Programming Change Among Nonprofit Human Service Organizations During the COVID-19 Pandemic

ABSTRACT

Research shows that nonprofit human service organizations are nimble in times of crisis. The surprising nature of the pandemic posed unique challenges to both the supply and demand sides of the human service sector. One way that nonprofit human service organizations responded to the pandemic environment was through programming change, including adding new programs, serving new populations, and discontinuing previous programs. Drawing from a two-wave statewide survey, our results indicate that a sizable proportion of nonprofit human service organizations engaged in these changes within the first five months of the pandemic. Such decisions were associated with both resource and mission considerations. Extant research shows how strategic change made in response to environmental shifts often leaves an imprint on organizations. As such, pandemic-era programming change may have a lasting impact on the human service sector, further evidenced by leaders’ intentions to sustain them in the years to come.

PRACTICE POINTS

1. The COVID-19 pandemic has forced a number of nonprofit human service organizations (NHSOs) to change their programming. Many NHSOs added programs, added new service populations, and discontinued programs.

2. NHSOs seem to have balanced resource and client needs as they enacted programming change, but as pandemic resources wane NHSOs will need to change further.

3. Programming change enacted during the pandemic was intended to persist after the pandemic ends. This may exert a toll as NHSOs recognize that the pandemic has required them to increase their programming load.

KEYWORDS:

• COVID-19 pandemic
• programming change
• strategic change

Introduction

Every several years, the human service sector equilibrium is punctuated with a crisis that impacts the sector (Alexander, 2000; Chen, 2022; Horvath et al., 2018; Lin & Wang, 2016; Never, 2011). In many ways, however, the challenges posed by the COVID-19 pandemic were unique (Dass et al., 2020; Kuenzi et al., 2021), being referred to as a “perfect storm” (Stewart et al., 2021) and a “black swan” event (Irvin & Furneaux, 2022). Indeed, the pandemic was unparalleled in its pervasiveness and intensity (Dass et al., 2020; Kuenzi et al., 2021; Shi et al., 2020), posing many challenges for nonprofit human service organizations (NHSOs) and their service populations (Vogel et al., 2022).

At a time when NHSO services were deeply needed, pandemic-related environmental changes altered both the supply and the demand sides of NHSO service provision (Ben-Ner & Hoomissen, 1993; Shi et al., 2020). On the supply side, NHSOs were faced with an uncertain and shifting funding environment. In many cases the pandemic had a dampening impact on funding, but there were some new opportunities such as the Payroll Protection Program (PPP) and pandemic relief grants from foundations (Stewart et al., 2021). On the demand side, both the intensity and scale of client need were heightened due to worsening economic and social conditions (Shi et al., 2020). In response to these challenges, many NHSOs needed to change the very ways they delivered their programming and to develop new services (Newby & Branyon, 2021). When making decisions about programming change – including program addition, service population expansion, and program discontinuation – NHSO leaders had to carefully consider their available resources (Addison & Rubin, 2023) and the needs of the populations they serve (Vogel et al., 2022). To date, it is unclear how common or how meaningful NHSO programming change was during the pandemic and which factors were the most important considerations for organizational leaders as they enacted them. Therefore, in this study, we ask two research questions: first, what was the prevalence and perceived permanence of programming change among NHSOs in the wake of the pandemic; second, what factors are associated with programming change among NHSOs?

To answer these questions, we draw on data from a two-wave statewide survey of NHSOs asking about programming change during the first five months of the pandemic, as well as the NHSOs’ leaders’ perceived permanence of these changes. We describe the prevalence of the changes and investigate the contributing factors, including the role of organization size (Mosley et al., 2012), capacity (Barman & MacIndoe, 2012), new funding sources (MacIndoe, 2021; Never & de Leon, 2014), financial stability (Kim & Mason, 2020), and concerns for clients regarding disruptions to service and increased demand (Mion & Chiaramonte, 2022; Stewart et al., 2021). To assist in the interpretation of our results, we utilize the strategic change literature from the field of management (Müller & Kunisch, 2018). When programming change is theorized as strategic change, our findings have important implications for the future of the human service nonprofit sector. Research on strategic change tells us that environmental jolts, like the pandemic (M. Meyer et al., 2023), often lead to revolutionary shifts and leave an imprint on organizational fields (Dieleman, 2010; M. Meyer et al., 2019). If we want to understand the long-term impact of the pandemic on NHSOs, we can begin by combining the strategic change literature with data on initial programming change, especially when these changes were intended to outlast the pandemic.

Strategic change & nonprofit human service pandemic programming

Management scholarship has long focused on the related yet distinct concepts of organizational change and strategic change (Müller & Kunisch, 2018; Nadler & Tushman, 1989), both of which became important in the context of the pandemic. To respond to the rapidly changing environment of the pandemic, all types of organizations needed to make changes. They engaged in organizational change, which focuses on changes to structure, processes, and systems (Cummings & Worley, 2013), and many engaged in one specific type of organizational change that this study focuses upon – strategic change (Pereira et al., 2023), which focuses on “goals, products and services, resources and capabilities, and the like” (Agarwal & Helfat, 2009). Gioia et al. (1994) define strategic change as “a redefinition of organizational mission and purpose or a substantial shift in overall priorities and goals to reflect new emphases or direction. It is usually accompanied by significant changes in patterns of resource allocation and/or … processes to meet changing environmental demands” (p. 364, emphasis added). When there is environmental uncertainty, such as after an environmental shock or jolt, strategic changes are more likely to occur in order to achieve organization-environment realignment (Barr, 1998). The research on strategic change suggests that leaders can be forced into strategic change as a reaction to the environment (deterministic perspective), make change proactively (voluntaristic perspective), or a combination of the two (dialectical perspective; Müller & Kunisch, 2018). In the case of the pandemic, strategic change was often the only option if organizations were to ensure continuity of services (Pereira et al., 2023).

We argue that programming change among NHSOs in response to the pandemic can be conceptualized as strategic change because programming represents the core services of the organization and is fundamental to the accomplishment of an NHSO’s mission and its outcomes (Lewis et al., 2012). As such, NHSO programming change during the pandemic represented a substantial shift in priorities and reflected new emphases. Research conducted by Newby and Branyon (2021) provides tangible evidence for the ways programming change in response to the pandemic reflects strategic change. Their interviews with leaders suggested that “many nonprofits realized that they needed to completely shift their services and venture into some new areas” (p. 452). The authors emphasize that “pivoting services” was fundamental to mission-achievement and involved important prioritization processes, balancing daily operations with special operations due to the pandemic (p. 452). While NHSO programming change may have been more or less substantial,¹ research has consistently found that the pandemic notably changed organizations of investigation (Chui, 2022; Shi et al., 2020; Wang & Cheng, 2021). In a study by Neely-Barnes et al. (2021), some NHSO leaders explained that they even needed to reexamine or expand their mission in order to meet the needs of the community. Ultimately, the overall programming portfolio of NHSOs changed as new programs were added for existing clients (e.g., adding a store to a housing site so clients didn’t need to leave; Books Holliday et al., 2020, p. 6), as new target populations were added (e.g., elderly had a new need for food delivery; Pitowsky-Nave, 2022, p. 6), and as existing programs were discontinued (e.g., educational events that posed a health risk due to potential exposure; Newby & Branyon, 2021, p. 449). These are the types of programming change – also treated as strategic change – that this study examines.

Theorizing NHSO programming change as strategic change has scholarly and practical value. Drawing on the strategic change literature can help us understand the nature and importance of programming change among NHSOs during the pandemic. Research shows that strategic change offers performance benefits, including enhanced organization forethought, greater public support, and better overall performance (Choi, 2008; Zajac et al., 2000). It also shows that strategic change is subject to inertia (Amburgey et al., 1990; Kelly & Amburgey, 1991) and is quite durable (Amburgey & Dacin, 1994; Mantere et al., 2012).

Knowledge of nonprofit strategy, including that of NHSOs, is evolving, and more research is needed (Laurett & Ferreira, 2018; Stone et al., 1999). Despite some nonprofit scholars’ support for traditionality over strategic change for the sake of continuity and commitment to the mission (Salipante & Golden Biddle, 1995), the external environment undoubtedly leads to strategic change in NHSOs (Schmid, 1993). When initiating strategic change, nonprofits consider many factors on both the supply and demand sides (Akingbola, 2020; Ben-Ner & Hoomissen, 1993; Shi et al., 2020). A key concern for scholars is whether decisions to initiate change prioritize economic or social rationales (Jäger & Beyes, 2010; Mosley et al., 2012), with scholars encouraging nonprofits to emphasize their social values (Frumkin & Andre-Clark, 2000; Salipante & Golden Biddle, 1995). We suspect that these two sets of factors – resource and mission factors – shaped NHSO programming change during the pandemic. Below, we discuss these two broad factors in more detail, proposing hypotheses motivated by research on strategic change and nonprofit organizing.

Resource factors

Research shows that resource factors are important antecedents of NHSOs’ responses in times of crisis (Boris et al., 2010; Chen, 2022; Horvath et al., 2018; Kim & Mason, 2020; Lin & Wang, 2016; Searing et al., 2021; Young & McGuire, 2018). Nonetheless, evidence is mixed with respect to how these factors relate to strategic change. On the one hand, research shows that larger organizations are likely to have more financial slack and are therefore better equipped to enact strategic change, such as enhancing and diversifying program offerings in times of uncertainty (Auer et al., 2011; Barker & Duhaime, 1997; Barnett & Carroll, 1995; Dawley et al., 2002; Mosley et al., 2012). Further, financial slack enables strategic change that is positively associated with performance (Bentley & Kehoe, 2020), and NHSOs with more slack may have been better able to position themselves for new revenue opportunities, for example those from the government (M. Meyer et al., 2023). Similarly, organizations with greater capacity, may be both more capable of change and also face greater expectations to adapt from external stakeholders during environmental shifts (Barman & MacIndoe, 2012).

On the other hand, some research provides a different mechanism for how existing resources and financial stability influence strategic change. Larger organizations with greater stocks of valuable resources may actually engage in fewer changes during environmental turbulence (Levinthal & March, 1993; Levitt & March, 1988) and choose to enact changes more slowly (Hannan & Freeman, 1984). This is because they can better “exploit” existing resources rather than “explore” new funding opportunities, which are often required for strategic change (Kraatz & Zajac, 2001, p. 633). From a cognitive perspective, the availability of resources also alters the way organizational leaders perceive environmental threats (Kraatz & Zajac, 2001; Levinthal, 1995; Milliken, 1990; Weick, 2006), where leaders of resource-rich organizations tend to see less environmental uncertainty, but leaders of resource-poor organizations search the environment more often and are quicker to interpret environmental cues and act upon surfacing environmental threats. For example, in a study of U.S. liberal arts colleges during the 1970s and 1980s, Kraatz and Zajac (2001) found that colleges with greater stocks of historically valuable resources enacted fewer changes in times of environmental turbulence, which facilitated rather than hindered their performance. Further, large and well-resourced organizations may be slower to change due to high levels of formalization and structural inertia (Hannan & Freeman, 1984). These insights may apply to NHSOs during the pandemic. Perceived or actual financial stability may allow organizational leaders to be more circumspect when it comes to making decisions about change, especially because change can lead to long-term repercussions, such as unintended shift in mission (Jones, 2007), and less financially secure organizations might be forced to pivot or scale back on existing programming during the pandemic.

In sum, organizations that are larger, with greater capacity, and with greater perceived or tangible financial stability may be either more likely to engage in programming change due to their greater slack and ability, or less likely to change due to bureaucratic intractability and the capacity to choose stability rather than change in an uncertain environment. Thus, we suspect financial and resource factors will influence NHSO leaders’ decisions about change during the pandemic, but the direction of influence is unclear. The following hypotheses allow us to examine these mechanisms empirically without suggesting specific directions of influence.

Hypothesis 1a:

NHSO organizational size will be associated with programming change during the pandemic.

Hypothesis 1b:

NHSO organizational capacity will be associated with programming change during the pandemic.

Hypothesis 1c:

NHSO financial stability will be associated with programming change during the pandemic.

With great consistency, and in line with the resource dependence of the sector, prior studies show that new resources are an important precursor to strategic change (Stone et al., 1999). Organizations may even shift their strategic focus and alter their mission when new or powerful funders come on board (Bennett & Savani, 2011), and several such funders emerged during the pandemic (Benavides & Nukpezah, 2020; Finchum-Mason et al., 2020; Hoch et al., 2022; Johnson et al., 2021). For example, many NHSOs applied and received forgivable loans from the federal government’s Payroll Protection Program (PPP) to continue paying their employees and sustain service provision during the pandemic (Hutton et al., 2021). PPP loans may also have contributed to available financial slack (Mumford, 2022). Similarly, NHSOs saw new opportunities to apply for foundation funding, including grants from high-profile community foundations that distributed COVID-19 relief (Azevedo et al., 2022). Evidence also suggests that large individual donations increased in several areas with some greater flexibility during the pandemic (Johnson et al., 2021). These new opportunities in the funding environment may have facilitated programming change. Therefore, we propose:

Hypothesis 2:

NHSO receipt of new funding during the pandemic will be positively associated with programming change.

Mission factors

NHSOs are mission-driven organizations that strive to be responsive to the needs of their communities and clients (Alexander, 2000; Mosley et al., 2012; Smith, 2012). Throughout many natural disasters and fiscal difficulties, NHSOs have been at the forefront of essential services (Benavides & Nukpezah, 2020; Mosley et al., 2012; Smith, 2012; Stewart et al., 2021). Similarly, the pandemic disrupted services to a broad spectrum of clients and activities, especially in the early months (Kim & Mason, 2020; Shi et al., 2020). Furthermore, the pandemic intensified the needs of many vulnerable and marginalized populations, including the unhoused (Benavides & Nukpezah, 2020), youth (Carvalho et al., 2022; Hopkins & Pedwell, 2021), and the elderly (Morrow-Howell et al., 2020).

During the pandemic, NHSOs took on additional lines of programming (Wang & Cheng, 2021), often at the expense of the organization, as the intensified and stretched programming loads were sometimes outside of their core mission and resulted in increased staff workload and burnout (Stewart et al., 2021; Vito et al., 2022; Wang & Cheng, 2021). Aside from resource factors, the reasons for programming change may also be due to NHSO leaders’ concern, compassion, and care for the heightened needs of their clients and communities (Mion & Chiaramonte, 2022; Wang & Cheng, 2021). Interviews with nonprofit leaders during the pandemic provide evidence of such. As Newby and Branyon (2021, p. 450) noted: “Nonprofit leaders spoke to us about the heartbreak of knowing their clients were being affected and having unmet needs while they were often not able to help with direct services.” And Pitowsky-Nave (2022, p. 9) explained that the hardships facing target populations “served to confirm the importance of the [social service nonprofits] for their directors, enhancing the sense of social mission that motivated them, and providing an added justification to keep struggling for their clients’ welfare.” NHSO leaders who were concerned with the increased and disrupted client needs may have decided to step up to meet those needs even in the face of limited resources, provided that they had the means to do so. Given this rationale, we propose that:

Hypothesis 3:

NHSOs leaders’ concern about increased demand and service disruption will be positively associated with programming change during the pandemic.

Method

Survey data collection

To examine programming change among NHSOs during the pandemic, we administered a multi-wave, statewide survey of NHSOs in Ohio (citation omitted for peer review). The data collection was part of a larger project targeting all public charities and private foundations in the state. The data collection effort was enabled by a collaboration with the Ohio Attorney General’s Office, Philanthropy Ohio, and the Ohio Association of Nonprofit Organizations. After obtaining a complete list of the nonprofits registered in Ohio from the Ohio Attorney General’s Office, we distributed the survey to the leaders of these organizations based on their registered e-mail addresses.

Two waves of the survey were fielded – the first wave in April 2020 and the second wave in August 2020. Both were sent to all 501(c)3 organizations registered with the state at the time. We administered the surveys at these time points to align with the timing of pandemic-related policy changes in Ohio. Specifically, the outbreak of the pandemic was declared a state of emergency in Ohio on March 9th, 2020, and a stay-at-home order was in effect from March 23rd through May 1st, 2020 (Ohio.gov, 2020). Our surveys were fielded about one month and five months after the official announcement of the emergency.

The first-wave survey contained thirteen questions, capturing organizational characteristics such as annual revenue, capacity, service populations, and organization leaders’ concerns about their clients and demand during the COVID-19 pandemic. We added several new questions in the second-wave survey. The second-wave survey asked about programming change due to the pandemic, including adding new programs, adding new service populations, and discontinuing programs. We collectively refer to these as indicators of programming change – the central inquiry and the dependent variables of the present study. The survey asked follow-up questions about whether these changes would still be in effect three years from the survey date.

The second-wave survey also asked about NHSOs’ financial reserves prior to the start of the pandemic and about new funding acquired since the beginning of the pandemic. Because the second wave survey was fielded to all 501(c)3 organizations registered in the state whether they had responded in the first wave or not, those responding for the first time in the second wave were also asked questions from the first wave including organizational characteristics like service populations.

Data preparation procedures and description of sample

The first step to clean and prepare our data for analysis involved removing duplicate responses based on an organization’s EIN. To note, we could receive multiple responses from the same organization, because multiple contacts for the same organization can register their e-mails with the Ohio Attorney General’s (AG’s) Office. For example, a CEO and a CFO can register their e-mails with the AG’s Office under the same EIN. Therefore, if multiple responses were provided from the same organization, we ranked the survey by completeness and seniority and preserved the most complete response of the more senior contact (e.g., if one survey is complete and another from the same organization is incomplete, the complete response was preserved; if the CEO and CFO both provided complete responses, the CEO’s response was preserved).

In our second step of data preparation, we complemented the survey with National Center for Charitable Statistics (NCCS) Business Master File (BMF) data and limited our sample to only NHSOs based on each organization’s BMF National Taxonomy of Exempt Entities (NTEE) code (i.e., HU).

We calculated the sample response rates for both surveys. In total, there are 13,367 unique NHSOs that are both registered in the state of Ohio and included in the BMF. The first-wave study sample included responses from 2,740 unique NHSOs, and the second wave included 1,425 NHSOs, resulting in the respective response rates of 20.50% and 10.66%.

The survey sample has an average organization age of 20.26 years and total revenue of $1.76 million compared to an average age of 16.39 years and size of $1.50 million in the state of Ohio. Therefore, our survey sample is older and slightly larger compared to the state average. Table 1 provides a detailed description on the number of observations and descriptive statistics for all variables in this study.

Table 1. Descriptive statistics.

Descriptive Statistics	n*	Mean	Std. Dev.	Min	Max
Programming Change
Adding New Program	1,261	0.19	0.39	0	1
Adding New Population	1,263	0.11	0.31	0	1
Discontinuing Program	1,264	0.18	0.38	0	1
Permanency: New Program	367	0.56	0.5	0	1
Permanency: New Population	219	0.59	0.49	0	1
Permanency: Discontinuing Program	358	0.14	0.34	0	1
Annual Revenue	1,301	2.31	1.63	1	7
Capacity	1,253	5.75	2.33	0	9
Financial Stability: Financial Reserve	1,095	0.78	0.42	0	1
Financial Stability: Expected Revenue	1,193	62.74	34.91	0	150
Revenue Addition: PPP Loan	1,188	0.26	0.44	0	1
Revenue Addition: Government Funding	1,184	0.12	0.33	0	1
Revenue Addition: Foundation Grants	1,182	0.11	0.31	0	1
Revenue Addition: Individual Donations	1,186	0.15	0.36	0	1
Concern about Client Disruptions	1,296	2.13	0.92	0	3
Concern about Client Demand	1,292	1.63	1.01	0	3
Age	1,421	20.26	18.35	1	87
Mission
Crime, Legal Related	1,425	0.05	0.22	0	1
Employment, job related	1,425	0.03	0.17	0	1
Food, Agriculture and Nutrition	1,425	0.07	0.25	0	1
Housing and Shelter	1,425	0.06	0.24	0	1
Public Safety, Disaster Preparedness and Relief	1,425	0.05	0.220	0	1
Recreation and Sports	1,425	0.3	0.46	0	1
Youth Development	1,425	0.08	0.28	0	1
Multipurpose and other	1,425	0.36	0.48	0	1
Service Population
Serving Racial Minority	1,425	0.114	0.318	0	1
Serving Children	1,425	0.493	0.5	0	1
Serving Families	1,425	0.273	0.445	0	1
Serving Disabled	1,425	0.183	0.387	0	1
Serving LGBT	1,425	0.062	0.24	0	1
Serving Women	1,425	0.123	0.328	0	1
Serving Immigrants	1,425	0.049	0.215	0	1
Serving Unemployed	1,425	0.087	0.283	0	1
Serving Seniors	1,425	0.156	0.363	0	1
Serving Other	1,425	0.135	0.342	0	1
Serving General Populations	1,425	0.241	0.428	0	1

*: n is the total respondents to the survey question, which may exceed the responses included in subsequent analysis depending upon data availability.

Variables and measurement

We utilize several variables in our descriptive and regression analyses. Below we outline our dependent variables (indicators of programming change) and independent variables (indicators of resource and mission considerations) as hypothesized, as well as relevant organizational characteristics we control for in our logistic regressions. Our descriptive analysis draws on some additional variables that we also summarize below.

Dependent variables: indicators of programming change

Our measure of programming change during the pandemic is motivated by Mosley et al. (2012) instrument. Our survey included three items that provide indicators of programming change. The survey question asked: “As a result of the COVID-19 pandemic, which actions have you or will you take?” “Add new program and or service offerings that you did not offer before;” “Provide programs and/or services to populations that you did not serve before;” and “Discontinue program and/or service offerings that you offered before.” These questions are coded as 0 = “Not applicable,” “Are not considering,” or “Planning to or considering,” and 1 = “Have already taken.”

Independent variables: financial and mission factors

Hypotheses 1a-c predict a relationship between programming change and resource-related factors, including organizational size, capacity, and financial stability. Organizational size was measured by the organization’s level of annual revenue. The survey asked: “What was the annual revenue for your organization last year?” Respondents can select from the following categories: “Under $50,000;” “$50,0000–$100,000;” “$100,000–500,000;” “$500,000-$1 million;” “$1 million - $5 million;” “$5 million - $10 million;” and “over $10 million.”²

Organizational capacity was measured by the following question set: “Does your organization have the following?” “Computerized financial records;” “Computerized client/member/program records;” “Written sexual harassment policy;” “Written fundraising plan;” “Annual report produced within the last year;” “Evaluation or assessment of program outcomes;” “Annual audited financial statement;” “Dedicated office space;” and “Social networking accounts (e.g., Facebook, Twitter, LinkedIn).” We measured organizational capacity as an ordinal variable by counting the number of “yes” responses for each organization.

Financial stability was measured using two variables: Financial reserve is measured by a survey question asking: “Does your organization have:” “Before the pandemic (March 2020), an operating reserve (liquid and unrestricted assets like cash) to cover at least three months of expenses,” to which respondents could choose “yes” or “no.” Expected revenue is measured by the question: “What percent of your annual revenue from the previous fiscal year do you expect for this fiscal year.” The question provided an example of how to calculate the appropriate percentage and respondents used a sliding scale to provide an answer.

Hypothesis 2 predicts a relationship between programming change and increased funding, so we measured a variety of funding sources. Revenue additions concerning PPP loans were measured by the following question: “Did your organization apply and/or receive a Payroll Protection Program (PPP) loan?” Respondents who selected “Yes, applied and received” were coded as 1, while all other responses were coded as 0. The additions of other sources of funding during the pandemic were measured by the following set of questions: “How have the following sources of income changed for your organization since the beginning of COVID-19 (March 2020)?” There were several items: “Government grants or contracts;” “Foundation grants;” “Individual donations.” For each item, respondents could select “decreased;” “my organization does not have this revenue;” “increased;” or “added this source of revenue.” We recoded “decreased” and “my organization does not have this revenue” as 0, and “increased” and “added this source of revenue” as 1.

Hypothesis 3 predicts a relationship between programming change and client-oriented factors, including concern about service disruption and concern about increased demand. Concern about client disruptions and concern about client demand were measured by the following survey question: “How concerned are you about the following?” with items for “Disruption of services to clients or communities” and “Increased demand for services” (Deitrick et al., 2020). For each item, respondents could choose from “not applicable to my organization” (coded as 1), “not at all concerned” (coded as 1), “somewhat concerned” (coded as 2), and “very concerned” (coded as 3).

Control variables

We included two control variables in our analyses: organizational age and organizational mission. We provide a brief rationale for each control variable as this practice facilitates transparency and replicability (Bernerth & Aguinis, 2016). Organizational age may influence the level of structural flexibility of an organization (Hannan & Freeman, 1984), thus mattering to programming change during the pandemic. Organizational age was measured as a continuous variable by the survey year, 2020, minus the NCCS Business Master Files (BMF) Internal Revenue Service (IRS) Ruling Date (NCCS Data Archive, 2016). Organizational mission may also influence programming change due to the varying effects of the pandemic on nonprofits based on their mission and service populations (Mumford, 2022). Mission was measured by BMF NTEE major group 10, providing mutually exclusive categories.

Additional variables for descriptive statistics

Two additional variables were not necessary for our regression analysis but were helpful for our descriptive analysis. One such variable provides evidence of the indented permanency of programming change. Perceived permanency is measured using a follow-up question for those respondents that indicated that the organization had engaged in one of the three programming change indicators. For example, the question posed if a respondent indicated they had added a new program or service stated: “You said you have added new program or service offerings that you did not offer before the pandemic, do you anticipate that this change will be in effect three years from now?” This set of questions is used in the subsequent descriptive analysis only.

The population(s) an NHSO serves was also relevant because different populations confronted different challenges during the pandemic, so we may see differences in programming change depending on the populations served, which may be related to but different from their mission. Service population is measured by the following question: “Please indicate the groups your organization aims to serve through its programs and activities. (Select all that apply):” “Children/adolescents/youth;” “Families;” “Disabled/special needs;” “LGBT;” “Women;” “Immigrants or refugees;” “Racial or Ethnic Minority Groups;” “Unemployed;” “Seniors;” “Other, please specify.” Because respondents could choose multiple categories, service populations are not mutually exclusive, and that is one reason we only used these measures in the descriptive rather than the regression analyses.

Analytical strategy

Given our dual research questions, data analysis took place in two stages. To answer our first question about the prevalence of programming change among NHSOs during the pandemic, we employed descriptive analyses to examine the proportion of NHSOs that enacted programming change – i.e., adding new service programs, adding new service populations, and discontinuing prior service programs during the pandemic. We examined these proportions overall, based on their mission area, and based on their target service populations. We also provide statistics on the perceived permanency of these changes by examining whether programming change was expected to be in effect in three years.

Our second research question asks about the factors associated with NHSO programming change during the pandemic and we developed several hypotheses related to this question. To test these hypotheses, we applied multivariate probit logistic regression because our three dependent variables measuring programming change (i.e., adding new services, adding new service populations, and discontinuing services) are binary. Probit regression is advantageous over linear probability models when the dependent variable is binary because the model assumes that the values of the dependent variable change nonlinearly with the values of the independent variable through maximum likelihood estimation (Gujarati, 2015). Further, probit models have more relaxed assumptions than linear probability models regarding homoskedasticity and normality of errors (Hosmer & Lemeshow, 2005; Long, 1997). Among the variations of probit models, we selected the multivariate probit model, as it accounts for correlations among multiple dependent variables (Cappellari & Jenkins, 2003), given that our three dependent variables are positively correlated.

In our results, we report the odds ratios (ORs) for associations between the independent variables and dependent variables to facilitate interpretation. Robust standard errors were included in the tables to account for heteroscedasticity (Gujarati, 2015). The regression analyses were limited to the sample of 1,041 NHSOs that responded to the second wave survey and had all the necessary data for the analyses. Because we supplemented the second wave data with first wave data, which included organization capacity, organization size prior to the pandemic, and service populations, one assumption of these analyses is that these responses would not change between the two waves of survey administration.

Results

Prevalence & perceived permanence of NHSO programming change

At five months into the pandemic (wave two survey administration in August 2020), 33% of NHSOs had made a programming change – either added a service program, added a new service population, or discontinued a service program. Specifically, 18.87% of NHSOs in our sample reported adding new programs or services that they did not offer before the pandemic; 10.53% reported adding new service populations they did not serve before the pandemic; 18.04% reported discontinuing at least one program since the beginning of the pandemic. Thus, a notable proportion of NHSOs had enacted these changes. Further, Pearson’s correlations reveal significant correlations among the three indicators of programming change. Adding new programs and populations are moderately correlated (coefficient = 0.41, p < .01), adding new programs and discontinuing programs are weakly correlated (coefficient = 0.24, p < .01), and adding new populations and discontinuing programs are also weakly correlated (coefficient = 0.19, p < .01) (Akoglu, 2018). Table 2 describes these results in detail.

Table 2. Extent and permanence of programming change and correlations.

Overall	Adding New Program	Adding New Population		Discontinuing Program
Observations (n = 1,261)	%	%		%
Percentage Making Change	18.87	10.53		18.04
Percentage Indicating Permanency	58.40	61.65		11.84
Programming Change by Organization Characteristic
Crime and Legal Related	18.46		7.58	18.18
Employment	26.32		18.42	23.68
Food, Agriculture & Nutrition	25.29		18.39	11.49
Housing & Shelter	18.92		4.05	16.22
Public Safety, Disaster Preparedness & Relief	4.69		3.13	21.88
Recreation & Sports	12.57		6.81	18.80
Youth Development	25.93		16.82	17.59
Multipurpose and Other	22.44		12.39	18.04
Serving Racial Minority	34.03		23.61	22.07
Serving Children	21.02		12.94	18.53
Serving Families	23.62		13.33	18.31
Serving Disabled	24.56		12.28	21.93
Serving LGBT	33.33		21.98	20.00
Serving Women	31.76		21.48	19.59
Serving Immigrants	39.34		25.81	22.58
Serving Unemployed	31.82		20.72	22.52
Serving Seniors	27.85		16.36	22.73
Serving Other	24.40		8.38	22.62
Serving General Populations	14.33		9.80	15.31
Correlations
Added New Program	1.00
Added New Population	0.41***		1.00
Discontinued Program	0.24***		0.19***	1.00

***p < .01, **p < .05, *p < .1.

We also investigated NHSOs’ programming change based on mission and service population. Below, we describe these results. Additionally, we summarize NHSO leaders’ response on expectations that programming change would be in effect in the next three years, providing an early indication of permanency.

Adding New Programs. NHSOs with the primary mission of employment were the most likely to add new programs (26%), followed by youth development (26%) and food, agriculture, and nutrition (25%). In terms of service populations, NHSOs serving immigrant populations (39%), racial minorities (34%), and lesbian, gay, and bisexual and transgender (LGBT) populations (33%) had the highest proportion of program addition.

Adding New Service Populations. NHSOs focused on employment (18%), food, agriculture, and nutrition (18%), and youth development (17%) were again the top three mission areas to add new service populations. NHSOs serving immigrant populations (26%), racial minorities (24%), and LGBT populations (22%) were the most likely to add new service populations.

Discontinuing Programs. NHSOs aimed at employment (24%), public safety and disaster-preparedness services (22%), and recreation and sports (19%) reported the highest proportions of program discontinuation, closely followed by crime- and legal-related organizations, multipurpose and other, and youth development organizations, all with 18% discontinuing previous programs and services. Organizations serving almost all other populations reported consistent proportions of discontinuing programs, within a narrow range from 18% to 23%. One exception was a lower proportion among those that serve the general population (15%).

Perceived permanency of programming change. Table 2 shows the results for NHSOs’ expected continuation of programming change in the next three years. Overall, 58.4% of NHSOs reporting newly added programs and 61.65% of NHSOs reporting newly added populations said the changes are likely to be sustained for the next three years; far fewer of the program discontinuations were perceived as long-term. Only 12% expected this action to be in effect three years from the survey date.

Factors associated with NHSO programming change

Table 3 reports the results of our multivariate probit regression analyses with odds ratios and robust standard errors as separate columns for each indicator of programming change during the pandemic.

Table 3. Multivariate probit regression results on programming change.

VARIABLES	Adding New Program		Adding New Population		Discontinuing Program
Observations (n = 1,041)	Odds Ratio	S.E.	Odds Ratio	S.E.	Odds Ratio	S.E.
Annual Revenue	1.107**	0.046	1.044	0.051	1.032	0.045
Capacity	1.056*	0.032	1.056	0.0362	1.043	0.029
Financial Stability: Financial Reserve	0.953	0.111	0.930	0.123	0.995	0.115
Financial Stability: Expected Revenue	1.003	0.0018	0.999	0.002	0.993***	0.0017
Revenue Addition: PPP Loan	1.339**	0.178	1.018	0.159	1.022	0.142
Revenue Addition: Government Funding	1.409**	0.192	1.414**	0.218	0.820	0.124
Revenue Addition: Foundation Grants	1.632***	0.230	1.247	0.202	1.223	0.180
Revenue Addition: Individual Donations	1.438***	0.189	1.464**	0.217	1.111	0.157
Concern about Client Disruptions	1.261***	0.091	1.040	0.081	1.332***	0.088
Concern about Client Demand	1.149**	0.067	1.303***	0.093	0.987	0.053
Organization Age	0.995*	0.003	0.996	0.003	1.004	0.003
Crime and Legal Related	1.197	0.266	0.956	0.231	1.171	0.244
Employment	1.246	0.309	1.447	0.402	1.529*	0.392
Food, Agriculture & Nutrition	1.204	0.240	1.117	0.236	0.916	0.202
Housing & Shelter	0.839	0.174	0.486***	0.134	1.119	0.234
Public Safety, Disaster Preparedness & Relief	0.751	0.218	0.713	0.251	1.314	0.295
Recreation & Sports	1.104	0.153	1.045	0.157	1.143	0.149
Youth Development	1.452**	0.250	1.438*	0.270	0.981	0.185
Multipurpose and Other (Base)
Constant	0.073***	0.0205	0.106***	0.031	0.200***	0.048
Log pseudolikelihood	−1152.597
Wald chi2(19)	311.94
Prob > chi2	0.0000

***p < .01, **p < .05, *p < .1; Robust standard error reported.

Hypotheses 1a-c anticipate a difference in programming change based on organizational size, capacity, and financial stability – although the literature was equivocal on the direction. Results show partial support for Hypothesis 1a and 1b. Everything else equal, one unit higher in the annual revenue scale was associated with a 10.7% increase in the odds of adding new programs (odds ratio = 1.107, p < .05). Similarly, organizations with one unit higher in capacity were associated with a 5.6% increase in the odds of adding new programs (odds ratio = 1.056, p < .1). However, neither organizational size nor capacity was significantly related to adding new populations or discontinuing programs. Results also show partial support for Hypothesis 1c. Regarding financial stability factors, one unit higher in expected revenue was not significantly related to adding new programs or service populations, yet significantly related to a 7% decrease in the odds of discontinuing programs (odds ratio = 0.993, p < .01). Moreover, the presence of a prior financial reserve was not significantly related to any of the three indicators of programming change.

Hypothesis 2 suggests that new funding is associated with programming change. We examined several sources of new funding, including government funding, individual donations, and PPP loans. Hypothesis 2 is partially supported. All else equal, receiving new funding, including government funding and individual donations, was significantly related to a 40.9% and a 43.8% increase in the odds of adding new programs (odds ratio = 1.409, p < .05 for new government funding; odds ratio = 1.438, p < .01 for individual donations) and a 41.4% and a 46.4% increase in the odds of adding new populations (odds ratio = 1.414, p < .05 for new government funding; odds ratio = 1.464, p < .05 for individual donations) compared to NHSOs that did not receive new funding from these sources. Receiving PPP loans was significantly associated with a 33.9% increase in the odds of adding new programs (odds ratio = 1.339, p < .05). Additionally, the addition of foundation grants was significantly related to a 63.2% increase in the odds of adding new programs (odds ratio = 1.632, p < .01). Nonetheless, receiving PPP loans and foundation grants was not significantly related to adding new populations. Further, none of the new funding was significantly associated with program discontinuations.

Hypothesis 3 suggests NHSO leaders’ concerns about service disruption to clients as well as increasing demand for services, are positively associated with programming change. This hypothesis received partial support. All else equal, leaders’ concerns about disruptions of service to clients were related to 26.1% higher odds (odds ratio = 1.261, p < .01), and concerns about increasing demand were related to 14.9% higher odds (odds ratio = 1.149, p < .05) of adding new programs. Additionally, all else equal, leaders’ concerns about increasing demand were significantly associated with 30.3% higher odds of adding new service populations (odds ratio = 1.303, p < .01). Concerns about service disruptions were also significantly associated with 33.2% higher odds in discontinuing existing programs (odds ratio = 1.332, p < .01).

The results reveal several significant control variables. Organizational age was marginally significantly related to adding new programs. All else equal, one year increase in an organization’s age was related to a 0.5% decrease in the odds of adding new programs (odds ratio = 0.995, p < .1). Regarding mission, all else equal, NHSOs focused on youth development had a 45.2% increase in the odds of adding new programs (odds ratio = 1.452, p < .05) and a 43.8% increase in the odds of adding new service populations (odds ratio = 1.438, p < .1) compared to multipurpose and other organizations; NHSOs focused on housing and shelter had a 48.6% lower odds of adding new populations (odds ratio = 0.486, p < .01). Additionally, organizations with employment-related missions had 52.9% higher odds of discontinuing their existing programs (odds ratio = 1.529, p < .1) compared to multipurpose and other organizations.

Discussion

Our empirical findings speak to ongoing conversations about programming change among NHSOs during the pandemic (Chui, 2022; Deitrick et al., 2020; Kim & Mason, 2020; Newby & Branyon, 2021; Pitowsky-Nave, 2022; Vito et al., 2022; Wang & Cheng, 2021). Research on the pandemic to date has largely focused on a small number of NHSOs’ responses during the early days of the outbreak. Through a two-wave study and a relatively large sample, we provide a macro-level view of programming change in the nonprofit human service sector during the pandemic. Our findings are, in some ways, expected and, in other ways, surprising, evidenced by the partial support for many of our hypotheses. Our findings help to understand programming change dynamics due to the pandemic and, when combined with the literature on nonprofit strategy and strategic change, point to broader implications for the human service sector. The contributions of this study are to our understanding of NHSO strategy (Frumkin & Andre-Clark, 2000; Laurett & Ferreira, 2018), especially in times of crisis (Horvath et al., 2018; Mosley et al., 2012).

Programming change dynamics

We find that a substantial number of NHSOs engaged in programming change as a response to the pandemic, and each form of programming change was in response to slightly different drivers. The first form of programming change we investigated was program and service addition, which was the most common type of programming change in our sample. Decisions to add programs and services seemed to be driven relatively equally by resource and mission factors. The resource factors at play were somewhat surprising. Larger and higher capacity organizations tended toward program addition, but organizations with greater financial stability (financial reserve and higher expected revenue) were not. This finding supports research showing that financial reserves may be essential to counter program reductions in the immediate term but may be a less meaningful factor as a crisis goes on (Irvin & Furneaux, 2022; Kim & Mason, 2020). This finding also echoes the argument that slack resources act as a buffer rather than a catalyst to change during environmental turbulence (Kraatz & Zajac, 2001).

Beyond size and capacity, what really mattered for program addition was the receipt of new funding sources, evidenced by the larger effect sizes. Organizations that received new funding from any source (PPP, government, foundations, and individuals) were the most likely to add programs. Because overall expected revenue is controlled for, this effect has less to do with the funding itself and must be attributed instead to the source and its newness. One explanation attributes the effect to the source itself and suggests that these funders incentivized (or even made funds conditional upon) program addition. Funders had specific goals for their funding during the pandemic (Beebe, 2020; Benavides & Nukpezah, 2020). For instance, in Boston during the pandemic, the city devised a solution for reducing COVID exposure among unhoused people – using dorm space as shelter. A local university provided the space, a foundation located in the city funded the effort, and a local housing NHSO used those resources to run the program (Benavides & Nukpezah, 2020; O’Neil, 2020). An alternative explanation attributes the effect to funding newness and suggests that the receipt of these new funding sources signaled to NHSO leaders that the funding environment could be counted on to support the sector through the pandemic. PPP loans were conditional only on maintaining employment levels (Neely et al., 2023), so that funding source did not explicitly incentivize program addition. Instead, PPP loans likely took pressure off NHSO recipients to cover overhead costs and allowed them to redirect their efforts toward best serving their clients. Research investigating these alternative explanations is welcomed. If the former explanation about funding mandates is supported, it will be important to consider the possibility of mission drift as a result of the pandemic.

Mission-oriented concerns were seemingly equally important drivers of program addition. Program and service additions were made when NHSO leaders were most concerned about heightened demand for their services and/or about disruption of services to their clients (Wang & Cheng, 2021). This combination of factors is apparent in the case of youth development, the group of NHSOs that was most likely to add programs when all else is controlled for. Because schools were closed during the pandemic, NHSOs were stepped up to provide more resource and services for children, especially concerning their needs for food security and mental health, such as providing community-building activities amid pandemic isolation (S. J. Lee et al., 2021; Sacks & Jones, 2020). Leaders of youth development NHSOs likely experienced heightened demand for their programming and were concurrently concerned about their ability to deliver that programming effectively in a virtual environment. In addition to youth serving NHSOs, NHSOs serving marginalized populations (immigrants, racial minorities, LGBT people) had higher rates of program addition, descriptively. It is important to note that the first five months of the pandemic coincided with the Black Lives Matter social justice movement in timing, which may have also contributed to greater program addition for certain NHSOs.

The second form of programming change we investigated was the addition of new service populations. Service population addition was less common among the sample than program addition. The relative reluctance among NHSOs to enact service population changes might indicate a preference for maintaining mission integrity as research shows that service population change can result in mission drift (Jaquette, 2013). When population expansion was used, it was seemingly a response to greater demand for the NHSO’s services, possibly because with demand came a wider heterogeneity of needs. Youth development NHSOs, which were also among the most likely to expand their service population, may have expanded to a wider age group or began to serve a wider geographic region. Conversely, housing NHSOs were unlikely to expand their service population perhaps because the geographic scope of shelters is fixed and/or because these organizations may broadly define their service population as the unhoused. Our descriptive findings support this interpretation since, unsurprisingly, NHSOs serving “the general population” were unlikely to expand their service population.

Like program additions, service population addition was motivated by resource factors. None of the size, capacity, or financial stability indicators were significant, but a subset of the new funding sources were. Specifically, it seems new funding from the government and individuals were key inputs for service population expansion. It is possible that these funders were effectively funding NHSOs that were experiencing the highest demand, which led to service population expansion. It is also possible that these funders were actively incentivizing or mandating service population expansion through their contributions.

The third and final form of programming change we investigated was the discontinuation of programs and services. Our findings suggest that nearly as many NHSOs discontinued programs as those that added programs, and these two forms of programming change were weakly correlated. It seems NHSOs were making tradeoffs and prioritizing their programming efforts – forgoing existing programs in favor of new ones. Employment-related NHSOs were most likely to discontinue programs, but, at least descriptively, were also very likely to add programs, representing a sort of programming churn. NHSOs of all sizes and capacities were just as likely to discontinue programs. Notably, a greater concern about disruptions of service to clients is related to program discontinuation. This greater concern was likely born out of these discontinuations rather than the other way around. This explanation helps to make sense of why NHSO leaders’ concerns about disruptions was associated with program discontinuations whereas their concerns about client demand was not. For example, an employment training program likely had to pause service delivery early in the pandemic, which would have led NHSO leaders to be concerned about that disruption regardless of whether demand was the same or higher than prior to the pandemic. When leaders felt forced to discontinue a program, perhaps because it was not safe to deliver it, their concern for clients intensified.

Overall, we see that both resource and mission-related factors, at seemingly equal levels, contributed to programming change among NHSOs during the pandemic, though these factors operated differently for different types of programming change. Given the relative frequencies of program addition, service population addition, and discontinuation, it seems there were more human service programs serving more people during the pandemic than prior to it, especially in mission areas like food, agriculture, and nutrition where the percentage of NHSOs adding programs far outweighs those discontinuing programs. We cannot be certain whether this higher service load is likely to carry into the post-pandemic environment, but we can make some assumptions based on NHSO leaders’ intentions. When we asked NHSO leaders about the longevity of their program additions, many of those changes were intended to be long-term (despite the presumed short-term nature of the pandemic in August 2020 at the time of the survey). Meanwhile, program discontinuations were expected to be short-term changes. We turn to the literature on nonprofit strategy and strategic change to make further sense of our findings.

NHSO strategy, programming change, and strategic change

Our findings contribute to research on NHSO strategy by examining the extent to which different resource, mission, and organizational characteristics were at play during the first five months of the COVID-19 pandemic (Alexander, 2000; Horvath et al., 2018; Jäger & Beyes, 2010; Laurett & Ferreira, 2018; Schmid, 1993; Stone et al., 1999). Identifying the factors associated with different types of programming change during the pandemic can help us gain a deeper understanding of the mechanisms that facilitate strategic change in a time of crisis.

Previously we argued that programming change among NHSOs during the pandemic can be conceptualized as strategic change. By focusing on NHSOs’ responses as strategic change, we contribute a distinct perspective that deepens our understanding. Programming change is strategic because it deals with the core services of the organization, which are directly tied to mission accomplishment and outcomes. Michael (1996, p. 45) famously stated: “The essence of strategy is choosing what not to do. Without tradeoffs there would be no need for choice and thus no need for strategy.” For this reason, we considered not only added programs and added service populations during the pandemic, but the tradeoffs made – namely program discontinuations. NHSOs were engaging in complicated prioritization processes.

Research on NHSOs early in the pandemic has emphasized the opportunities enabled by the disruption (Chui, 2022; Neely-Barnes et al., 2021; Pitowsky-Nave, 2022; Shi et al., 2020) and has begun to look at the lasting impacts (M. Meyer et al., 2023). According to research and theory (Dieleman, 2010), programming change enacted during the a crisis period could be “imprinted” on NHSOs and have long-term implications. Strategic change due to an environmental shock often leaves a lasting imprint on sectors and industries (Dieleman, 2010; A. Meyer et al., 1990) and strategic change can be hard to undo (Mantere et al., 2012). For instance, while programming additions during the pandemic may entail converting existing programs online or providing extra lines of programming, changes that could be reverted relatively easily if NHSOs’ leaders wish to, discontinuing services to a new population after the pandemic could be more challenging.

The results of this study foreshadow an increasingly stressed sector. Nonprofits are known for operating on minimal resources (Lecy & Searing, 2015), but for many NHSOs, the pandemic may have exacerbated this problem. Our results show that most programs and populations added during the pandemic are intended to be permanent. Meanwhile, the discontinuations made during the pandemic were largely intended as temporary. If these two intentions materialize, there will be a higher programming load within the average NHSO.

During the pandemic, much of these programming and service population additions were funded through new revenue sources, including PPP loans. Without this type of government support and COVID-19 dedicated funds from foundations, it is unclear how a higher program capacity can be financed. Research and funding to help NHSOs operate with a heavier programming and service load, specifically in the mission categories with the most programming change, could be important in the near future. Without necessary funding streams, NHSOs may be faced with tough choices about which programs to discontinue. Achieving a balance between scale, scope, and quality of services will be an increasing opportunity and challenge for future strategic consideration. NHSO leaders should keep the most vulnerable populations in mind as they make these decisions (Hassett, 2021). Scholars can offer support by providing data upon which to base these challenging and complex decisions.

Limitations

The findings of this study should be read with several limitations in mind. First, our method is based on NHSO leaders’ perceptions and projections for the next three years at a particular point in time (August 2020). These perceptions and projections will continually shift as time moves further from the initial outbreak. It would be prudent to follow up with the leaders of these organizations on the execution of their plans to investigate the consistency between intentions and outcomes. Related, because our sample was collected in the first five to six months of the pandemic, it is difficult to assess the long-term sustainability of NHSOs’ programming change. In prior studies examining how financial crises impacted the nonprofit sector, Mosley et al. (2012) covered a two-year timeframe through longitudinal surveys. We acknowledge that having a longer span of surveys would have been helpful, especially to investigate the scope and duration of programming change and make inferences on organizational resiliency (Searing et al., 2021).

Second, we acknowledge that the scope of our study only encompasses particular forms of programming and strategic change. There are many others that we could have investigated, including the pandemic’s effects on NHSOs’ reduction or addition of staff, as well as changes in mission, advocacy, and new partnerships (Mosley et al., 2012). These will be important areas to investigate through future research endeavors. We also recognize that our method does not allow us to account for the size and scope of programming change made by NHSOs in our study. We maintain that the changes investigated in this study fit the current definition of strategic change. However, we also appreciate that some sources provide an alternative definition (e.g., Nadler & Tushman, 1989).

Finally, our study has limited generalizability. Our sample was taken from a single state. While NHSOs in the state of Ohio are, on average similar to NHSOs in other states, the pandemic was experienced uniquely based on state policy and culture. This may limit the generalizability of our findings. Given that the pandemic differed in duration and impact on NHSOs compared to other types of natural and financial crises, the implications of our study are only generalizable to the pandemic and similar disaster contexts. Further, the inclusion of NHSOs based on NTEE code has methodological implications. Although it is consistent with the categorization of NHSOs based on their major purposes in other research (e.g., Y. Lee, 2019), the NTEE code for NHSOs may not capture the diversity of larger, multipurpose organizations (Grønbjerg, 1994). To mitigate this issue, our descriptive and regression analyses included self-reported service population groups to complement the results from NTEE categories.

Conclusion

Navigating a rapidly changing environment, many NHSOs engaged in programming change during the pandemic. In these decisions, NHSOs strove to couple available resources with fulfilling the most urgent needs in society. We have identified factors that influenced programming change as well as several new and continuing tensions that are likely to arise, as indicated by the strategic change literature. Future research will need to investigate the extent to which these opportunities, challenges, and tensions may permanently shape the identity and core programming of NHSOs.

Acknowledgments

The authors gratefully acknowledge the research assistance of Glenn College graduate, Elizabeth Colchin, and the active support and involvement of Beth Short at the Ohio Attorney General’s Office.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by The Ohio State University Office of Outreach and Engagement.

Notes

¹ We take the term “substantial” in the definition of strategic change to mean sufficiently important due to the change’s proximity to the core of the organization (as opposed to the size of the change). Programming is always important regardless of the size of the change because it is core to reaching organizational goals/mission. By way of corollary, much of the research on strategic change comes from the management literature and any changes to products would be considered strategic in nature. NHSOs are service-providing organizations, and programs are the equivalent of products in such a setting.

² We used a $1 overlap instead of using 99 cents at the end of each category due to practitioners’ recommendations during survey testing.