AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Emerging Applications on Smart Phones: The Role of Privacy Concerns and Its Antecedents on Smart Phones Usage


Affiliations
1 Department of Information Systems, Shaqra University, (Imam Muhammad Ibn Saud Islamic University), Riyadh, Saudi Arabia
 

Many applications on smart Phones can use various sensors embedded in the mobiles to provide users’ private information. This can result in a variety of privacy issues that may lessening level of mobile apps usage. To understand this issue better the researcher identified the ischolar_main causes of privacy concerns. The study proposed a model identifies the ischolar_main causes of privacy concerns and perceived benefits based on our interpretation for information boundary theory. The proposed model also addresses the usage behavior and behavioral intention toward using mobile apps by using the Theory of Planned Behavior. The result shows that “Cultural values” alone explains 70% of “Perceived privacy concerns” followed by “Self-defense” which explains around 23% of “Perceived privacy concerns”, and then “Context of the situation” with 5%. Whereas, the findings show that “Perceived effectiveness of privacy policy” and “Perceived effectiveness of industry self-regulation” both are factors which have the ability to reduce individuals “Perceived privacy concerns” by 9% and 8% respectively.

Keywords

Mobile Phone, Information Boundary Theory, Communication Privacy Management Theory, Perceived Privacy Concerns, Theory of Planned Behavior.
User
Notifications
Font Size

  • CISCO. (2020). Cisco Annual Internet Report (2018–2023). Retrieved from CISCO Website https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf.

  • Sutanto, J., Palme, E., Chuan-Hoo, T., & Chee Wei, P. (2013). ADDRESSING THE PERSONALIZATION-PRIVACY PARADOX: AN EMPIRICAL ASSESSMENT FROM A FIELD EXPERIMENT ON SMARTPHONE USERS. MIS Quarterly, 37(4), 1141-A5.

  • Angwin, J., & Valentino-DeVries, J. (2011, April 22). Apple, Google Collect User Data. The Wall Street Journal, U.S. Edition. Retrieved from http://www.wsj.com/articles/SB10001424052748703983704576277101723453610

  • Hutchinson, R. (2011, January 26). 50 Percent of iPhone Apps Can Track User Data. Applie News. Retrieved from http://www.geeky-gadgets.com/50-percent-of-iphone-apps-can-track-user-data-26-01-2011/

  • Federal Trade Commission. (2015). Internet of things: Privacy & Security in a Connected World. Preliminary FTC Staff Report. Retrieved from Federal Trade Commission Website: (https://www.ftc.gov/system/files/documents/reports/federal-trade-commission-staff-report-november-2013-workshop-entitled-internet-things-privacy/150127iotrpt.pdf).

  • Banisar, D. (2014, December 8). National Comprehensive Data Protection/Privacy Laws and Bills 2014 Map. Retrieved from SSRN Website: http://ssrn.com/abstract=1951416

  • Conger, S., Pratt, J. H. & Loch, K. D. (2013). Personal information privacy and emerging technologies. Info Systems J, 23, 401–417.

  • DLA Piper. (2015). DLA Piper's Data Protection Laws of the World Handbook. Retrieved from DLA Piper Website: http://dlapiperdataprotection.com/#handbook/world-map-section

  • Arab Social Media Report. (2014). Citizen Engagement and Public Services in the Arab World: The Potential of Social Media. Mohammed bin Rashid School of government, 1(6). Retrieved from Arab Social Media Report Website: http://www.arabsocialmediareport.com/

  • Phelps, J., Nowak, G., & Ferrell, E. (2000). Privacy concerns and consumer willingness to provide personal information. Journal of Public Policy and Marketing, 19(1), 27-41.

  • Al-ghaith, W. (2015). Understanding Social Network Usage: impact of co-presence, intimacy, and immediacy. International Journal of Advanced Computer Science and Applications (IJACSA), 6(8), 99-111.

  • RFID World Canada. (2015). Billion Dollar Mobile Payment Industry Using Secure NFC Technology Projected to Continue Strong Growth. Retrieved from RFID World Canada Website: http://www.rfidworld.ca/billion-dollar-mobile-payment-industry-using-secure-nfc-technology-projected-to-continue-strong-growth/2418

  • Giorgia, G., Teunissena, P., Verhagena, S. & Buista, P. (2010). GNSS remote sensing: testing a new multivariate GNSS carrier phase attitude determination method for remote sensing platforms. Advances in Space Research, 46, 118–129.

  • Daniel, O. (2007). Security and privacy on the semantic web. In Milan Petkovic and Willem Jonker, editors, Security, Privacy and Trust in Modern Data Management, Data-Centric Systems and Applications. Berlin Heidelberg: Springer.

  • Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour. Englewood Cliffs, NJ: Prentice-Hall.

  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179-211.

  • Pavlou, P. & Fygenson, M. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly, 30(1), 115-143.

  • Hernandez M., & Mazzon J. (2007). Adoption of internet banking: proposition and implementation of an integrated methodology approach. The International Journal of Bank Marketing, 25(2), 72-88.

  • Marler, Janet H., Fisher, Sandra L., & Ke, Weiling. (2009). employee self-service technology acceptance: a comparison of pre-implementation and post-implementation relationships. Personnel Psychology, 62(2), 327-358.

  • Warren, S., & Brandeis, L. (1890). The Right to Privacy. Harvard Law Review. IV (5).

  • Li, Y. (2014). The impact of disposition to privacy, website reputation and website familiarity on information privacy concerns. Decision Support Systems, 57(1), 343–354.

  • Li, H., Sarathy, R., & Xu, H. (2010). Understanding situational online information disclosure as a privacy calculus. The Journal of Computer Information Systems, 62–71.

  • Koohikamali, M., Gerhart, N. & Mousavizadeh, M. (2015). Location disclosure on LB-SNAs: The role of incentives on sharing behavior. Decision Support Systems, 71(1), 78–87.

  • Alghaith, W., Sanzogni, L., & Sandhu, K. (2010). Factors Influencing the Adoption and Usage of Online Services in Saudi Arabia. Electronic Journal of Information Systems in Developing Countries (EJISDC), 40(1), 1-32.

  • Petronio, S. (1991). Communication boundary management: a theoretical model of managing disclosure of private information between marital couples. Communication Theory, 311-335.

  • Petronio, S. (2002). Boundaries of privacy: Dialectics of disclosure (xix,pp. 268). Albany, NY: State University of New York Press.

  • Chen, J., Ping, W., Xu, Y., & Tan, B.C.Y. (2009). Am i afraid of my peers? Understanding the antecedents of information privacy concerns in the online social context. In: Proceedings of the Thirtieth International Conference on Information Systems.

  • Metzger, M.J. (2007). Communication privacy management in electronic commerce. Journal of Computer-Mediated Communication, 12(2), 335–361.

  • Ji, P. & Lieber, P. S. (2010). Am I safe? Exploring relationships between primary territories and online privacy. Journal of Internet Commerce, 9(1), 3–22.

  • Zikmund, W. G. (2003). Business research methods (7th ed.). Cincinnati, OH: Thomson.

  • Xu, C., Ryan, S., Prybutok, V., & Wen, C. (2012). It is not for fun: An examination of social network site usage. Information and Management, 49(5), 210–217.

  • Taylor, S., & Todd, P.A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.

  • Moore, C., & Benbasat, I. (2001). Development of an instrument to measure the perception of adopting an information technology innovation. Information Systems Research, 2, 192-222.

  • Al-Debei, M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54.

  • Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer's dilemma. Information Technology and Management, 6(2-3), 181-202.

  • Premkumar, G., & Ramamurthy, K. (1995). The role of Interorganizational and organizational factors of the decision mode for adoption of interorganizational systems. Decision Science, 26(3), 303-336.

  • Comrey, A.L., & Lee, H.B. (1992). A first course in factor analysis. N.J.: L. Erlbaum Associates.

  • Asher, H.B. (1983). Causal modeling. Newbury Park: Sage University Press.

  • Lee, M. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141.

  • Alsharif, A. (2014). Teaching the customs, traditions and culture of Saudi Arabia through the arts in high school to promote cultural understanding and appreciation (Order No. 1589365). Available from ProQuest Dissertations & Theses Global. (1691800000).

  • Alotaibi, M. (2015). Mobile Computing Trends in Saudi Arabia: An Exploratory Study. I.J. Information Technology and Computer Science, 01, 21-32.


Abstract Views: 281

PDF Views: 115




  • Emerging Applications on Smart Phones: The Role of Privacy Concerns and Its Antecedents on Smart Phones Usage

Abstract Views: 281  |  PDF Views: 115

Authors

Waleed Al-Ghaith
Department of Information Systems, Shaqra University, (Imam Muhammad Ibn Saud Islamic University), Riyadh, Saudi Arabia

Abstract


Many applications on smart Phones can use various sensors embedded in the mobiles to provide users’ private information. This can result in a variety of privacy issues that may lessening level of mobile apps usage. To understand this issue better the researcher identified the ischolar_main causes of privacy concerns. The study proposed a model identifies the ischolar_main causes of privacy concerns and perceived benefits based on our interpretation for information boundary theory. The proposed model also addresses the usage behavior and behavioral intention toward using mobile apps by using the Theory of Planned Behavior. The result shows that “Cultural values” alone explains 70% of “Perceived privacy concerns” followed by “Self-defense” which explains around 23% of “Perceived privacy concerns”, and then “Context of the situation” with 5%. Whereas, the findings show that “Perceived effectiveness of privacy policy” and “Perceived effectiveness of industry self-regulation” both are factors which have the ability to reduce individuals “Perceived privacy concerns” by 9% and 8% respectively.

Keywords


Mobile Phone, Information Boundary Theory, Communication Privacy Management Theory, Perceived Privacy Concerns, Theory of Planned Behavior.

References