Older adults who exhibit concern for guiding and contributing to future generations, a quality known as generativity, are more likely to enjoy psychological well-being and lead fulfilling lives, according to a recent study published in The Journals of Gerontology: Series B. The study identified several predictors of generativity, with traits like social potency, openness to new experiences, social integration, personal growth, and achievement orientation emerging as the strongest contributors.
“What I like about generativity is that it not only enhances the well-being of those who practice it, but also positively impacts the lives of others,” said study author Mohsen Joshanloo, an associate professor at Keimyung University and honorary principal fellow at the University of Melbourne.
“Despite its many benefits, ageist stereotypes can undermine older adults’ capacity to fully express their generativity. Through this line of research, I aim to challenge these stereotypes and highlight the value of generative mindsets and behaviors as a catalyst for an active and fulfilling life in old age.”
“Contrary to popular perceptions of old age as a period of decline and inactivity, a growing body of empirical research and the lived experiences of many people reveal a different reality. Old age can be a time of fulfillment, productivity, and meaningful social contribution, with generativity playing a critical role in promoting active and successful aging.”
Joshanloo conducted this study using data from the third wave of the Midlife in the United States project, a comprehensive dataset designed to explore various aspects of aging in American adults. The dataset included responses from 2,830 participants aged 39 to 93, with a mean age of approximately 64. This sample was nationally representative and balanced across genders. Joshanloo’s aim was to identify the most important predictors of generativity—a concern for guiding and nurturing future generations—by analyzing a broad set of variables spanning personality, social, health, and psychological domains.
To measure generativity, the study used the contributions domain of the Loyola Generativity Scale, which captures behaviors and attitudes that reflect generative concerns (e.g., “I try to pass along the knowledge I have gained through my experiences” and “I have a responsibility to improve the neighborhood in which I live”).
The initial dataset included over 70 variables, but not all were retained for analysis. Variables with poor reliability, significant overlap with the generativity measure, or substantial missing data were excluded. After this refinement, 34 variables were selected as potential predictors. These included personality traits, indicators of mental and emotional well-being, measures of social integration, and other psychosocial factors.
Joshanloo applied machine learning techniques, specifically random forest regression, to analyze the data. This approach is particularly well-suited to exploring complex relationships among variables because it can handle non-linear interactions and high-dimensional data. The random forest model also incorporates internal cross-validation to ensure robust and reliable predictions. By running multiple decision trees and averaging their results, the method identifies which variables are most predictive of generativity.
“Comprehensive empirical investigations of predictors of generativity remain scarce,” Joshanloo explained. “To address this gap, I applied machine learning techniques to analyze a large dataset and explore a wide range of potential predictors. The overarching goal of the study was to gain a deeper understanding of the predictors of generativity and to provide data-driven insights to inform the development of new interventions and theoretical frameworks for generativity.”
The findings revealed that social potency—a trait encompassing assertiveness, persuasiveness, and leadership abilities—was the strongest predictor of generativity. This suggests that generativity is deeply social in nature, relying on the capacity to engage with and influence others. Openness to new experiences was the second most important predictor, indicating that curiosity and a willingness to embrace diversity are crucial for generative behaviors. Social integration, or the extent to which individuals feel connected to their communities, also emerged as a significant factor, underscoring the importance of social relationships in fostering generativity.
Personal growth and achievement orientation were other key predictors. These traits reflect a focus on self-improvement and industriousness, which may motivate individuals to contribute meaningfully to the lives of others. Psychological factors such as purpose in life, self-acceptance, and daily spiritual experiences also played an important role, highlighting the connection between generativity and a sense of meaning and fulfillment.
“The findings of this study portray generativity as a dynamic process of development and contribution rather than a static trait,” Joshanloo told PsyPost. “It is less about merely settling into routines or being passively benevolent, and more about active pursuit and exploration. Generativity involves taking the initiative to shape one’s social environment and build meaningful connections with others. This proactive approach challenges negative perceptions of aging that often portray later life as a period of stagnation or decline. By engaging in generative behaviors, older adulthood can become a vibrant and dynamic phase marked by activity, growth, and impact on others.”
Variables tied to hedonic well-being, such as life satisfaction, and homeostasis-oriented traits, such as emotional stability, were less predictive of generativity. This suggests that generativity is more influenced by dynamic and growth-driven psychological processes than by a pursuit of emotional comfort or stability.
“The results provide another significant insight worth highlighting,” Joshanloo said. “Pursuing a generative lifestyle is not without its challenges. Generativity can require individuals to venture out of their comfort zones, change, and take intentional action. This transformative process transcends conventional pursuits of happiness and comfort, prioritizing instead social engagement and personal growth. It demands a willingness to explore, experiment, take risks, and develop social skills. Consequently, generativity necessitates continued and sustained effort, requiring a long- term commitment to personal and communal growth.”
Interestingly, demographic variables like income and gender, as well as health-related factors such as chronic conditions, were much less predictive. In other words, generativity appears to be influenced more by psychological and social characteristics than by external or physical conditions.
“This suggests that generativity can be cultivated, even in challenging circumstances,” Joshanloo said. “It serves as a reminder that our capacity to make a positive impact is not solely shaped by external circumstances or demographic factors; rather, our inner resources and determination play a crucial role.”
The study highlights the psychological and social factors associated with generativity in older adults. However, the cross-sectional design means that causal relationships cannot be established; it remains unclear whether the identified traits lead to generativity or if generative individuals develop these traits over time. Additionally, the analysis relied on the variables available in the MIDUS dataset, which, although extensive, may not capture all relevant factors. For example, cultural or environmental influences on generativity were not directly assessed.
Future research could address these gaps by incorporating longitudinal designs to explore how generativity evolves over time and across different life stages. Expanding the range of variables to include cultural, environmental, and systemic factors could also provide a more holistic understanding of what drives generativity.
“Identifying the factors that predict and potentially contribute to generativity can provide a solid foundation for creating targeted interventions that empower older adults to realize their potential for generativity,” Joshanloo explained. “This vision calls for further research to design, implement, and evaluate such interventions, along with psychoeducational programs that promote generativity. These initiatives can help older adults challenge the myth that aging inevitably leads to passivity, decline, and disengagement. I believe this study offers novel insights to guide the development of generativity-focused interventions and hope it inspires more research in this area.”
The study, “Key Predictors of Generativity in Adulthood: A Machine Learning Analysis,” was published December 12, 2024.
