Conceptualizing Productive Engagement in a System Dynamics Framework

Abstract

Gerontologists have argued that the growing human capital of the aging population can be better marshaled as a resource for families, communities, and society at large. Additionally, this active, purposeful engagement can produce positive outcomes for older adults themselves. In this manuscript, we propose that existing conceptual frameworks articulating antecedents and outcomes of productive engagement, including working, volunteering, and caregiving can be improved using a system dynamics (SD) approach. Through a series of five unstructured group model-building sessions, experts from gerontology and systems science developed a qualitative SD model of the productive engagement of older adults. The model illustrates the reciprocal and dynamic nature of the stocks of human capital of older adults, social capital of older adults, and family resources; the engagement of older adults in productive activities; and the social and organizational variables that affect the flow and depletion of these stocks. Given this is the first attempt to develop a SD model for productive engagement in later life, the model is preliminary and heuristic. However, it offers a new approach to advancing theory and research on productive engagement in later life. Further, it can guide the development of mathematical models to estimate the effects of changes in any part of this system.

Sense of Community Mediating Between Age-Friendly Characteristics and Life Satisfaction of Community-Dwelling Older Adults

“…On a more positive note, a larger proportion of this age cohort is also likely to remain healthy as they age. More of them will thus be capable of continuing in employment or other forms of contribution to society for longer (Chan and Cao, 2015;Morrow-Howell et al, 2017). The psychological sense of community has been found to be a predictor of volunteerism in older adults (Omoto and Packard, 2016).…”

A community-based system dynamics approach suggests solutions for improving healthy food access in a low-income urban environment

“…Once a preliminary hypothesized structure is established, the logical consistency of the model could be tested through quantification of parameters and initial conditions, and the specification of the model structure as a system of coupled and ordinary differential equations [30]. This translation of a qualitative causal map to a formal quantitative simulation model allows for confidence building through identification of logical inconsistencies, tests of the model’s ability to replicate empirical data of historical trends, and identification of data gaps where quantitative data is not available for the specification of the model, as described in [31]. With greater confidence in the model, this formal simulation model would then allow researchers and policymakers to ask more complex “what if” questions about the potential impact of policy proposals and the relative leverage of various discrete and combined intervention strategies on healthy food access.…”

The diversity of interest in later-life entrepreneurship: Results from a nationally representative survey of Americans aged 50 to 70

“…Social capital was assessed using two variables, including marital status (1 = married, 0 = not married) and conducting volunteer work (1 = a few times per year or more, 0 = less than a few times per year). Our operationalization of social capital follows the individual-focused usage that is common in the literature on working and volunteering in later life (e.g., [54–56]). However, a deep body of scholarship has operationalized social capital as factors related to social norms, reciprocity, trust, the structure of relationships between and among actors, and neighborhood cohesion, among other factors (e.g., [41,57]) that may also play an important role in relation to self-employment in later life.…”