Threatened individuals prefer positive information during Internet search: An experimental laboratory study
Vol.12,No.1(2018)
The Internet is the main source for information search and it is increasingly used in the health domain. Such self-relevant Internet searches are most probably accompanied by affective states such as threat (e.g., being afraid of a serious illness). Thus, threat can influence the entire Internet search process. Threat is known to elicit a preference for positive information. This positive bias has recently been shown for separate steps of the Internet search process (i.e., selection of links, scanning of webpages, and recall of information). To extend this research, the present study aimed at investigating the influence of threat across the Internet search process. We expected that threatened individuals similarly prefer positive information during this process. An experimental laboratory study was conducted with undergraduate students (N = 114) enrolled in a broad range of majors. In this study, threat was manipulated and then participants were to complete a preprogrammed, realistic Internet search task which was used to assess selection of links, scanning of webpages, and recall of information. The results supported our hypothesis and revealed that, during the Internet search task, threatened individuals directed more attention to positive information (i.e., selected more positive links and scanned positive webpages longer) and, as a consequence, also recalled more positive information than non-threatened individuals. Thus, our study shows that not only separate steps but also the Internet search process as such is susceptible to being influenced by affective states such as threat.
Internet search; health-related information; threat; counter-regulation; self-relevance
Andrewes, D. (2001). Neuropsychology: From theory to practice. New York, NY: Psychology Press.
Becker, D., Grapendorf, J., Greving, H., & Sassenberg, K. (2018). Perceived threat and Internet use predict intentions to get bowel cancer screening (colonoscopy): A longitudinal questionnaire study. Journal of Medical Internet Research, 20, e46. https://doi.org/10.2196/jmir.9144
Blascovich, J., & Tomaka, J. (1996). The biopsychosocial model of arousal regulation. Advances in Experimental Social Psychology, 28, 1–51. https://doi.org/10.1016/S0065-2601(08)60235-X
Brand-Gruwel, S., Wopereis, I., & Vermetten, Y. (2005). Information problem solving by experts and novices: Analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487–508. https://doi.org/10.1016/j.chb.2004.10.005
Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using internet. Computers & Education, 53, 1207–1217. https://doi.org/10.1016/j.compedu.2009.06.004
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Davis, M. J. (2010). Contrast coding in multiple regression analysis: Strengths, weaknesses, and utility of popular coding structures. Journal of Data Science, 8, 61–73.
Fallows, D. (2008). Search engine use. Pew Research Center. Retrieved from: http://www.pewinternet.org/files/old-media/Files/Reports/2008/PIP_Search_Aug08.pdf.pdf
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Thousand Oaks, CA: Sage Publications.
Fox, S. (2011). Health topics. Pew Research Center. Retrieved from: http://www.pewinternet.org/files/old-media/Files/Reports/2011/PIP_Health_Topics.pdf
Fox, S., & Duggan, M. (2013). Health online 2013. Pew Research Center. Retrieved from: http://www.pewinternet.org/files/old-media/Files/Reports/PIP_HealthOnline.pdf
Fox, S., & Jones, S. (2009). The social life of health information. Pew Research Center. Retrieved from: http://www.pewinternet.org/files/old-media/Files/Reports/PIP_HealthOnline.pdf
Fu, W.-T., & Pirolli, P. (2007). SNIF-ACT: A cognitive model of user navigation on the World Wide Web. Human–Computer Interaction, 22, 355–412.
Gerjets, P., Kammerer, Y., & Werner, B. (2011). Measuring spontaneous and instructed evaluation processes during web search: Integrating concurrent thinking-aloud protocols and eye-tracking data. Learning and Instruction, 21, 220–231. https://doi.org/10.1016/j.learninstruc.2010.02.005
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457, 1012–1014. https://doi.org/10.1038/nature07634
Greving, H., & Sassenberg, K. (2015). Counter-regulation online: Threat biases retrieval of information during Internet search. Computers in Human Behavior, 50, 291–298. https://doi.org/10.1016/j.chb.2015.03.077
Greving, H., Sassenberg, K., & Fetterman, A. (2015). Counter-regulating on the Internet: Threat elicits preferential processing of positive information. Journal of Experimental Psychology: Applied, 21, 287–299. https://doi.org/10.1037/xap0000053
Jonas, E., McGregor, I., Klackl, J., Agroskin, D., Fritsche, I., Holbrook, C., … Quirin, M. (2014). Threat and defense: From anxiety to approach. Advances in Experimental Social Psychology, 49, 219–286. https://doi.org/10.1016/B978-0-12-800052-6.00004-4
Kammerer, Y., & Gerjets, P. (2011). Searching and evaluating information on the WWW: Cognitive processes and user support. In K.-P. L. Vu & R. W. Proctor (Eds.), Handbook of human factors in Web design (2nd ed., pp. 283–302). Boca Raton, FL: CRC Press.
Kammerer, Y., & Gerjets, P. (2012). Effects of search interface and internet-specific epistemic beliefs on source evaluations during web search for medical information: An eye-tracking study. Behaviour & Information Technology, 31, 83–97. https://doi.org/10.1080/0144929X.2011.599040
Kammerer, Y., & Gerjets, P. (2014). The role of search result position and source trustworthiness in the selection of Web search results when using a list or a grid interface. International Journal of Human-Computer-Interaction, 30, 177–191. https://doi.org/10.1080/10447318.2013.846790
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Free Press.
Lo, B., & Parham, L. (2010). The impact of Web 2.0 on the doctor–patient relationship. Journal of Law, Medicine, & Ethics, 38, 17–26. https://doi.org/10.1111/j.1748-720X.2010.00462.x
Morahan-Martin, J. M. (2004). How internet users find, evaluate, and use online health information: A cross-cultural review. CyberPsychology & Behavior, 7, 497–510. https://doi.org/10.1089/cpb.2004.7.497
Murray, E., Lo, B., Pollack, L., Donelan, K., Catania, J., White, M., … Turner, R. (2003). The impact of health information on the Internet on the physician–patient relationship. Archives of Internal Medicine, 163, 1727–1734. https://doi.org/10.1001/archinte.163.14.1727
Neter, J., Kutner, M. H., Nachtschiem, C. J., & Wasserman, W. (1996). Applied linear statistical models (4th ed.). Boston: McGraw-Hill.
Pirolli, P. (2005). Rational analyses of information foraging on the Web. Cognitive Science, 29, 343–373. https://doi.org/10.1207/s15516709cog0000_20
Pirolli, P. (2007). Information foraging theory. Adaptive interaction with information. New York: Oxford University Press.
Pirolli, P., & Card, S. (1999). Information foraging. Psychological Review, 106, 643–675. https://doi.org/10.1037/0033-295X.106.4.643
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. https://doi.org/10.3758/BRM.40.3.879
Purcell, K. (2011). Search and email still top the list of most popular online activities. Pew Research Center. Retrieved from: http://www.pewinternet.org/files/old-media/Files/Reports/2011/PIP_Search-and-Email.pdf
Purcell, K., Brenner, J., & Rainie, L. (2012). Search engine use 2012. Pew Research Center. Retrieved from: http://www.pewinternet.org/files/old-media/Files/Reports/2012/PIP_Search_Engine_Use_2012.pdf
Rothermund, K. (2011). Counter-regulation and control-dependency. Social Psychology, 42, 56–66. https://doi.org/10.1027/1864-9335/a000043
Rothermund, K., Gast, A., & Wentura, D. (2011). Incongruency effects in affective processing: Automatic motivational counter-regulation or mismatch-induced salience? Cognition and Emotion, 25, 413–425. https://doi.org/10.1080/02699931.2010.537075
Rothermund, K., Voss, A., & Wentura, D. (2008). Counter-regulation in affective attentional biases: A basic mechanism that warrants flexibility in emotion and motivation. Emotion, 8, 34–46. https://doi.org/10.1037/1528-3542.8.1.34
Rouet, J.-F., Ros, C., Goumi, A., Macedo-Rouet, M., & Dinet, J. (2011). The influence of surface and deep cues on primary and secondary school students’ assessment of relevance in web menues. Learning and Instruction, 21, 205–219. https://doi.org/10.1016/j.learninstruc.2010.02.007
Sassenberg, K., & Greving, H. (2016). Internet searching about disease elicits a positive perception of own health when severity of illness is high: A longitudinal questionnaire study. Journal of Medical Internet Research, 18, e56. https://doi.org/10.2196/jmir.5140
Sassenberg, K., Sassenrath, C., & Fetterman, A. K. (2015). Threat ≠ prevention, challenge ≠ promotion: The impact of threat, challenge, and regulatory focus on attention to negative stimuli. Cognition and Emotion, 29, 188–195. https://doi.org/10.1080/02699931.2014.898612
Schwager, S., & Rothermund, K. (2013a). Counter-regulation triggered by emotions: Positive/negative affective states elicit opposite valence biases in affective processing. Cognition and Emotion, 27, 839–855. https://doi.org/10.1080/02699931.2012.750599
Schwager, S., & Rothermund, K. (2013b). Motivation and affective processing biases in risky decision making: A counter-regulation account. Journal of Economic Psychology, 38, 111–126. https://doi.org/10.1016/j.joep.2012.08.005
Schwager, S., & Rothermund, K. (2014). On the dynamics of implicit emotion regulation: Counter-regulation after remembering events of high but not of low emotional intensity. Cognition and Emotion, 28, 971–992. https://doi.org/10.1080/02699931.2013.866074
Shepperd, J., Malone, W., & Sweeny, K. (2008). Exploring causes of the self-serving bias. Social and Personality Compass, 2, 895–908. https://doi.org/10.1111/j.1751-9004.2008.00078.x
Taylor, S. E. (1991). Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psychological Bulletin, 110, 67–85. https://doi.org/10.1037/0033-2909.110.1.67
Tomaka, J., Blascovich, J., Kibler, J., & Ernst, J. M. (1997). Cognitive and physiological antecedents of threat and challenge appraisal. Journal of Personality and Social Psychology, 73, 63–72. https://doi.org/10.1037/0022-3514.73.1.63
Walraven, A., Brand-Gruwel, S., & Boshuizen, H. P. A. (2013). Fostering students‘ evaluation behaviour while searching the internet. Instructional Science, 41, 125–146. https://doi.org/10.1007/s11251-012-9221-x
Ward, A. F. (2013). One with the Cloud: Why people mistake the Internet’s knowledge for their own [Unpublished doctoral dissertation]. Harvard University, Cambridge, MA.
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