The Influence of Technology Characteristics on Privacy Calculus: A Theoretical Framework

The notion of privacy calculus has been used to explain the risk-benefit analysis information technology users perform when asked to provide personal information. This study extends the privacy calculus model by proposing a theoretical framework in which technology characteristics (radicalness and complexity) have moderating effects on the benefit-value and risk-value relationships. The framework also suggests that perceive benefit is a multidimensional construct formed by utilitarian, hedonic, and social benefits. This study is contextualized for smartphone users who are faced with the decision to allow access to their personal information in order to use mobile applications. Propositions to guide future research are developed and implications of the proposed framework are discussed.

[1]  Viswanath Venkatesh,et al.  Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research , 2010, J. Manag. Inf. Syst..

[2]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[3]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[4]  Linda L. Price,et al.  An Investigation into the Social Context of Early Adoption Behavior , 1992 .

[5]  G. Soutar,et al.  The Role of Perceived Risk in the Quality-Value Relationship: A Study in a Retail Environment , 1999 .

[6]  R. Staelin,et al.  A Model of Perceived Risk and Intended Risk-handling Activity , 1994 .

[7]  Hock-Hai Teo,et al.  The Role of Push-Pull Technology in Privacy Calculus: The Case of Location-Based Services , 2009, J. Manag. Inf. Syst..

[8]  Viswanath Venkatesh,et al.  Model of Adoption and Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle , 2005, MIS Q..

[9]  K. B. Monroe,et al.  The Effects of Price - Comparison Advertising on Buyers'''' Perceptions of Acquisition Value, Transa , 1998 .

[10]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[11]  A. Markman,et al.  Entrenched Knowledge Structures and Consumer Response to New Products , 2001 .

[12]  Mayuram S. Krishnan,et al.  The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization , 2006, MIS Q..

[13]  Joseph S. Valacich,et al.  Technology Adoption by Groups: A Valence Perspective , 2005, J. Assoc. Inf. Syst..

[14]  Anthony D. Miyazaki,et al.  Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility , 2010 .

[15]  Gary Anthes,et al.  Invasion of the mobile apps , 2011, Commun. ACM.

[16]  V. Mahajan,et al.  Delight by Design: The Role of Hedonic versus Utilitarian Benefits , 2008 .

[17]  H. Jeff Smith,et al.  Information Privacy: Measuring Individuals' Concerns About Organizational Practices , 1996, MIS Q..

[18]  Hun Choi,et al.  Contribution to Quality of Life: A New Outcome Variable for Mobile Data Service , 2007, J. Assoc. Inf. Syst..

[19]  V. Zeithaml.  Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence: , 1988 .

[20]  K. Zhou,et al.  The Effects of Strategic Orientations on Technology- and Market-Based Breakthrough Innovations , 2005 .

[21]  R. Chandy,et al.  Organizing for Radical Product Innovation: The Overlooked Role of Willingness to Cannibalize , 1998 .

[22]  Mun Y. Yi,et al.  Understanding the Role of Individual Innovativeness in the Acceptance of IT-Based Innovations: Comparative Analyses of Models and Measures , 2006, Decis. Sci..

[23]  Gregory J. Brush,et al.  Comparing the Perceived Value of Information and Entertainment Mobile Services , 2008 .

[24]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[25]  Suprateek Sarker,et al.  Understanding mobile handheld device use and adoption , 2003, CACM.

[26]  Alladi Venkatesh,et al.  Beyond Adoption: Development and Application of a Use-Diffusion Model , 2004 .

[27]  Viswanath Venkatesh,et al.  A Longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges , 2001, MIS Q..

[28]  B. Schneier.  Inside risks , 1997 .

[29]  Wesley J. Johnston,et al.  Cooperative adoption of complex systems: a comprehensive model within and across networks , 2005 .

[30]  Maria Sääksjärvi,et al.  Assessing multifunctional innovation adoption via an integrative model , 2011 .

[31]  R. Teas,et al.  Perceived Value: Mediating Role of Perceived Risk , 2001 .

[32]  Scott B. MacKenzie,et al.  Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques , 2011, MIS Q..

[33]  P. Attewell.  Technology Diffusion and Organizational Learning: The Case of Business Computing , 1992 .

[34]  Detmar W. Straub,et al.  Specifying Formative Constructs in Information Systems Research , 2007, MIS Q..

[35]  S. Fournier,et al.  Paradoxes of Technology: Consumer Cognizance, Emotions, and Coping Strategies , 1998 .

[36]  Thiagarajan Ravichandran,et al.  Organizational assimilation of complex technologies: an empirical study of component-based software development , 2005, IEEE Transactions on Engineering Management.

[37]  J. Gentry,et al.  Characteristics of Adopters and Non-Adopters of Home Computers , 1983 .

[38]  Paul A. Pavlou,et al.  Predicting E-Services Adoption: A Perceived Risk Facets Perspective , 2002, Int. J. Hum. Comput. Stud..

[39]  Lynda Aiman-Smith,et al.  Implementing New Manufacturing Technology: The Related Effects of Technology Characteristics and User Learning Activities , 2002 .

[40]  Naresh K. Malhotra,et al.  Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model , 2004, Inf. Syst. Res..

[41]  Chin-Lung Hsu,et al.  Why do people play on-line games? An extended TAM with social influences and flow experience , 2004, Inf. Manag..

[42]  Joseph A. Cazier,et al.  The Impact of Consumer Perceptions of Information Privacy and Security Risks on the Adoption of Residual RFID Technologies , 2008, Commun. Assoc. Inf. Syst..

[43]  H. Raghav Rao,et al.  A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents , 2008, Decis. Support Syst..

[44]  Mary Beth Rosson,et al.  The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing , 2011, Decis. Support Syst..