How perceived security risk affects intention to use smart home devices: A reasoned action explanation

Abstract Smart home devices form a significant part of the consumer IoT market yet they have potential security risks. Little is known about how security risk perceptions influence householders’ decisions to adopt smart home devices. In order to examine how perceptions of security risks influence intentions to use smart home devices, we test a new model of how perceived security risk affects intention to use smart devices. This model draws on the reasoned action approach from social psychology and proposes that security risk perceptions have an indirect effect on smart home adoption decisions by influencing cognitions that have a more immediate, effect on adoption intentions. The results demonstrated that perceived security risk does have an effect on intentions to use smart home devices and both attitude to use of smart home devices and perceived control over secure use of these devices play a role in this effect, confirming the value of modelling perceived risks as determinants of more direct cognitive antecedents of consumer intentions. We also investigated the role of age and education in smart home adoption decisions and found that older and more highly educated people are more likely to take their own assessments of security risk into account when they make decisions about adoption of smart home devices. Given the role of perceived security risk in influencing consumers’ intentions to use smart home devices, enabling, influencing, and guiding consumers to develop the knowledge and skills they need for secure use of smart home devices is of particular importance. The findings of this study suggest several paths of action for information security professionals to achieve this.

[1]  Steven Furnell,et al.  Information security conscious care behaviour formation in organizations , 2015, Comput. Secur..

[2]  Xuebing Dong,et al.  Understanding usage of Internet of Things (IOT) systems in China: Cognitive experience and affect experience as moderator , 2017, Inf. Technol. People.

[3]  I. Ajzen,et al.  Predicting and Changing Behavior: The Reasoned Action Approach , 2009 .

[4]  Jui-Sheng Chou,et al.  Smart meter adoption and deployment strategy for residential buildings in Indonesia , 2014 .

[5]  Xuequn Wang,et al.  "Security begins at home": Determinants of home computer and mobile device security behavior , 2017, Comput. Secur..

[6]  Dong-Hee Shin,et al.  Conceptualizing and measuring quality of experience of the internet of things: Exploring how quality is perceived by users , 2017, Inf. Manag..

[7]  Patricia Baudier,et al.  Smart home: Highly-educated students' acceptance , 2020, Technological Forecasting and Social Change.

[8]  Lóránt Dávid,et al.  Applying RFID technology in the retail industry – benefits and concerns from the consumer’s perspective , 2015 .

[9]  Jinyoung Han,et al.  Comprehensive Approaches to User Acceptance of Internet of Things in a Smart Home Environment , 2017, IEEE Internet of Things Journal.

[10]  I. Ajzen,et al.  A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action , 1992 .

[11]  Ponnurangam Kumaraguru,et al.  Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions , 2010, CHI.

[12]  Franziska Roesner,et al.  End User Security and Privacy Concerns with Smart Homes , 2017, SOUPS.

[13]  Fatemeh Zahedi,et al.  Individuals' Internet Security Perceptions and Behaviors: Polycontextual Contrasts Between the United States and China , 2016, MIS Q..

[14]  Per E. Pedersen,et al.  Consumer adoption of RFID-enabled services. Applying an extended UTAUT model , 2014, Information Systems Frontiers.

[15]  Andreas Pitsillides,et al.  Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures , 2014, IEEE Communications Surveys & Tutorials.

[16]  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..

[17]  Yi Zhou,et al.  Understanding the Mirai Botnet , 2017, USENIX Security Symposium.

[18]  Hangjung Zo,et al.  Industrial Management & Data Systems , 2017 .

[19]  Chin-Lung Hsu,et al.  An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives , 2016, Comput. Hum. Behav..

[20]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[21]  Jungwoo Shin,et al.  Who will be smart home users? An analysis of adoption and diffusion of smart homes , 2018, Technological Forecasting and Social Change.

[22]  Paul van Schaik,et al.  Familiarity with Internet threats: Beyond awareness , 2017, Comput. Secur..

[23]  Zied Mani,et al.  Drivers of consumers’ resistance to smart products , 2017 .

[24]  Mo Adam Mahmood,et al.  Employees' adherence to information security policies: An exploratory field study , 2014, Inf. Manag..

[25]  Charlie Wilson,et al.  Benefits and risks of smart home technologies , 2017 .

[26]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[27]  R. W. Rogers,et al.  Attitude Change and Information Integration in Fear Appeals , 1985 .

[28]  Yonghee Kim,et al.  A study on the adoption of IoT smart home service: using Value-based Adoption Model , 2017 .

[29]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[30]  Younghwa Lee,et al.  An empirical investigation of anti-spyware software adoption: A multitheoretical perspective , 2008, Inf. Manag..

[31]  Earlence Fernandes,et al.  Security Analysis of Emerging Smart Home Applications , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[32]  Sunil Hazari,et al.  An Empirical Investigation of Factors Influencing Information Security Behavior , 2008 .

[33]  Sadie Creese,et al.  The Perfect Storm: The Privacy Paradox and the Internet-of-Things , 2016, 2016 11th International Conference on Availability, Reliability and Security (ARES).

[34]  Lingling Gao,et al.  A unified perspective on the factors influencing consumer acceptance of internet of things technology , 2014 .

[35]  Steven Furnell,et al.  Assessing the security perceptions of personal Internet users , 2007, Comput. Secur..

[36]  Jane Klobas,et al.  I Want It Anyway: Consumer Perceptions of Smart Home Devices , 2018, J. Comput. Inf. Syst..

[37]  Sanjay Goel,et al.  Shared Benefits and Information Privacy: What Determines Smart Meter Technology Adoption? , 2017, J. Assoc. Inf. Syst..

[38]  J. Hair Multivariate data analysis : a global perspective , 2010 .

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

[40]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[41]  Chin-Lung Hsu,et al.  Exploring Factors Affecting the Adoption of Internet of Things Services , 2018, J. Comput. Inf. Syst..

[42]  Verlin B. Hinsz,et al.  The Value of the Theory of Planned Behavior, Perceived Control, and Self-Efficacy Expectations for Predicting Health-Protective Behaviors , 1993 .

[43]  Malcolm Robert Pattinson,et al.  Factors that Influence Information Security Behavior: An Australian Web-Based Study , 2015, HCI.

[44]  I. Ajzen The theory of planned behavior , 1991 .

[45]  D. Bastos,et al.  Internet of Things: A survey of technologies and security risks in smart home and city environments , 2018, IoT 2018.