Abstract
Although computer technology may be particularly useful for older adults (e.g., for communication and information access), they have been slower adopters than their younger counterparts. Perceptions about computers, such as perceived usefulness and perceived ease of use, can pose barriers to acceptance and universal access (Davis in MIS Q 13(3):319–340, 1989). Therefore, understanding the precursors to these perceptions for older adult non-computer users may provide insight into the reasons for their non-adoption. The authors examined the relationship between perceived usefulness and perceived ease of use of a computer interface designed for older users and demographic, technology experience, cognitive abilities, personality, and attitudinal variables in a sample of 300 non-computer-using adults between the ages 64 and 98, selected for being at high risk for social isolation. The strongest correlates of perceived usefulness and perceived ease of use were technology experience, personality dimensions of agreeableness and openness to experience, and attitudes. The emotional stability personality dimension was significantly correlated with perceived ease of use though not perceived usefulness. Hierarchical regression analysis revealed that attitudes (i.e., self-efficacy, comfort, and interest) remained predictive of perceptions of usefulness and ease of use when technology experience and personality variables were accounted for. Given that attitudes are more malleable than other variables, such as demographic and cognitive abilities, these findings highlight the potential to increase technology acceptance through positive experiences, appropriate training, and educational campaigns about the benefits of computers and other technologies.
Similar content being viewed by others
References
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)
Czaja, S.J., Lee, C.C.: Older adults and information technology: opportunities and challenges. In: Jacko, J.A. (ed.) The human computer-interaction handbook, pp. 825–840. CRC Press, Boca Raton, FL (2012)
Bureau, U.S.C.: Projections of the population by selected age groups and sex for the United States: 2015 to 2060. United States Census Bureau, Washington, DC (2012)
Williams, D., Roper Starch Worldwide, Inc., AARP: The grandparent study 2002 report. American Association for Retired Persons, Washington, DC (2002)
Charness, N., Boot, W.R.: Aging and information technology use potential and barriers. Curr. Dir. Psychol. Sci. 18(5), 253–258 (2009)
Green, C.S., Bavelier, D.: Exercising your brain: a review of human brain plasticity and training-induced learning. Psychol. Aging. 23(4), 692–701 (2008)
Boot, W.R., Blakely, D.P., Simons, D.J.: Do action video games improve perception and cognition? Front. Psychol. 2, 226 (2011)
Falling through the net: a survey of the “Have Nots” in rural and urban America. National Telecommunications and Information Administration [NTIA], Washington, DC (1995)
Falling through the net: toward digital inclusion. A report on Americans’ access to technology tools. National Telecommunications and Information Administration [NTIA], Washington, DC (2000)
A nation outline: how Americans are expanding their use of the internet. National Telecommunications and Information Administration [NTIA], Washington, DC (2002)
Project, P.I.A.L.: Demographics of internet users (2013)
Fox, S.: Americans living with disability and their technology profile. Pew Research Center, Washington, DC (2011)
Zickuhr, K., Madden, M.: Older adults and internet use. PEW Research Center, Washington, DC (2012)
Morrell, R.W., Dailey, S.R., Stoltz-Loike, M., Mayhorn, C.B., Echt, K.V.: Older adults and information technology: a compendium of scientific research and web site accessibility guidelines. National Institute on Aging, Washington, DC (2005)
Boot, W.R., Charness, N., Czaja, S.J., Sharit, J., Rogers, W.A., Fisk, A.D., Mitzner, T.L., Lee, C.C., Nair, S.: The computer proficiency Questionnaire (CPQ): assessing low and high computer proficient seniors. The Gerontologist (2013) [Epub ahead of print]
Fox, S.: Older Americans and the internet. Pew Internet and American Life Project, Washington, DC (2004)
Morrell, R.W., Mayhorn, C.B., Bennett, J.: A survey of World Wide Web use in middle-aged and older adults. Hum. Factors. 42, 175–182 (2000)
King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manag. 43, 740–755 (2006)
Davis, F.D., Venkatesh, V.: Toward preprototype user acceptance testing of new information systems: implications for software project management. IEEE Trans. Eng. Manag. 51(1), 31–46 (2004)
Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008)
Porter, C.E., Donthu, N.: Using the technology acceptance model to explain how attitudes determine internet usage: the role of perceived access barriers and demographics. J. Bus. Res. 9, 999–1007 (2006)
Venkatesh, V., Morris, M.G.: Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q. 24(1), 115–139 (2000)
Dishaw, M.T., Strong, D.M.: Extending the technology acceptance model with task–technology fit constructs. Inf. Manag. 36(1), 9–21 (1999)
Davis, F.D., Venkatesh, V.: A critical assessment of potential measurement biases in the technology acceptance model: three experiments. Int. J. Hum Comput Stud. 45(1), 19–45 (1996)
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)
Plouffe, C.R., Hulland, J., Vandenbosch, M.: Richness versus parsimony in modeling technology adoption decisions: understanding merchant adoption of a smart card-based payment system. Inf. Syst. Res. 12(2), 208–222 (2001)
Karahanna, E., Agarwal, R., Angst, C.: Reconceptualizing compatibility beliefs in technology acceptance. MIS Q. 30(4), 781–804 (2006)
Venkatesh, V., Davis, F.D.: A model of the antecedents of perceived ease of use: development and test. Decis. Sci. 27, 451–481 (1996)
Nov, O., Ye, C.: Users’ personality and perceived ease of use of digital libraries: the case for resistance to change. J. Am. Soc. Inform. Sci. Technol. 59(5), 845–851 (2008)
Wixom, B.H., Todd, P.A.: A theoretical integration of user satisfaction and technology acceptance. Inf. Syst. Res. 16(1), 85–102 (2005)
Zhou, T., Lu, Y.: Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput. Hum. Behav. 27(2), 883–889 (2011)
Czaja, S.J., Charness, N., Fisk, A., Hertzog, C., Nair, S., Rogers, W., Sharit, J.: Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (CREATE). Psychol. Aging 21(2), 333–352 (2006)
De Haan, J.: A multifaceted dynamic model of the digital divide. IT Soc. 1(7), 66–88 (2004)
Freese, J., Rivas, S.: Cognition, personality, and the sociology of response to social change: the case of internet adoption. Paper presented at the annual meetings of the Gerontological Society of America, Orlando, Florida (2005)
Pratt, R.M.C., K. M. Extraversion, the predictor of system adoption? The effect of personality traits on acceptance. In: The Academy of Management (AOM) Meeting. Atlanta, GA (2006)
Punnoose, A.: Determinants of intention to use eLearning based on the technology acceptance model. J. Inf. Technol. Educ. Res. 11(1), 301–337 (2012)
Digman, J.M.: Personality structure: emergence of the five-factor model. Annu. Rev. Psychol. 41, 417–440 (1990)
Goldberg, L.R.: The development of markers for the big-five factor structure. Psychol. Assess. 4(1), 26–42 (1992)
John, O.P., Srivastava, S.: The big-five trait taxonomy: history, measurement, and theoretical perspectives. Handb. Personal. Theory Res. 2, 102–138 (1999)
Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the big-five personality domains. J. Res. Pers. 37, 504–528 (2003)
Zweig, D., Webster, J.: Personality as a moderator of monitoring acceptance. Comput. Hum. Behav. 19, 479–493 (2003)
Mitzner, T.L., Boron, J.B., Fausset, C.B., Adams, A.E., Charness, N., Czaja, S.J., Dijkstra, K., Fisk, A.D., Rogers, W.A., Sharit, J.: Older adults talk technology: technology usage and attitudes. Comput. Hum. Behav. 26(6), 1710–1721 (2010)
Burnett, J.S., Mitzner, T.L., Charness, N., Rogers, W. A.: Understanding predictors of computer communication technology use by older adults. In: The Human Factors and Ergonomics Society 55th Annual Meeting. Human Factors and Ergonomics Society, Santa Monica, CA (2011)
Madden, M.: America’s online pursuits-the changing picture of who’s online and what they do. The pew internet and American life project, Washington, DC (2003)
Reitan, R.M.: The validity of the trail making test as an indicator of organic brain damage. Percept. Mot. Skills 8, 271–276 (1958)
Ekstrom, R.B., French, J. W., Harman, H. H., Dermen, D.: Manual for kit of factor-referenced cognitive tests. Education Testing Service, Princeton, NJ (1976)
McCabe, D.P., Robertson, C.L., Smith, A.D.: Age differences in stroop interference in working memory. J. Clin. Exp. Neuropsychol. 27, 633–644 (2005)
Rosen, W.: The UCLA loneliness scale (Version 3): reliability, validity, and factor structure. J. Pers. Assess. 66, 20–40 (1980)
Shipley, W.C.: A self-administering scale for measuring intellectual impairment and deterioration. J. Psychol. 9, 371–377 (1940)
Zachary, R.A.: Shipley institute of living scale: revised manual. Western Psychological Services, Los Angeles (1986)
Wilkinson, G.S.: Wide range achievement test administration manual. Wide Range, Inc., Wilmington, DE (1993)
Loyd, B.H., Gressard, C.: Reliability and factoral validity of computer attitude scale. Educ. Psychol. Measur. 44(2), 501–505 (1984)
Hall, C.D., et al.: Cognitive and motor mechanisms underlying older adults’ ability to divide attention while walking. Phys. Ther. 91(7), 1039–1050 (2011)
Chesney, T.: Measuring the context of information systems use. J. Inf. Technol. Manag. 29(3), 9–20 (2008)
Szajna, B.: Empirical evaluation of the revised technology acceptance model. Manag. Sci. 42(1), 85–92 (1996)
Cresci, M.K., Yarandi, H., Morrell, R.W.: Pro-nets versus no-nets: differences in urban odler adults’ predilections for Internet use. Educ. Gerontol. 36, 1–21 (2010)
Cresci, M.K., Yarandi, H., Morrell, R.W.: Digital divide and urban older adults. Comput. Inf. Nursing 28(2), 88–94 (2010)
Stolp, S., Zabrucky, K.M.: Contributions of metacognitive and self-regulated learning theories to investigations of calibration of comprehension. Int. Electron. J. Element. Educ. 2(1), 7–31 (2009)
Older adults and technology use. Pew Research Center (2014)
Jay, G.M., Willis, S.L.: Influence of direct computer experience on older adults’ attitudes towards computers. J. Gerontol. 47(4), 250–257 (1992)
Acknowledgments
This research was supported in part by a grant from the National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE; www.create-center.org). The authors would also like to thank Chin Chin Lee, Laura Matalenas, Sank Nair, Shih-Hua Fu, and Minsun Park for their assistance with various aspects of the project.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mitzner, T.L., Rogers, W.A., Fisk, A.D. et al. Predicting older adults’ perceptions about a computer system designed for seniors. Univ Access Inf Soc 15, 271–280 (2016). https://doi.org/10.1007/s10209-014-0383-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10209-014-0383-y