Exploring predictors of technology adoption among older adults

The purpose of this study was to investigate predictors of older adult technology adoption through a mixed methods perspective. One hundred and seventy-six older adults responded to a quantitative survey assessing their technology adoption. Four participants were selected for qualitative interviews. The mean age of participants was 74.71 years old that included an age range of 65-96 year old participants. The majority of older adults lived independently, and no participants lived in care facilities. In the quantitative phase, structural equation modeling in Mplus was used to evaluate the fit of a technology adoption model using personality, self-efficacy, perceptions of technology, and attitudes of technology as predictors. Noteworthy findings indicated the model showed a good fit predicting technology adoption. Education, perceived usefulness, and attitudes toward using technology were positively associated with technology adoption. Participant age was negatively associated with technology adoption, indicating younger older adults were significantly more likely to adopt technology. Greater levels of agreeableness predicted greater levels of perceived usefulness and self-efficacy. Additionally, a significant indirect effect was obtained from perceived usefulness via attitudes toward using technology to technology adoption. This finding indicated that greater levels of perceived usefulness influenced more positive attitudes toward technology which in turn predicted greater levels of technology adoption. The qualitative phase indicated three themes specifically highlighting the importance of 1) earlier life experiences (e.g., workplace experiences), 2) personal preferences (e.g., choices regarding keeping up with technology), and 3) societal perspectives (e.g., concern for human interaction) on technology adoption. A revised theoretical model of technology adoption is suggested, tying together the quantitative and vii

[1]  Leon G. Schiffman,et al.  Cybersenior Empowerment: How Some Older Individuals Are Taking Control of Their Lives , 2002 .

[2]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[3]  Maria Karavidas,et al.  The effects of computers on older adult users , 2005, Comput. Hum. Behav..

[4]  Maryalice Jordan-Marsh,et al.  Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective , 2010, Comput. Hum. Behav..

[5]  M. Patton.  Qualitative research and evaluation methods , 1980 .

[6]  De Nevers,et al.  Technology and Society , 1972 .

[7]  Robert F. Easley,et al.  Research Note - How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use , 2008, Inf. Syst. Res..

[8]  Katherine M. White,et al.  Personality and Self-Esteem as Predictors of Young People's Technology Use , 2008, Cyberpsychology Behav. Soc. Netw..

[9]  Arthur D. Fisk,et al.  Designing for Older Adults: Principles and Creative Human Factors Approaches , 2004 .

[10]  Marita A. O'Brien,et al.  Diffusion of Technology: Frequency of use for Younger and Older Adults , 2011, Ageing international.

[11]  Daniel Cervone,et al.  Perceived self-efficacy and everyday problem solving among young and older adults. , 2003, Psychology and aging.

[12]  Martina Ziefle,et al.  Different Perspectives on Technology Acceptance: The Role of Technology Type and Age , 2009, USAB.

[13]  A. Bandura,et al.  Self-evaluative and self-efficacy mechanisms governing the motivational effects of goal systems. , 1983 .

[14]  Elsa Marziali,et al.  A Systematic Review of Practice Standards and Research Ethics in Technology-Based Home Health Care Intervention Programs for Older Adults , 2005, Journal of aging and health.

[15]  Eulàlia Hernández-Encuentra,et al.  ICT and Older People: Beyond Usability , 2009 .

[16]  P. Costa,et al.  Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five-Factor Inventory (NEO-FFI) , 1992 .

[17]  A. Onwuegbuzie,et al.  Toward a Definition of Mixed Methods Research , 2007 .

[18]  Paul B. Baltes,et al.  Theoretical propositions of life-span developmental psychology : On the dynamics between growth and decline , 1987 .

[19]  P. Baltes,et al.  On the incomplete architecture of human ontogeny. Selection, optimization, and compensation as foundation of developmental theory. , 1997, The American psychologist.

[20]  L. Nygård,et al.  Everyday Technology Use Questionnaire: Psychometric Evaluation of a New Assessment of Competence in Technology Use , 2009 .

[21]  Sharan B. Merriam,et al.  Qualitative Research: A Guide to Design and Implementation , 2009 .

[22]  D C Park,et al.  Technology and the promise of independent living for older adults: A cognitive perspective., Impact of Technology on Successful Aging , 2003 .

[23]  R. W. Rogers,et al.  The Self-Efficacy Scale: Construction and Validation , 1982 .

[24]  S. Paunonen,et al.  Self-efficacy and the prediction of domain-specific cognitive abilities. , 2010, Journal of personality.

[25]  R. P. McDonald,et al.  Structural Equations with Latent Variables , 1989 .

[26]  Crossing the Digital Divide , 2003 .

[27]  N. Schmitt,et al.  Developing a biodata measure and situational judgment inventory as predictors of college student performance. , 2004, The Journal of applied psychology.

[28]  Miguel P Caldas,et al.  Research design: qualitative, quantitative, and mixed methods approaches , 2003 .

[29]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[30]  Paul B. Baltes,et al.  On the incomplete architecture of human ontogeny: Selection, optimization, and compensation as foundation of developmental theory , 1997 .

[31]  Richard E. Lucas,et al.  The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. , 2006, Psychological assessment.

[32]  Jean Claude Marquié,et al.  Do older adults underestimate their actual computer knowledge? , 2002, Behav. Inf. Technol..

[33]  Barbara Jean Monroe.  Crossing the digital divide , 2004 .

[34]  N. Selwyn.  The information aged: A qualitative study of older adults' use of information and communications technology , 2004 .

[35]  M. Skubic,et al.  Older adults' attitudes towards and perceptions of ‘smart home’ technologies: a pilot study , 2004, Medical informatics and the Internet in medicine.

[36]  Samuel Juni,et al.  Revised NEO personality inventory , 1995 .

[37]  U. Staudinger,et al.  Lifespan psychology: theory and application to intellectual functioning. , 1999, Annual review of psychology.

[38]  Joseph Sharit,et al.  Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). , 2006, Psychology and aging.

[39]  R. Kline.  Principles and practice of structural equation modeling, 2nd ed. , 2005 .

[40]  Galit Nimrod.  Seniors' online communities: a quantitative content analysis. , 2010, The Gerontologist.

[41]  H. Umemuro.  Computer attitudes, cognitive abilities, and technology usage among older Japanese adults , 2004 .

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

[43]  Sara J. Czaja,et al.  The impact of aging on access to technology , 2005, ASAC.

[44]  Teresa Correa,et al.  Who interacts on the Web?: The intersection of users' personality and social media use , 2010, Comput. Hum. Behav..

[45]  Diana Kuh,et al.  Do Positive Psychological Characteristics Modify the Associations of Physical Performance With Functional Decline and Institutionalization? Findings From the Longitudinal Aging Study Amsterdam , 2011, The journals of gerontology. Series B, Psychological sciences and social sciences.

[46]  P. Chisnall.  Mail and Internet Surveys: The Tailored Design Method , 2007, Journal of Advertising Research.

[47]  Carl K. Chang,et al.  Perceptions of technology among older adults. , 2013, Journal of gerontological nursing.

[48]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[49]  Gerda G. Fillenbaum,et al.  Multidimensional Functional Assessment of Older Adults: The Duke Older Americans Resources and Services Procedures , 1988 .

[50]  Todd H Wagner,et al.  The effect of age on the use of health and self-care information: confronting the stereotype. , 2003, The Gerontologist.

[51]  Arthur D. Fisk,et al.  Older adults talk technology: Technology usage and attitudes , 2010, Comput. Hum. Behav..

[52]  Richard P. Bagozzi,et al.  The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift , 2007, J. Assoc. Inf. Syst..

[53]  Yair Amichai-Hamburger,et al.  Social network use and personality , 2010, Comput. Hum. Behav..

[54]  Jennifer M. Kinney,et al.  Challenges in caregiving and creative solutions: Using technology to facilitate caring for a relative with dementia , 2003 .

[55]  M. Albert,et al.  Self-efficacy beliefs and change in cognitive performance: MacArthur Studies of Successful Aging. , 1996, Psychology and aging.

[56]  David Devins,et al.  Lifelong Learning and Digital Exclusion: Lessons from the Evaluation of an ICT Learning Centre and an Emerging Research Agenda , 2002 .

[57]  G. Jay,et al.  Influence of direct computer experience on older adults' attitudes toward computers. , 1992, Journal of gerontology.

[58]  James G. Phillips,et al.  Personality and self reported mobile phone use , 2008, Comput. Hum. Behav..

[59]  S. Thompson.  Social Learning Theory , 2008 .

[60]  Christopher B. Mayhorn,et al.  A Survey of World Wide Web Use in Middle-Aged and Older Adults , 2000, Hum. Factors.