Abstract
Cloud computing is spreading rapidly, but little research has been conducted to understand the effects of the protective perspective on acceptance by individuals. The goal of this study was to explore the impact of protective behavior on individuals’ adoption of cloud computing. Specifically, the study will concentrate on personal cloud storage service (CSS), widely recognized as a protective technology. Our theoretical framework endeavored to combine protection motivation theory with the extended unified theory of acceptance and use of technology and focused on the effect of threat in the context of CSS. This study tested two proposed models (Model A, Model B) by structural equation modeling using data from 392 individuals. Data were collected for people in voluntary CSS-usage settings through an online survey. Model A and model B explained 61.0% and 60.4% of the variance in the behavioral intention to accept CSS, respectively. Results showed that CSS adoption is a protective measure against data loss and identified that threat influence CSS adoption. Threat elements directly do not influence behavioral intention to adopt CSS. Threat elements mostly acted as antecedents of independent variables; perceived severity and perceived vulnerability directly influenced technology attributes (performance expectancy, effort expectancy), and showed slightly different results in contextual attributes (facilitating conditions, social influence) and consumer attributes (hedonic motivation, habits). Our study offers a unique perspective for researchers and professionals in understanding the impact of threat factors on information technology domains where protective technologies are operated. Thus, we recommend that academics and business practitioners in the realm of cloud computing should consider user’s protective motivation view of adopting CSS when designing, developing, delivering and spreading it.
Acknowledgments
The draft of this study was presented at the 2020 Academy of Management Annual Conference.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data will be available on request from the authors.
Additional information
Notes on contributors
Chi-Hoon Song
Chi-Hoon Song is an assistant professor at Department of IT Management, Sunmoon University, South Korea. His current research interest includes data intelligence, machine learning (anomaly detection) and AI-driven method for the impact of technology on organizations and individuals.
Ji-Hwan Lee
Ji-Hwan Lee is an associate professor of strategic and international management in the College of Business and Kim Jaechul Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology, Seoul, South Korea. His current research interest includes corporate governance, international business, and technology management.
Taewoo Roh
Taewoo Roh is an associate professor of strategy and international business at School of International Studies, Hanyang University, South Korea. His research interests center on institutional theory in environmental sustainability, green management, global strategy, knowledge and innovation, and business ethics.