtL, et al. Information in crisis: health and technology-related information behaviors of parents in emergency departments. Proc Assoc Inf Sci Technol2016; 53(1): 1–10.
28.
KhosrowjerdiM. National culture and trust in online health information. J Librarianship Inf Sci2020; 52(2): 509–528.
29.
ThonFMJucksR. Believing in expertise: how authors’ credentials and language use influence the credibility of online health information. Health Commun2017; 32(7): 828–836.
30.
ZimmermannMJucksR. How experts’ use of medical technical jargon in different types of online health forums affects perceived information credibility: randomized experiment with laypersons. J Med Internet Res2018; 20(1): e30.
31.
LiYZhangX. College students’ capability of screening health information in social media. Doc Inf Knowl2018; 18(1): 66–77.
LiaoQVFuWT. Age differences in credibility judgment of online health information. In: Proceedings of the 2nd ACM SIGHIT international health informatics symposium, Miami, FL, 28–30 January 2012, Vol. 108, pp. 353–362. New York: Association for Computing Machinery.
34.
SmithD. Health care consumer’s use and trust of health information sources. J Commun Healthc2011; 4(3): 200–210.
35.
TennantBStellefsonMDoddV, et al. EHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res2015; 17(3): e70.
36.
ChoudhuryMDMorrisMRWhiteRW. Seeking and sharing health information online: comparing search engines and social media. In: Proceedings of the 32nd annual ACM conference on human factors in computing systems, Toronto, ON, Canada, 26 April–1 May 2014, pp. 1365–1376. New York: Association for Computing Machinery.
37.
KellyDArguelloJEdwardsA, et al. Development and evaluation of search tasks for IIR experiments using a cognitive complexity framework. In: Proceedings of the 2015 international conference on the theory of information retrieval, Northampton, MA, 27–30 September 2015, pp. 101–110. New York: Association for Computing Machinery.
38.
ZhangYSunYKimY. The influence of individual differences on consumer’s selection of online sources for health information. Comput Hum Behav2017; 67: 303–312.
39.
JansenBJBoothDSmithB. Using the taxonomy of cognitive learning to model online searching. Inf Process Manag2009; 45(6): 643–663.
RiehSYCollins-ThompsonKHansenP, et al. Towards searching as a learning process: a review of current perspectives and future directions. J Inf Sci2016; 42(1): 19–34.
42.
VakkariP. Searching as learning: a systematization based on literature. J Inf Sci2016; 42(1): 7–18.
43.
BhattacharyaNGwizdkaJ. Measuring learning during search: differences in interactions, eye-gaze, and semantic similarity to expert knowledge. In: Proceedings of the 2019 conference on human information interaction and retrieval (CHIIR2019), Glasgow, 10–14 March 2019, pp. 63–71. New York: Association for Computing Machinery.
44.
BhattacharyaNGwizdkaJ. Relating eye-tracking measures with changes in knowledge on search tasks. In: Proceedings of the 2018 ACM symposium on eye tracking research and applications (ETRA 2018), Warsaw, 14–17 June 2018, p. 62. New York: Association for Computing Machinery.
45.
QuYFurnasGW. Model-driven formative evaluation of exploratory search: a study under a sensemaking framework. Inform Process Manag2008; 44(2): 534–555.
46.
BatesM. The design of browsing and berrypicking techniques for the online search interface. Online Rev1989; 13: 407–424.
47.
KilicogluHBen AbachaABMrabetY, et al. Semantic annotation of consumer health questions. BMC Bioinformatics2018; 19(1): 34.
48.
CoonDMittererJO. Introduction to psychology: gateways to mind and behavior with concept maps. Wadsworth, CA: Belmont, 2010.
49.
MarchioniniG. Search, sense making and learning: closing gaps. Inform Learn Sci2019; 120(1/2): 74–86.
50.
VakkariP. Task-based information searching. Ann Rev Info Sci Tech2003; 37(1): 413–464.
51.
VakkariP. The usefulness of search results: a systematization of types and predictors. In: CHIIR 2020: conference on human information interaction and retrieval, Vancouver, BC, Canada, 14–18 March 2020, pp. 243–252. New York: Association for Computing Machinery.
52.
SyedRCollins-ThompsonK. Retrieval algorithms optimized for human learning. In: SIGIR’17: the 40th international ACM SIGIR conference on research and development in information retrieval, Tokyo, Japan, 7–11 August 2017, pp. 555–564. New York: Association for Computing Machinery.
53.
VakkariPVölskeMPotthastM, et al. Predicting retrieval success based on information use for writing tasks. In: 22nd international conference on theory and practice of digital libraries, Porto, 10–13 September 2018, pp. 161–173. Cham: Springer.
54.
VakkariPVölskeMPotthastM, et al. Modeling the usefulness of search results as measured by information use. Inform Process Manag2019; 56(3): 879–894.
55.
KashyapAHristidisV. Comprehension-based result snippets. In: CIKM’ 12: proceedings of the 21st ACM international conference on information and knowledge management, Maui, HI, 29 October–2 November 2012, pp. 1075–1084. New York: Association for Computing Machinery.
56.
NagallaS. From focused elements to snippets. MS Thesis, University of Minnesota USA, 2013.
57.
LiYYeSZhouY, et al. Web-based medical information searching by Chinese patients with breast cancer and its influence on survival: observational study. J Med Internet Res2020; 22(4): e16768.
58.
ShiYLuoL. Chinese college students’ health information seeking behavior: implications for academic libraries. J Acad Librarianship2019; 45(2): 69–74.
