Do Entrepreneurial Assistance Programs Create Value for Agri-Food Entrepreneurs?
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Adam Lovgren
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Andrea Leschewski
Abstract
This study analyzes how agri-food Entrepreneurial Assistance Programs (EAPs) create value for entrepreneurs. Using the MSU Product Center as a unit of analysis, the effect of agri-food EAP assistance on firms’ venture evaluation, perceived legitimacy, and performance is examined. Results indicate that agri-food EAP assistance prevents untenable business ideas from launching, improves the survival of launched ventures and develops entrepreneurs’ perceived legitimacy with trading partners. Further, results imply that targeted EAPs are a viable policy approach for promoting entrepreneurial activity in the agri-food industry, and that they can be particularly well suited to assisting nascent entrepreneurs.
Introduction
Entrepreneurship has long been considered a primary driver of economic growth in society, with one study finding that nearly 70 % of economic growth in the United States can be attributed to entrepreneurial activity (Reynolds, Hay, and Camp 1999). Research has also consistently shown that over 50 % of new ventures fail within the first five years (Shane 2008). Given that new venture creation is both valuable to society, but difficult to achieve, it is not surprising that a significant amount of public dollars are allocated to programs that assist entrepreneurs. These programs are broadly defined as Entrepreneurial Assistance Programs (EAPs) (Yusuf 2010). The primary goal of many EAPs is to aid prospective small business owners in new venture creation by providing them with pre-venture assistance (Chrisman, Hoy, and Robinson 1987). While prior research generally indicates that EAP assistance positively impacts new venture formation, these programs are costly (Gu, Karoly, and Zissimopoulos 2010). One of the US’ largest publicly funded EAPs, the Small Business Development Center (SBDC) Program, was established under the Small Business Development Center Act of 1980 and had an initial funding level of $20 million per year (Dilger and Lowry 2015). This number has since grown to $200 million dollars, funding 63 lead SBDCs and more than 900 SBDC local outreach locations (Dilger and Lowry 2015), indicating that EAPs have become a key weapon in the economic development arsenal. Furthermore, the literature tends to suggest that business creation is a low cost economic development strategy (Lyons 2002). Evaluation of the effectiveness of EAPs is therefore of non-trivial importance (Yusuf 2010) and we need to know more about their efficacy.
In his work, Lerner (2009) examines the determinants of effective government intervention programs aimed at promoting entrepreneurship. Lerner finds that government intervention programs, such as EAPs, generally fail when they are designed to provide incentives for entrepreneurial activities in target industries. An example of one such EAP targeting the agri-food industry is the USDA’s Agriculture Innovation Center (AIC) Program. Authorized by the 2002 Farm Bill, the program provided grants to eligible entities to assist agricultural producers in establishing and enhancing value-added agricultural commodity and product businesses (U.S. Congress 2002). Under the program, the USDA funded 10 AICs at land grant universities (Holcomb and Johnson 2007). The USDA further funded similar agri-food EAP centers through the Rural Cooperative Development and Rural Business Enterprise Grant Programs (Holcomb and Johnson 2007). While analysis of general EAPs (e. g. SBDCs) is widespread, analysis of agri-food EAPs is less common. To date, only two studies have analyzed the effect of agri-food EAP assistance on firm performance (Peake and Marshall 2009; Cranwell et al. 2005). Findings suggest agri-food EAP assistance has a positive effect on firm performance; however, these analyses are limited only to entrepreneurs who have received assistance and rely on subjective measures of performance.
The primary objective of this study is to analyze how agri-food EAPs create value for entrepreneurs. In an application of Behavioral Decision Theory, Legitimacy Theory, and the Theory of Guided Preparation, we examine the effect of assistance from an agri-food EAP, the Michigan State University (MSU) Product Center, on 467 entrepreneurs’ venture evaluation, perceived legitimacy with resource holders and trading partners, and firm performance. This study contributes to the limited applied entrepreneurship literature on agri-food EAPs by providing a quantitatively rigorous, contemporary analysis of the impact of assistance from a single agri-food EAP on entrepreneurial performance. While results are not generalizable to all agri-food EAPs, they provide agricultural and food policymakers with a knowledge base from which to create effective entrepreneurship policy.
The remainder of this study is organized as follows. An overview of the literature on EAP effectiveness is provided, followed by a description of the mechanisms by which EAPs create value for entrepreneurs. Research methods are then outlined, followed by a presentation of the results. Finally, a discussion of this research’s implications as well as limitations and directions for future research is provided.
The Effect of EAP Assistance on Firm-Level Performance
Since the mid-1980s, general EAPs targeting entrepreneurs in all industries have been extensively studied in the literature. The majority of studies analyzed the Small Business Administration’s (SBA) SBDC Program (Robinson 1982; Chrisman et al. 1985; Chrisman, Hoy, and Robinson 1987; Chrisman et al. 1990; Nahavandi and Chesteen 1988; Chrisman 1999; McMullan, Chrisman, and Vesper 2001; Chrisman, Gatewood, and Donlevy 2002; Chrisman and McMullan 2004; Chrisman, McMullan, and Hall 2005; Chrisman et al. 2012). Other studies more generally considered the effect of all SBA entrepreneurial development programs (Solomon and Weaver 1983; Solomon et al. 2013; Seo et al. 2014), as well as international EAPs (Chrisman 1999; Wren and Storey 2002; Rotger, Gørtz, and Storey 2012; Cummings and Fischer 2012).
Early EAP studies primarily focused on the effect of EAP assistance on firm-level performance. Generally, these studies found EAP assistance leads to increased profits, sales, job creation, and firm survival (Robinson 1982; Solomon and Weaver 1983; Pelham 1985; Chrisman et al. 1985; Nahavandi and Chesteen 1988; Bates 1995). However, later studies note that these analyses did not account for sample selection bias. Accounting for sample selection bias, Wren and Storey (2002) and Kosters and Obschonka (2010) found that assistance has no impact on firm performance measures, including start-up rates and job creation.
Subsequent studies considered the effect of EAP assistance by the type of: (1) entrepreneur, (2) firm, and (3) assistance received. Several studies analyze the effect of EAP assistance on the performance of nascent entrepreneurs. Results consistently indicate that nascent entrepreneurs receiving EAP assistance experience increased start-up rates (Chrisman, Hoy, and Robinson 1987; Chrisman 1999; Delanoë 2013) and firm survival rates (Chrisman, Hoy, and Robinson 1987; Chrisman and McMullan 2004; Chrisman, McMullan, and Hall 2005; Rotger, Gørtz, and Storey 2012; Yusuf 2014). Chrisman et al. (1990) further distinguish entrepreneurs by gender, finding that assisted females’ start-up rates were proportionally higher than their male counterparts. Several studies also assess the effectiveness of EAPs by type of firm. Chrisman, Gatewood, and Donlevy (2002) examine whether EAP effectiveness varies for firms located in rural versus urban areas, finding that EAPs are at least as effective, if not more so, in rural states. Further, Wren and Storey (2002) and Solomon et al. (2013) consider whether EAP assistance has a differential effect based on firm size. Solomon et al. (2013) finds that assistance has a greater effect on the financial outcomes of larger firms, while Wren and Storey (2002) find that assistance has a positive impact on the survival and growth of medium-sized firms, but no impact on smaller firms. This may be explained, in part, by the fact that assistance is not strategic in that it typically does not vary by the stage in the business life cycle of the business in question or by the skill level of the entrepreneur or entrepreneurial team (Lichtenstein and Lyons 2006). Assistance that is not strategic tends to favor later stage companies with more skilled and experienced entrepreneurs because they tend to be more open to and ready for most types of assistance than less skilled and experienced entrepreneurs.
Recent studies further analyze how the type and amount of EAP assistance received affects firm performance. Chrisman et al. (2012) find that the number of EAP courses taken has a positive effect on start-ups, while the amount of EAP counseling hours received has a positive effect on firm growth and survival. Cummings and Fischer (2012) and Solomon et al. (2013) similarly find that the amount of counseling received has a positive effect on sales, survival, and financial outcomes. Two studies also consider how the type of EAP assistance received affects performance. Solomon et al. (2013) find that both managerial and technical assistance have a positive effect on survival and growth. Seo et al. (2014) further find that assistance for primary business functions, such as marketing, financial management, and general management, is more effective for low-performing firms. In contrast, assistance for secondary business functions, such as human resources and capital obtainment, is more effective for high-performing firms.
Despite the extensive literature on general EAPs, few studies have focused specifically on the assessment of agri-food EAPs. Ulmer et al. (2005) conduct an economic impact assessment of the Oklahoma Food and Agricultural Product Center. Results indicate that the center has a positive impact on the state economy in terms of job creation and sales. Woods and Hoagland (2000) examine the objectives, development strategies, and effectiveness of agri-food EAPs from seven states. While quantitative measures of program effectiveness were limited, findings indicate that agri-food EAP assistance increases employment at the state level. Two additional studies in the literature assess the effect of agri-food EAP assistance on entrepreneurs’ performance. Cranwell et al. (2005) evaluate the effect of assistance from the Northeast Center for Food Entrepreneurship and find that agri-food EAP assistance has a positive effect on business growth, new venture start-ups, and job creation among entrepreneurs. Similarly, Peake and Marshall (2009) analyze the Purdue University Cooperative Extension Programs for Entrepreneurs, finding that the agri-food EAP has a positive effect on the rate of new venture start-ups.
While these studies provide a first look at the effect of agri-food EAPs on entrepreneurs’ performance, both studies are limited to entrepreneurs receiving EAP assistance and do not include a counterfactual sample of unassisted entrepreneurs in their analysis. As a result, the causal effect of assistance from the EAP cannot accurately be assessed. Further, both studies rely on subjective measures of performance, which have been shown to differ from actual performance in prior studies on general EAPs (McMullan, Chrisman, and Vesper 2001). In this study, we aim to address these issues in our analysis of how agri-food EAPs create value for entrepreneurs. In order to do so, we compare objective performance measures of assisted agri-food entrepreneurs to those of non-assisted entrepreneurs, while attempting to control for sample selection bias.
Mechanisms by Which EAPs Create Value
In this section, mechanisms by which EAPs create value for entrepreneurs are described. Specifically, the extant entrepreneurship literature suggests three key mechanisms through which EAPs create value: (1) venture evaluation, (2) legitimacy, and (3) knowledge sharing. Venture evaluation refers to the decision-making process used by the entrepreneur to determine the potential value of a new venture and the subsequent decision to launch the venture. Legitimacy refers to the process of building acceptance for the new venture among resource holders and consumers. Finally, knowledge sharing refers to the process by which entrepreneurs acquire new knowledge about venture development. In each of these cases, the extant entrepreneurship literature suggests the EAPs can play a positive role in improving each of these processes and can lead to positive entrepreneurial outcomes. Below, we provide a review of each of these value creating mechanisms and introduce the relevant supporting theoretical frameworks from the extant entrepreneurship literature.
Venture Evaluation
In the extant entrepreneurship literature, Behavioral Decision Theory suggests that EAPs create value for nascent entrepreneurs, i. e. individuals who are actively involved in starting a new business, by helping them to develop realistic and effective valuations of their proposed ventures (Parker and Belghitar 2006). According to Busenitz and Barney (1997), entrepreneurs facing uncertainty rely on their own heuristics and cognitive biases rather than rational decision-making models to evaluate opportunities and potential courses of action. Heuristics and cognitive biases provide simplifying strategies or “rules of thumb” to guide entrepreneurial behavior and decision-making when outcomes are unknowable or costly to ascertain. Two common types of heuristics and cognitive biases often considered in the context of entrepreneurship are overconfidence and optimism (Forbes 2005; Busenitz and Barney 1997; Palich and Bagby 1995); that is, entrepreneurs may systematically overestimate the value of the potential returns of a new venture or significantly discount the risks associated with it. Behavioral Decision Theory suggests that such heuristics and cognitive bias often inhibit effective decision-making, leading entrepreneurs to make major mistakes in venture evaluation that could have been avoided if the entrepreneur was able to recognize their bias beforehand (Forbes 2005).
EAPs serve to protect nascent entrepreneurs from both overconfidence and optimism bias. Behavioral Decision Theory suggests that by leveraging an EAP counselor’s knowledge and experience, nascent entrepreneurs can build in and engage safety nets that provide checks on the appropriateness of their heuristics and/or identify the presence of cognitive biases. These checks can lead to more realistic and better informed decisions when evaluating whether or not to exploit a particular opportunity and/or how to successfully do so. This type of assistance, furthermore, often leads to a “weeding out” of untenable ideas as well as the “planting in” of viable new ideas. As a result, the more EAPs can assist nascent entrepreneurs in building and engaging safety nets that provide checks on their heuristics and cognitive biases, the more likely the nascent entrepreneur will be successful in exploiting an entrepreneurial opportunity. To determine if this argument holds true for agri-food EAPs, the following hypotheses will be tested:
Hypothesis 1: Nascent entrepreneurs who receive assistance from an agri-food EAP prior to launch of a new venture will be less likely to launch than those who do not receive assistance (weeding out) (H1).
Hypothesis 2: Nascent entrepreneurs who receive assistance from an agri-food EAP prior to launch of a new venture and decide to launch, will be more likely to stay in business than those that did not receive assistance (planting in) (H2).
Legitimacy
The entrepreneurship literature also suggests that EAPs create value for entrepreneurs by acting as a badge or signal of “legitimacy” for a new venture (Rotger, Gørtz, and Storey 2012; Bell, Taylor, and Thorpe 2002; Zimmerman and Zeitz 2002). Nascent entrepreneurs face a complex hurdle in their ability to establish viable new ventures; specifically, they often lack the ability and/or track record to acquire the necessary resources (e. g. financial capital) or to establish the necessary relationships required to move a new venture forward (Shane 2003; Yang and Aldrich 2017). This barrier is known as the “liability of newness” and is associated with the high failure rates typically observed among new ventures (Shane 2008; Stinchcombe 1965). To overcome this barrier, Legitimacy Theory suggests that entrepreneurs who are able to establish legitimacy are further able to gain access to the resources and relationships that are needed for their new ventures to survive and grow (Zimmerman and Zeitz 2002; Aldrich and Martinez 2001). Legitimacy, in this case, refers to the perception (or social judgment) of resource holders that a new venture is acceptable, appropriate, and desirable (Zimmerman and Zeitz 2002). For entrepreneurs without an observable track record, resource providers must judge a venture’s legitimacy with limited information. This decision is further complicated by the uncertain nature of all entrepreneurial outcomes. As a result, it is often particularly difficult for entrepreneurs to acquire necessary resources such as financing since a resource holder may have significant questions about the entrepreneur’s ability to repay a loan or generate an adequate return on their investment. Similarly, an entrepreneur’s lack of legitimacy also makes establishing trading partnerships in a supply chain difficult. This is because cooperation from partners is based on trust, reliability, and reputation, which in turn are based on familiarity and evidence (Bateson 2000). In situations where entrepreneurs lack legitimacy and no discernable track record of previous trading activity, trading partners are less likely to exchange goods and services with the new venture for fear that the goods and services obtained from the new venture will not meet expectations (Aldrich and Fiol 1994).
Tornikoski and Newbert (2007) find that nascent entrepreneurs can establish legitimacy through resource combination and networking activities, including obtaining outside assistance from EAPs. EAPs help entrepreneurs establish legitimacy through assistance in business plan development. By developing a business plan, entrepreneurs are able to describe their venture in detail, reduce asymmetric information, and signal legitimacy. EAPs enhance this legitimacy signal by acting as a third-party auditor of the business plan. Following Legitimacy Theory, EAPs are thus expected to assist entrepreneurs in establishing legitimacy, and in turn, improve their ability to obtain external financing and establish trading partnerships. Thus, we test the following hypotheses in order to discern whether agri-food EAPs increase entrepreneurs’ legitimacy:
Hypothesis 3: Entrepreneurs who receive assistance from an agri-food EAP will be more likely to obtain external financing than those not receiving assistance (H3).
Hypothesis 4: Entrepreneurs who receive assistance from an agri-food EAP will have access to a greater marketing opportunity set than those not receiving assistance (H4).
Knowledge Sharing
Chrisman, McMullan and Hall (2005), Chrisman and McMullan’s (2000) Theory of Guided Preparation and New Firm Performance further suggests that EAP assistance improves the performance of new ventures through sharing knowledge with entrepreneurs. This stream of the entrepreneurship literature proposes that EAPs can help improve a client’s new venture survival and success by providing access to outside advisors and facilitating the development of new knowledge (e. g. training programs, mentoring/coaching), as a special type of resource available to the firm. The knowledge possessed by the entrepreneur creates the foundation for many, if not all of the new venture’s competitive advantages (Chrisman and McMullan 2005; Alvarez and Busenitz 2001; Chrisman 1999). Given that individual entrepreneurs have imperfect knowledge of market conditions (Hayek 1945) and may not know how to write a business plan, obtain financing, optimally locate their business, or deal with trading partners, EAPs can help improve new venture performance by providing the tacit and explicit knowledge needed to fill those gaps (Chrisman, McMullan, and Hall 2005). Chrisman, McMullan, and Hall (2005), Chrisman and McMullan (2004; 2000), and Kolvereid, Isaksen, and Ottosson (2009) find evidence that EAP assistance improves several measures of new venture performance, including survival, obtaining external financing, sales, and employment growth. In addition to the impact of EAP assistance on survival (H2) and obtaining external financing (H3), the effect of EAP assistance on new venture’s sales and employment performance will be tested in the following hypotheses:
Hypothesis 5: Entrepreneurs who receive assistance from an agri-food EAP will have higher gross annual sales rates than those not receiving assistance (H5).
Hypothesis 6: Entrepreneurs who receive assistance from an agri-food EAP will have higher employment rates than those not receiving assistance (H6).
Methods
The Michigan State University Product Center
In order to assess how agri-food EAPs create value for entrepreneurs, this study examines the value that was created for entrepreneurs who received assistance from the Michigan State University (MSU) Product Center. The Product Center was created by a memorandum of understanding among the MSU College of Agriculture and Natural Resources, MSU Extension, and the Michigan Agricultural Experiment Station on March 1, 2003. Partially funded by the AIC Program, the Center’s original mission was to be a catalyst for the creation of future businesses and industries engaged in Michigan’s agriculture, food, and natural resources. This mission was then expanded into a three-part framework that emphasized the Product Center’s role as a business and technical assistance program, an entrepreneurship education provider, and a market research organization. However, over time, it became clear that the education component was not highly valued by entrepreneurs, and this component was dropped.
The Product Center’s team consists of a core group of staff members, an executive group who take actions and make commitments on behalf of the center, and two operating subgroups: (1) a research subgroup composed of university faculty and students who engage in interdisciplinary research aimed at identifying and supporting clients’ needs and (2) a venture development subgroup that works directly with business clients, and internal and external partners, to provide the analysis and services clients require. The Product Center’s central offices are housed on the MSU campus, but its innovation counselor network is dispersed throughout Michigan, operating through MSU’s extension network. Selected extension agents are trained to be Innovation Counselors who serve as a first contact for individuals interested in receiving Product Center services. The on-campus specialized service unit assists entrepreneurs by directly providing services to clients or connecting them to on-campus departments. Provided services include strategic advice, assistance obtaining financing, feasibility studies, assistance in supply-chain entry, packaging, nutritional labeling, food-safety testing, product testing, and legal assistance. This combination of a dedicated on-campus staff to provide research and technical expertise with an off-campus counselor network is a unique feature of the Product Center. In addition to the above services, the Product Center has developed strategic programs with key food retailers to distribute products from Product Center clients and hosts an annual Michigan food trade show. The Product Center, however, does not connect clients to financing. From 2004 to 2012, the Product Center’s staff had 15,805 one-on-one client sessions, helped 1,434 clients develop their venture concept, led 881 clients to the start-up stage, and helped launch 164 ventures. These outcomes were achieved without direct promotion of their services.
Data Collection
Online surveys were administered to agri-food entrepreneurs across the state of Michigan in order to assess how agri-food EAPs, specifically the MSU Product Center, create value for entrepreneurs. Developed in collaboration with the Product Center staff, the survey instrument aimed to collect information on entrepreneurs’ demographics, resource obtainment, agri-food EAP assistance, business orientation, venture characteristics, and venture performance. The entrepreneurs surveyed were identified through two sources: (1) the Product Center database and (2) the Michigan Department of Agriculture’s food license applicant database. For the period of January 2004 to August 2012, the Product Center database provided a comprehensive list of all entrepreneurs that had contact with the center, while the Michigan Department of Agriculture’s food license applicant database provided a list of all individuals applying for a food license in the State of Michigan. Food license applicants were surveyed to obtain a control group of individuals similar to clients of the Product Center, but that did not obtain agri-food EAP assistance. Online surveys were sent out to all 2,200 entrepreneurs in the Product Center and Michigan Department of Agriculture databases between August and October of 2012. Two weeks following the initial mailing of the survey, a follow-up email was sent to all non-responding entrepreneurs as a reminder to complete the online survey.
Of the 2,200 agri-food entrepreneurs surveyed, 617 responses were received. This response rate of 28 % is consistent with that of recent studies assessing general EAP effectiveness (Solomon et al. 2013; Seo et al. 2014). Within this sample, 16 respondents reported receiving assistance from general EAPs. These respondents were removed from the sample so as to not confound the effect of assistance from an agri-food EAP. Further, a total of 134 surveys were incomplete and screened out of the sample. This resulted in a final sample size of 467 agri-food entrepreneurs, of which 312 received assistance from the Product Center and 155 did not receive assistance.
Variables and Measures
The variables obtained from the survey of entrepreneurs are presented and defined in Table 1. Detailed descriptive statistics for each variable are further provided in Table 2.
Variable definitions.
| Variable | Definition |
|---|---|
| Treatment Variables | |
| Pcassist | Respondent received assistance from Product Center |
| Prepcassist | Respondent received assistance from Product Center prior to launch |
| Performance Variables | |
| Launched | Whether respondent’s venture was launched |
| Survival | Whether respondent’s venture is still in operation |
| Sales | The venture’s current gross annual sales |
| Employment | The venture’s current total employment |
| Externfinance | The venture’s level of external financing |
| Volretail | The venture’s sales sold through retail |
| Selection Bias Variables | |
| Propinfoseekb | Respondent’s propensity to seek information |
| Independent Variables | |
| Ventureage | Time since the launch of new venture |
| Age | Respondent’s age |
| Indusexp | Respondent’s years of relevant work experience |
| College | Respondent’s received a bachelor’s degree or higher |
| Male | Respondent’s is a male |
| White | Respondent’s ethnicity is Caucasian |
| Familyentrep | Respondent’s has an entrepreneur in their family |
| Inherit | Respondent’s received significant inheritance |
| Pushed | Respondent’s self-employed out of necessity |
| Proprisktakeb | Respondent’s propensity to seek risk |
| Propinnovateb | Respondent’s propensity to innovate |
| Propgrowbizb | Respondent’s propensity to grow business |
Descriptive statistics.
| Variable | Obs.a | Unit | Range | Full Sample |
PC Clients |
Non-PC Clients |
|||
|---|---|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | ||||
| Treatment Variables | |||||||||
| Pcassist | 467 | Binary | 0–1 | 0.67 | – | 1 | – | 0 | – |
| Prepcassist | 467 | Binary | 0–1 | 0.49 | – | 0.49 | – | – | – |
| Performance Variables | |||||||||
| Launched | 464 | Binary | 0–1 | 0.75 | – | 0.73 | – | 0.81 | – |
| Survival | 341 | Binary | 0–1 | 0.93 | – | 0.95 | – | 0.89 | – |
| Sales | 281 | $ | 0–200,000 | 47,136.00 | 47,135.57 | 43,696.00 | 60,619.93 | 52,645.00 | 73,715.29 |
| Employment | 298 | # | 0–250 | 7.76 | 27.85 | 7.26 | 27.82 | 8.69 | 28.01 |
| Externfinance | 342 | $ | 0–200,000 | 10,682.87 | 37,437.31 | 8,199.00 | 33,358.91 | 13,348.00 | 41,305.67 |
| Volretail | 271 | $ | 0–62,911 | 4,551.96 | 13,328.10 | 5,854.00 | 15,681.91 | 2,959.00 | 8,669.87 |
| Selection bias Variables | |||||||||
| Propinfoseekb | 411 | Scale | 0–7 | 4.88 | 1.77 | 5.05 | 1.73 | 4.50 | 1.80 |
| Independent Variables | |||||||||
| Ventureage | 341 | Years | 0.75–8.75 | 3.87 | 2.32 | 3.84 | 2.33 | 3.93 | 2.31 |
| Age | 431 | Years | 20–74 | 48.80 | 11.41 | 48.57 | 11.58 | 49.30 | 11.05 |
| Indusexp | 404 | Years | 0–42 | 12.09 | 12.17 | 12.10 | 12.64 | 12.07 | 11.19 |
| College | 433 | Binary | 0–1 | 0.64 | – | 0.65 | – | 0.60 | – |
| Male | 431 | Binary | 0–1 | 0.47 | – | 0.48 | – | 0.46 | – |
| White | 433 | Binary | 0–1 | 0.86 | – | 0.84 | – | 0.90 | – |
| Familyentrep | 426 | Binary | 0–1 | 0.64 | – | 0.68 | – | 0.54 | – |
| Inherit | 426 | Binary | 0–1 | 0.09 | – | 0.09 | – | 0.07 | – |
| Pushed | 426 | Binary | 0–1 | 0.16 | – | 0.14 | – | 0.18 | – |
| Proprisktakeb | 411 | Scale | 1–7 | 3.69 | 3.70 | 3.64 | 1.59 | 3.82 | 1.65 |
| Propinnovateb | 411 | Scale | 1–7 | 3.72 | 1.89 | 3.75 | 1.88 | 3.66 | 1.91 |
| Propgrowbizb | 411 | Scale | 1–7 | 3.66 | 1.98 | 3.85 | 1.98 | 3.24 | 1.90 |
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aNote that the number of usable observations varies for each explanatory variable due to skipped survey questions.
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bMinimum value of 1 indicates lowest propensity, while value of 7 indicates highest propensity.
In order to examine the impact of assistance from the Product Center, two treatment variables are constructed from the survey data. Used to test H2-H6, Pcassist is a binary indicator for whether the entrepreneur received assistance from the Product Center. Entrepreneurs receiving counseling or specialized services were classified as having received assistance. However, individuals who contacted the Product Center, but did not receive counseling or services, were classified as not receiving assistance. Used to test H1, the second treatment variable, Prepcassist, is a binary indicator for whether the entrepreneur received Product Center assistance prior to the launch of their venture, as opposed to after.
Six performance variables, corresponding to each of this study’s six hypotheses, are obtained from the survey data: (1) launched, (2) survival, (3) externfinance, (4) volretail, (5) sales, and (6) employment. Corresponding with H1, launched, is a binary indicator of whether the surveyed entrepreneur launched an agri-food venture. The second variable, survival, corresponds to H2 and indicates whether the entrepreneur’s agri-food venture was still in existence at the time of the survey. As shown in Table 2, 95 % of entrepreneurs assisted by the Product Center were in business at the time of the survey. This finding mirrors that of Cranwell et al. (2005), who find that entrepreneurs receiving assistance from the Northeast Center for Entrepreneurship experienced a 96 % survival rate. Externfinance and volretail correspond to H3 and H4, and represent entrepreneur’s ability to obtain external financing and sell through retail channels. The final two performance variables, sales and employment, correspond to H5 and H6, respectively, and represent entrepreneurs’ annual gross sales and number of employees.
Comparison of the dependent variables across entrepreneurs receiving and not receiving assistance from the Product Center inherently suffers from self-selection bias (Wren and Storey 2002). This selection bias is the result of unobserved factors that determine whether an entrepreneur seeks assistance also influencing the performance of the entrepreneur’s venture. Prior EAP studies find evidence of both upward and downward selection bias. Kösters and Obschonka (2010) explain that the act of seeking assistance likely indicates entrepreneurs have lower entrepreneurial ability that will in turn affect the performance of their venture. Therefore, any comparison of assisted and non-assisted entrepreneurs would have an inherent downward bias. On the other hand, the propensity to seek information before making strategic business decisions has been shown to increase the overall probability of venture success (Baron 2004). Given that entrepreneurs seeking assistance from the Product Center are signaling a higher propensity to seek information, comparison of assisted and non-assisted entrepreneurs would have an inherent upward bias (Rotger, Gørtz, and Storey 2012).
Randomized control trial (RCT) and instrumental variable methods are the preferred approaches for controlling selection bias. However, the data used in this analysis does not lend itself to RCT methods and valid instruments could not be identified. Instead, this analysis attempts to control for selection bias by proxying for entrepreneurial ability and the propensity to seek information. The entrepreneur’s propensity to seek information (propinfoseek) is representative of upward selection bias, while their entrepreneurial ability (captured through two variables, college and indusexp) is representative of downward selection bias. The propensity to seek information has a significant, positive effect on seeking assistance from the Product Center, while the entrepreneurial ability variables are not significant. Thus, inclusion of propinfoseek in the regression analyses presented in this study will control for upward selection bias. However, without valid proxies of entrepreneurial ability, we are unable to control for downward selection bias. Given the presence of downward selection bias, all positive regression coefficients should be considered conservative estimates and are thus still valid for hypothesis testing (Wooldridge 2010). However, negative coefficients may be confounded by downward selection bias and thus, cannot provide causal evidence of Product Center’s effect on performance.
This analysis also controls for demographic and entrepreneurial orientation variables in order to isolate the relationship between assistance and performance. Demographics include the entrepreneur’s age, work experience, education, gender, race, family entrepreneurial experience, obtainment of inheritance, reason for interest in self-employment, and venture age.[1] Entrepreneurial orientation variables represent the processes, practices, and decision-making activities that lead to venture creation (Lumpkin and Dess 1996). In this study, we consider three commonly used measures of entrepreneurial orientation developed by Covin and Slevin (1989): propensity to seek risk, propensity to innovate, and propensity to grow one’s business. Propensity to seek risk is measured on a scale of 1 to 7, with 1 indicating the entrepreneur prefers low-risk projects (with normal and certain rates of return) and 7 indicating they prefer high-risk projects (with chances of high returns). Similarly, propensity to innovate is measured on a scale of 1 to 7, with 1 indicating a stronger emphasis on the marketing of tried and true products or services and 7 indicating a strong emphasis on R&D, technology leadership, and innovation. The final entrepreneurial orientation variable, propensity to grow one’s business, is also measured on a scale of 1 to 7, with 1 indicating the entrepreneur’s primary goal is to provide a fair income for their family or self and 7 indicating their goal is to grow a multimillion dollar business. Comparatively, those assisted by the Product Center are more likely to be male, have a college degree, have obtained a substantial inheritance, have an entrepreneur in the family, and have a higher propensity to innovate and grow their business. Those assisted are also less likely to be white, pursue self-employment out of necessity, and have a lower propensity for risk.
Statistical Analysis
A combination of Probit, Tobit, and Ordinary Least Squares (OLS) regressions are used to test the six hypotheses on how agri-food EAPs create value for entrepreneurs.
Probit regression analysis is employed to test H1 and H2. The Probit model has the following functional form:
where y is the performance measure, t is the treatment variable, x is a vector of the explanatory and self-selection bias proxy variables defined in Table 1, and
The Tobit model is used to test H3–H5, in which the performance variable under consideration has either an upper or lower bound. Left censored Tobit models, which have a lower bound, have the following functional form:
where a is the lower bound and y, t, and x are as previously defined. Similarly, right censored Tobit models, which have an upper bound, have the following functional form:
where b is the upper bound. For H3, the performance variable, y, is externfinance, which is censored below at 0. The respective performance variables for H4 and H5 are volretail and sales, which are censored above at $200,000.[2] For all three hypotheses, the treatment variable is pcassist. Estimates of the three Tobit models are obtained using MLE.
To test the final hypothesis, H6, OLS is used to estimate the following function:
where the performance variable, y, is employment, the treatment variable, t, is pcassist, and x is as previously defined.
Results
Estimates of the six regression models utilized to test H1–H6 are presented in Table 3.
Probit, Tobit, and OLS regressions: the effect of Product Center assistance on new venture launch, survival, legitimacy, and performance.
|
Launch and Survival
|
Legitimacy
|
Performance
|
||||
|---|---|---|---|---|---|---|
| Variable |
Launcha
(N = 346) |
Survivala
(N = 256) |
External Financingb
(N = 251) |
Trading Partnersb
(N = 192) |
Gross Annual Salesb
(N = 219) |
Total Employmentc
(N = 227) |
| Prepcassist | −0.77*** | – | – | – | – | – |
| (0.16) | – | – | – | – | – | |
| Pcassist | – | 0.57* | 3,159 | 12,939*** | −16,157 | −4.94 |
| – | (0.31) | (31,852) | (3,817) | (10,354) | (4.22) | |
| Propinfoseek | −0.07 | 0.03 | −4,087 | 271 | −1,856 | 0.93 |
| (0.05) | (0.07) | (8,658) | (956) | (2,553) | (1.15) | |
| Ventureage | – | −0.13** | 15,689** | 1,390* | 4,258** | −0.12 |
| – | (0.06) | (6,859) | (735) | (2,172) | (0.85) | |
| Age | 0.00 | 0.02* | −472 | −214 | −775* | 0.03 |
| (0.01) | (0.01) | (1,435) | (156) | (411) | (0.18) | |
| Indusexp | 0.00 | −0.02** | 3,143** | −12 | 1,222*** | 0.34* |
| (0.01) | (0.01) | (1,376) | (156) | (491) | (0.18) | |
| Baromore | 0.19 | −0.06 | −6,032 | −6,664* | 8,143 | 5.63 |
| (0.17) | (0.24) | (31,510) | (3,520) | (10,140) | (4.14) | |
| Male | −0.14 | 0.74** | 50,821* | 4,519 | 32,930*** | 7.28* |
| (0.16) | (0.32) | (30,762) | (3,303) | (9,238) | (3.92) | |
| White | 0.29 | 0.71* | 95,987 | 8,806 | 31,641*** | 5.68 |
| (0.23) | (0.41) | (72,172) | (5,816) | (10,402) | (6.52) | |
| Familyentrep | 0.11 | 0.48* | 10,102 | −1,045 | −16,242 | −2.22 |
| (0.17) | (0.28) | (32,271) | (3,596) | (10,182) | (4.13) | |
| Inherit | 0.02 | −1.00** | −19,790 | −1,102 | −10,405 | −7.84 |
| (0.32) | (0.44) | (57,764) | (6,308) | (17,109) | (7.21) | |
| Pushed | 0.27 | −0.04 | −44,328 | −5,468 | −8,421 | 3.25 |
| (0.24) | (0.32) | (42,181) | (4,443) | (11,463) | (5.36) | |
| Proprisktake | 0.08 | −0.15 | 13,431 | 11 | 2,655 | −1.84 |
| (0.05) | (0.10) | (10,824) | (1,185) | (3,370) | (1.42) | |
| Propinnovate | −0.13*** | 0.00 | −18,906** | 1,240 | 610 | 2.16* |
| (0.05) | (0.07) | (9,126) | (1,048) | (3,161) | (1.17) | |
| Propgrowbiz | 0.03 | 0.17** | 5,852 | 114 | 9,864*** | 2.50** |
| (0.04) | (0.07) | (7,908) | (914) | (2,541) | (1.07) | |
| Constant | 1.13* | −0.13 | −336,361*** | −18,205 | −9,272 | −19.48 |
| (0.60) | (0.82) | (133,853) | (12,781) | (31,713) | (14.24) | |
| Log-Likelihood | −167.82 | −50.06 | −492.34 | −1,146.80 | −2,439.39 | – |
| Likelihood Ratio | 45.62 | 30.17 | 25.57 | 27.70 | −52.43 | – |
| p-Value | 0.00 | 0.01 | 0.03 | 0.02 | 0.00 | – |
| Pseudo R2 | 0.12 | 0.23 | 0.03 | 0.01 | 0.01 | – |
| R2 | – | – | – | – | – | 0.09 |
| Censored Below | – | – | 212 | 95 | 0 | – |
| Censored Above | – | – | 7 | 0 | 27 | – |
-
Note: Standard errors are given in parantheses.
-
Significant at the *** 0.01 level. ** 0.05 level, and * 0.10 level.
-
aProbit, bCensored Tobit, and cOLS.
Hypotheses 1 and 2: Venture Evaluation
H1 and H2 conjecture that agri-food EAPs create value for nascent entrepreneurs by improving venture evaluation. In H1, we consider whether agri-food EAP assistance leads to fewer new venture launches or a weeding out of untenable business ideas. Probit regression results in Column 1 of Table 3 indicate that receiving assistance from the Product Center prior to launch is inversely related to the probability of new venture launch. Significant at the 0.01 level, this relationship strongly supports H1. In H2, we consider whether agri-food EAP assistance leads to planting in, or higher rates of survival among ventures. Presented in Column 2 of Table 3, Probit regression estimates suggest that assistance from the Product Center has a significant, positive effect on the probability of venture survival. Thus, H2 is also supported. Overall, the joint support for H1 and H2 implies that the Product Center is effective at assisting nascent entrepreneurs in venture evaluation, which in turn results in the weeding out of untenable business ideas and the planting in of tenable business ventures.
The results also highlight several demographic and entrepreneurial orientation factors that affect the probability of venture launch and survival. Significant at the 0.01 level, the propensity to innovate is inversely related to the probability of launch, potentially reflecting that Product Center counselors advise nascent entrepreneurs about the added risk associated with launching innovative products. As expected, older, white, male entrepreneurs with a propensity to grow their business have a greater probability of survival. Further, venture age and, somewhat surprisingly, inheritance obtainment and industry experience are inversely related to the probability of survival. Receiving an inheritance may cause entrepreneurs to hastily launch a new venture, thus resulting in lower probability of survival. Prior studies also show that entrepreneurial skillsets and personalities differ from those of management (Teal and Carroll 1999; Chen, Greene, and Crick 1998). Thus, the negative effect of industry experience on venture survival may reflect that some entrepreneurs with industry experience lack entrepreneurial skills, resulting in lower survival rates. Further, entrepreneurs with greater industry experience likely face increased opportunities to return to industry and leave their venture. It should be noted, however, that this finding is contrary to previous literature that suggests that industry experience can lead to more accurate and less biased expectations for new venture performance (Cassar 2014).
Hypotheses 3 and 4: Legitimacy
Whether assistance from agri-Food EAPs aids in the development of entrepreneurs’ venture legitimacy is tested in H3 and H4. Specifically, H3 tests whether agri-food EAP assistance improves entrepreneurs’ ability to obtain external financing, while H4 tests assistance’s effect on the volume of sales sold through retail. Shown in Columns 3 and 4 of Table 3, respectively, the Tobit results reveal that obtaining assistance from the Product Center does not significantly affect the level of external financing entrepreneurs obtain. However, Product Center assistance does have a significant, positive effect on the entrepreneur’s volume of sales sold through retail. Thus, H3 is rejected, while H4 is supported. This finding indicates that Product Center assistance is effective at improving entrepreneurs’ perceived legitimacy with trading partners, but ineffective at doing so with financial resource holders.
As expected, results further highlight that male entrepreneurs with greater industry experience and older ventures are able to obtain greater levels of external financing. However, entrepreneurs with a greater propensity to innovate obtain significantly less external financing. This could potentially indicate that banks, especially during the 2008 recession, were not willing to take the risk of financing non-traditional, innovative products. Further, entrepreneurs’ venture age has a significant positive effect and education has a negative effect on their ventures’ level of sales through retail.
Hypotheses 5 and 6: Performance
In H5 and H6, we consider how the knowledge sharing generated by agri-food EAP assistance affects entrepreneurs’ venture performance. Specifically, H5 tests whether agri-food EAP assistance has a positive effect on entrepreneurs’ annual gross sales, while H6 tests whether assistance has a positive effect on total employment. Tobit and OLS results presented in Columns 5 and 6 of Table 3, respectively, suggest that assistance from the Product Center does not significantly effect gross annual sales or total employment. Both H5 and H6 are thus rejected.
While Product Center assistance does not affect sales or employment, results indicate several demographic and entrepreneurial orientation factors have a significant effect. Gross annual sales are positively affected by venture age, industry experience, propensity to grow one’s business, being male and white, and negatively affected by the entrepreneur’s age. Similarly, industry experience, the propensity to innovate and grow one’s business and being male positively affect entrepreneur’s total venture employment.
Discussion
The primary objective of this study was to analyze how agri-food EAPs create value for entrepreneurs. Using the MSU Product Center as a case, the effect of agri-food EAP assistance on entrepreneurs’ venture evaluation, perceived legitimacy with resource holders and trading partners, and firm performance was examined. To conduct this analysis, 467 entrepreneurs receiving and not receiving assistance from the MSU Product Center were surveyed. Controlling for upward sample selection bias, Probit, Tobit, and OLS models were estimated. Results suggest that the Product Center is effective at preventing untenable business ideas from launching, improving the survival of launched ventures, and developing entrepreneurs’ perceived legitimacy with trading partners. However, results indicate that Product Center assistance has no significant effect on employment, firm sales, or legitimacy with financial resource holders. This result is consistent with Kutzhanova, Lyons, and Lichtenstein (2009) who finds that EAPs like the Product Center are capable of helping start-ups survive because they offer long-term counseling/coaching that helps to build skills.
Implications for Policymakers and Agri-Food EAP Management
In a previous assessment of the determinants of effective government intervention programs aimed at promoting entrepreneurship, Lerner (2009) finds that intervention programs generally fail when they are designed to promote entrepreneurial activities in target industries. Results from this analysis suggest that either Lerner’s (2009) finding does not hold for EAPs targeting the agri-food industry or that it may depend on how the program is designed. The agri-food EAP analyzed in this study, the MSU Product Center, successfully promotes entrepreneurship in the agri-food industry by weeding out untenable business ideas, improving the survival of launched ventures, and developing entrepreneurs’ perceived legitimacy with trading partners. While these results are based on the analysis of a single agri-food EAP, they nevertheless support the notion that agri-food EAPs can successfully foster entrepreneurial activity. This implies that policymakers should not discount targeted EAPs as a potential approach to promoting entrepreneurship in the agri-food industry. This finding is also consistent with Cranwell et al. (2005) and Peake and Marshall (2009) who find that agri-food EAPs have a positive effect on entrepreneurial performance.
Findings from this analysis further suggest that agri-food EAPs may be particularly well suited to local, state, and federal policies aimed at inducing economic development in the agri-food industry through the promotion of new entrepreneurial ventures, as opposed to assisting existing entrepreneurial ventures. Analysis of the MSU Product Center indicates that agri-food EAPs can be particularly successful at assisting nascent entrepreneurs. Aiding entrepreneurs prior to the launch of their business is critical in that it gives the agri-food EAP the ability to help entrepreneurs assess whether their business idea is viable and to develop of strategic plan. This results in the launch of fewer untenable business ideas and increased survival among launched ventures. While these conclusions are drawn from the analysis of a single agri-food EAP, they do provide evidence that given a program design similar to the MSU Product Center, agri-food EAPs can be well suited to assisting nascent entrepreneurs. This latter observation may also reflect that the support offered by the Product Center may be relatively unique among EAPs. Through its more holistic and longer-term approach, one might argue that the Product Center works a “pipeline” of entrepreneurs and enterprises that allows an entrepreneur to take their idea and grow it, ultimately, into a Stage 2 company (Lichtenstein and Lyons 2006). Most EAPs engage in “knowledge hand-offs” or resource brokering (Kutzhanova, Lyons, and Lichtenstein 2009). In order to build skills, entrepreneurs may also need to practice what they learn and receive feedback on that practice (Lichtenstein and Lyons 2006).
Managerial implications for agri-food EAPs can also be drawn from this analysis, most notably, how agri-food EAPs can most effectively allocate limited resources. As previously discussed, agri-food EAPs can be particularly well suited to assisting nascent entrepreneurs. However, results from this analysis suggest that the MSU Product Center, and likely other similar agri-food EAPs, may be less effective at assisting experienced entrepreneurs. In particular, business ventures of entrepreneurs seeking assistance after launch, with relevant industry experience and that have recently obtained a large inheritance, are less likely to survive. These findings suggest that in order to maximize the economic impact of their services, agri-food EAPs constrained by limited public funding should prioritize assisting nascent entrepreneurs before more experienced entrepreneurs. In other words, they should find their niche in the pipeline of entrepreneurs and enterprises (Lichtenstein and Lyons 2006).
Limitations
Despite its many implications for agri-food EAPs and policymakers, this study is not without limitations. A key limitation of this study is that it provides an analysis of a single case study of an agri-food EAP. With data from only one agri-food EAP, which serves entrepreneurs in a single state, the results obtained are not generalizable to all agri-food EAPs in the US. Supplementing this research with evaluations of entrepreneurs assisted by other agri-food EAPs is needed in order to determine if the manner by which agri-food EAPs create value for entrepreneurs is homogenous across programs and geographic regions.
This analysis is also limited by the presence of sample selection bias, resulting from unobserved factors that affect both an entrepreneur’s decision to seek assistance and their level of performance. In this analysis, proxy variables for the propensity to seek information were used in order to control for upward selection bias. However, no proxy variable could be identified for entrepreneurial ability, and thus, downward selection bias could not be controlled. In the presence of downward selection bias, positive coefficients were viewed as conservative estimates and could be used to make inferences on whether assistance had a positive effect on entrepreneurs’ performance. However, the magnitude of these effects could not be ascertained and the interpretation of negative coefficients was uncertain. Future studies could improve upon this analysis by explicitly accounting for selection bias through the use of randomized controlled trials or instrumental variable two-stage modeling.
Similar to Peake and Marshall (2009) and Cranwell et al.’s (2005) analyses of agri-food EAPs, this analysis was unable to control for potential non-response bias. Non-response bias could be present if there is significant variation in performance indicators across entrepreneurs who responded and did not respond to the survey. Sample weighting is commonly used to correct for non-response bias; however, a suitable reference dataset on Michigan entrepreneurs was not available and thus survey weights could not be utilized in this analysis.
Conclusions and Future Research
While this article provides a first look at the effect of agri-food EAP assistance on an entrepreneur’s venture performance, further research is also needed to fully understand how agri-food EAPs create value for entrepreneurs. Potential areas of future research include assessing how agri-food EAPs create value for different types of entrepreneurs, particularly nascent entrepreneurs and entrepreneurs with small businesses. A central question in this line of research might be to ask: how do the assistance needs of nascent entrepreneurs differ from entrepreneurs that are more established and are now looking for assistance with growing (or exiting) their ventures? If the assistance needs differ, are they complementary or does an agri-food EAP need to invest in substantially different expertise and resources to provide assistance to both types of entrepreneurs? Future research should examine the extent to which agri-food EAPs are able to assist several types of entrepreneurs without destroying value for the types of entrepreneurs they intend to serve. To put it another way, researchers might examine if and how agri-food EAPs can create value by developing a suite of assistance programs that evolves as an entrepreneur’s assistance needs change over time.
Another area of interest for future research includes understanding how the effect of agri-food EAP assistance on entrepreneurs’ performance varies with the level and type of assistance received. For instance, while this study examines the effect of knowledge sharing by an agri-food EAP with an entrepreneur, it does not distinguish between types of knowledge shared. In the case of the MSU Product Center, the Center shares expertise on a range of knowledge areas, including business model design, market research, and technical product development (e. g. food labelling, packaging, food safety, etc.). Further research is needed to evaluate the effectiveness of each of these types of knowledge sharing on entrepreneurial performance. Are certain types of knowledge more valuable to entrepreneurs? At the same time, future research might examine the extent to which entrepreneurs need to assimilate new knowledge in order to receive the full value of certain types of knowledge. In other words, to what degree (in-depth vs. cursory) does an entrepreneur need to understand the intricacies of the shared knowledge in order to extract its value.
Funding statement: This work was supported by USDA-NIFA Hatch projects 1014886 and 1004126, and the Homer Nowlin Chair of Consumer-Responsive Agriculture.
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Articles in the same Issue
- The Practical Intelligence of Social Entrepreneurs: Managing the Hybridity of Social Enterprises
- Do Entrepreneurial Assistance Programs Create Value for Agri-Food Entrepreneurs?
- Creative Entrepreneurs’ Well-Being, Opportunity Recognition and Absorptive Capacity: Self-Determination Theory Perspective
- New Venture Growth: Role of Ecosystem Elements and Prior Experience
- Not All Entrepreneurs are Viewed Equally: A Social Dominance Theory Perspective on Access to Capital
Articles in the same Issue
- The Practical Intelligence of Social Entrepreneurs: Managing the Hybridity of Social Enterprises
- Do Entrepreneurial Assistance Programs Create Value for Agri-Food Entrepreneurs?
- Creative Entrepreneurs’ Well-Being, Opportunity Recognition and Absorptive Capacity: Self-Determination Theory Perspective
- New Venture Growth: Role of Ecosystem Elements and Prior Experience
- Not All Entrepreneurs are Viewed Equally: A Social Dominance Theory Perspective on Access to Capital
