Factors Influencing Future Employees' Decision-Making to Participate in a BYOD Program: Does Risk Matter?

As people use mobile devices (smartphones, tablets, etc.) more and more in daily life, their desire grows to use the same devices at work. In response to this demand, many firms are considering to offer BYOD programs allowing their employees to bring their own devices to work and use them for business purposes. This study analyses how employees´ perceive the benefits and risk associated with BYOD. Additionally, it is investigated whether innovativeness traits can serve as predictors of BYOD program participation. For this account, a theoretical model building on net valence approaches from decision making and perceived risk theory is proposed and tested. German students with relevant work experience and close to finishing their studies (i.e. future employees) were surveyed (n = 71). \ The study demonstrates that benefits matter more than risks, at least for the suspected drivers of IT consumerization. The results show that the intention to enrol in a BYOD program is a function of perceived risk, perceived benefits and level of personal innovativeness. Among these factors, perceived benefit most strongly affects behavioural intention, whereas only safety risks proved to inhibit an individual´s intention to use privately-owned devices at work. Knowledge acquired from this study is particularly beneficial to IT executives to guide their decision to set up or adjust BYOD initiatives.

[1]  Blake Ives,et al.  IT Consumerization: When Gadgets Turn Into Enterprise IT Tools , 2012, MIS Q. Executive.

[2]  J. Jacoby,et al.  The Components of Perceived Risk , 1972 .

[3]  Björn Niehaves,et al.  AIS Electronic , 2017 .

[4]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[5]  Martin Fishbein,et al.  Readings in attitude theory and measurement , 1968 .

[6]  Naresh K. Malhotra,et al.  Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research , 2006, Manag. Sci..

[7]  M. S. Cunningham.  The Major Dimensions of Perceived Risk , 1967 .

[8]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[9]  H. Winklhofer,et al.  Index Construction with Formative Indicators: An Alternative to Scale Development , 2001 .

[10]  C. Brenan.  The Genie Is Out of the Bottle. , 2015, IEEE pulse.

[11]  K. Grønhaug,et al.  Perceived Risk: Further Considerations for the Marketing Discipline , 1993 .

[12]  V. Mitchell,et al.  Understanding Consumers’ Behaviour: Can Perceived Risk Theory Help? , 1992 .

[13]  Ming-Chi Lee,et al.  Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit , 2009, Electron. Commer. Res. Appl..

[14]  H. Campbell,et al.  A Multiple Account Framework For Cost-Benefit Analysis , 2005 .

[15]  Yong Liu,et al.  A Unified Risk-Benefit Analysis Framework for Investigating Mobile Payment Adoption , 2012, ICMB.

[16]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

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

[18]  Jeffrey A. Ingalsbe,et al.  Threat Modeling the Cloud Computing, Mobile Device Toting, Consumerized Enterprise - an overview of considerations , 2011, AMCIS.

[19]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[20]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[21]  Björn Niehaves,et al.  The Effect of Private IT Use on Work Performance - Towards an IT Consumerization Theory , 2013, Wirtschaftsinformatik.

[22]  M. Prensky.  Digital Natives, Digital Immigrants , 2001 .

[23]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[24]  C. Ruiz-Mafé,et al.  The role of consumer innovativeness and perceived risk in online banking usage , 2009 .

[25]  Enrico Ciavolino,et al.  Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models , 2013 .

[26]  Richard Oliver.  Why the BYOD boom is changing how we think about business it , 2012 .

[27]  J. Twenge.  Generation Me: Why Today's Young Americans Are More Confident, Assertive, Entitled--and More Miserable Than Ever Before , 2006 .

[28]  W. H. Rodgers.  Consumer behavior as risk taking; a new model and new hypotheses. , 1969 .

[29]  Jason Bennett Thatcher,et al.  What Cell Phones Mean in Young People's Daily Lives and Social Interactions , 2011 .

[30]  Stacy Campbell,et al.  Generational differences in psychological traits and their impact on the workplace , 2008 .

[31]  Heiko Gewald,et al.  Risks and benefits of business process outsourcing: A study of transaction services in the German banking industry , 2009, Inf. Manag..

[32]  Hsi‐Peng Lu,et al.  An empirical study of the effect of perceived risk upon intention to use online applications , 2005, Inf. Manag. Comput. Security.

[33]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[34]  E. Rogers.  Diffusion of Innovations , 1962 .

[35]  Jan Marco Leimeister,et al.  Consumerization - IT Innovations from the Consumer Market as a Challenge for Corporate IT , 2012, Bus. Inf. Syst. Eng..

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

[37]  Walter Brenner,et al.  European Conference on Information Systems ( ECIS ) 5-15-2012 EXPLORING THE SHADOWS : IT GOVERNANCE APPROACHES TO USER-DRIVEN INNOVATION , 2012 .

[38]  J. Foster,et al.  Why do narcissists take more risks? Testing the roles of perceived risks and benefits of risky behaviors. , 2009 .

[39]  W. Keith Campbell,et al.  The lure of the noisy ego: Narcissism as a social trap. , 2008 .

[40]  Mark A. Harris,et al.  The Need for BYOD Mobile Device Security Awareness and Training , 2013, AMCIS.

[41]  Bill Holtsnider,et al.  IT Manager's Handbook: Getting your New Job Done , 2010 .

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

[43]  Naveen Donthu,et al.  Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics , 2006 .

[44]  Veronica Liljander,et al.  Estimating Zones of Tolerance in Perceived Service Quality and Perceived Service Value , 1993 .

[45]  Wynne W. Chin.  How to Write Up and Report PLS Analyses , 2010 .

[46]  Kurt Lewin,et al.  Level of aspiration. , 1944 .

[47]  J. P. Peter,et al.  A Comparative Analysis of Three Consumer Decision Strategies , 1975 .

[48]  Jan-Bernd Lohmöller,et al.  Latent Variable Path Modeling with Partial Least Squares , 1989 .

[49]  Lorne Olfman,et al.  Determinants of Corporate Web Services Adoption: A Survey of Companies in Korea , 2011, Commun. Assoc. Inf. Syst..

[50]  Venkataraman Ramesh,et al.  Web 2.0 proclivity: understanding how personal use influences organizational adoption , 2009, SIGDOC '09.

[51]  Cheryl Burke Jarvis,et al.  The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. , 2005, The Journal of applied psychology.

[52]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[53]  Richard Baskerville,et al.  Individual information systems as a research arena , 2011, Eur. J. Inf. Syst..

[54]  Jeffrey M. Voas,et al.  BYOD: Security and Privacy Considerations , 2012, IT Professional.

[55]  J. Arnett,et al.  The Evidence for Generation We and Against Generation Me , 2013 .