Exploring Digital Legacy Planning Among Older Adults: A Proposed Study Through Diffusion of Innovations
DOI:
https://doi.org/10.55578/fepr.2601.004Keywords:
Digital Legacy Planning, Preventive Innovation, Older Adults, Social Influence, Behavioral IntentionAbstract
Digital legacy planning refers to the intentional organization and management of digital assets and digital identity in anticipation of incapacity or death. Despite the growing volume and personal significance of digital materials, engagement in digital legacy planning remains limited, particularly among older adults. This article presents a conceptual and methodological foundation for a proposed quantitative study examining factors that shape older adults’ behavioral intention to engage in digital legacy planning, conceptualized as a preventive innovation and a socially embedded process situated within postdigital adult life. Guided by Diffusion of Innovations theory, Moore and Benbasat’s Perceived Characteristics of Innovation framework, and insights from adult development and intergenerational learning, the paper outlines a cross-sectional survey design. The proposed study introduces a partially standardized collaborative prototype of digital legacy planning through a brief informational video to anchor participants’ understanding of the innovation and involves an online questionnaire administered to adults aged 60 and older residing in an age-restricted community in central Texas. The instrument adapts validated measures of perceived relative advantage, compatibility, complexity, trialability, and observability, and extends the framework by incorporating two social influence constructs: explicit encouragement and perceived social value. Behavioral intention to engage in digital legacy planning is specified as the dependent variable, with planned analyses including descriptive statistics, reliability assessment, and multiple linear regression. The proposed design is intended to clarify whether digital legacy planning functions as a preventive innovation among older adults and to identify key perceptual and relational factors shaping intention. By articulating a theoretically grounded and educationally informed study design, the paper aims to inform future empirical research and the development of adult education and communication interventions that support socially meaningful digital legacy planning.
References
1. Albers, R., Sadeghian, S., Laschke, M., & Hassenzahl, M. (2023, April). Dying, death, and the afterlife in human–computer interaction: A scoping review. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–16).
2. American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.
3. Brubaker, J. R., Hayes, G. R., & Dourish, P. (2013). Beyond the grave: Facebook as a site for the expansion of death and mourning. The Information Society, 29(3), 152–163.
4. Cupar, D., Dragija Ivanović, M., & Grgeč, A. (2023). Personal digital legacy: Findings from an exploratory study among citizens of Croatia. Technology in Society, 72, 102196.
5. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
7. Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37(2), 183–190.
8. Dearing, J. W. (2009). Applying diffusion of innovation theory to intervention development. Research on Social Work Practice, 19(5), 503–518.
9. Dingfelder, H. E., & Mandell, D. S. (2011). Bridging the research-to-practice gap in autism intervention: An application of diffusion of innovation theory. Journal of Autism and Developmental Disorders, 41(5), 597–609.
10. Doyle, D. T., & Brubaker, J. R. (2023). Digital legacy: A systematic literature review. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), Article 100.
11. Dunkel, C. S., & Harbke, C. R. (2016). A review of measures of Erikson’s stages of psychosocial development: Evidence for a general factor. Journal of Adult Development, 24(1), 58–76.
12. Erikson, E. H. (1950). Childhood and society. W. W. Norton & Company.
13. Feenberg, A. (2019). Postdigital or predigital? Postdigital Science and Education, 1(1), 8–9.
14. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage.
15. Hayes, S., & Jandrić, P. (2021). Postdigital artistic positionality and its potentials for cultural education. In Transdisziplinäre Begegnungen zwischen postdigitaler Kunst und Kultureller Bildung: Perspektiven aus Wissenschaft, Kunst und Vermittlung (pp. 17–28). Springer Fachmedien Wiesbaden.
16. Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations (2nd ed.). Sage Publications.
17. Kawashima, D., Kempe, T., Koga, Y., Kubota, S., Urata, Y., Watanabe, T., … Tsujimoto, T. (2025). Attitudes toward digital immortality and related factors among older adults: An exploratory study from a developmental perspective. OMEGA—Journal of Death and Dying, 00302228251336498.
18. Jarana‐Díaz, T., Romero‐Martín, M., Ponce‐Blandón, J. A., & Jiménez‐Picón, N. (2021). Integrative review of related factors and defining characteristics of lack of family integrity. International Journal of Nursing Knowledge, 32(1), 44–52.
19. King, D. A., & Wynne, L. C. (2004). The emergence of family integrity in later life. Family Process, 43(1), 7–21.
20. Mazman, S. G., Usluel, Y. K., & Çevik, V. (2009). Social influence in the adoption process and usage of innovation: Gender differences. International Journal of Behavioral, Cognitive, Educational and Psychological Sciences, 1(4), 229–232.
21. Merriam, S. B., & Baumgartner, L. M. (2020). Learning in adulthood: A comprehensive guide (4th ed.). Jossey-Bass.
22. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
23. Murray, C. E. (2009). Diffusion of innovation theory: A bridge for the research–practice gap in counseling. Journal of Counseling & Development, 87(1), 108–116.
24. Park, L. Y. J., Oh, Y. W., & Sang, Y. (2024). Digital access, digital literacy, and afterlife preparedness: Societal contexts of digital afterlife traces. Social Media + Society, 10(3), 20563051241274676.
25. Peters, M. A., Jandrić, P., & Hayes, S. (2023). Postdigital-biodigital: An emerging configuration. Educational Philosophy and Theory, 55(1), 1–14.
26. Peters, M. A., Jandrić, P., & Hayes, S. (2021). Revisiting the concept of the edited collection: Bioinformational philosophy and postdigital knowledge ecologies. Postdigital Science and Education, 3(2), 283-293.
27. Rogers, E. M. (2002). Diffusion of preventive innovations. Addictive Behaviors, 27(6), 989–993.
28. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
29. Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In D. W. Stacks & M. B. Salwen (Eds.), An integrated approach to communication theory and research (pp. 432–448). Routledge.
30.Savin-Baden, M. (2021). Digital afterlife and the spiritual realm. Chapman and Hall/CRC.
31. Savin-Baden, M. (2019). Postdigital afterlife? Postdigital Science and Education, 1(2), 303–306.
32. Savin-Baden, M., Burden, D., & Taylor, H. (2017). The ethics and impact of digital immortality. Knowledge Cultures, 5(2), 178–196.
33. Savin-Baden, Maggi, John Reader, and Ibrar Bhatt. “The Many Faces of Postdigital Theologies.” Postdigital Science and Education 5, no. 3 (2023): 933-944.
34. Sinclair, C., & Hayes, S. (2019). Between the post and the com-post: Examining the postdigital ‘work’ of a prefix. Postdigital Science and Education, 1(1), 119–131.
35. Stephan, A. (2021). Intergenerational learning in the family as an informal learning process: A review of the literature. Journal of Intergenerational relationships, 19(4), 441-458.
36. Young, H. P. (2009). Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review, 99(5), 1899–1924.
37. Young, J., Pekane, A., & Kautondokwa, P. (2025). Behavioural predictors that influence digital legacy management intentions among individuals in South Africa. arXiv preprint arXiv:2502.18542.
38. Ünal, E., & Özdemir, A. (2019). Old age and aging. In Recent studies in health sciences. Akademisyen Kitabevi.
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