A Model of End Users' Web Threats Information Processing

References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
   Web of Science ®Google Scholar
• Ajzen, I., & Sexton, J. (1999). Depth of processing, belief congruence, and attitude behavior correspondence. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology (pp. 117–138). New York: Guilford.
   Google Scholar
• Armitrage, C. J. & Connor, M. (2001). Efficacy of the theory of planned behaviour: A meta analytic review. British Journal of Social Psychology, 40 (4), 471–499.
   PubMed Web of Science ®Google Scholar
• Andrews, J.C. (1988). Motivation, ability and opportunity to process information: conceptual and experimental manipulation issues. Advances in Consumer Research, 15, 219–225.
   Google Scholar
• Baron, R.M., & Kenny, D.A. (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
   PubMed Web of Science ®Google Scholar
• Bohner, G., Rank, S., Reinhard, M., Einwiller, S., & Erb, H. (1998). Motivational determinants of systematic processing: expectancy moderates effects of desired confidence on processing efforts. European Journal of Social Psychology, 28, 185–206.
   Web of Science ®Google Scholar
• Burnkraut, R., & Sawyer, A. (1983). Effects of involvement and message content on Information-processing intensity. In Harris, R.J. (Ed.). Information-Processing Research in Advertising (pp.43–64). Hillsdale NJ: Erlbaum.
   Google Scholar
• Celsi, R.L., & Olson, J.C., (1988). The role of involvement in attention and comprehension processes. Journal of Consumer Research, 15, 210–224.
   Web of Science ®Google Scholar
• Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology 39(5), 752–766.
   Web of Science ®Google Scholar
• Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic information processing within and beyond the persuasion process. In J. S. Uleman, & J. A. Bargh (Eds.), Unintended thought (pp. 212–252). New York: Guilford Press.
   Google Scholar
• Chaiken, S., & Stangor, C. (1987). Attitude and attitude change. Annual Review of Psychology 38(1), 575–630.
   Google Scholar
• Eagly, A.H., & Chaiken, S. (1993). The Psychology of Attitudes, San Diego, Harcourt Brace.
   Google Scholar
• Eagly, A. H., & Chaiken, S (1995). Attitude strength, attitude structure, and resistance to change. In R. E. Petty, & J. A. Krosnick Attitude strength: Antecedents and consequences. Ohio State University series on attitudes and persuasion, Vol. 4, (pp. 413–432). Hillsdale, NJ, England: Lawrence Erlbaum Associates.
   Google Scholar
• Fiske, S.T., & Taylor, S.E. (1991). Social cognition (2nd ed). New York: McGraw-Hill.
   Google Scholar
• Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
   Web of Science ®Google Scholar
• Gefen, D., Straub, D.W., & Boudreau, M.C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4 (7), 2–75.
   Google Scholar
• Griffin, R. J., Dunwoody, S. & Neuwirth, K. (1999). Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environmental Research, 80(2), S230–S245.
   PubMed Web of Science ®Google Scholar
• Griffin, R.J., Neuwirth, K., Giese, J. & Dunwoody, S. (2002). Linking the heuristic-systematic model and depth of processing. Communication Research, 29 (6), 705–732.
   Web of Science ®Google Scholar
• Griffin, R.J., Powell, M., Dunwoody, S., Neuwirth, K., Clark, D., & Novotny, V. (2004). Testing the robustness of the risk information processing model. Paper presented at the annual convention of the AEJMC, Toronto, Canada.
   Google Scholar
• Grossman, J. (2010). Cross-site scripting worms & viruses. Retrieved October 11th, 2010, from http://www.whitehatsec.com/home/resource/whitepapers/XSS_cross_site_scripting.html
   Google Scholar
• Hazlewood, D., & Chaiken, S. (1990, August). Personal relevance, majority influence, and the law of large numbers. Poster presented at the 98th Annual Convention of the American Psychological Association, Boston, MA.
   Google Scholar
• Hann, I-H., Hui, K-L., Lee, S-Y T., & Png, I.P.L. (2007). Overcoming online information privacy concerns: an information-processing theory approach. Journal of Management Information Systems, 24(2), 13–42.
   Web of Science ®Google Scholar
• Hovland, C.I. (1949). Reconciling conflicting results derived from experimental and survey studies of attitude change. American Psychologist, 14, 8–17.
   Web of Science ®Google Scholar
• Johnson, B.B. (2005). Testing and expanding a model of cognitive processing of risk information. Risk Analysis, 25(3), 631–650.
   PubMed Web of Science ®Google Scholar
• Johnson, A.C., & Warkentin, M. (2010). The influence of perceived source credibility on end user attitudes and intentions to comply with recommended IT actions. Journal of Organizational and End User Computing, 22(3), 1–21.
   Google Scholar
• Kahlor, L., Dunwoody, S., Griffin, R.J., & Neuwirth, K. (2006). Seeking and processing information about impersonal risk. Science Communication, 28, 163–194.
   Web of Science ®Google Scholar
• Kahlor, L., Dunwoody, S., Griffin, R.J., Neuwirth, K., & Giese, J. (2003). Studying heuristic-systematic processing of risk communication. Risk Analysis, 23(2), 355–368.
   PubMed Web of Science ®Google Scholar
• LaRose, R., Rifon, N.J., and Enbody, R. (2008), Promoting personal responsibility for Internet safety, Communications of the ACM, 51 (3), 71–76.
   Web of Science ®Google Scholar
• Lopes, A. B., & Galletta, D. F. (2006). Consumer perceptions and willingness to pay for intrinsically motivated online content. Journal of Management Information System, 23(2), 203–31.
   Web of Science ®Google Scholar
• Loewenstein, G., Weber, E., Hsee, C. & Welch, N. (2001). Risk as feelings. Psychological Bulletin. 127, 267–286.
   PubMed Web of Science ®Google Scholar
• Lynch, J.G., Jr. (1982). On the external validity of experiments in consumer research. Journal of Consumer Research, 9 (December), 225–239.
   Web of Science ®Google Scholar
• McGuire, W.J. (1969). The nature of attitudes and attitude change. In G. Lindzey & E. Aronson (Eds), The Handbook of Social Psychology (2nd ed., Vol. 3, pp. 136–314). Reading, MA: Addison-Wesley.
   Google Scholar
• Mowen, J. C. & Minor, M.S. (2006). Consumer Behavior: A Framework. Mason, OH: Thomson.
   Google Scholar
• Neuwirth, K., Frederick, E., & Mayo, C. (2002), Person-effects and heuristic systematic processing, Communications Research, 29(3), 320–359.
   Web of Science ®Google Scholar
• Qing, T., Ng, B-Y, & Kankanhalli (2007). Individual's response to security messages: A decision-making perspective,” Annals of Information Systems, 2 (2), 177–191.
   Google Scholar
• Peterson, R.A. (2001). On the use of college students in social science research: insights from a second-order meta-analysis. Journal of Consumer Research, 28, 450–461.
   Web of Science ®Google Scholar
• Petty, R. E., & Cacioppo, J. T. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York: Springer-Verlag.
   Google Scholar
• Petty, R. E. & Cacioppo, J. T. (1984). Source factors and the elaboration likelihood model of persuasion. Advances in Consumer Research, 11, 668–672.
   Google Scholar
• Petty, R. E., & Cacioppo, J.T. (1979). Issue involvement can increase or decrease persuasion by enhancing message-relevant cognitive response. Journal of Personality and Social Psychology, 37, 1915–1926.
   Web of Science ®Google Scholar
• Petty, R.E., Cacioppo, J.T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10, 135–146.
   Web of Science ®Google Scholar
• Pomerantz, E.M., Chaiken, S., and Tordesillas, S. (1995). Attitude strength and resistance processes. Journal of Personality and Social Psychology, 69(3), 408–419.
   PubMed Web of Science ®Google Scholar
• Ringle, C.M., Wende, S., & Will, A. (2005). SmartPLS (Release 2.0 (beta)). Hamburg, Germany, University of Hamburg. (http://www.smartpls.de).
   Google Scholar
• Trend Micro (2008) Web Threats: Challenges and Solutions from Accessed from http://us.trendmicro.com/imperia/md/content/us/pdf/webthreats/wp01_webthreats_080303.pdf on August 17th, 2012.
   Google Scholar
• Trumbo, C.W. (1999). Heuristic-systematic information processing and risk judgment. Risk Analysis, 19(3), 391–400.
   PubMed Web of Science ®Google Scholar
• Trumbo, C.W. (2002). Information processing and risk perception: an adaptation of the heuristic-systematic model. Journal of Communication, June, 367–382.
   Google Scholar
• Van de Velde, L., Vandermeulin, V., Hylenbroeck, G.V., Verbeke, W. (2010). Consumer information (In)sufficiency in relation to biofuels: determinants and impact, Biofuels, Bioproducts, and Biorefining, 5, 125–131.
   Web of Science ®Google Scholar
• Wadlow, T., & Gorelik, V. (2009). Security in the browser. Communications of the ACM, 52(5), 40–45.
   Web of Science ®Google Scholar
• Wash, R. (2010), Folk models of home computer security. Symposium on Usable Privacy and Security, July 14th–16th, Redmond, WA.
   Google Scholar
• White Hat. (2011). WhiteHat website security statistic report. Winter, Retrieved May 25th, 2011, from https://www.whitehatsec.com/resource/stats.html.
   Google Scholar
• Yang, Z.J., McComas, K., Gay, G., Leonard, J.P., Dannenberg, A.J., and Dillon, H. (2010). From information processing to behavioral intentions: exploring cancer patients' motivations for clinical trial enrollment. Patient Education and Counseling, 79, 231–238.
   PubMed Web of Science ®Google Scholar
• Zaichkowsky, J.L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12, 341–352.
   Web of Science ®Google Scholar