Lifestyle-technology fit: Theorizing the role of self-identity in IS research |
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| Affiliation: | 1. VA HSR&D Center for Health Information and Communication, Roudebush VA Medical Center, Indianapolis, IN, United States;2. Regenstrief Institute, Indianapolis, IN, United States;3. Department of Communication Studies, Indiana University-Purdue University, Indianapolis, IN, United States;4. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States;5. Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, United States;6. VA Center for Clinical Management Research, Ann Arbor, MI, United States;7. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States;8. Department of Psychology, Indiana University-Purdue University, Indianapolis, IN, United States;1. Department of Psychology, University of British Columbia, Kelowna, British Columbia, Canada;2. Department of Family Medicine, Brody School of Medicine, East Carolina University, Greenville, NC, USA;3. Center for Health Disparities, East Carolina University, Greenville, NC, USA;4. School of Social Work, University of South Florida, Tampa, FL, USA;5. Department of Psychology, East Carolina University, Greenville, NC, USA |
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| Abstract: | Sociology and modernist theories have long emphasized the central role of lifestyle in processes of self-identity and attitude formation. Furthermore, lifestyle has been used to great effect in marketing and health research to predict attitudes, cognitions, and behaviors, but has largely been ignored in the IS field. In this study, we demonstrate the potential usefulness of incorporating lifestyle into IS research by using lifestyle cluster segmentation in the context of technology adoption. Based on a U.S. national random sample of 402 non-cloud service users, we propose, analyze, and validate a multi-faceted model of cloud technology adoption that integrates technology attributes—the dominant predictors in IS adoption and acceptance models—with a range of demographic, domestic, leisurely and professional variables for providing a holistic theoretical understanding of and practical insights into the technology adoption process. |
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| Keywords: | IT diffusion and adoption Mobile computing Cloud computing Lifestyle-technology fit Cluster analysis |
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