It probably happened long before you managed an employee project for the first time, long before you cracked open your first management book and even before you got your first job. Such gestures conferred favored status upon you while engendering snickers and jealousy on the part of your peers. Little did you know that you were engaging in organizational politics, though of course you didn't refer to it in such heady terms. You were simply learning to get along in the world, as young people do. Now that you're a small-business owner, you might be surprised at how little the world has changed — and how that youthful lesson about power and politics resonates with adults.
The difference now is that the proverbial shoe is on the other foot, making it vital that you expand your world view to the four domains of organizational politics. As you gain a firmer grasp on these domains, you can do more than understand the r ole of power in effective leadership; you can learn to manage the power you possess at your fingertips to enhance productivity among your employees.
Some of these connotations still exist, especially when they're linked to the internal politics in business. Even the Business Dictionary defines organizational politics as:. In this case, you may favor a less ego-centric definition of organizational politics from the Harvard Business Review:. In other words, it's possible that organizational politics has gotten a bad rap, the University of Minnesota says:.
You may have experienced it when your classmates dissed you in the lunchroom or promised to meet you in the hallway after the last bell — only to leave you waiting and stranded. The political landscape of youth may have left you feeling confused and befuddled, so you'll be relieved to know that the terrain is actually easier to identify and navigate as an adult — if you're willing to get an up-close view.
For example, we could expand the Senior Community Service Employment Program aimed at job training and placement for low-income older adults, or increase financial and psychosocial support for older caregivers through the expansion of consumer-directed care programs. These factors that involve programs, policies, and organizations correspond to the institutional or organizational capacity concepts used in previous frameworks on productive engagement in later life Sherraden et al.
In sum, this model suggests that there are modifiable conditions to increase the utilization of human capital in productive activities. The lower half of the model depicts the flow of older workers and volunteers into organizations, including businesses, nonprofit and public organizations, and educational institutions. As shown in Table 1 , reinforcing feedback loop R1 is illustrated by the arrow from productive activity to change in organizational capacity. This represents the flow of older adults into organizations, providing person-power and enabling organizations to better fulfill their missions, leading to the increased engagement of older adults in work and volunteer roles.
Reinforcing feedback loop R2 is illustrated by the arrow from productive activity to changes in attitudes and expectations about older workers and volunteers; ultimately, this may reduce age bias and increase organizational capacity by creating more supportive work environments for older adults—thus increasing productive activity. This model also depicts the process of building and depleting the stock of human capital.
The human capital of health depletes as individuals reach the end of their lives, and too often in these extended years, financial capital also depletes. Yet, older adult human capital is depreciating more rapidly than it might due to the failure of current social structures to maintain and replenish it.
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Much of the effort of gerontologists has been to prevent this depletion e. Reinforcing feedback loop R3 illustrates that productive activity leads to building human capital , which increases the stock of human capital , further increasing productive activity.
In sum, active engagement can lead to better health, increased education, and increased financial security. Again, this reinforcing feedback loop is not as vital as it could be due to of the current constraints on productive engagement. A similar story is depicted about the building and depletion of the stock of social capital in older adults. Currently, social capital is depleted when older adults separate from work and education institutions or reduce their community participation.
This depletion of social capital reduces productive engagement because social capital—for example, a professional network—can lead to paid and unpaid work. However, a reinforcing feedback loop can be created if older adults are productively engaged, thereby building social capital.
The engagement of older adults in productive activities can also have negative effects on the human capital of older adults, represented by the red link from productive activity to depreciation of human capital. For example, working longer in certain employment conditions can reduce health and mental health, and the negative effects of caregiving on older adults are widely documented.
For example, working older adults may not be able to provide caregiving services to the family. The model also shows how policies to support productive engagement can mitigate the negative effects on the rate of human capital depletion. Currently, these efforts are perhaps best represented by caregiver and grandparent support programs.
The arrow from productive activity to building family resources represents the direct contribution by older adults to family resources. For example, if older adults work longer and achieve higher levels of economic security, they can contribute to family finances. In other words, via productive activity , older adults can contribute to the stock of family resources rather than drain it. Higher levels of family resources , then, lead to an increased ability to provide family caregiving to older adults when they need it.
This is illustrated by reinforcing feedback loop R4. There are several subsystems in the larger model that can be detailed. If we change attitudes, expectations, programs, and policies to support older adults as employees, the years of paid employment may be extended. These more sustainable public insurance systems lead back to increased human capital of older adults e.
This is an example of a reinforcing feedback loop. There is another subsystem that regards family caregiving to older adults with functional and financial limitations.http://derivid.route1.com/map370.php
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Family resources—a stock of physical, psychological, and financial assets—are often depleted when older family members require assistance due to the depletion of their human capital. Balancing feedback loop B1 illustrates how family caregiving to older adults leads to the depreciation of family resources.
As families provide caregiving assistance to older adults, their resources are depleted, leaving a smaller stock of family resources from which to provide additional caregiving. This loop is balanced because as there are fewer family resources to use for caregiving of older adults, there are also fewer resources to be depleted. As depicted in balancing feedback loop B2 , the provision of family caregiving to older adults slows the depreciation of human capital in older adult care recipients, serving as a protective factor. This feedback loop may also be true for older adults with dwindling finances who need financial support from family members to maintain certain conditions.
On the other hand, if the stock of human capital of older adults is not diminished—if they remain physically, mentally, and financially healthier—there will not be as much demand for care from family. This would lead to a far healthier society on several measurements. This manuscript describes the development of a dynamic and complex conceptual model of the productive engagement of older adults, building on frameworks that have come before. We aimed to demonstrate the effects of multiple and interlocking factors associated with engaging older adults as workers, volunteers, and caregivers.
The model illustrates how changes in one factor can have wide-ranging and reciprocal impacts on other factors. We believe that the model illuminates more clearly than previous models several key points about the productive engagement of older adults. First, scholars studying productive engagement have long argued that the level of participation by older adults in paid and unpaid work can be most effectively influenced by extra-individual factors like programs, policies, and organizations. This model visually highlights the prominent and widely reciprocal effects of factors outside the individual attributes of older people.
For example, this model depicts the system-wide effects of expanding programs that engage older volunteers e. Previous models seem to include individual factors and extra-individual factors more symmetrically and do not call out as clearly the fundamental assumptions about the primary role of organizational and policy arrangements in promoting productive engagement. This model spotlights that increasing the flow of human capital into productive activities is essential to achieve these multiple benefits. Positive effects on society are achieved by increasing the number of experienced workers and volunteers flowing into organizations, the extension of working years that affect public entitlement programs, and the number of older caregivers who aid family members.
Positive effects on older individuals are achieved by the reciprocal relationship between productive activities and building or replenishing human and social capital. As such, this model can be used to explicitly identify and illustrate the unintended consequences of changes to the system. Indeed, Alvor Svanborg suggested that the biggest dividend of productive engagement would come from postponing decline associated with aging.
The central role of human capital—both in building and depleting it—is clear in this model. Going forward, SD offers both theoretical and analytical means to guide program and policy developments aimed at maintaining and using the human capital of the older population. In this paper, we have presented an initial and conceptual SD model that underlies the individual-, family-, and societal-level interrelationships of productive engagement in later life.
As such, this conceptual model represents an early stage of theory specification e. Figure 3 represents a first and necessary step in developing a more complex, accurate, and testable model of productive engagement in later life through the lens of SD. The process of improving the model is iterative, in which we first lay out our assumptions and qualitatively test the logic of the model. Going further, we can apply quantitative parameter values—such as estimates of initial conditions and rates of change that determine flows—drawn from the existing literature and extant data.
The ultimate goal of SD modeling and simulation is to more accurately articulate theory while empirically testing leverage points for future interventions and social change. The following two sections discuss in more detail the potential next steps for theory development and empirical development using SD. The goal of theory development is to increase confidence that the SD model reflects the actual structure of the system. Once a preliminary conceptual model is developed such as in Figure 3 , we can then build confidence in the model by identifying errors and omissions through an iterative process of qualitative review using the most current literature.
This iterative process can be enhanced through the development a simulation model based on this qualitative structure. SD provides a set of meta-theoretical rules for formulating causal relationships in a similar way as multivariate regression analysis provides a set of meta-theoretical rules for formulating statements about the associations among variables. The chief difference is that SD models focus on feedback relationships that are represented as a system of coupled and ordinary differential equations Forrester, In other words, SD relies heavily on calculus-based mathematics instead of the more traditional statistics-based mathematics used in the social sciences.
Essentially, we are trying to ground the model in rough estimates from empirical data and the literature. Consider a simple reinforcing feedback loop between the stock of social capital and productive activity shown in Figure 3. Productive activity —which we will operationalize as volunteering for this example—has been shown to increase the number of friends Morrow-Howell, To complete this feedback loop, having higher levels of social capital have been shown to lead to higher levels of volunteering i. Estimates of the effect of friendship network size on levels of volunteering could be used to provide rough estimates for early versions of the quantified simulation models.
The process of building a coherent model in which the units of these stocks, flows, and auxiliary variables are consistent and the effects are both logically and mathematically sound occurs through error identification and correction. A basic test of model structure asks whether the quantified simulation model will allow us to consider whether the structure of the system and parameter estimates can reproduce past behavior in the system.
Through running multiple iterations and refinements, revisions to the structure allow scholars to develop new understanding how and at what rate social capital may grow and depreciate. Adding complexity to the model, then, we could further explore the link between the capacity of organizations and the development of social capital , and so on. Even though this process uses extant data, it is important to stress that this work focuses primarily on theory development. Further, using tests of statistical significance to test SD models is problematic, as the ultimate goal of the model-building process is to fail to reject the null hypothesis that our model replicates the real world Barlas, However, one often can rule out a number of theories that seemed plausible through verbal reasoning and grounding in the published studies, due to the fact that these models could not generate the observed patterns of behavior.
Part of the challenge of confidence building in SD models is that the very nature of complexity makes it hard to draw logically valid inferences about the nonlinear relationships between variables that involve accumulations, delays, and feedback Sterman, Face validity and replications of empirical trends are not sufficient to build confidence that a model is an adequate representation of the structure of a system. An example includes how education and training influence the rate of building human capital in our model while accounting for factors in other subsystems that have dynamic relationships with these variables.
Although simple linear relationships between factors may be evident, human capital appreciation is embedded in and influenced by multiple feedback loops, such as productive activity and the development and depreciation of social capital. These loops may interact with education and training in a way that cannot be inferred through simple linear relationships.
Thus, the result of using SD modeling with simulation is a formal verification of the logical consistency of a core theory around which one can build a progressive program of research. Having formally verified a theory in terms of its logical consistency is valuable but can be pushed further through the development and testing of hypotheses through simulation. A simulation model might lead to a specific hypothesis about the influence of feedback mechanisms that ultimately lead to a final result. For example, a series of simulations could test the hypothesis that programs and policies to support productive engagement of older adults as workers and volunteers do more to reduce age bias in the workplace than programs and policies that directly target the age bias of younger colleagues.
Simulations of this sort could lead to the identification of stronger leverage points to reduce age bias. In many cases, the data needed for SD models already exist and come from observational and prospective studies, natural experiences, and re-analyzing results from systematic reviews and meta-analyses. Extant trends, associations, and prevalence estimates from secondary data and published studies—such as from the Health and Retirement Study and its sister studies, MIDUS, the Current Population Survey, and others—can be used to test, calibrate, and further refine this simulation model and examine potential leverage points.
A well-established experience from SD is that no single source of quantitative data contains all of the information needed to build a model and run a series of simulations regarding the productive engagement of older adults, or any other complex system Forrester, Instead, this process involves utilizing estimates from the various data sources that exist and, when needed, collecting or estimating new data points.
For example, a hypothesis may exist where conditions have been observed but the data have not been collected e. Or, a hypothesis may exist about the relative relationship between two variables under a condition that has not yet been observed e. This type of simulation modeling would enable scholars to test hypotheses that cannot be currently tested in the real world due to time, resource, or other natural constraints. Furthermore, simulation models can also reveal the absence of important data, which can then be generated through primary research efforts and used to test the model or enhance the robustness of its specification.
Finally, SD models can also be used to design pilot studies to explore novel hypotheses in the real world. For example, one might use a formal simulation model to discover and develop policy interventions that increase human capital through productive engagement in later life, and then test these interventions in a pilot study while paying close attention to the intermediate mechanisms and predictors of outcomes suggested in the simulation model. The model could thus serve as a guide for the design and implementation strategy for the pilot study, as well as a framework for understanding both the outcomes and the specific inflection points and mechanisms through which an outcome failed to be met.
SD can help to identify and assess the effects of changes at key leverage points. SD models could then estimate the short, intermediate, and long-term ramifications within the larger system, including changes in organizational capacity, human capital of older adults, demand for caregiving, and attitudes and expectations about older adults.
Conceptualizing Productive Engagement in a System Dynamics Framework
Our model, created through a synthesis of the fields of productive engagement in later life and SD, suggests that these and other high-impact, system-level leverage points may exist. Future simulations—following the iterative procedures outlined in this report—will determine when, and under what conditions, these changes make a meaningful difference.
Supplementary data are available at Innovation in Aging online. All authors participated in the group model building sessions. Morrow-Howell and C. Halvorsen represented the expertise on productive engagement. Hovmand, C. Lee, and E. Ballard represented the expertise on system dynamics. All authors contributed to production of the system dynamics model and contributed to the writing of the manuscript. National Center for Biotechnology Information , U. Journal List Innov Aging v. Innov Aging.
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Published online Sep Find articles by Nancy Morrow-Howell. Find articles by Cal J Halvorsen. Find articles by Peter Hovmand. Find articles by Carmen Lee. Find articles by Ellis Ballard. Author information Copyright and License information Disclaimer. Louis, MO E-mail: ude. This article has been cited by other articles in PMC.
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. Translational Significance System dynamics can illuminate dynamic, complex, and reciprocal relationships and guide theoretical and empirical understanding to increase productive engagement in later life. Current Conceptualizations of Productive Engagement Bass and Caro , presented the first conceptual framework regarding the antecedents of productive engagement.
Open in a separate window. Figure 1. Auxiliary Variables In principle, all equations in a dynamic model can be written as a function of stock and flow variables; however, this often obscures the logic of the hypothesized causal mechanisms forming a feedback loop. Figure 2. Feedback Loops Feedback mechanisms or loops are created as causal chains and can generally be divided into reinforcing and balancing feedback loops. Our Model: A New Perspective on Productive Engagement in Later Life The results of the group model building include a stock and flow diagram of productive engagement in later life and a set of explanations regarding its concepts and relationships.
Figure 3. Table 1. Exemplar Feedback Loops. Reinforcing Feedback Loops Title R1. This, in turn, leads to increased capacity for organizations to fulfill their missions. Title R2. More older adults contributing to the organization could lead to changes in attitudes and expectations about older adults, thereby reducing aging bias. Reductions in age discrimination could further expand the capacity of organizations.
Title R3. Title R4. Increased family resources can be utilized to provide assistance to the older adults when needed. Balancing Feedback Loops Title B1. With fewer resources, members provide less caregiving to older adults, reducing the amount of family resource depreciation. Title B2. Contributions of the New Model This manuscript describes the development of a dynamic and complex conceptual model of the productive engagement of older adults, building on frameworks that have come before.
Going Forward In this paper, we have presented an initial and conceptual SD model that underlies the individual-, family-, and societal-level interrelationships of productive engagement in later life. Theory Development The goal of theory development is to increase confidence that the SD model reflects the actual structure of the system.
Empirical Development Having formally verified a theory in terms of its logical consistency is valuable but can be pushed further through the development and testing of hypotheses through simulation. Conclusion SD can help to identify and assess the effects of changes at key leverage points. Supplementary Material Supplementary data are available at Innovation in Aging online. Conflict of Interest None reported. Supplementary Material Supplementary Materials Click here for additional data file. Acknowledgments All authors participated in the group model building sessions.
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