The Connection between Entrepreneurial Intentions and Community Member Priorities for Asset-Based, Sustainable Development to Improve Well-Being

Abstract

This chapter analyzes survey data collected from 5487 residents across 85 communities in Texas, USA, ranging from rural areas to large urban centers. Our analysis examines the impact of a community’s degree of urbanity and rurality, as well as individual factors such as social position, values, and satisfaction with local assets, on their economic and quality-of-life development project preferences. We argue that community development processes should take a broader, multicriteria approach that considers a range of factors impacting community needs and well-being. Our findings demonstrate the viability of this human-centered approach, highlighting the opportunity to direct societal capitals toward enhancing well-being within various systems. We analyze six community development project concepts, including renovations to downtown buildings, opening a community health center, deploying high-speed internet, enhancing public libraries, offering early-college-credit programs, and creating a co-working and startup space. Our study shows that there is no one-size-fits-all approach to community development, and participatory processes can guide collaboration between experts and community members. We find that entrepreneurial intention can positively or negatively impact community development concepts and should be considered as part of a well-being strategy. However, social governance structures, both governmental and non-governmental, need to address common-cause aspects of well-being such as community health and education.

1. Introduction

A condensed version of this research was published by Kammer-Kerwick et al. [1], where two economic development theories, Sustainable Local Economic Development (SLED) and Asset-Based Community Development (ABCD), were connected to address sustainability across multiple objectives and decision-making criteria. SLED integrates community perspectives into the development process with an emphasis on equitable and sustainable solutions, while ABCD views the community as a system of subsystems with the economy being one component that needs to be developed to maintain system equilibrium. Our human-centered, participatory approach builds on SLED and ABCD by starting from community needs to assess projects that serve the broader well-being of the community. The previous study only presented models for three of the six community development intervention concepts, but this chapter includes all six intervention concepts with models that predict interest and investment amount compared to other concepts. The chapter also includes modeling analysis of the connections among entrepreneurial intention, its antecedents, and community member priorities for asset-based, human-centered, sustainable development to improve community well-being. Additionally, the research presented here connects to related research by the authors exploring collaboration in a variety of other community and business settings, including large and small capital improvement projects [1, 2] as well as a study that uses empirically grounded simulation to generate synthetic data as an approach to studying collaboration in complex sociological systems that are difficult to observe [3]. We frame our study in this chapter with a focus on mapping various types of capital to community systems. More specifically, we previously connected location and human, social, and community capitals to the built environment, health system, and communications system. In this chapter, we extend the examination of capitals to include business capital and we add additional systems for civic, education, and business.

2. The current study

Extending the previous study, we shift our framing in this chapter to one that maps various types of capital to community systems. More specifically, we connect location and human, social, community, and business capitals to the built environment, health, communications, civic, education, and business systems. We examine these issues formally by addressing the following research questions:

1. Does location predict preferences for and priorities among various community development project concepts?

2. Does human capital (as represented by community members’ social position—for instance, age, race, and income—predict preferences for community development project concepts?

3. Does social capital (as represented by personality types and personal values) help predict preferences for community development project concepts?

4. Does community capital (as represented by community satisfaction with and the perceived importance of assets available in a community) further improve predictions about preferences for various community development projects?

5. Does business capital (as represented by entrepreneurial intentions) connect with various community development project concepts?

Our analysis provides insights into how these factors improve our understanding of community interest in and willingness to allocate resources to innovations in various systems serving the community. For simplicity in this chapter, we will refer to the types of capital (location, human, social, community, and business) associated with each question in the narrative that follows.

To investigate economic and quality-of-life priorities in Texas (USA) communities, we conducted a survey of 5487 residents from 85 different communities, including those in rural areas, small towns, medium-sized cities, and large urban centers. The survey asked participants to allocate interest points among six economic development project concepts that were strategically chosen to fit their town or neighborhood. We aimed to predict respondent priorities based on factors such as degree of urbanity and rurality, social position, community values, personality types, community asset satisfaction and importance, and entrepreneurial intentions. Our approach prioritizes the perspectives of community members in determining which projects should be prioritized, taking into account how individual needs and community needs are connected and impact each other. By taking a human-centered approach to economic and quality-of-life development, we hope to improve community well-being by adapting decision-making processes to the unique needs of each community (Table 1).

Broad-based community services (Chronbach’s α = 0.85)

Arts and culture options

Nature and outdoor options

Walking and biking options

Public transportation

Infrastructure conditions

Institutions of higher education

Economic environment (Chronbach’s α = 0.85)

Housing affordability

Housing availability

Employment options

Cost of living

Incentives to start or expand a business

Family-oriented community services (Chronbach’s α = 0.80)

K-12 education

Library

Healthcare

Childcare options

Safe environment

Communications services (Chronbach’s α = 0.78)

Broadband (internet)

Cellular or mobile telephone options

Table 1.

Community asset satisfaction subscales.

3. Expanding the concept of well-being: the importance of community and quality of life in economic development

A well-being economy refers to an economy that prioritizes the well-being of collective human society and the ecological environment, instead of material growth [4]. For example, developing local entrepreneurship is one way to directly involve community members in economic development. Instead of imposing external answers to supposed community needs, community members have embedded knowledge of community needs and embedded networks that could meet those needs [5]. The Well-being Economy Alliance in Scotland [6] reports that Scotland’s craft brewing sector is an example of how local production supports the local economy and is good for the environment, especially in rural areas. Our study builds on research [7] studying the degree to which, if at all, entrepreneurial intentions predict investment in collective well-being interventions. The availability of good jobs, opportunities for human flourishing, and options for community creation are as important—and possibly more important—than a narrow focus on economic growth. The concept of a human-centered approach to sustainable community development is inspired by human-centered design literature, which emphasizes involving the human perspective in every step of planning and development to create product solutions. However, this approach is not widely used in psychosocial interventions and implementation strategies [8]. In sustainable community development, quality of life is used as a framework to plan for locally appropriate and sustainable economic development, which requires information at the individual, community, and economic levels. Quality of life provides a domain of observation that allows for measuring the balance between economic and community concerns. Individual needs are shaped by their personality type, personal values, well-being, and levels of perceived community attachment and satisfaction [9, 10]. Community well-being, on the other hand, is influenced by individual-level needs, underlying community assets, levels of community satisfaction with those assets, and shared culture and values among individuals in the community [11, 12, 13], Hanscott 2016. McGregor, Camfield, and Woodcock [14] found that people identify their needs and interpret their quality of life in a local context, which suggests that economic development should assess not only satisfaction with community assets but also their importance to community members.

4. Method

We utilize mixed multi-level hurdle models to analyze community member responses and explain the outcomes of an investment allocation exercise in economic development and quality-of-life development concepts. Our analysis incorporates a range of factors that could influence decision-making. For example, we consider community size, population density, and proximity to major urban centers to assess the impact of urbanity and rurality. We also include values, personality traits such as agreeableness and openness, satisfaction with and perceived importance of community resources, and intentions for entrepreneurial behavior as well as related attitudes, social norms, and perceived behavioral control. Additionally, we analyze the effects of demographic variables and the perceived impact of COVID-19 on the community.

We hypothesize that values and agreeableness will predict preferences for economic and quality-of-life interventions given limited resources. The satisfaction with and importance of available resources are also expected to play a role in resource allocation decisions. Furthermore, we suggest that entrepreneurship may be an effective mechanism for implementing certain types of projects, but some interventions may be better approached as a common good with less salience for entrepreneurs.

5. Procedure and participants

This chapter’s data were gathered in 2020, during the COVID-19 pandemic, in two phases. A purposeful community sampling methodology was employed to gather information from eighty rural and small-town communities situated outside of the five most populous metropolitan areas in Texas, namely Dallas/Fort Worth, Houston, Austin, San Antonio, and El Paso. Student researchers were hired to promote the project and share links to a web-based survey in the rural and small-town communities involved in the study. To encourage participation, these researchers employed various methods, such as social media, telephone calls to community leaders and members, and disseminating electronic flyers to local groups to post on their websites. Funding for school computer equipment was provided to the community with the highest number of completed surveys per capita. The participants from the five largest metro areas were obtained through a commercial panel, Dynata. The rural and small-town surveys were conducted during the summer of 2020, while the major metro surveys were conducted during the fall of 2020. Our institutional review board approved the study. However, survey response rates were not disclosed as we used community promotion and referral sampling methods that make it difficult to determine the total number of community members made aware of the study.

Our analysis is based on survey data collected from 3363 rural and small-town residents and 2124 individuals residing in the five largest metro areas in Texas. Table 2 outlines the sample’s demographic descriptive statistics. We intentionally selected rural and small-town communities from a larger research initiative that deployed students across the state of Texas, USA, to evaluate community resilience. These communities varied in population size, with an average of 24,692 and a median of 7325, ranging from Bandera, with a population of 910 in 2021, to Amarillo, with a population of 199,747 in 2021. The complete list of rural communities is available in the manuscript’s data supplement. The five metro areas were chosen for their large population sizes, with all having a 2021 population of over one million, except for El Paso, which has a population of 963,000.

Gender (n = 5487)	Male	33.0%
	Female	67.0%
Race/ethnicity (n = 5487)	White, not Hispanic or Latino	56.8%
	Hispanic or Latino	24.2%
	Black or African American	8.7%
	American Indian or Alaskan Native	1.0%
	Asian	5.5%
	Native Hawaiian or Pacific Islander	0.3%
	Two or more races	3.6%
Education (n = 5487)	Less than 9th grade	0.4%
	9th to 12th grade, without diploma	1.6%
	High school diploma or GED	12.6%
	Some college	21.7%
	Associate degree	10.5%
	Bachelor’s degree	30.1%
	Graduate or professional degree	23.1%
Age (n = 5487)	18–24	10.3%
	25–34	17.3%
	35–44	22.8%
	45–54	19.5%
	55–64	16.5%
	65–74	10.8%
	75 or older	2.8%
Household income (n = 5439)	Less than $10,000	4.8%
	$10,000 to $14,999	3.4%
	$15,000 to $24,999	5.9%
	$25,000 to $34,999	8.4%
	$35,000 to $49,999	11.3%
	$50,000 to $74,999	17.9%
	$75,000 to $99,999	15.8%
	$100,000 to $149,999	18.1%
	$150,000 to $199,999	7.7%
	$200,000 or more	6.7%

Table 2.

Sample demographics.

6. Measures

Our assessment of community decision-making utilized measures described in depth in Kammer-Kerwick et al. [1]. We have included a summary of the measures here for continuity.

6.1 Schwartz value theory

According to Schwartz’ theory of basic human values [15], all cultures are shaped by ten distinct personal values. These values comprise self-direction, stimulation, hedonism, achievement, power, security, conformity, tradition, benevolence, and universalism, forming a circular motivational continuum that reflects the compatibility and conflict among values. Schwartz’s theory proposes that this continuum is organized along two bipolar dimensions: one dimension compares “openness to change” and “conservation,” while the other compares “self-enhancement” and “self-transcendence.” To assess these dimensions, we have included resultant self-transcendence (self-transcendence—self-enhancement) and resultant conservation (conservation—openness to change) in our survey. This approach is commonly used in various marketing and business strategy contexts [16, 17, 18, 19]. Additionally, we included community size, population density, and distance to the nearest major metropolitan center in Texas as additional variables to explain variance in interest in the community development concepts. Furthermore, to account for our multi-site data collection design, we included community as a random effect in our analysis. We also assessed the role of social position by including the age, race, gender, education, and income of community members in our sample.

6.2 Big five personality traits

The Big Five Inventory (BFI) is a well-established measure of five personality dimensions that have been shown to be relatively stable and distinct over the life course and applicable across cultures [20, 21, 22]. We included agreeableness and openness because the prior research indicates that these personality dimensions should predict the likelihood of a community member investing in an intervention. Agreeableness relates to a desire to engage in prosocial actions. Openness was viewed to be a personality trait that might predict respondent likelihood to invest in community development interventions. We omitted conscientiousness, extraversion, and neuroticism from the BFI to avoid participant fatigue.

More specifically, we hypothesized that agreeableness would be the personality factor that would best predict whether community members would cooperate toward an intervention. Openness to experience includes three higher-order measures of intellectual curiosity, active experiencing of senses and emotions, and open-mindedness toward different cultural ideas and values [23]. Central aspects of openness include a willingness to entertain novel ideas and unconventional values, intellectual curiosity, and independent judgment. Openness has been studied in relation to creativity and innovation in social entrepreneurship [24], adjustment to change, identification and maintenance of specific communities [25]; Füller et al. (2008), and engagement with community development interventions [26].

6.3 Asset satisfaction and importance

Respondents rated how satisfied they were with eighteen community assets, on a 7-point scale where 1 is “not at all satisfied” and 7 is “extremely satisfied”. Participants also rated the importance of these same eighteen assets in the context of considering relocating to another community (where 1 is “not at all important” and 7 is “extremely important”. We performed an exploratory factor analysis (EFA) and reliability analysis to create four mean asset satisfaction subscales from the original eighteen survey items, previously listed, see Table 3. This analysis is discussed in Kammer-Kerwick et al. [1]. We utilized these same four themes to calculate asset importance subscales. Table 3 shows the eighteen community assets included in the analysis the following items, grouped by theme.

Broad-based community services (Chronbach’s α = 0.85)

Arts and culture options

Nature and outdoor options

Walking and biking options

Public transportation

Infrastructure conditions

Institutions of higher education

Economic environment (Chronbach’s α = 0.85)

Housing affordability

Housing availability

Employment options

Cost of living

Incentives to start or expand a business

Family-oriented community services (Chronbach’s α = 0.80)

Primary and secondary education of children prior to college

Library

Healthcare

Childcare options

Safe environment

Communications services (Chronbach’s α = 0.78)

Broadband (internet)

Cellular or mobile telephone options

Table 3.

Community asset themes.

6.4 Entrepreneurial intentions and antecedents

We incorporated the measures from the entrepreneurial intention questionnaire (EIQ) [27]—based on Ajzen’s [28] Theory of Planned Behavior (TPB)—to assess the level of entrepreneurial intention and its antecedents. The EIQ is a cognitive model based on the assumptions that becoming an entrepreneur is a voluntary and conscious decision and that intention is the single best predictor of behavior. The EIQ measures three motivational antecedent factors: a person’s positive or negative attitudes toward being an entrepreneur (personal attitude), a person’s perception of whether significant personal relationships would approve or disapprove of being an entrepreneur (subjective norms), and a person’s perception of how easy or difficult it would be for them to do entrepreneurial behaviors (perceived behavioral control). These three antecedents measure the effort needed for a person to make the decision to practice entrepreneurial behavior. Demographic and situational factors will influence entrepreneurial intention, like educational experiences and time constraints. We include several demographic variables, like level of education, and variables representing the social context of the community in our models predicting community intervention concepts.

In addition to entrepreneurial intention, Linan and Chen [27] include measures for personal attitudes, subjective norms, and perceived behavioral control in the EIQ, with four all measured on 7-point rating scales. Personal attitudes are measured through agreement ratings for 5 items, including “if I had the opportunity and resources, I’d like to start a firm” and “being an entrepreneur implies more advantages than disadvantages to me.” Subjective norms were measured through approval rating from close family, friends, and colleagues for “if you decided to create a firm, would people in your close environment approve of that decision.” Perceived behavioral control was measured through agreement ratings for 6 items, including “to start a firm and keep it working would be easy for me” and “I know how to develop an entrepreneurial project.” Entrepreneurial intention was measured through agreement ratings for 6 items, including “my professional goal is to become an entrepreneur” and “I am determined to create a firm in the future.” The EIQ has been utilized in diverse countries with results that demonstrate satisfactory measurement properties and strong support for the model. The EIQ has elucidated insights into how cultural values modify the way individuals in different communities perceive entrepreneurship [27].

6.5 Additional explanatory variables

To account for variability in interest across the different community development concepts, we incorporated several additional variables in our analysis. These variables encompassed community size, population density, and proximity to the nearest major metropolitan center in Texas. Moreover, we utilized community as a random effect to accommodate our multi-site data collection design. To evaluate the influence of social position, we considered age, race, gender, education, and income of community members in our sample. Lastly, as novel variables, because this study was conducted during the first year of the COVID-19 pandemic, we included variables measuring participants’ perceptions of how COVID-19 impacted their community’s health and economy. Specifically, we asked, “How has your community’s health been affected by COVID-19?” and “How has your community’s financial condition been affected by COVID-19?” Both questions used a rating scale where 1 is “not very affected” and 5 is “extremely affected”.

6.6 Community development concepts

Our study focused on understanding how community members prioritize and make trade-offs among a set of community development project concepts, which were selected based on need expressed in literature as well as from comments obtained during interviews and community meetings conducted during the planning phase for this survey. These concepts were all popular options that have been considered and implemented in various community settings. The specific societal systems covered by these concepts included: Downtown Renovation for Mixed Use Facilities (Built Environment), Community Health Centers (Health System), Gigabit Fiber Broadband Downtown (Communications System), Adding more Computers and Meeting Spaces in the Public Library (Civic System), Early College Credit and Vocational Programs for High School Students (Education System), Co-Working and Startup Working Space for Entrepreneurs (Business System).

To assess community members’ preferences, we employed an exercise that asked participants to allocate 100 points across the different projects based on how well they thought the projects would fit the needs of their community. We included a seventh category for any additional concepts that participants deemed salient. The wording of the options was localized to the context of the survey, with the phrase “in my neighborhood” included in surveys conducted in major metro areas. Further details about the concepts tested can be found in Kammer-Kerwick et al. [1].

7. Data analysis strategy

We conducted descriptive statistics as well as exploratory factor and reliability analyses using SPSS 27.0. To address our research questions, we employed Generalized Linear Mixed Hurdle Models (GLMMs), using the logit link function and binomial distribution to fit each interest model. Interest in each project concept was classified as either yes (allocation of any points) or no (allocation of zero points). The allocation models were also GLMMs, with an identity link function and a Gaussian distribution, and used centered log-ratio transformation to counter skewing resulting from the point allocation exercise. The allocation model for each concept was fitted using only those participants who expressed interest in that concept. To account for multiple sites involved in the study, random intercept mixed hurdle models were used to model interest in each project concept. All predictive models were run using the glmmTMB package 1.0.2.1 in R [29].

We tested each model in several stages, starting with community characteristics, followed by social position, COVID impact, values, personality, asset satisfaction and importance, and finally entrepreneurial intention.

The improvement of the models with the addition of variables after each step was assessed with the reduction in AIC. In summary, the specification for the models employed in the present study is shown in Table 4. It is important to note that for the sake of parsimony and model convergence issues, we are presenting models with simplistic representations of gender and race/ethnic identities. This choice has empirical support, e.g., consistent with the communities, our rural sample includes few participants with nonbinary gender identities and fewer brown and black community members than the larger metro areas. Nonetheless, we recognize that this is a limitation in our study, which we discuss further below.

Dependent variables

Interest (0/1) and allocation (centered log ratio) in each of: Downtown Renovation for Mixed Use Facilities (Built Environment) Community Health Centers (Health System) Gigabit Fiber Broadband Downtown (Communications System) Adding more Computers and Meeting Spaces in the Public Library (Civic System) Early College Credit and Vocational Programs for High School Students (Education System) Co-Working and Startup Working Space for Entrepreneurs (Business System)

Stage 1 Predictors: Location—RQ1

Population (numeric, log(population)) Population Density (numeric, people/square mile) Distance to the nearest major metro area (numeric, km)

Stage 2 Predictors: Human Capital—RQ2 (and COVID Controls)

Age (numeric, with the value taken at the lower end of each categorical range) Gender (reference = male) Minority Race/Ethnicity (reference = White) Education (Reference = Less than college degree) COVID impact on community’s health (numeric, 1–7 agreement rating) COVID impact on community’s economy (numeric, 1–7 agreement rating)

Stage3 Predictors: Social Capital—RQ3

Resultant Self-Transcendence, Self-Transcendence – Self Enhancement (numeric, difference between two 1–7 subscale mean agreement ratings) Resultant Conservation, Conservation – Openness to Change (numeric, difference between two 1–7 subscale mean agreement rating) Agreeableness (numeric, 1–7 subscale mean agreement rating) Openness (numeric, 1–7 subscale mean agreement rating)

Stage 4 Predictors: Community Capital—RQ4

Satisfaction with broad-based community services (numeric, 1–7 mean rating) Satisfaction with economic environment (numeric, 1–7 mean rating) Satisfaction with family-oriented community services (numeric, 1–7 mean rating) Satisfaction with communications services (numeric, 1–7 mean rating) Importance of broad-based community services (numeric, 1–7 mean rating) Importance of economic environment (numeric, 1–7 mean rating) Importance of family-oriented community services (numeric, 1–7 mean rating) Importance of communications services (numeric, 1–7 mean rating)

Stage 5 Predictors: Business Capital—RQ5

Entrepreneurial attitudes (numeric, 1–7 mean agreement rating) Social norms (numeric, 1–7 mean approval rating) Perceived behavioral control (numeric, 1–7 mean agreement rating) Entrepreneurial intentions (numeric, 1–7 mean agreement rating)

Table 4.

Variables included in project concept interest models.

8. Results

In Tables 5 and 6, we provide an overview of the results of the hierarchal hurdle models for both interest and allocation related to the six community development project concepts that address the “if at all” portions of our five research questions. These tables also include fit statistics for each model as information is added to the hierarchy. The Akaike information criteria (AIC) for each model, the change in the X2 statistic as information is added, and the significance of the change in model fit are presented.

		Interest					Allocation
Concept	Model	df	AIC	Δ X^2	Δ X^2 df	Sig.	df	AIC	Δ X^2	Δ X^2 df	Sig.
Downtown renovation for mixed use facilities	Intercept	1	5194.1				2	11682.6
	Random Intercept	2	5079.1	117.0	1	0.000	3	11643.4	41.2	1	0.000
	Location	5	5082.4	2.6	3	0.453	6	11636.8	12.6	3	0.006
	Social Position	11	5066.2	28.3	6	0.000	12	11612.2	36.7	6	0.000
	Values & Personality	15	5036.4	37.8	4	0.000	16	11581.6	38.5	4	0.000
	Asset Satisfaction and Importance	23	5021.7	30.7	8	0.000	24	11507.4	90.2	8	0.000
	Entrepreneurial Intentions	27	5008.3	21.5	4	0.000	28	11498.6	16.8	4	0.002
Community health centers	Intercept	1	5661.2				2	10430.4
	Random Intercept	2	5489.3	173.9	1	0.000	3	10419.9	12.4	1	0.000
	Location	5	5483.7	11.6	3	0.009	6	10422.2	3.7	3	0.296
	Social Position	11	5454.8	40.9	6	0.000	12	10400.5	33.8	6	0.000
	Values & Personality	15	5460.1	2.7	4	0.614	16	10379.6	28.9	4	0.000
	Asset Satisfaction and Importance	23	5394.3	81.8	8	0.000	24	10338.0	57.6	8	0.000
	Entrepreneurial Intentions	27	5397.2	5.2	4	0.271	28	10337.1	8.9	4	0.065
Gigabit fiber broadband downtown	Intercept	1	6524.9				2	9347.8
	Random Intercept	2	6387.2	139.7	1	0.000	3	9297.5	52.3	1	0.000
	Location	5	6384.9	8.3	3	0.040	6	9288.3	15.2	3	0.002
	Social Position	11	6338.5	58.3	6	0.000	12	9262.0	38.3	6	0.000
	Values & Personality	15	6341.9	4.7	4	0.321	16	9257.8	12.1	4	0.016
	Asset Satisfaction and Importance	23	6252.8	105.1	8	0.000	24	8992.7	281.2	8	0.000
	Entrepreneurial Intentions	27	6239.2	21.6	4	0.000	28	8971.4	29.3	4	0.000
Adding more computers and meeting spaces in the public library	Intercept	1	7010.4				2	5720.4
	Random Intercept	2	6890.3	122.2	1	0.000	3	5713.7	8.6	1	0.003
	Location	5	6878.0	18.3	3	0.000	6	5719.2	0.5	3	0.918
	Social Position	11	6857.2	32.8	6	0.000	12	5713.3	17.9	6	0.006
	Values & Personality	15	6854.0	11.3	4	0.024	16	5715.3	6.0	4	0.197
	Asset Satisfaction and Importance	23	6804.0	66.0	8	0.000	24	5712.6	18.8	8	0.016
	Entrepreneurial Intentions	27	6804.8	7.2	4	0.126	28	5704.7	15.8	4	0.003
Early college credit and vocational programs for high school students	Intercept	1	6744.6				2	8582.8
	Random Intercept	2	6685.7	60.9	1	0.000	3	8302.9	281.9	1	0.000
	Location	5	6686.1	5.6	3	0.134	6	8292.1	16.8	3	0.001
	Social Position	11	6665.6	32.5	6	0.000	12	8253.4	50.7	6	0.000
	Values & Personality	15	6634.8	38.8	4	0.000	16	8246.1	15.2	4	0.004
	Asset Satisfaction and Importance	23	6548.4	102.4	8	0.000	24	8228.0	34.1	8	0.000
	Entrepreneurial Intentions	27	6517.3	39.1	4	0.000	28	8208.7	27.3	4	0.000
Co-working and startup working space for entrepreneurs	Intercept	1	7032.6				2	6270.5
	Random Intercept	2	7010.9	23.7	1	0.000	3	6263.6	8.9	1	0.003
	Location	5	7003.4	13.5	3	0.004	6	6260.0	9.6	3	0.022
	Social Position	11	6945.8	69.6	6	0.000	12	6257.8	14.2	6	0.027
	Values & Personality	15	6869.1	84.7	4	0.000	16	6231.0	34.7	4	0.000
	Asset Satisfaction and Importance	23	6812.7	72.4	8	0.000	24	6194.2	52.9	8	0.000
	Entrepreneurial Intentions	27	6739.9	80.8	4	0.000	28	6138.5	63.7	4	0.000

Table 5.

Hierarchical modeling summary for interest and allocation models.

Research questions	S1	S2	S3	S4	S5	S6
1. Location	A	I + A	I + A	I	A	I + A
2. Human capital	I + A	I	I + A	I + A	I + A	I + A
3. Social capital	I+ A	A	I	I	I + A	I + A
4. Community capital	I+ A	I+A	I + A	I + A	I + A	I + A
5. Business capital	I+ A	Neither	I + A	A	I + A	I + A

Table 6.

Summary of results for each research question by system.

Caption. System-Focused Community Development Concept Key:

S1, Downtown Renovation for Mixed Use Facilities (Built Environment).

S2, Community Health Centers (Health System).

S3, Gigabit Fiber Broadband Downtown (Communications System).

S4, Adding more Computers and Meeting Spaces in the Public Library (Civic System).

S5 ,Early College Credit and Vocational Programs for High School Students (Education System).

S6 ,Co-Working and Startup Working Space for Entrepreneurs (Business System).

I, Predicts Interest.

A, Predicts Allocation among Those with Interest.

Table 5 specifically displays the changes in model fit as the stages of information are added to the models. The inclusion of the random intercept improved fit for all models, confirming the need to account for random differences between the communities where we collected the data (p < 0.01). The sequential addition of location variables significantly improved fit for all models except for interest in downtown renovation and early college credit/vocational training (p = 0.453 and p = 0.134, respectively) and allocation to a community health center and allocation to a public library hub (p = 0.296 and p = 0.918, respectively). Fit was improved significantly after the addition of human capital and community perceptions of the impact of COVID for all models (p < 0.01). The addition of social capital (via personal values and personality variables) significantly improved the fit of all models except interest in community health center (p = 0.614), broadband (p = 0.321) and allocation to a public library hub (p = 0.197). The fit of all models was improved by the addition of information about community capitals (via perceptions about satisfaction and importance of community assets), all significant at p ≤ 0.001. The fit of all models was further improved by the addition of information about business capitals (via entrepreneurial intention and its antecedents) except for interest in and allocation to community health center (p = 0.271 and p = 0.065, respectively) and interest in public library hub (p = 0.126).

These results confirm that interest in and degree of allocation to various development concepts to improve community well-being are broadly connected, with exceptions, to the characteristics of community members, the values they hold, aspects of their personality, and how they perceive the adequacy of the assets available to the community. All our research questions are at least partial answered in the affirmative.

As a focus of the current study, most of development concepts are connected to entrepreneurial intentions, with important exceptions. Entrepreneurship did not predict interest in or allocation to community health centers nor did it predict interest in public library hubs. However, among those who are interested in public library hubs, entrepreneurial intentions are connected to a willingness to allocate resources to those hubs. These findings are summarized in Table 5. Table 6 simplifies this presentation down to a mapping of capitals to systems.

After establishing the broad support for the connections posited in our research questions that connect capitals to systems, we will present the detailed results of our models that characterize the nature of these connections. The results will reveal which variables in each category increase or decrease interest and degree of allocation for each community development project concept, as perceived by communities.

Tables 7 and 8 provide the adjusted odds ratio (AOR), standard error (SE), and significance (Sig.) for all terms in each model, and we will focus on model effects (AOR) that are significant at 0.05 or less. We organize our discussion around our research questions, starting with the results from the hierarchical modeling process across all six concepts, followed by a presentation of the results for each model taken in turn. These models correspond to research questions 1 (community characteristics), 2 (social position), 3 (personal values and personality types), and 4 (asset satisfaction and importance). We will focus on the final, or full, model in our discussion, and restrict it to only those results with a significance of p < 0.01. To provide a comprehensive qualitative review of these models, we have included Table 6.

	Downtown renovation for mixed use facilities			Community health center			Gigabit fiber broadband downtown			Adding more computers and meeting spaces in the public library			Early college credit and vocational programs for high school students			Co-working and startup working space for entrepreneurs
	AOR	SE	Sig	AOR	SE	Sig	AOR	SE	Sig	AOR	SE	Sig	AOR	SE	Sig	AOR	SE	Sig
Intercept	2.477	0.464	0.051	2.338	0.436	0.051	2.143	0.392	0.052	0.154	0.361	0.000	0.754	0.361	0.434	0.153	0.361	0.000
Log Population (000)	0.942	0.063	0.343	1.065	0.059	0.287	0.972	0.053	0.590	1.011	0.048	0.820	0.973	0.038	0.463	0.980	0.045	0.649
Population Density	1.000	0.000	0.656	1.000	0.000	0.969	1.000	0.000	0.099	1.000	0.000	0.095	1.000	0.000	0.714	1.000	0.000	0.117
Distance to nearest Urban Center (km)	1.000	0.001	0.352	1.000	0.000	0.504	0.999	0.000	0.020	0.999	0.000	0.079	1.000	0.000	0.889	1.000	0.000	0.950
Age	0.994	0.003	0.019	0.996	0.003	0.157	1.004	0.002	0.135	1.002	0.002	0.371	0.995	0.002	0.023	1.004	0.002	0.081
Female	0.983	0.084	0.841	0.998	0.079	0.979	0.758	0.072	0.000	0.870	0.066	0.036	0.848	0.068	0.016	0.677	0.066	0.000
Minority	0.959	0.092	0.650	1.311	0.087	0.002	0.966	0.079	0.661	0.975	0.072	0.724	0.843	0.074	0.022	1.048	0.072	0.519
Undergrad degree or more	1.097	0.076	0.222	1.040	0.072	0.585	1.410	0.065	0.000	1.254	0.061	0.000	1.264	0.062	0.000	1.246	0.061	0.000
COVID health impact	1.052	0.036	0.156	1.081	0.034	0.021	1.057	0.031	0.075	1.086	0.029	0.005	1.051	0.030	0.092	1.019	0.029	0.508
COVID economic impact	0.986	0.039	0.715	0.994	0.036	0.869	0.924	0.033	0.017	0.974	0.031	0.397	1.016	0.032	0.615	0.993	0.031	0.833
Agreeableness	1.104	0.045	0.027	0.964	0.042	0.393	0.957	0.039	0.254	1.002	0.036	0.946	0.920	0.037	0.024	0.935	0.036	0.063
Openness	1.062	0.045	0.188	1.053	0.043	0.229	0.971	0.039	0.461	0.977	0.037	0.530	0.961	0.038	0.297	0.919	0.037	0.022
Self Transcendence—Self Enhancement	0.922	0.022	0.000	1.013	0.021	0.521	0.986	0.019	0.460	1.022	0.018	0.223	1.006	0.018	0.754	1.045	0.018	0.014
Conservation—Openness to Change	0.970	0.024	0.211	1.002	0.023	0.923	0.963	0.021	0.075	0.984	0.020	0.433	0.961	0.020	0.050	0.957	0.020	0.030
Sat. with broad-based community services	0.882	0.042	0.003	1.069	0.039	0.086	1.043	0.036	0.238	1.043	0.033	0.203	0.993	0.034	0.846	0.959	0.033	0.209
Sat. with economic environment	1.053	0.038	0.167	1.008	0.035	0.824	1.008	0.033	0.815	0.975	0.030	0.403	0.962	0.032	0.217	0.975	0.030	0.400
Sat. with family-oriented community services	1.099	0.044	0.032	0.811	0.042	0.000	0.981	0.038	0.619	0.945	0.036	0.119	0.983	0.037	0.637	1.039	0.036	0.277
Sat. with broadband and cellular/mobile	1.088	0.026	0.001	1.103	0.024	0.000	0.829	0.023	0.000	1.091	0.022	0.000	1.122	0.022	0.000	1.066	0.022	0.003
Imp. of broad-based community services	1.064	0.046	0.179	1.154	0.043	0.001	0.929	0.040	0.069	1.055	0.038	0.153	0.933	0.039	0.076	0.996	0.038	0.923
Imp. of economic environment	0.883	0.051	0.015	0.848	0.048	0.001	1.005	0.044	0.918	0.926	0.041	0.061	1.116	0.042	0.008	1.221	0.042	0.000
Imp. of family-oriented community services	1.045	0.048	0.360	1.185	0.045	0.000	1.003	0.042	0.943	1.185	0.040	0.000	1.242	0.040	0.000	0.964	0.040	0.355
Imp. of broadband and cellular/mobile	0.965	0.033	0.275	0.923	0.031	0.010	1.213	0.028	0.000	1.014	0.027	0.596	0.946	0.028	0.044	0.976	0.027	0.366
Entrepreneurial attitudes	1.026	0.030	0.394	0.944	0.029	0.048	1.049	0.026	0.066	1.028	0.025	0.264	1.064	0.025	0.015	1.099	0.025	0.000
Social norms	0.966	0.028	0.212	0.991	0.027	0.746	1.026	0.025	0.304	1.011	0.023	0.642	1.067	0.024	0.006	1.035	0.023	0.143
Perceived behavioral control	1.039	0.036	0.291	0.988	0.034	0.720	0.899	0.032	0.001	0.947	0.030	0.069	0.969	0.031	0.306	0.971	0.030	0.329
Entrepreneurial intentions	1.064	0.034	0.068	1.040	0.032	0.218	0.986	0.029	0.620	0.988	0.028	0.657	0.894	0.028	0.000	1.099	0.027	0.001

Table 7.

System-focused concept interest model results (fixed effects from GLMM logistic with RI).

Caption: AOR, adjusted odds ratio; SE, standard error; Sig, significance.

	Downtown renovation for mixed use facilities			Community health center			Gigabit fiber broadband downtown			Adding more computers and meeting spaces in the public library			Early college credit and vocational programs for high school students			Co-working and startup working space for entrepreneurs
	Mean	SE	Sig	Mean	SE	Sig	Mean	SE	Sig	Mean	SE	Sig	Mean	SE	Sig	Mean	SE	Sig
Intercept	12.178	0.202	0.000	7.346	0.200	0.000	4.264	0.199	0.000	1.424	0.223	0.113	2.811	0.202	0.000	2.150	0.242	0.002
Log Population (000)	0.988	0.020	0.532	1.028	0.023	0.245	1.016	0.022	0.452	0.976	0.023	0.299	0.992	0.025	0.733	1.026	0.024	0.297
Population Density	1.000	0.000	0.225	1.000	0.000	0.050	1.000	0.000	0.385	1.000	0.000	0.530	1.000	0.000	0.107	1.000	0.000	0.058
Distance to nearest Urban Center (km)	1.000	0.000	0.009	1.000	0.000	0.397	1.000	0.000	0.013	1.000	0.000	0.362	1.000	0.000	0.202	1.000	0.000	0.474
Age	0.997	0.001	0.010	1.000	0.001	0.929	1.003	0.001	0.020	1.000	0.001	0.765	0.998	0.001	0.179	1.001	0.001	0.669
Female	1.163	0.037	0.000	1.155	0.036	0.000	1.020	0.036	0.581	1.130	0.041	0.003	1.142	0.036	0.000	1.060	0.042	0.167
Minority	0.968	0.040	0.410	1.074	0.039	0.070	1.021	0.040	0.604	0.999	0.043	0.982	0.924	0.039	0.043	0.949	0.046	0.247
Undergrad degree or more	0.900	0.035	0.002	0.921	0.034	0.015	1.042	0.034	0.225	0.981	0.038	0.620	0.938	0.033	0.051	0.960	0.040	0.311
COVID health impact	0.970	0.017	0.067	0.984	0.016	0.319	0.982	0.016	0.271	1.025	0.019	0.187	0.955	0.016	0.003	0.942	0.019	0.002
COVID economic impact	1.008	0.017	0.633	1.009	0.017	0.612	0.989	0.017	0.519	1.006	0.020	0.764	1.022	0.017	0.184	1.024	0.021	0.248
Agreeableness	1.017	0.020	0.402	1.018	0.020	0.355	0.974	0.020	0.188	1.022	0.022	0.321	1.001	0.020	0.968	0.959	0.024	0.080
Openness	1.003	0.021	0.904	1.022	0.020	0.277	1.019	0.020	0.367	1.013	0.024	0.602	1.023	0.020	0.263	0.940	0.025	0.016
Self-Transcendence—Self Enhancement	0.967	0.010	0.001	1.019	0.010	0.053	1.005	0.010	0.610	1.009	0.011	0.416	1.008	0.010	0.398	1.019	0.012	0.122
Conservation—Openness to Change	1.032	0.012	0.007	1.010	0.011	0.385	1.002	0.011	0.850	0.998	0.013	0.896	1.006	0.011	0.605	0.999	0.013	0.952
Sat. with broad-based community services	0.948	0.019	0.005	1.036	0.019	0.056	0.993	0.019	0.709	1.027	0.022	0.212	1.008	0.018	0.665	0.999	0.022	0.952
Sat. with economic environment	1.023	0.017	0.182	0.993	0.017	0.665	1.037	0.017	0.032	0.979	0.019	0.268	0.986	0.016	0.392	0.967	0.020	0.085
Sat. with family-oriented community services	1.062	0.020	0.003	0.954	0.020	0.018	1.017	0.020	0.388	1.003	0.023	0.905	1.042	0.020	0.036	1.035	0.023	0.134
Sat. with broadband and cellular/mobile	0.984	0.012	0.180	0.995	0.012	0.656	0.868	0.012	0.000	1.025	0.014	0.077	1.000	0.012	0.988	1.026	0.014	0.071
Imp. of broad-based community services	1.024	0.021	0.274	1.019	0.021	0.364	0.919	0.021	0.000	1.010	0.024	0.683	0.934	0.020	0.001	0.942	0.025	0.017
Imp. of economic environment	0.955	0.023	0.050	0.929	0.023	0.001	1.076	0.023	0.001	0.936	0.026	0.013	1.080	0.023	0.001	1.119	0.027	0.000
Imp. of family-oriented community services	0.913	0.023	0.000	0.988	0.022	0.576	0.879	0.022	0.000	1.013	0.026	0.631	1.032	0.021	0.140	0.899	0.026	0.000
Imp. of broadband and cellular/mobile	0.967	0.015	0.028	0.965	0.015	0.019	1.179	0.015	0.000	1.021	0.018	0.232	0.972	0.014	0.047	1.003	0.018	0.889
Entrepreneurial attitudes	0.971	0.014	0.040	0.980	0.014	0.142	0.975	0.014	0.062	0.955	0.016	0.003	0.997	0.013	0.831	1.031	0.016	0.059
Social norms	0.973	0.014	0.042	0.973	0.013	0.033	1.011	0.013	0.420	1.017	0.015	0.269	1.032	0.013	0.013	1.012	0.016	0.478
Perceived behavioral control	1.054	0.017	0.002	1.003	0.017	0.861	0.992	0.017	0.624	1.010	0.019	0.605	0.997	0.016	0.850	0.981	0.019	0.324
Entrepreneurial intentions	0.997	0.016	0.836	1.011	0.015	0.472	0.969	0.015	0.038	0.986	0.017	0.419	0.954	0.015	0.001	1.083	0.017	0.000

Table 8.

Concept allocation model results (fixed effects from GLMM logistic with RI).

Caption: Mean, mean allocation; SE, standard error; Sig, significance.

Downtown renovation of mixed-use buildings: Now consider the results for the community development project concept of downtown renovation for mixed-use facilities, as shown in Table 7. Increasing age predicts a lower likelihood of interest in a downtown renovation for mixed-use facilities (AOR = 0.922, p < 0.001). A more agreeable personality predicts a higher likelihood of interest in a downtown renovation for mixed-use facilities (AOR = 1.110, p = 0.017). Greater resultant self-transcendence predicts a lower likelihood of interest in a downtown renovation for mixed-use facilities (AOR = 0.882, p = 0.003). Higher satisfaction with available broadband and cellular/mobile predicts a higher likelihood of interest in a downtown renovation for mixed-use facilities (AOR = 1.088, p = 0.001).

As shown in Table 8, among community members with interest: being female increases allocation (AOR = 1.163, p < 0.001); having an undergrad degree or more decreases allocation (AOR = 0.900, p = 0.002); higher resultant self-transcendence decreases allocation (AOR = 0.967, p = 0.001); higher resultant conservation increases allocation (AOR = 1.032, p = 0.007); higher satisfaction with broad-based community services decreases allocation (AOR = 0.948, p = 0.005); higher satisfaction with family-oriented community services increases allocation (AOR = 1.062, p = 0.003); higher importance of family-oriented community services decreases (AOR = 0.913, p < 0.001); and a higher degree of perceived behavioral control increases allocation to a downtown renovation (AOR = 1.054, p = 0.002).

Community health center. Table 7 reveals that minority status is a significant predictor for increased interest in a community health center (AOR = 1.311, p = 0.002). Furthermore, lower satisfaction with and a higher perceived importance of available family-oriented community services are associated with a greater likelihood of interest in a community health center (AOR = 0.811, p < 0.001) and (AOR = 1.185, p < 0.001), respectively. In addition, higher satisfaction with and lower perceived importance of available broadband and cellular/mobile predict a higher likelihood of interest in a community health center (AOR = 1.104, p < 0.001) and (AOR = 0.923, p = 0.010), respectively. A greater perceived importance of broad-based community services predicts a higher likelihood of interest in a community health center (AOR = 1.154, p = 0.001), whereas a greater perceived importance of the economic environment predicts a lower likelihood of interest in a community health center (AOR = 0.848, p = 0.001).

As shown in Table 8, among community members with interest, being female increases allocation (AOR = 1.155, p < 0.000) and higher perceptions of the importance of the economic environment reduces allocation to a community health center (AOR = 0.929, p = 0.001).

Adding gigabit fiber to a downtown area.Table 7 illustrates that interest in installing gigabit fiber in a downtown area is influenced by several factors. Females in the community are less likely than males to show interest in this development (AOR = 0.758, p < 0.001). Conversely, community members with at least an undergraduate degree are more likely to express interest compared to those with less education (AOR = 1.410, p < 0.001). Additionally, lower satisfaction with current broadband and cellular/mobile services and higher perceived importance of these services lead to a higher likelihood of interest in adding gigabit fiber to a downtown area (AOR = 0.839, p < 0.001) and (AOR = 1.213, p < 0.001), respectively. Interestingly, a higher perceived importance of broad-based community services results in a lower likelihood of interest in adding gigabit fiber to a downtown area (AOR = 0.924, p = 0.048).

As shown in Table 8, among community member with interest, higher satisfaction with broadband and cellular/mobile reduces allocation (AOR = 0.868, p < 0.000), higher perceived importance of broadband and cellular/mobile increases allocation (AOR = 1.179, p < 0.000), higher perceived importance of broad-based community services and family-oriented community services reduce allocation (AOR = 0.919, p < 0.000 and AOR = 0.879, p < 0.000, respectively), and higher perceived importance of economic environment increases allocation to gigabit fiber (AOR = 1.076, p = 0.001).

Adding more Computers and Meeting Spaces in the Public Library (Public Library Hub). As shown in Table 7, community members with an undergraduate degree or more formal education are more likely to be interested in adding more computers and meeting spaces in the public library than community members with less formal education than an undergraduate degree (AOR = 1.254, p = 0.001). Community members that perceived a greater impact of COVID-19 on the health of their community are more likely to be interested in adding more computers and meeting spaces in the public library than community members that perceived a lower impact of COVID-19 on the health of their community (AOR = 1.086, p = 0.005). Community members with higher satisfaction with available broadband and cellular/mobile predict a higher likelihood of interest in adding more computers and meeting spaces in the public library (AOR = 1.091, p = 0.000). Community members with higher perceived importance of family-oriented community services are more likely to be interested in adding more computers and meeting spaces in the public library (AOR = 1.185, p = 0.000).

As shown in Table 8, among community members with interest in adding more computers and meeting spaces in the public library, being female increases allocation (AOR = 1.130, p = 0.003) whereas community members with higher perceived importance of the economic environment and community members with more positive attitudes about being an entrepreneur reduce allocation.

Early College Credit and Vocational Programs for High School Students. As shown in Table 7, community members with at least an undergrad degree (AOR = 1.264, p = 0.0002) are more likely to express interest. Increased satisfaction with broadband and cellular/mobile (AOR = 1.122, p < 0.000) and higher perceptions of the importance of economic environment (AOR = 1.116, p = 0.008) and family-oriented community services (AOR = 1.242, p < 0.001) increase the likelihood to express interest. Stronger perceptions of entrepreneurial social norms (AOR = 1.067, p = 0.006) increase the likelihood of interest, but stronger entrepreneurial intentions decrease the likelihood of interest in early college credit and vocational programs (AOR = 0.894, p < 0.001).

As shown in Table 8, among community members with interest, females are predicted to allocate more resources (AOR = 1.142, p < 0.001). Stronger perceptions of the health impact of COVID (AOR = 0.955, p = 0.003) reduce allocation. Increased importance of broad-based community services decreases allocation (AOR = 0.934, p < 0.001) but increased importance of the economic environment increases allocation (AOR = 1.08, p < 0.001). And higher entrepreneurial intentions decrease allocation to early college credit and vocational programs (AOR = 0.954, p = 0.001).

Co-Working and Startup Working Space for Entrepreneurs. As shown in Table 7, female community members are less likely than males to have interest in co-working and startup working spaces (AOR = 0.677, p < 0.001). Having an undergraduate degree or more increases the likelihood of interest (AOR = 1.246, p < 0.001). Higher satisfaction with broadband and cellular/mobile (AOR = 1.066, p = 0.003) and having higher perceptions of the importance of the economic environment (AOR = 1.221, p < 0.001) increase the likelihood of interest. Having higher entrepreneurial attitudes (AOR = 1.099, p < 0.001) and stronger entrepreneurial intentions (AOR = 1.099, p < 0.001) increase the likelihood of interest in co-working and startup working spaces.

As shown in Table 8, among community members with interest, stronger perceptions of the health impact of COVID on the community decrease allocation (AOR = 0.942, p = 0.002). Higher perceptions of the importance of the economic environment increase allocation (AOR = 1.119, p < 0.001) but higher perceptions of the importance of family-oriented community services decrease allocation (AOR = 0.899, p < 0.001). And stronger entrepreneurial intentions increase allocation (AOR = 1.083, p < 0.001) to co-working and startup working spaces.

9. Discussion

This study demonstrates that the degree of interest and willingness to invest resources in diverse community development projects is associated with various factors such as the individual identities, personalities, personal values, and satisfaction levels of community members with the assets available in their community. Additionally, entrepreneurship exhibits both positive and negative associations with different project concepts. Tables 7 and 8 provides a summary of the relationships between various community capitals and systems, in terms of their impact (positive or negative) on the interest and willingness to allocate resources toward these concepts.

The associations among the factors and interest in community development projects demonstrate consistent patterns, irrespective of the degree of urbanization or rurality, as measured in the current study. However, these findings do suggest that the interrelationships among communities might influence these processes.

The inclination toward downtown renovation for mixed-use facilities is positively correlated with agreeableness, but negatively associated with resultant self-transcendence. Conversely, neither of these personality traits exhibits any significant correlation with interest in the development of a community health center or the addition of Gigabit fiber. Furthermore, the satisfaction levels with broadband and cellular/mobile services exhibit a connection with the interest in downtown renovation for mixed-use facilities.

Minority status is positively linked to interest in a community health center. Nevertheless, the level of interest is also related to the satisfaction levels and perceived significance of various community assets. Specifically, a decreased satisfaction level and a heightened perception of the importance of family-oriented community services are associated with an increased interest in a community health center. Interestingly, a heightened satisfaction level and a decreased perception of the importance of broadband and cellular/mobile services are also connected to interest in a community health center.

The findings emphasize the significance of incorporating the opinions and viewpoints of local individuals while pursuing sustainable community development, aligning with the SLED model. Furthermore, the results demonstrate that decision-making concerning community development is influenced by both the emotional perspectives of community members and their rational evaluation of the effectiveness of the available assets in their community. Additionally, the relative impact of these emotional and rational decision-making drivers differs depending on the context of the decision being made.

Entrepreneurship and its antecedents are associated with all the concepts tested, and critically, positively for some and negatively for others. Not surprisingly, higher entrepreneurial intentions predict higher interest in and willingness to allocate resources to co-working and startup working space for entrepreneurs but are not predictive for community health centers. Additionally, stronger entrepreneurial attitudes predict a lower likelihood of interest in community health centers. Similarly, stronger entrepreneurial social norms reduce the predicted degree of resource allocation among those with interest. The results presented in this study strengthen the intricate comprehension of the interplay among community, personality, and satisfaction/importance variables in community economic development, as highlighted in the SLED and ABCD frameworks. These frameworks concentrate on individuals, relationships, and organizations to enhance decision-making for community development. This research has demonstrated that personality traits also impact the connections people establish, their incentives for engaging with others, and the categories of activities they participate in, with community development being one, but by no means the only, such activity.

This paper has also demonstrated that entrepreneurship has some, albeit limited, potential as means for community members to participate in some community development efforts, but it cannot be the only strategy utilized. Some community development targets, like health centers, public library hubs, high educational programs either do not connect with entrepreneurship or connect negatively.

Although community-level characteristics had a minimal impact on development project preferences, with distance from an urban center leading to decreased support for the broadband intervention, their inclusion in this study is significant and extends beyond the ABCD and SLED frameworks, highlighting the connections between communities. A crucial aspect of ABCD is community formation through interaction. The proximity of a community to an urban center, where assets, resources, and services are typically centralized, can make individuals feel as connected to the center as to their own community, thus reducing the sense of lacking resources. Population size and density can also enhance interaction opportunities, enabling different types of development activities and increasing the assets available. In contrast, communities that are distant from urban centers or have low population densities and scattered individuals face greater challenges in fostering interaction among community members, leading to gaps in agreement on investments in improvement. The models and data presented in this study only partly validate this assumption, suggesting the need for further research on community distance from major urban metropolitan areas, community identity, and gaps in satisfaction and importance to be undertaken. Table 9 simplifies the presentation of our interest and allocation results by mapping of capitals to systems.

	S1	S2	S3	S4	S5	S6
Location
Log Population (000)
Population Density
Distance to nearest Urban Center (km)			I−
Human Capitals
Age	I− A−		A+		I−
Female	A+	A+	I−	I−, A+	I−, A+	I−
Minority		I+			I− A−
Undergrad degree or more	A−	A−	I+	I+	I+	I+
COVID health impact		I+		I+	A−	A−
COVID economic impact			I−
Social Capitals
Agreeableness	I+				I−
Openness						I− A−
Self-Transcendence – Self Enhancement	I− A−					I+
Conservation – Openness to Change	A+				I−	I−
Community Capitals
Sat. with broad-based community services	I− A−
Sat. with economic environment			A+
Sat. with family-oriented community services	I+ A−	I− A−			A+
Sat. with broadband and cellular/mobile	I+	I+	I− A−	I+	I+	I+
Imp. of broad-based community services		I+	A−		A−	A−
Imp. of economic environment	I−	I− A−	A+	A−	I+ A−	I+ A−
Imp. of family-oriented community services	A−	I+	A−	I+	I+	A-
Imp. of broadband and cellular/mobile	A−	I− A−	I+ A−		I− A−
Business Capitals
Entrepreneurial attitudes	A−	I−		A−	I+	I+
Social norms	A−	A−			I+ A−
Perceived behavioral control	A+		I−
Entrepreneurial intentions			A−		I− A−	I+ A−

Table 9.

Mapping capitals to systems.

Caption: Community Development Concept Key.

S1, Downtown Renovation for Mixed Use Facilities (Built Environment).

S2, Community Health Centers (Health System).

S3, Gigabit Fiber Broadband Downtown (Communications System).

S4, Adding more Computers and Meeting Spaces in the Public Library (Civic System).

S5, Early College Credit and Vocational Programs for High School Students (Education System).

S6, Co-Working and Startup Working Space for Entrepreneurs (Business System).

I+/−, Predicts Higher/Lower Interest.

A+/−, Predicts Great/Lower Allocation among Those with Interest.

10. Limitations

There are limitations to the present study that should be acknowledged. The data collection process was impacted by the COVID-19 pandemic, making it difficult to include small-town and rural communities in the sample, despite the outreach methods we employed, as discussed earlier. Although we utilized social media, phone calls to community leaders, and electronic fliers, future research should aim to conduct more in-person outreach in selected communities to ensure that marginalized community members, who may have faced systemic barriers and inequalities, are better represented. This deeper human-centered engagement would help establish greater trust with the community and allow researchers to connect with members in places where they physically gather, such as places of worship, town squares, and public parks, enabling both social networking and intercept interviewing to increase randomness of participation, and would complement the electronic outreach methods used during the pandemic. Furthermore, the present study operationalized the degree of urbanity/rurality using only three measures: community population size, population density, and distance from one of the five major metropolitan areas in the state. Future research should consider selecting variables that capture variation across the urban-rural continuum, including spatial, cultural, ecological, and demographic factors, to test which variables best explain the processes being studied. In addition, our analysis had limited representation of gender and racial/ethnic identities due to the COVID-19 restrictions on our ability to field the survey, which effectively limited nonbinary gender and black and brown participants. Future research should enhance equity in sampling.

11. Conclusions

The present analysis underscores the intricate interplay of community characteristics, individual values and personality types, and community members’ perceptions of satisfaction and importance regarding various assets available in the community. These factors have varying impacts on interest in different community development concepts, emphasizing the need to consider local community perspectives and approach development as a system of subsystems. Different community development concepts serve different community segments by enhancing or expanding different subsystems, presenting opportunities for practical interventions within the community development process. The results highlight the importance of human-centered, participatory processes in guiding further interactions between experts and community members. Specifically, they suggest the need to identify groups in the community who may be most affected by the choice of one intervention over another and to facilitate community dialogs in areas where further discussion and deliberation are necessary. Such dialogs could take the form of round tables, town halls, workshops, and other similar initiatives.

The first practical intervention aims to identify community members who are most interested in a particular type of community development project. This helps in determining the target audience for outreach and involving them further in the development process. The study results for the Community Health Center project intervention revealed that minority status played a significant role in predicting support for the intervention. Minorities were found to be more supportive of the project than white community members, which is reasonable given the historical disparities in healthcare provision along racial lines. Such concerns over inclusion and exclusion are common in various community development processes and should prompt researchers and practitioners to examine the representation of voices and perspectives in decision-making processes. Success in such projects depends on engaging a diverse range of community members in decision-making through outreach.

The second type of intervention aims to identify and target specific areas where community preferences and priorities require further discussion and deliberation. For instance, the study found that support for the broadband project intervention declined among respondents living farther away from urban centers, despite its significance in promoting community well-being and economic growth. In this case, organizing a round table discussion where experts and members from other communities that implemented broadband projects can share their experiences can benefit the community. Such a dialog can help community members become aware of the challenges and unknowns related to the project while fostering collaboration and forming relationships that can guide the community development process.

Both types of interventions require a participatory approach to research, which involves engaging community members in discussions with policymakers, development professionals, and researchers from the outset of planning and deliberation. This process allows for input from all parties and facilitates the emergence of a design that reflects the needs and desires of the community [30, 31].

To engage communities and promote implementation, entrepreneurship and its underlying factors have been found to have a positive association with interest in and willingness to invest in certain systems, such as education, business, and the built environment. However, this connection has a negative correlation with other systems, including community health, communications, and civic systems.

As we analyzed community development priorities across different regions of a large U.S. state, we discovered, perhaps surprisingly, that there are more similarities than differences between urban and rural residents. Interestingly, preferences regarding economic and quality-of-life development choices were not determined solely by urban or rural identity but were influenced by a complex set of factors. Economic development professionals must recognize this complexity in their communities when assessing available options for the betterment of the entire community’s well-being.