Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Performance Enhancement of OMP Algorithm for Compressed Sensing Based Sparse Channel Estimation in OFDM Systems Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

9. How Risk Tolerance Constrains Perceived Risk on Smartphone Users’ Risk Behavior

verfasst von : Shwu-Min Horng, Chia-Ling Chao

Erschienen in: Information Technology - New Generations

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This study focused on smartphone users’ intention to install anti-virus software on their devices. In addition to the three tested factors, risk tolerance, perceived, and risk awareness, the models also include several demographic and behavioral variables as controlled factors including tenure of using smartphone, average online time per day, average online time using smartphone per day, gender, age, education level, and monthly expenditure. The results showed that lower risk tolerance, higher perceived risk, and higher risk awareness will lead to higher intention to installation. The constraining effect of risk tolerance on the relationship between perceived risk and intention to installation was also tested significantly. When smartphone users have risk tolerance higher than the threshold, their intention to installation will not be affected by the perceived risk.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Implementing an In-Home Sensor Agent in Conjunction with an Elderly Monitoring Network
Nächstes Kapitel When Asteroids Attack the Moon: Design and Implementation of an STK-Based Satellite Communication Simulation for the NASA-Led Simulation Exploration Experience
Literatur
1.
2.
Dagon, D., Martin, T., & Starner, T. (2004). Mobile phones as computing devices: The viruses are coming! IEEE Pervasive Computing, 3(4), 11–15.CrossRef
3.
4.
Gross, G. (2007). Groups raise concerns about cybersecurity standards, PC World, April 25.
5.
Matsubara, M. (2012). A long and winding road for cybersecurity cooperation. Harvard Asia Quarterly, 14(1/2), 103–111.
6.
Kshetri, N. (2010). Diffusion and effects of cyber-crime in developing economies. Third World Quarterly, 31(7), 1057–1079.CrossRef
7.
8.
Fitton, O., & Prince, D. (2014). The future of mobile devices: security and mobility. Lancaster University, Report ID 73272, pp. 34.
9.
Enck W. (2011). Defending users against smartphone apps: Techniques and future directions, ICISS 2011. LYNC, 7093, 49–70.
10.
Mylonas, A., Kastania, A., & Gritzalis, D. (2013). Delegate the smartphone user? Security awareness in smartphone platforms. Computers & Security, 34, 47–66.CrossRef
11.
Felt, A. P., Egelman, S., & Wagner, D. (2012). I’ve got 99 problems, but vibration ain’t one: A survey of smartphone users’ concerns. Proceedings of SPSM’, 12, 33–38.
12.
Anderson, C. L., & Agarwal, R. (2010). Practicing safe computing: A multimedia empirical examination of home computer user security behavioral intentions. MIS Quarterly, 34(3), 613–643.
13.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
14.
Egelman, S., Felt, A. P., & Wagner, D. (2013). Choice architecture and smartphone privacy: There’s a price for that. In R. Böhme (Ed.), The economics of information security and privacy (pp. 211–236). New York: Springer.CrossRef
15.
Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2010). Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3), 242–255.CrossRef
16.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly, 13(3), 319–340.CrossRef
17.
Dowling, G. R. (1986). Perceived risk: The concept and its measurement. Psychology and Marketing, 3(3), 193–210.CrossRef
18.
Tan, S. J. (1999). Strategies for reducing consumers’ risk aversion in internet shopping. Journal of Consumer Marketing, 16(2), 163–180.CrossRef
19.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474.CrossRef
20.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
21.
Kim, J. (2012). Developing an empirical model of college students’ online shopping behavior. International Journal of Interdisciplinary Social Sciences, 6(10), 81–109.CrossRef
22.
Lu, H., Hsu, C., & Hsu, H. (1993). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106–120.CrossRef
23.
Arachchilage, N. A. G., & Love, S. (2013). A game design framework for avoiding phishing attacks. Computers in Human Behavior, 29, 706–714.CrossRef
24.
Udo, G. J. (2001). Privacy and security concerns as major barriers for e-commerce: A survey study. Information Management & Computer Security, 9(4), 165–174.CrossRef
25.
Bulgurcu, B., Cavusoglu, H., & Benbasat, I. (2010). Information security policy compliance: An empirical study of rationality-based beliefs and information security awareness. MIS Quarterly, 34(3), 523–548.
26.
Schaninger, C. M. (1976). Perceived risk and personality. Journal of Consumer Research, 3(2), 95–100.CrossRef
27.
28.
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.CrossRef
29.
Constant, D., Kiesler, S., & Sproull, L. (1994). What’s mine is ours, or is it? A study of attitudes about information sharing. Information Systems Research, 5(4), 400–421.CrossRef
30.
Lee, H., Cho, H. J., Xu, W., & Fairhurst, A. (2010). The influence of consumer trait and demographics on intention to use retail self-service checkouts. Marketing Intelligence & Planning, 28(1), 46–58.CrossRef
31.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
32.
Kankanhalli, Q., Tan, B. C. Y., & Wei, K.-K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly, 29(1), 113–143.
33.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18(1), 39–50.CrossRef
34.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.CrossRef
35.
Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Hove: Psychology Press.
36.
Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 655–690). Berlin: Springer.CrossRef
37.
Bagozzi, R. P., & Edwards, J. R. (1998). A general approach for representing constructs in organizational research. Organizational Research Methods, 1(1), 45–87.CrossRef
38.
Siemsen, E., Roth, A. V., & Balasubramanian, S. (2008). How motivation, opportunity, and ability drive knowledge sharing: The constraining-factor model. Journal of Operations Management, 26(3), 426–445.CrossRef
Metadaten
Titel
How Risk Tolerance Constrains Perceived Risk on Smartphone Users’ Risk Behavior
verfasst von
Shwu-Min Horng
Chia-Ling Chao
Copyright-Jahr
2018
Buch
Information Technology - New Generations

Print ISBN: 978-3-319-54977-4

Electronic ISBN: 978-3-319-54978-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-54978-1_9