Abstract
Change is frequently afoot in the nonprofit sector, both in the wider institutional environment in which nonprofits operate and within the organizations themselves. Environmental transformations—funding sources, supply and demand for collective goods, and administrative norms—create the circumstances in which organizations operate. Internally, change involves the alteration of goals, practices, and personnel. To explore how multiple aspects of change intersect across levels, we ask how organizations’ practices influence their experience of and reaction to changes in the environment. Turning open systems theories inside out, we argue that internal planning, routines, and missions give rise to organizational mindsets that imbue evolving environmental circumstances with meaning. We illustrate our argument using a unique longitudinal dataset of 196 representative 501(c)(3) public charities in the San Francisco Bay Area from 2005 to 2015 to assess both accelerators and obstacles of change. Empirically, we investigate predictors of organizational insolvency and the ability to serve constituents in the wake of the Great Recession. We find that strategic planning decreases the likelihood of insolvency whereas an orientation toward the needy increases spending. We conclude with our contributions to understanding of multi-level organizational change and nonprofit strategy.
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Notes
The findings presented in this paragraph are from a separate analysis of the entire population of nonprofit organizations in the San Francisco Bay Area during the time of the financial crisis. Additional information on this analysis is available upon request.
In a markedly different approach, the literature on organizational ecology places populations of organizations at center stage, examining change as a question of population-level selection processes rather than as organization-level adaptation processes (cf. Hannan and Freeman 1989).
The Powell et al. (2005) project built on more than a decade of rich network data but lacked detailed organizational data. In contrast, our sample of organizations is not as densely interconnected, but we have considerable information on the organizations themselves over time, providing a rare opportunity to see how see how structures and routines evolve through time.
All four organizations with missing data closed prior to the onset of the crisis in late 2007. By the end of 2010, a total of eight organizations had closed. Two closed in 2006, one in 2007 and another in early 2008; four closed in 2010. Between 2005 and their closure, two had been insolvent during five or more years, two were insolvent once, and four had not been insolvent. Though insolvency does not guarantee closure, it raises the risk significantly. In a separate fixed-effects analysis of organizational closure (available on request), we find that insolvency in the previous year increases the likelihood of closure 6.8 times (p < .01).
Although a healthy financial mix insulated organizations from financial turmoil, executive directors lamented difficulties regardless of the primary funding source. Informants reported problems with funding from community foundations as they struggled to report success metrics associated with activities aimed at mitigating the crisis for their constituents. Finally, if organizations provide services of any sort to clients, they are far less likely to experience insolvency (IRR = .086, p < .001), but if they rely on fees for service, they are slightly more likely (IRR = 1.021, p < .05). One performing arts group in our sample shifted their funding model from government income to fees for services as government funding was cut during the crisis, but this was associated with a significant drift in the organization’s mission.
The frequency with which plans are revised is suggestive of this sentiment: 12% revised annually, 80% revised biannually, and the remainder either do not revise plans or do so less frequently.
As a robustness check, we also modeled the average spending changes across the entire period, finding significant effects supportive of our argument and consistent with the findings presented here. Such models, however, offer low-overall explanatory power. Additionally, the effects of annual funding changes on annual expenditure changes, for example, are muddled when average change is used as an outcome. Because the inclusion of sector (NTEE) controls complicates straightforward interpretation of the interaction effects in models 3.2, 4.2, and 5.2, such controls are excluded here. Still, coefficients and standard errors are robust to the inclusion of sector controls.
Annual changes in revenue explain a good deal of variance in each of the models. In 2007-2008, the addition of this variable increases the R2 .10, in 2008-2009, the variable increases R2 .25, and in 2009-2010, the variable increases the R2 .09.
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Horvath, A., Brandtner, C. & Powell, W.W. Serve or Conserve: Mission, Strategy, and Multi-Level Nonprofit Change During the Great Recession. Voluntas 29, 976–993 (2018). https://doi.org/10.1007/s11266-017-9948-8
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DOI: https://doi.org/10.1007/s11266-017-9948-8