Assessing Capacity in Rural Nonprofits

Abstract

Organizational capacity is necessary for nonprofits to meet their missions and contribute to society. Less is known about rural nonprofits’ organizational capacity and characteristics, as most research focuses on urban nonprofits. The present study first considers the utility of an organizational capacity assessment and identifies pertinent areas of organizational capacity in rural nonprofits. The second objective of the study is to examine relationships between areas of organizational capacity and organizational-level characteristics. In May 2019, nonprofits (n = 290) from persistently poor, rural counties in 14 southern states participated in a survey providing organizational characteristics and assessing organizational capacity. An exploratory factor analysis on the organizational capacity assessment revealed four organizational capacity areas: Organizational Identity, Fund Development, Volunteers, and Organizational Procedures. Then, descriptive, bivariate, and multivariate analyses were conducted to understand the relationships between the organizational capacity areas and organization characteristics, including organizational age, expenses, life stage, NTEE classification, and executive director tenure. Findings indicate significant relationships exist between organizational capacity and characteristics, consistent with previous studies. Older, mature, and/or larger rural nonprofits have increased capacity in several areas. Thus, targeting younger and smaller rural organizations for capacity-building efforts may be impactful.

1. Introduction

Nonprofits help empower their communities and lift burdens of poverty, crisis, inequity, and injustice, especially in persistently poor communities (Partners for Rural Transformation 2019). Nonprofits are vital for rural communities, particularly in the rural South, where persistent poverty rates in rural areas are high (USDA/ERS 2019). Fulfilling these roles requires nonprofits to have organizational capacity, which is generally understood as a measure of resources an organization possesses to meet its mission, including leadership, finances, knowledge, and services (Hung and Hager 2019; Light 2004; Walters 2020). Less is known about rural nonprofits and their organizational capacity strengths and weaknesses; thus, questions are emerging about the effectiveness and sustainability of these organizations (Walters 2020). Rural nonprofits typically receive less funding in comparison to their urban counterparts (Partners for Rural Transformation 2019). With many funders desiring high levels of organizational capacity in nonprofits (Shah and McGill 2020), organizational capacity struggles may be one reason rural nonprofits are viewed as less competitive for funding (Hall 2010; Walters 2020).

Assessing organizational capacity, formally and informally, is necessary for nonprofits to recognize and address strengths and challenges in an effort to ensure effectiveness and sustainability (Hall 2008; Lee 2011). Many organizational capacity assessment tools are available, and choosing the most appropriate assessment can be confusing because organizational capacity is viewed in many different ways (Informing Change 2017a, 2017b). Minimal consensus exists on capacity areas that are most important for nonprofits and, more specifically, rural nonprofits (Informing Change 2017a). As Walters (2021) noted, the areas of organizational capacity that are important for rural nonprofits differ from their urban and suburban counterparts. Additionally, few studies exist on the relationships between rural nonprofit organizational capacity and organizational characteristics, such as age, size, organizational life stage, leadership, and the National Taxonomy of Exempt Entities (NTEE) (Andersson et al. 2016).

The current study first sought to evaluate the utility and dimensionality of an existing organizational capacity assessment for use in rural nonprofits. Identifying a practical, cost-effective tool specifically for rural nonprofits and their stakeholders will help target capacity areas needing building and investment. This investigation also examined relationships between organizational-level characteristics and areas of organizational capacity. This knowledge may help capacity builders, funders, and nonprofits understand how certain characteristics may impact organizations in the rural South so they may work to improve where weaknesses are more likely and sustain their important efforts in their communities.

2. Background

2.1. Nonprofits in Rural Communities

Just over 19 percent of the U.S. population live in rural areas, and 51 percent of all rural residents live in poverty in the South (U.S. Census Bureau 2016; USDA/ERS 2019). Nonprofits are essential to the functioning and well-being of society (Salamon 2003; Berman 2002), and that is no different in the rural South (Walters 2021). In addition to providing vital services to communities, such as human services, education, and healthcare, nonprofits are often voices for communities they serve as they have hands-on knowledge and a depth of understanding of social and economic needs (Camper 2016). Strengths of operating a nonprofit in the rural South include not only relationships among members of the community, but they also have a willingness to utilize all available resources, regular meetings with board members to discuss financial health and client needs, dedication of staff and leadership to the mission, and innovation in problem-solving (Scales et al. 2013; Walters 2021).

Barriers to rural nonprofit success include fewer providers, geographical isolation, and financial disparities (Fanburg 2011; Campbell University 2018). Rural nonprofits often experience more financial stress than their urban counterparts as they struggle to secure grants and donations (Lin and Wang 2015). An estimated seven percent of all donor funding is delegated to rural nonprofits (Campbell University 2018). These financial insecurities hinder organizational capacity, such as securing and retaining enough staff and funding to travel to meet their clients (Walters 2020, 2021). Rural nonprofits identify a strong commitment to their mission and desire to serve the community, which allows for advantageous problem-solving abilities (Walters 2021). Even with this strong sense of creativity, the continued lack of funding (including grant dollars and fundraising) can often limit rural nonprofits’ reach. Moreover, the relationship between high organizational capacity and successful funding requests, as well as the funders’ inexperience and lack of knowledge related to the needs of rural nonprofits, is detrimental to rural communities as a whole (Fanburg 2011; Walters 2021).

2.2. Nonprofits and Organizational Capacity—Frameworks and Measurement

Nonprofit systems usually consist of a mission, leadership, outreach, services, and resources (De Vita and Fleming 2001). All nonprofits need organizational capacity for each part of the organization to function and perform effectively. Light (2004) defines organizational capacity as the necessary element to accomplish nonprofits’ overall goals. This multi-dimensional construct is prescriptive: capacity required by one organization may not be as important to another, often creating difficulties in measuring the construct (Andersson et al. 2016; Despard 2016). Scholars and practitioners alike struggle to agree upon the best model of organizational capacity (i.e., what do nonprofits need to be successful?). As Andersson et al. (2016) point out, these models often come in two forms: best practices—very much theory-driven—and “how-to” approaches—more practical in nature.

The diversity in measurement and operationalization of nonprofit organizational capacity is addressed comprehensively in a report by Informing Change and the Hewlett Foundation, where they identified and analyzed publicly available organizational capacity assessments (Informing Change 2017a). Additionally, they created a database of 48 organizational capacity assessment tools and 43 resource guides (Informing Change 2017a, 2017b). Many commonalities were identified across the tools, yet many differences existed in capacity areas measured and associated processes (Informing Change 2017a). The most commonly employed capacity areas were the following (definitions can be found in the database glossary): adaptive capacity; aspirations; community and external relations; content and sector expertise; culture, values, and ethics; learning, evaluation, and accountability; finances; fundraising and development; governance; human resources; infrastructure and technology; leadership; management and organizational structure; operations; planning; strategy; programmatic; and constituents (Informing Change 2017b). Many of these align with empirical research (e.g., Andersson et al. 2016).

The range of capacity areas assessed was 3 to 17, with the number of items extending from 24 to 264. Some assessments were meant to be adapted for each organization, while others required internal or external facilitation. Some assessments were to be completed by board members, executive leadership, staff, external funders, or a combination of people, and most were not based on theory or evaluated (Informing Change 2017a). Many assessments required hours to complete and sometimes days and weeks with multiple people taking the assessment. Moreover, the process of conducting an organizational capacity assessment is as significant as the items on the assessment because it allows organizations to learn about organizational capacity, recognize strengths and weaknesses, and facilitate a discussion among leadership and staff about the organizational needs (Informing Change 2017a).

In the past few decades, a stronger focus has emerged on organizational capacity with funders of nonprofits (e.g., foundations, governments), with significant concerns regarding an organization’s ability to effectively manage and utilize money (Brown 2014; Karsh and Fox 2014; Minzner et al. 2014). This expectation, along with a “do more with less” fiscal environment (Luma Consulting 2016), has manifested in hesitations and failures in investing in small or grassroots organizations and increased competition for funding across the nonprofit sector (Karsh and Fox 2014). Hence, there is a great importance for rural nonprofits to assess their organizational capacity, identify areas of strength and limitation, and address them accordingly.

2.3. Organizational Characteristics Associated with Nonprofit Organizational Capacity

Limited research exists about relationships between nonprofit organizational capacity areas and organizational characteristics, especially in rural nonprofits. However, understanding these relationships provides knowledge to funders who must prioritize dollars for capacity building and capacity builders who desire to customize their education materials for nonprofits.

2.4. Organization Age and Size

Previous research examined the age and size of organizations related to total expenses. Larger and older human-service nonprofits had a lower likelihood of capacity problems with regards to mission, programming, leadership, fund development, technology, human resources, and legal matters when compared to smaller and younger organizations (Andersson et al. 2016). Furthermore, smaller minority health-serving nonprofits tended to have decreased capacity relating to program evaluation (Yung et al. 2008). Despard (2016) observed that younger and smaller human-service organizations were less likely to increase capacity related to evaluation over time compared to older and larger nonprofits. Conversely, Minzner et al. (2014) found no effects associated with age and size in a study examining the impact of a capacity-building program.

2.5. Organization Life Stage

Organizational life stage refers to the idea that organizations are like organisms. Through natural development, organizations enter, go through, and re-enter stages of existence (e.g., birth, death; Andersson et al. 2016). Andersson et al. (2016) used a life stage framework that included five stages—startup, growth, maturity, decline, and turnaround. An organization, no matter its age, can be at any life stage. Capacity strengths and struggles differ among life stages. Organizations in the maturity stage are often competent in capacity areas such as mission, board, human resources, and fund development. In contrast, those in the startup, decline, and turnaround phases often encounter challenges with financial and program capacity (Andersson et al. 2016). Previous studies on nonprofits point out life stage differences in the ability for nonprofits to survive. In young organizations, strong ideas, leadership, and financial resources are crucial for moving into the next phase (Gibb and Scott 1986; Stevens 2001). While studies exist on life stage needs and organizational capacity in nonprofits, no studies specifically examine relationships between organizational life stage and capacity in rural nonprofits.

2.6. Leadership

Few studies have explored how a nonprofit’s leadership impacts its organizational capacity. Two studies indicated that leadership turnover caused stress to the organizational capacity of rural nonprofits (Knudsen 2016; Lee 2011). Knudsen (2016) and Lee (2011) found that stress due to insufficient resources led to high turnover in leadership in rural nonprofits. This in turn caused the nonprofits to experience overall instability. While one study found no relationship between executive director experience and the retention and recruitment capacity of a nonprofit (Gilmer 2012), others have noted the cyclical nature of leadership and staffing. With agencies being understaffed, employees often take on heavier workloads (Neuhoff and Dunckelman 2011; Skinner et al. 2018). The stress of inadequate resources can lead to higher turnover in executive positions (Knudsen 2016; Lee 2011). Though rural nonprofits employ inventive strategies to overcome challenges, this cycle of inadequate staffing and leadership turnover can lead to rural nonprofits’ inability to meet client needs (Neuhoff and Dunckelman 2011).

2.7. NTEE

The Internal Revenue Service (IRS) and National Center for Charitable Statistics use the National Taxonomy of Exempt Entities (NTEE) to classify nonprofits (Jones 2019). The system uniformly collects information on nonprofits so that the comparison and analysis of data can be conducted (Jones 2019). The NTEE categorizes types of services provided (e.g., health, education, human services, etc.) and major types of funding received (Jones 2019). To the authors’ knowledge, no research exists that examines differences and relationships between categories of the NTEE and organizational capacity as a whole; however, research related to fund development and revenue (aspects of organizational capacity) has been conducted. Fischer et al. (2011) found that the more public and known a nonprofit’s services, the more likely they are to fundraise. While this provides beneficial knowledge for organizational capacity, this study only examined nonprofits within three NTEE subfields: arts and culture, human services, and health. During the COVID-19 pandemic, nonprofits that engage in human service work across the country saw a decrease in revenue, leading some to close their doors or drastically reduce their services provided (Morris 2020). Assessing organizational capacity related to NTEE category may give insights into how to help nonprofits continue to operate during future times of financial hardship.

2.8. Current Study

With challenges in measuring organizational capacity, the first study objective focuses on assessing the utility and dimensionality of an existing organizational capacity assessment for use in rural nonprofit settings. Identifying a tool and understanding its usefulness can provide more knowledge to organizations in their effort to become more effective and work through change. Furthermore, healthy, functioning nonprofits with higher levels of organizational capacity are more likely to respond to adversity while still providing services to clients (Children’s Bureau n.d.). The current investigation is important because there are no studies that exist with the primary focus on the measurement of organizational capacity in rural nonprofits in the U.S. Assessments are a key way to learn which core capacity dimensions require more attention to continue growing and sustaining the nonprofit organization (National Council of Nonprofits 2021). The second study objective investigates the relationships between organizational capacity (as measured by the present assessment) and organizational-level characteristics (e.g., tenure of executive director) in the same rural nonprofits. Few studies exist that examine this query, and while capacity building is often prospective, understanding potential relationships between organizational capacity and characteristics can provide some direction for those funders and capacity builders seeking to help in rural areas but who do not know where to target their efforts.

3. Methodology

3.1. Design

Data for the present study were collected as part of a larger investigation conducted in the summer of 2019 and approved by the University of Tennessee Institutional Review Board. The study employed a cross-sectional survey approach.

3.2. Sample and Sampling Procedures

Participants were IRS-registered, 501(c)3 nonprofits of all types. The geographic focus was rural counties with the persistently poor designation in the southern region of the U.S. as designated by the U.S. Census Bureau. As there are many definitions, rural in the present investigation meant counties that have cities with less than 50,000 residents and are not within boundaries of metro areas (USDA/ERS 2017). The persistently poor designation is a county with 20% or more of their population in poverty over the past four census rounds (USDA/ERS 2017). Thus, 3530 nonprofits across 301 counties across 15 states were included in the study population.

All nonprofits meeting the inclusion criteria were identified from a dataset from Candid (2019) which included IRS Form 990 information for registered organizations. A total population sampling strategy was applied as the study population was discernable (Lund Research 2012). Participants with email addresses received up to three recruitment emails with a survey link. Nonprofits with no response to email invitations were sent surveys via postal mail. The respondents were nonprofit executives such as CEOs, executive directors, program directors, or board presidents. For contributing to the study, participants received access to a free nonprofit professional development course (USD 25 to USD 75 value) developed by the Alliance for Better Nonprofits, a capacity building organization in Knoxville, Tennessee.

3.3. Measurement

Data about the participating nonprofits were obtained from IRS Form 990s and a survey designed for the purposes of the present study. For the full description of survey questions, see (redacted for review); only the variables for the present study are described below.

Age of Organization. Age of the organization was computed from the date of legal formation listed in the 990.

National Taxonomy of Exempt Entities (NTEE) Classification Major Category. With the exception of the unknown category, nonprofits in all NTEE categories were selected (26 major groups of nonprofits in nine categories; National Center for Charitable Statistics n.d.).

Tenure of executive director. On the survey, respondents reported the number of years that the current executive director has been in place.

Total expenses. The expenses figure was extracted from the 990 data, which consists of funds paid out by the organization (e.g., employee compensation).

Organizational life stage. Employing the organizational life-stage framework (i.e., Andersson et al. 2016), the survey provided definitions of each stage (e.g., start-up, growth, maturity, decline, and turnaround), and respondents selected the stage which they believed best represented their organization.

Organizational capacity. The larger investigation (Walters and Wallis 2021) selected an assessment to measure the organizational capacity of nonprofits in persistently poor, rural counties in the South. The assessment choice needed to represent parts of the nonprofit system (De Vita and Fleming 2001) and parallel frequently employed capacity areas as examined by Andersson et al. (2016). Publicly available assessments in the Informing Change (2017a, 2017b) database as well as in the peer-reviewed and gray literature were examined, and most were found to be time-intensive, and in many cases mandated that more than one organization representative participate. Thus, the lead investigator worked with a local capacity building nonprofit to adapt their assessment (ABN/KLF 2017).

The ABN/KLF assessment and process, like many tools for assessing organizational capacity, was too time-consuming and complex for the present study (2017). With 11 capacity area subscales, there are two main foci of the ABN/KLF assessment—structural capacity and effectiveness (D. Meyers, personal communication, 14 November 2019; ABN/KLF 2017). However, the structural capacity items alone matched well with study objectives. Structural capacity is meant to assess if nonprofits have basic components related to the areas; these items are dichotomous (i.e., yes/no; ABN/KLF 2017). Structural capacity items are answered by the executive director, or if no executive director exists, the board president. In all, there are 158 structural capacity items (ABN/KLF 2017).

Using previous research and practice knowledge, the number of items was narrowed, two capacity area subscales were combined (community engagement and marketing strategy), and another subscale, technology, was added based on Fink and Engel (2006). To assess content validity, the researcher met with ABN, KLF, and two nonprofit scholars. Finally, the modified assessment was piloted with ten rural nonprofits in East Tennessee who were not part of the study population. The pilot process consisted of nonprofits taking the survey and being interviewed about accessibility, content, and item comprehension. Upon receiving feedback, the final survey, which took about 10 to 12 min to complete, included:

• Two screening items: county of nonprofit and role of respondent.
• Nine identification items (e.g., employee identification number).
• Five demographic items (e.g., tenure of the executive director).
• Sixty-one dichotomous (i.e., yes/no) organizational capacity items.

3.4. Data Analysis

To assess dimensionality of the organizational capacity assessment, an exploratory factor analysis (EFA) was performed with Mplus (8.0; Muthén and Muthén 2017). Mplus analysis defaults were used: estimator was weighted least square mean and variance adjusted (WLSMV), goemin rotation, and oblique as rotation type. One to five factor solutions and associated scree plots and test statistics were generated. First, scree plots were inspected (Costello and Osborne 2005). Then, model fit statistics were reviewed including chi-square (decreasing values indicate a better fit); comparative fit index (CFI; >0.95 indicates a good fit); and root mean square error of approximation (RMSEA; <0.05 indicates a close fit) (Bowen and Guo 2012; Fabrigar and Wegener 2011). Once the best fitting model was selected, factor loadings of items were assessed—items with factor loadings of 0.5 or greater were retained (Tabachnick and Fidell 2007). Additionally, items with high cross-loadings—a difference of 0.2 or less—were removed as this indicated discrepancies relating to the factor on which the item best loads. To better comprehend new factors (or capacity areas) and their respective items, internal consistency reliability using Cronbach’s alpha and correlations between factors were analyzed.

To examine relationships between new organizational capacity area scores and organizational-level characteristics, data analyses were conducted using SPSS 27.0.1.0. Descriptive statistics were conducted for all variables, and bivariate analyses were conducted between organizational characteristics and organizational capacity areas. To look at the bivariate relationships between NTEE classifications and organizational procedures, a one-way ANOVA test was conducted with a post-hoc Tukey test. For the relationships between NTEE classifications and all other organizational capacity areas, Kruskal–Wallis tests were used with post-hoc Mann–Whitney tests. Likewise, for bivariate analyses between organizational life stage and organizational capacity areas, one-way ANOVA tests with post-hoc Tukey tests and Kruskal–Wallis tests with post-hoc Mann–Whitney tests were conducted. Kendall rank correlation coefficients and Pearson’s product moment r correlation coefficients were used to examine the bivariate relationships between organizational capacity areas and organizational age, tenure of executive director, and expenses.

To test which characteristics were associated with each capacity area when controlling for other characteristics, multiple regression analyses were conducted with each of the organizational capacity areas as outcomes. Ordinal logistic regressions were conducted for organizational identity, fund development, and volunteer practices scores. However, the proportional odds assumption of ordinal logistic regression was not met for organizational identity and fund development scores, so these variables were dichotomized and binary logistic regressions were used instead (Orme and Combs-Orme 2009). The distribution of organizational procedures scores was negatively skewed, and standardized residuals from a linear regression were not normally distributed. Hence, a generalized linear model was fit to the data by specifying a gamma distribution and using a log link function (Ng and Cribbie 2017). Standardized effect sizes were generated for all four multiple regression models (Crowson 2021).

There were no missing data for any variables indicating organizational characteristics; however, pairwise deletion was used to handle missing data for the organizational capacity variables. Little’s (1988) missing completely at random (MCAR) test provided evidence that data used in analyses were MCAR (χ²[48] = 55.83, p = 0.204). Before conducting bivariate and multivariate analyses, all continuous variables used in these analyses were screened for outliers and winsorized according to Aggarwal’s (2015) criterion of values +/− 3 standard deviations from the mean. Outliers on the high end of the distributions of three variables were winsorized prior to bivariate and multivariate analyses: three cases for organizational age, four cases for executive director tenure, and four cases for organizational expenses. Expenses were originally measured in whole dollars; however, this variable was recoded to represent increments of USD 10,000 before bivariate analyses and transformed into z-scores for multivariate analyses. Before being used as a predictor for regression analyses, organizational life stage was recoded into dummy variables, with the maturity life stage used as a reference category. Likewise, the variable indicating NTEE classification was dummy coded, and the human services classification was used as a reference category.

4. Results

4.1. Sample Descriptive Statistics

Among the sample of 290 agencies, the average organizational age was 21.14 years (SD = 15.32). About a third of the agencies had an NTEE classification of human services (36.9%). Most agencies (59.3%) classified themselves as being in the maturity stage. The average executive director tenure was 10.67 years (SD = 8.61). A majority of executive directors (60.7%) had been in place for ten years or less. The mean total expenses among agencies was USD 1,648,395.42 (SD = 8,184,375.28), and the median expenses amount was USD 143,551.50. A majority of agencies (51.0%) reported total yearly expenses of USD 150,000 or less. See

Table 1. Organizational characteristics. This table provides the organizational characteristics of participating nonprofits in rural, persistently poor counties in the South.

Organizational Characteristics (N = 290)
	n	%
Organizational age (years)	M = 3.46, SD = 2.65
0 to 5	48	16.55
6 to 10	36	12.41
11 to 15	38	13.1
16 to 20	41	14.14
21 to 25	27	9.31
26 to 30	28	9.66
31 to 40	34	11.72
>40	38	13.1
National Taxonomy of Exempt Entities classification
Arts	32	11.03
Education	40	13.79
Environment and animals	23	7.93
Health	32	11.03
Human services	107	36.90
Public society benefit	36	12.41
Religion	20	6.90
Organizational stage
Startup	7	2.41
Growth	67	23.1
Maturity	172	59.31
Decline	10	3.45
Turn-around	34	11.72
Tenure of Executive Director (years)	M = 10.67, SD = 8.61
0 to 5	97	33.45
6 to 10	79	27.24
11 to 15	41	14.14
16 to 20	33	11.38
>20	40	13.79
Expenses	M = USD 1,648,395.42, SD = 8,184,375.28
USD 0.00 to USD 50,000.00	60	20.69
USD 50,000.01 to USD 100,000.00	56	19.31
USD 100,000.01 to USD 200,000.00	53	18.28
USD 200,000.01 to USD 500,000.00	42	14.48
USD 500,000.01 to USD 1,000,000.00	26	8.97
USD 1,000,000.01 to USD 2,000,000.00	23	7.93
>USD 2,000,000.00	30	10.34

for organizational characteristic variable frequencies.

4.2. Exploratory Factor Analysis and Descriptive Statistics for Emerging Organizational Capacity Area Subscales

Prior to the EFA, distributions of item responses were reviewed, and those with minimal variability (10% or less; e.g., 90% yes/10% no responses on an item) were removed (15 items). Thus, there were 46 items included in the EFA. Upon reviewing scree plots and goodness-of-fit statistics as well as considering conceptual frameworks, a four-factor solution of organizational capacity emerged (χ² = 988.757 [815], p < 0.001; CFI = 0.97; RMSEA = 0.03 [90% CI = 0.020, 0.033]). Items with less than 0.5 factor loadings (5 items) and those with cross-loadings of 0.2 or less were removed (5 items), leaving 36 total items. Four new area subscales were named Organizational Identity (3 items; scores 0–3; α = 0.83), Fund Development (4 items; scores 0–4; α = 0.69), Volunteers (3 items; scores 0–3; α = 0.82), and Organizational Procedures (26 items; scores 0–26; α = 0.91). Organizational Identity, Fund Development, and Volunteers area subscales are ordinal scales, with higher levels indicating higher capacity. The Organizational Procedures area is continuous, with higher scores signifying higher capacity.

Table 2. Exploratory factor analysis—4-factor solution. This table provides the factor loadings from the exploratory factor analysis for each of the four newly emerged domains related to organizational capacity. Additionally, the original capacity area and corresponding item is noted in the first column by abbreviation.

Exploratory Factor Analysis—4-Factor Solution
Original Items/New Organizational Capacity Factors	Factor 1—Org. Identity	Factor 2—Fund Development	Factor 3—Volunteers	Factor 4—Org. Procedures
SP1	0.892
SP2	0.843
SP3	0.727
FD2		0.837
FD3		0.788
FD4		0.741
FD5		0.693
V1			0.763
V2			0.722
V3			0.715
SP4				0.573
FS1				0.678
FS2				0.545
FS5				0.732
FS6				0.652
BL4				0.554
BL6				0.680
EM3				0.768
EM4				0.646
HR1				0.843
HR2				0.838
HR3				0.814
HR4				0.807
HR5				0.742
HR6				0.785
LC3				0.648
LC5				0.732
LC6				0.748
PD1				0.560
PD2				0.740
PD3				0.687
PD4				0.674
PD5				0.579
PD6				0.682
T4				0.536
T5				0.580

S.P.—strategy and planning; F.D.—fund development; V—volunteers; F.S.—financial systems; B.L.—board leadership; E.M.—executive management; H.R.—human resources; L.C.—legal and compliance; P.D.—program development; T—technology.

illustrates factor loadings for items in each domain. Factor loadings ranged from 0.536 to 0.892.

Table 3. New organizational capacity area correlations and item examples. This table shows the correlations between the newly emerged organizational capacity areas.

New Organizational Capacity Area Correlations
Organizational Capacity Areas	Org. Identity	Fund Development	Volunteers	Org. Procedures
Org. Identity	1	0.115	0.257 **	0.327 **
Fund Development	0.115	1	0.217 **	0.094
Volunteers	0.257 **	0.217 **	1	0.346 **
Org. Procedures	0.327 **	0.094	0.346 **	1

** p < 0.01.

provides correlations among the four area subscales. Although there were several statistically significant correlations, most correlations were low to moderate, suggesting relative independence.

Table 4. Organizational capacity areas and items. This table provides measures of the central tendency for subscale scores along with frequencies of yes responses for items indicating each subscale.

Organizational Capacity Areas and Items
	n	% of Yes Responses
Organizational identity	(M = 2.27, SD = 1.10, Mdn = 3)
SP1—All of these tasks have been accomplished for our mission: developed, written, and posted.	229 of 272	84.19
SP2—All of these tasks have been accomplished for our vision: developed, written, and posted.	198 of 265	74.72
SP3—All of these tasks have been accomplished for our values: developed, written, and posted.	180 of 264	68.18
Fund development	(M = 2.77, SD = 1.24, Mdn = 3)
FD1—Our organization solicits gifts from individuals.	241 of 276	87.32
FD2—Our organization solicits gifts (both monetary and in-kind) from businesses.	232 of 277	83.75
FD3—Our funding development plan includes one or more of the following: major gifts, planned giving, and/or more of the following: major gifts, planned giving and/or endowment donors.	143 of 278	51.44
FD4—Our organization conducts well-planned fundraising events that maximize return on investment.	150 of 277	54.15
Volunteer practices	(M = 1.18, SD = 1.25, Mdn = 1)
V1—Our organization has a formal process for identifying the needs for volunteers across our organization.	127 of 279	45.52
V2—The organization has a formal process for recruiting volunteers.	106 of 279	37.99
V3—There are formal processes in place for assessing volunteer strengths and skills.	95 of 279	34.05

S.P.—strategy and planning; F.D.—fund development; V—volunteers.

provides measures of central tendency for subscale scores along with frequencies of yes responses for items indicating each subscale.

4.3. Relationships among Organizational-Level Characteristics and Organizational Capacity Area Subscales

4.3.1. Bivariate Analyses

Organizational age. There was a moderate correlation between organizational age and scores indicating organizational procedures (r = 0.23, p < 0.001), such that agencies that had been operating longer tended to have a higher number of organizational procedures in place.

4.3.2. NTEE Classifications

There were significant differences in fund development scores between agencies with different NTEE classifications (H[6] = 23.571, p < 0.001). Post-hoc tests showed that those in the arts sector had significantly higher fund development scores on average than those in the health sector (p = 0.003) or the public society benefit sector (p = 0.018). Those in the education sector had significantly higher fund development scores on average compared to those in the health sector (p = 0.001), the human services section (p = 0.043), and the public society benefit sector (p = 0.011). Agencies classified as environment and animals tended to have higher fund development scores compared to those classified as being in the health sector (p = 0.001), the human services sector (p = 0.013), the public society benefit sector (p = 0.004), and the religion sector (p = 0.020). Agencies classified as human services had significantly higher fund development scores than those in the health sector (p = 0.024).

There were significant differences in volunteer services scores between agencies based on NTEE classifications (H[6] = 13.405, p = 0.037). Post-hoc tests showed that agencies classified as human services had higher volunteer services scores on average compared to those in the arts sector (p = 0.004), the health sector (p = 0.025), and the public society benefit sector (p = 0.024).

There were significant differences in organizational procedures scores between agencies with different NTEE classifications (F[6,225] = 6.23, p < 0.001). Post-hoc tests showed that agencies classified as being in the arts sector had significantly lower organizational procedures scores on average compared to those in the education sector (p = 0.001), the health sector (p < 0.001), and the human services sector (p = 0.006). Those in the religion sector had lower organizational procedures scores on average compared to those in the education sector (p = 0.023) and the health sector (p < 0.001).

Organizational life stages. There were significant differences in organizational identity scores between organizations at different life stages (H[4] = 20.11, p < 0.001). Post-hoc tests showed that agencies in the growth stage (p < 0.001) and turnaround stage (p < 0.001) had significantly lower organizational identity scores compared to those in the maturity stage.

There were also significant differences in fund development scores via organizational stage (H[4] = 19.29, p < 0.001). Post-hoc tests showed that agencies classified as being in the maturity stage had higher fund development scores on average compared to those in the start-up (p = 0.038), growth (p = 0.047), decline (p = 0.005), and turnaround stages (p = 0.003).

There were significant differences in organizational procedures scores between agencies classified into different life stages (F[4,227] = 5.18, p < 0.001). Post-hoc tests showed that agencies classified as being in the maturity stage had significantly higher organizational procedures scores on average compared to those in the turnaround stage (p = 0.007)

Tenure of executive director. There was a significant positive relationship between the tenure of an agency’s current executive director and volunteer practices (τ_b = 0.10, p = 0.031), such that the longer an agency’s executive director had been in place, the more likely they were to have implemented practices relating to volunteering. There was also a significant positive relationship between executive director tenure and scores indicating organizational procedures (r = 0.16, p = 0.018).

Expenses. Agencies’ total expenses were positively associated with scores indicating organizational identity (τ_b = 0.10, p = 0.042) and organizational procedures (r = 0.37, p < 0.001).

4.4. Multivariate Analyses

Organizational identity subscale. When an ordinal logistic regression was conducted with organizational identity scores as the outcome, the proportional odds assumption was violated (χ²[26] = 51.34, p = 0.002), so scores were dichotomized based on observed frequencies (0 = scores of 0, 1, or 2; 1 = score of 3), and a binary logistic regression was conducted instead. Compared to agencies in the maturity stage, those in the turnaround (OR_SD = 0.69, p = 0.006) and growth stages (OR_SD = 0.65, p = 0.002) were less likely to have high organizational identity scores when controlling for other factors. As a test of sensitivity, a multinomial regression was conducted and yielded similar results, with the same factors being significant in both models.

Fund development subscale. An ordinal logistic regression predicting fund development scores violated the proportional odds assumption (χ²[39] = 78.69, p < 0.001), so the scores were dichotomized based on observed frequencies (0 = scores of 0, 1, or 2; 1 = scores of 3 or 4), and a binary logistic regression was conducted instead. Compared to agencies in the maturity stage, those in the decline (OR_SD = 0.65, p = 0.001), startup (OR_SD = 0.74, p = 0.026), turnaround (OR_SD = 0.73, p = 0.029), and growth stages (OR_SD = 0.75, p = 0.049) were less likely to have high fund development scores on average. Compared to agencies with an NTEE classification of human services, those in the environment and animals sector (OR_SD = 2.57, p = 0.012) and the education sector (OR_SD = 1.45, p = 0.023) were more likely to have high fund development scores. Those in the health sector were less likely to have high fund development scores compared to those in the human services sector (OR_SD = 0.70, p = 0.009). As a test of sensitivity, these results were compared to the results of the ordinal logistic regression which violated the proportional odds assumption. The same factors were significant in this model, except with the addition of tenure of executive director (OR_SD = 1.04, p = 0.014).

Volunteer practices subscale. An ordinal logistic regression estimating factors associated volunteer practices scores showed that compared to agencies in the maturity life stage, those in the turnaround stage (OR_SD = 0.73, p = 0.014) were less likely to have higher volunteer practices scores on average. When compared to agencies in the human services sector, those in the health (OR_SD = 0.71, p = 0.007), arts (OR_SD = 0.72, p = 0.009), and public society benefit sectors (OR_SD = 0.78, p = 0.042) were less likely to have higher volunteer practices scores on average. The proportional odds assumption was met for this model (χ²[26] = 6.924, p = 0.999).

Organizational procedures subscale. A generalized linear model specified with a gamma distribution and a log link function was used to estimate factors related to organizational procedures scores. Organizational age was positively associated with organizational procedures scores when controlling for other factors (see Figure 1). For every one-year increase in organizational age, there was a 0.4% increase in organizational procedures scores on average (Exp(B)_SD = 1.06, p = 0.016). Compared to agencies in the maturity life stage, those in the decline or turnaround stages had lower organizational procedures scores on average. Organizational procedures scores for agencies in the maturity stage were 42.7% higher on average than for those in the decline stage (Exp(B)_SD = 0.94, p = 0.013) and 25.6% higher than for those in the turnaround stage (Exp(B)_SD = 0.93, p = 0.004). Compared to agencies in the human services sector, those in the arts and religion sectors had lower organizational procedures scores on average. Organizational procedures scores for agencies in the human services sector were 33.2% higher on average than for those in the arts sector (Exp(B)_SD = 0.91, p < 0.001) and 28.4% higher than for those in the religion sector (Exp(B)_SD = 0.94, p = 0.017).

Table 5. Multiple regressions estimating organizational characteristics associated with capacity scores. This table provides results of the multiple regression analyses that were conducted with each of the organizational capacity areas as outcomes to test which characteristics were associated with each capacity area when controlling for other characteristics.

Multiple Regressions Estimating Organizational Characteristics Associated with Capacity Scores
	Organizational Identity (n = 263) a			Fund Development (n = 274) a			Volunteer Practices (n = 279) b			Organizational Procedures (n = 232) c
	B	SE	ORSD	B	SE	ORSD	B	SE	ORSD	B	SE	Exp(B)SD
Organizational age	−0.02	0.01	0.80	−0.02	0.01	0.78	0.01	0.01	1.08	0.01 *	0.01	1.06
Standardized expenses	0.36	0.24	1.43	0.03	0.14	1.03	0.17	0.12	1.19	0.03	0.02	1.03
Tenure of executive director	−0.02	0.02	0.86	−0.02	0.02	0.83	0.01	0.01	1.06	0.01	0.01	1.02
Life stage
Maturity d
Startup	−1.10	0.87	0.85	−1.98 *	0.92	0.74	−0.31	0.81	0.95	0.02	0.16	1.00
Growth	−1.01 **	0.33	0.65	−0.69 *	0.35	0.75	−0.01	0.28	0.99	−0.02	0.06	0.99
Decline	−0.31	0.78	0.95	−2.39 **	0.79	0.65	−0.58	0.59	0.90	−0.36 **	0.14	0.94
Turnaround	−1.18 **	0.43	0.69	−1.00 *	0.45	0.73	−1.00 *	0.42	0.73	−0.23 **	0.08	0.93
NTEE classification
Human services d
Arts	−0.21	0.44	0.94	0.96	0.53	1.35	−1.06 **	0.42	0.72	−0.29 **	0.08	0.91
Education	0.89	0.50	1.36	1.07 *	0.50	1.45	−0.24	0.35	0.92	0.06	0.07	1.02
Environment and animals	0.05	0.52	1.01	1.68 *	0.79	2.57	−0.35	0.44	0.91	−0.05	0.09	0.99
Health	−0.07	0.47	0.98	−1.13 **	0.44	0.70	−1.11 **	0.43	0.71	0.13	0.08	1.04
Public society benefit	−0.10	0.45	0.97	−0.69	0.44	0.80	−0.75 *	0.37	0.78	−0.03	0.08	0.99
Religion	−0.76	0.59	0.83	0.24	0.56	1.06	−0.55	0.48	0.87	−0.25*	0.10	0.94
LR χ2 (13)	28.84 (p = 0.007)			47.39 (p < 0.001)			25.94 (p = 0.017)			53.67 (p < 0.001)

Note: National Taxonomy of Exempt Entities (NTEE) * p < 0.05; ** p < 0.01. ^a Binary logistic regression. ^b Ordinal logistic regression. ^c Generalized linear model identified with gamma distribution and log link. ^d Reference category.

provides test statistics for all multiple regressions estimating organizational characteristics associated with capacity scores.

4.5. Limitations

The present study’s findings should be considered along with its limitations, the foremost of which was that the response rate was eight percent overall, and thus, findings cannot be generalized to the population of interest—nonprofits in rural, persistently poor counties in the South. To increase responses in future studies, additional outreach (e.g., reminders via postcard or phone call) or incorporation of another data collection method (e.g., phone survey) may be necessary. Another issue is using an assessment that has not been previously used empirically. However, content validity was assessed allowing for adjustments in the subscales, and the reliability analysis after the EFA had decent outcomes. Further, as noted in Section 2, the Background, many of the publicly available assessment tools are not founded in research. The present investigation serves as foundational support to provide evidence for a quick, introductory organizational capacity assessment that could be useful for nonprofits in rural communities.

Additionally, there are challenges related to the selection criteria and sampling procedures. McDougle (2015) notes that the accuracy of the NCCS and IRS data are questionable and consequently leave out potential participants that might meet the study criteria. Using these data also excludes small organizations who have not filed the 990 form with the IRS. Unfortunately, there are few alternatives to obtaining a full listing of nonprofits in areas of interest. Regarding selection criteria, the definition of rural adopted in the present study (i.e., non-metro as less than 50,000; USDA/ERS 2017) means that some argue that non-metro and rural are not synonyms (USDA/ERS 2021). Thus, it is possible that an alternative rural definition may yield different findings, and future studies examining rural nonprofits should consider employing the rural–urban continuum codes, for example, to identify potential variances.

5. Discussion

In the present study, investigators examined organizational capacity measurement issues by performing an exploratory factor analysis on a structural organizational capacity assessment (ABN/KLF 2017) for use in rural nonprofits. Upon identifying the new capacity area subscales, relationships between capacity areas and organizational-level characteristics were probed.

5.1. Post-EFA Organizational Capacity Areas Subscales

The EFA resulted in four organizational capacity area subscales with 36 items pared down from the original assessment, which had 11 organizational capacity areas and 61 items. The four new area subscales were named Organizational Identity (who is the organization and what does it do); Fund Development (fundraising practices); Volunteers (volunteer practices); and Organizational Procedures (functions related to human resources, technology, legal and compliance, program design and evaluation, executive and board leadership, and financial systems). The subscales were aligned with those in other instruments, and though on the lower end, the number of capacity areas and items identified were within the ranges of similar tools (Informing Change 2017a, 2017b). Several items that were removed would be considered very basic to nonprofit operations, such as an established fiscal year or number of board members as required in bylaws. Additionally, an entire subscale, External Relations, Communications, and Marketing Strategy, was removed. Compared to non-respondents, the responding organizations were older with larger bottom lines and high levels of capacity in most domains. Thus, it seems that the most basic items and even some of the more complex were superfluous for this sample. However, if younger and/or smaller organizations were to respond, the EFA outcomes might have been different. Additional research is needed to test the assessment’s utility with rural nonprofits with various budgets and ages.

The dichotomous nature of items and content of items that remained after the EFA create a rudimentary awareness of gaps in capacity areas, which is the foundational level of capacity assessment (Informing Change 2017a). The information gained from the assessment can provide organizations, capacity builders, and funders with direction as to which capacity areas may need the most attention immediately. What is not obtained from the assessment is quality or effectiveness within each subscale. For example, fundraising with donors is occurring, but how successful are those efforts? To acquire these data, more in-depth assessments of organizational capacity would be necessary, and these processes are costly and time-intensive, though very important to improving capacity functions.

The EFA opens up a dialogue about how to best measure organizational capacity while considering spatial and geographical differences. The study did not allow for closer examination of contextual factors related to operating in a rural area (e.g., isolation) that might affect organizational capacity in nonprofits. What is already known and used in practice is the prescriptive form of organizational capacity assessment. What is missing is knowledge about operating a nonprofit in a rural area that impacts solutions prescribed. Future research, especially qualitative studies, should be considered which might provide additional evidence that organizational capacity and its measurement might look different for rural nonprofits.

5.2. Relationships between Capacity and Organization Characteristics

Most of the rural organizations in the present study were small (budget-wise) but well-established, as they had been in existence for 20-plus years and were self-reported to be in the maturity stage. Leadership in these organizations seemed to be stable as executive directors were in their roles for more than 10 years on average. The organizations were a mixture of NTEE categories with human services, education, and public society benefit being the dominant areas.

Overall, related to capacity, organizations had high scores on the Organizational Identity subscale, moderate scores on the Fund Development and Organizational Procedures subscales, and low scores on the Volunteer subscale. Examining the relationships between organizational characteristics and the capacity subscales, organizational life stage (e.g., start-up, growth, maturity, decline, and turnaround) seems to be a good predictor of capacity in rural nonprofits across all of the present subscales, which is consistent with Andersson et al. (2016)’s previous study with metropolitan organizations.

In the present study, rural organizations in the maturity life stage had higher capacity in all areas than those in other stages. As the work of Stevens (2001) supports, nonprofit capacity builders and funders might use the organizational life stage marker to better understand organizations’ potential capacity levels, set expectations accordingly, and target investments and development efforts. Nonetheless, it is important to note that the organizational life stage marker (as presented in this study and others) is a self-report characteristic. While definitions of each stage are provided, respondents may identify in the wrong stage unintentionally or intentionally (i.e., they do not want to reveal organizational weaknesses or challenges). Other characteristics, including age, NTEE category, expenses, and tenure of executive director, were less dependable in predicting capacity using the present capacity assessment. In the subsections below, the most important relationships for each subscale are discussed.

Organizational Identity. Organizational life stage was the only organizational-level characteristic that predicted capacity in the Organizational Identity subscale. Aligned with Andersson et al. (2016), rural organizations in the maturity stage were more likely to have higher Organizational Identity scores than those in the turnaround or growth stages, meaning, in this assessment, they have developed, written, and posted missions, visions, and values. This finding is logical because, generally, organizations at the beginning or end of the organizational life cycle are less established or confident about their identity and purpose. Identities, which are “central, enduring, and distinctive attributes of an organization” (Saqib 2019, p. 234) guide staff in their work. Identities are important because they communicate to stakeholders what the organizations stand for and what they contribute to communities, and based on those identities, stakeholders pledge their support (Levine and Eckerd 2019). However, changing strategies, adapting to new external environments, and innovating to address ever-evolving social problems are necessary and critical for nonprofits to remain relevant in their communities (Jaskyte 2011). Nonprofit flexibility has been especially important during times of community crisis and emergency (e.g., the Great Recession; Horvath et al. 2018), as we saw in the COVID-19 pandemic (e.g., Giordano 2020). To foster innovation, maintain relevance, and yet also have a solid identity, organizations in all life stages might consider adopting an adaptive strategy in identity formation and planning:

“…a roadmap of the terrain that lies before an organization and develop a set of navigational tools, realizing that there will be many different options for reaching the destination. If necessary, the destination itself may shift based on what we learn along the way”.

(O’Donovan and Flower 2013, para. 9)

Fund Development. Along with the organizational life stage, the NTEE category of the participating nonprofits also predicted the fund development capacity scores. Rural organizations that identified as environment-, animal-, or education-focused had higher fund development capacity scores than those in human services. One possible explanation is competition for supporters within categories of nonprofits. The human services category has the most organizations in the rural South, while the environment and animal category and education category have considerably fewer. Human service agencies may need to devise methods to differentiate themselves from similar organizations. Another explanation is that donors in the rural South are less inclined to support human services due to rugged individualism—the belief that people should help themselves and avoid government aid (Bazzi et al. 2017). Previous research (e.g., Besel et al. 2011) indicates that donors in this region are more apt to give to churches and universities. In capacity-building efforts, educators should consider that the type of nonprofit may dictate fundraising strategies. For example, what works for education nonprofits may not be fruitful for human services organizations. Still, more research, qualitative in particular, is needed to understand the relationship between NTEE type and fund development capacity.

Volunteer Practices. In the Volunteer Practices capacity area, those in the human services NTEE category were more likely to have higher scores than health, public society benefit, and arts organizations. Where volunteer capacity differs among type of nonprofit, this finding is consistent with recent national research (e.g., Hager and Brudney 2021). Human services nonprofits typically utilize volunteer support more regularly (i.e., daily and weekly) than other categories that may only use volunteers sporadically. The increased need demands more capacity. Nonetheless, average scores were low across all items, dictating a need for attention to increasing Volunteer Practices capacity in rural nonprofits to meet programmatic needs. For rural human services nonprofits specifically, one key issue is that they may not be effectively courting volunteers to become donors, considering their low Fund Development capacity. Research finds that people who volunteer as well as people who attend special events are about 75 percent more likely to give to the organization (Dietz and Keller 2016), and these are two areas in which rural nonprofits need support from capacity builders.

Organizational Procedures. In the multivariate analyses, Organizational Procedures capacity was connected to the age of the organization, consistent with some previous findings (e.g., Andersson et al. 2016). This means that older, rural organizations have higher organizational procedures scores; over the years with time and investment, they have solidified functions related to human resources, technology, legal and compliance, program design and evaluation, executive and board leadership, and financial systems. While this finding seems common sense, funders and capacity builders should keep in mind when evaluating applications for support that younger rural nonprofits may not have existed long enough to develop a strong organizational foundation. Further, more attention should be given to the organizational procedures standards that rural nonprofits are held to by funders. Recent research suggests that rural nonprofits may find meeting funder standards impossible due to lack of funding, staff, and other contextual issues (Walters 2021). Some standards may be unnecessary for rural nonprofits to run successfully and effectively (Walters 2021).

6. Conclusions

Representing a smaller part of the organization makeup, existing nonprofit research highlights less about rural nonprofit characteristics and organizational capacity (Hung and Hager 2019; Lu et al. 2019; Neuhoff and Dunckelman 2011). Recent research (e.g., Walters 2021) indicates that rural nonprofit capacity issues may differ from their urban and suburban peer organizations, and thus how these nonprofits and their stakeholders consider and measure organizational capacity should be scrutinized. Thus, the purpose of the current study was to assess the utility of an existing organization capacity assessment for rural nonprofits.

Organizational capacity assessments are often time-consuming and complex (Informing Change 2017a). The EFA on the assessment in the current study—the ABN and KLF organizational structural capacity assessment—revealed a good-fitting model with four organizational capacity areas identified for the revised assessment, including Organizational Identity, Fund Development, Volunteers, and Organizational Procedures. While more testing is needed, the revised assessment is useful for rural nonprofits to quickly evaluate areas of strength and challenge. With funders placing greater concern on organizational capacity, rural nonprofits may be at a disadvantage for funding opportunities, and therefore, these organizations must carefully attend to perceived deficiencies to become more competitive while remaining true to their values and community context (Brown 2014; Karsh and Fox 2014; Minzner et al. 2014; Walters 2020). Conversely, nonprofit influencers (e.g., funders, capacity builders, policymakers) need to be less rigid and more thoughtful about how conventional standards—often made specifically for and by nonprofits in urban settings—may or may not fit nonprofits in rural settings. In short, context matters when setting standards around organizational capacity for nonprofits and their ability to address complex social problems.

The present study also examined the relationships between organizational capacity areas and organizational-level characteristics of nonprofits in rural communities. One of the most important findings is that organizations in the mature life stage were more likely to score better across all capacity areas. Though reasonable and logical, this finding underscores that nonprofits in the beginning and end stages of the organizational life cycle require abundant support in areas of identity, fundraising, and procedure development to progress to the maturity stage. These organizations, while young and sometimes struggling, often possess innovation and enthusiasm that mature organizations might lack. Mentoring of and investments in new organizations—and potentially new ideas and approaches—by funders and capacity builders could result in effective solutions for persistent problems, like poverty and poor health outcomes, that have plagued rural communities in the U.S. for decades.