Abstract
Neuroheadsets use electroencephalography (EEG) to record cognitive activity and some neuroheadsets are even capable of deciphering basic mental commands. As a result, users might believe there are privacy risks associated with these devices, which can hinder their usage. In this article, we postulate that Perceived Privacy Risk has both a direct negative influence on the Behavioral Intention to Use neuroheadsets and an indirect negative influence on the Behavioral Intention to Use neuroheadsets through Perceived Usefulness. After collecting 107 completed online questionnaires and applying a structural equation modeling approach, our findings indicate that neuroheadsets are at least partly utilitarian technologies whose usage is influenced by Perceived Usefulness. However, we were not able to confirm a significant influence of Perceived Privacy Risk on either the Behavioral Intention to Use neuroheadsets or their Perceived Usefulness. These findings suggest that neuroheadset manufacturers need to emphasize the instrumental benefits of their devices, but that they do not currently need to address people’s potential negative perceptions of neuroheadsets in terms of privacy risks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Since at the time of the survey (June 2015), the neuroheadset under study, Emotiv Insight, was not yet released to the general public, we only included Behavioral Intention to Use, and not Actual System Use, into our research model. Behavioral Intention to Use is a commonly accepted mediator between people’s beliefs and their actual behavior. It “capture[s] the motivational factors that influence a [person’s] behavior; they are indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behavior” (Ajzen 1991, p. 181).
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Alarcón-del-Amo, M.-C., Lorenzo-Romero, C., & Gomez-Borja, M.-A. (2012). Analysis of acceptance of social networking sites. African Journal of Business Management, 6(29), 8609–8619.
Brief, A. P., & Aldag, R. J. (1977). The intrinsic-extrinsic dichotomy: Toward conceptual clarity. Academy of Management Review, 2(3), 496–500.
Chen, R. (2013). Member use of social networking sites—an empirical examination. Decision Support Systems, 54(3), 1219–1227.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80.
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.
Egea, J. M. O., & González, M. V. R. (2010). Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27(1), 319–332.
Emotiv. (2015). Insight. http://emotiv.com/insight.php. Accessed July 24, 2015.
Ernst, C.-P. H., Pfeiffer, J., & Rothlauf, F. (2013). Hedonic and utilitarian motivations of social network site adoption. Johannes Gutenberg University Mainz, Working Paper.
Ernst, C.-P. H. (2014). Risk hurts fun: The influence of perceived privacy risk on social network site usage. In AMCIS 2014 Proceedings.
Featherman, M. (2001). Extending the technology acceptance model by inclusion of perceived risk. In AMCIS 2001 Proceedings. Paper 148.
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.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151.
Hu, T., Poston, R. S., & Kettinger, W. J. (2011). Nonadopters of online social network services: Is it easy to have fun yet? Communications of the Association for Information Systems, 29(1), 441–458.
Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1), 45–71.
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564.
Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online social networks: Why we disclose. Journal of Information Technology, 25(2), 109–125.
Lal, T. N., Schröder, M., Hill, N. J., Preissl, H., Hinterberger, T., Mellinger, J., Bogdan, M., Rosenstiel, W., Hofmann, T., Birbaumer, N., & Schölkopf, B. (2005). A brain computer interface with online feedback based on magnetoencephalography. In International Conference on Machine Learning 2005 Proceedings.
Miller, M. (2015). The Internet of things: How smart TVs, smart cars, smart homes, and smart cities are changing the world. Indianapolis, IN: Que.
Pavlou, P. A. (2001). Integrating trust in electronic commerce with the technology acceptance model: Model development and validation. In AMCIS 2001 Proceedings.
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.
Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of Marketing Research, 13(2), 184–188.
Poslad, S. (2009). Ubiquitous computing: Smart devices environments and interactions. Chichester, UK: Wiley.
PwC. (2014). Health wearables: Early days. White Paper.
Ringle, C. M., Wende, S., & Becker, J. -M. (2015). SmartPLS 3. http://www.smartpls.com.
Rose, J., & Fogarty, G. J. (2006). Determinants of perceived usefulness and perceived ease of use in the technology acceptance model: senior consumers’ adoption of self-service banking technologies. In Academy of World Business, Marketing and Management Development Conference Proceedings.
Sitkin, S. B., & Pablo, A. L. (1992). Reconceptualizing the determinants of risk behavior. Academy of Management Review, 17(1), 9–38.
Statista. (2014). Prognose zum Umsatz mit Wearable Technology in Europa von 2013 bis 2018 (in Milliarden Euro). http://de.statista.com/statistik/daten/studie/322222/umfrage/prognose-zum-umsatz-mit-wearable-computing-geraeten-in-europa. Accessed June 29, 2015.
Tan, S. J. (1999). Strategies for reducing consumers’ risk aversion in Internet shopping. Journal of Consumer Marketing, 16(2), 163–180.
Tehrani, K., & Andrew, M. (2014). Wearable technology and wearable devices: Everything you need to know. http://www.wearabledevices.com/what-is-a-wearable-device. Accessed June 29, 2015.
Vala, N., & Trivedi, K. (2014). Brain computer interface: Data acquisition using non-invasive Emotiv Epoc neuroheadset. International Journal of Software & Hardware Research in Engineering, 2(5), 127–130.
Van Der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Von Stetten, A., Wild, U., & Chrennikow, W. (2011). Adopting social network sites—the role of individual IT culture and privacy concerns. In AMCIS 2011 Proceedings. Paper 290.
Westin, A. F. (1968). Privacy and freedom. New York, NY: Atheneum.
Wu, Y. A., Ryan, S., & Windsor J. (2009). Influence of social context and affect on individuals’ implementation of information security safeguards. In ICIS 2009 Proceedings. Paper 70.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Mayer, F.M., Nguyen, D.T., Ernst, CP.H. (2016). An Analysis of the Potential Influence of Privacy Risk on Neuroheadset Usage. In: Ernst, CP. (eds) The Drivers of Wearable Device Usage. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-30376-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-30376-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30374-1
Online ISBN: 978-3-319-30376-5
eBook Packages: Business and ManagementBusiness and Management (R0)