Skip to main content
Log in

Students as information consumers: A focus on online decision making process

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

There are many decision-making theories that explain how people decide to buy, click, read, or skip something online. Studies integrating the eye tracking methodology with decision-making processes state many assumptions. Based on a group of assumptions, the current study aims to compare the two search contexts and find if the assumptions are valid for both situations. The simultaneous mixed research methodology led to benefitting from both qualitative and quantitative data. Participants consist of 15 grad/undergrad volunteers from different departments. They were given two scenarios: a hotel search; an academic topic search. They were assigned to decide on the best three options. As they engaged in searching, their eye movements accompanied with think-aloud were recorded. The results indicated a consistency between espoused and enacted academic criteria in addition to the order of importance dimension, but neither of them was observed in everyday context. On the contrary to academic context, eye movements and espoused criterion/criteria were parallel in everyday context. Price and rating were popular criteria for the final decisions, and participants had their own heuristics related to them. Source and content for the academic context were popular, and the participants utilized heuristics, too. The fixations and visits on the popular areas were the maximum ones among others.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Ackermann, E., & Hartman, K. (2013). The information specialist’ s guide to searching and researching on the internet and the world wide web (2nd ed.). New York: Routledge.

    Google Scholar 

  • Askew, K., & Coovert, M. D. (2013). Online decision making. In Y. Amichai-Hamburger (Ed.), The social net: understanding our online behavior (pp. 99–119). Oxford: Oxford University Press.

    Chapter  Google Scholar 

  • Blakeslee, S. (2004). The CRAAP test. LOEX Quarterly, 31(3) Retrieved from http://commons.emich.edu/cgi/viewcontent.cgi?article=1009&context=loexquarterly.

  • Chen, Y., Hsu, I., & Lin, C. (2010). Website attributes that increase consumer purchase intention: a conjoint analysis. Journal of Business Research, 63(9–10), 1007–1014.

    Article  Google Scholar 

  • Choi, Y. (2013). Analysis of image search queries on the web: query modification patterns and semantic attributes. Journal of the American Society for Information Science & Tecnhology, 64(7), 1423–1441.

    Article  Google Scholar 

  • Chung, N., & Koo, C. (2015). The use of social media in travel information search. Telematics and Informatics, 32(2), 215–229.

    Article  Google Scholar 

  • Clark, R.C., Nguyen, F., & Sweller, J. (2011). Efficiency in learning: Evidence-based guidelines to manage cognitive load. Wiley.

  • Darley, W. K., Blankson, C., & Luethge, D. J. (2010). Toward an integrated framework for online consumer behavior and decision making process: a review. Psychology and Marketing, 27, 94–116.

    Article  Google Scholar 

  • Dinet, J., Chevalier, A., & Tricot, A. (2012). Information search activity: an overview. European Review of Applied Psychology, 62(2), 49–62.

    Article  Google Scholar 

  • Gallarza, M. G., & Saura, I. G. (2006). Value dimensions, perceived value, satisfaction and loyalty: an investigation of university students’ travel behavior. Tourism Management, 27(3), 437–452.

    Article  Google Scholar 

  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.

    Article  Google Scholar 

  • Gwizdka, J. (2010). Distribution of cognitive load in web search. Journal of the American Society for Information Science and Technology, 61(11), 2167–2187.

    Article  Google Scholar 

  • Ha, L. (2008). Online advertising research in advertising journals: a review. Journal of Current Issues and Research in Advertising, 30(1), 31–48.

    Article  Google Scholar 

  • Hood, K. M., Shanahan, K. J., & Hopkins, C. D. (2015). The influence of interactivity on visit and purchase frequency: the moderating role of website informational features. Journal of Internet Commerce, 14, 294–315.

    Article  Google Scholar 

  • Jansen, B. J., Brown, A., & Resnick, M. (2007). Factors relating to the decision to click on a sponsored link. Decision Support Systems, 44(1), 46–59.

    Article  Google Scholar 

  • Karimi, S., Papamichail, K. N., & Holland, C. P. (2015). The effect of prior knowledge and decision-making style on the online purchase decision-making process: a typology of consumer shopping behavior. Decision Support Systems, 77, 137–147.

    Article  Google Scholar 

  • Khosrowjerdi, M., & Sundqvist, A. (2017). Students’ trust formation and credibility judgements in online health information – a review article. Tidsskriftet Arkiv, 8(1), 1–22.

    Article  Google Scholar 

  • Kim, H.-W., Xu, Y., & Gupta, S. (2012). Which is more important in internet shopping, perceived price or trust? Electronic Commerce Research and Applications, 11(3), 241–252.

    Article  Google Scholar 

  • Krathwohl, D. R. (2002). A revision of Bloom's taxonomy: an overview. Theory Into Practice, 41(4), 212–218.

    Article  Google Scholar 

  • Li, Y., & Belkin, N. J. (2008). A faceted approach to conceptualizing tasks in information seeking. Information Processing and Management, 44, 1822–1837.

    Article  Google Scholar 

  • Liu, J., Kim, C. S., & Creel, C. (2015). Exploring search task difficulty reasons in different task types and user knowledge groups. Information Processing and Management, 51(3), 273–285.

    Article  Google Scholar 

  • Mandalios, J. (2013). RADAR: an approach for helping students evaluate internet sources. Journal of Information Science, 39(4), 470–478.

    Article  Google Scholar 

  • Moat, H. S., Preis, T., Olivola, C., Liu, C., & Chater, N. (2014). Using big data to predict collective behavior in the real world. Behavioral and Brain Sciences, 37, 92–93.

    Article  Google Scholar 

  • Monchaux, S., Amadieu, F., Chevalier, A., & Marine, C. (2015). Query strategies during information searching: effects of prior domain knowledge and complexity of the information problems to be solved. Information Processing and Management, 51(5), 557–569.

    Article  Google Scholar 

  • Navarro-Prieto, R., Scaife M., Rogers Y. (1999). Cognitive strategies in web searching. In proceedings of the 5th conference on human factors and the web (pp. 43–56). Gaithersburg, Maryland: National Institute of standards and Technology Retrieved from http://zing.ncsl.nist.gov/hfweb/proceedings/navarro-prieto/index.html

  • Orquin, J. L., & Loose, S. M. (2013). Attention and choice: a review on eye movements in decision making. Acta Psychologica, 144(1), 190–206.

    Article  Google Scholar 

  • Ortiz-Cordova, A., Yang, Y., & Jansen, B. J. (2015). External to internal search: Associating searching on search engines with searching on sites. Information Processing and Management, 51(5), 718–736.

    Article  Google Scholar 

  • Pan, B. & Zhang, L. (2016). An eyetracking study on online hotel decision making: the effects of images and number of options. Tourism Travel and Research Association: Advancing Tourism Research Globally, 27. Retrieved from http://scholarworks.umass.edu//ttra/2010/Oral/27.

  • Phelan, K. V., Christodoulidou, N., Countryman, C. C., & Kistner, L. J. (2011). To book or not to book: the role hotel web site heuristics. Journal of Services Marketing, 25(2), 134–148.

    Article  Google Scholar 

  • Pirolli, P., & Card, S. (1999). Information foraging. Psychological Review, 106(4), 643–675.

    Article  Google Scholar 

  • Rieh, S.-Y., Collins-Thompson, K., Hansen, P., & Lee, H.-J. (2016). Towards searching as a learning process: a review of current perspectives and future directions. Journal of Information Science, 42(1), 19–34.

    Article  Google Scholar 

  • Roscoe, R. D., Grebitus, C., O’Brian, J., Johson, A. C., & Kula, I. (2016). Online information search and decision making: Effects of web search stance. Computers in Human Behavior, 56, 103–118.

    Article  Google Scholar 

  • Sanchiz, M., Chin, J., Chevalier, A., Fu, W. T., Amadieu, F., & He, J. (2017). Searching for information on the web: impact of cognitive aging, prior domain knowledge and complexity of the search problems. Information Processing and Management, 53, 281–294.

    Article  Google Scholar 

  • Sendurur, E., & Yildirim, Z. (2015). Students’ web search strategies with different task types: an eye-tracking study. International Journal of Human Computer Interaction, 31(2), 101–111.

    Article  Google Scholar 

  • Sharit, J., Taha, J., Berkowsky, R. W., Profita, H., & Czaja, S. J. (2015). Online information search performance and search strategies in a health problem-solving scenario. Journal of Cognitive Engineering & Decision Making, 9(3), 211–228.

    Article  Google Scholar 

  • Silverstein, C., Marais, H., Henzinger, M., & Moricz, M. (1999). Analysis of a very large web search engine query log. ACM SIGIR Forum, 33(1), 6–12.

    Article  Google Scholar 

  • Tarmin, L., & Chouquet, C. (2017). On the impact of domain expertise on query formulation, relevance assessment and retrieval performance in clinical settings. Information Processing and Management, 53(2), 332–350.

    Article  Google Scholar 

  • Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: combining qualitative and quantitative approaches. Thousand Oaks: Sage.

    Google Scholar 

  • Webb, N., & Renshaw, T. (2008). Eyetracking in HCI. In Cairns, P., & Cox, A. L. (Eds.), Research methods for human-computer interaction, (pp. 35-69). Cambridge: Cambridge University Press.

  • Wilson, T. D. (1999). Models in information behavior research. Journal of Documentation, 55(3), 249–270.

    Article  Google Scholar 

  • Wilson, T. D. (2000). Human information behavior. Informing Science, 3(2), 49–56.

    Article  Google Scholar 

  • Witten, I. H. (2008). Searching … in a web. Journal of Universal Computer Science, 14(10), 1739–1762.

    Google Scholar 

  • Wu, K.-C. (2015). Affective surfing in the visualized interface of a digital library for children. Information Processing and Management, 51(4), 373–390.

    Article  Google Scholar 

  • Zapato, L. (1998). The Pacific northwest tree Octopus. Save the endangered tree Octopus. Retrieved May 17, 2018, from http://zapatopi.net/treeoctopus/

  • Zhang, Y. (2008). The influence of mental models on undergraduate students’ searching behavior on the web. Information Processing and Management, 44(3), 1330–1345.

    Article  Google Scholar 

  • Zhou, M. (2013). A systematic understanding of successful web searches in nformation-based tasks. Educational Technology & Society, 6(1), 321–331.

    Google Scholar 

Download references

Acknowledgements

I would like to thank for the great efforts of the personnel at METU HCI lab.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emine Sendurur.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sendurur, E. Students as information consumers: A focus on online decision making process. Educ Inf Technol 23, 3007–3027 (2018). https://doi.org/10.1007/s10639-018-9756-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-018-9756-9

Keywords

Navigation