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Article

Intra-Stakeholder Heterogeneity Perspective on the Hybridity of Competing Institutional Logics for Social Enterprises

by
Tae Jun Bae
1,* and
James O. Fiet
2
1
Department of Entrepreneurship, Hanyang University, Seoul 04763, Republic of Korea
2
College of Business, University of Louisville, Louisville, KY 40292, USA
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3215; https://doi.org/10.3390/su15043215
Submission received: 16 January 2023 / Revised: 5 February 2023 / Accepted: 6 February 2023 / Published: 9 February 2023
(This article belongs to the Special Issue Social Business and Impact for Sustainable Growth)
Versions Notes

Abstract

:
Both academics and businesspeople are interested in how to make social enterprises sustainable. The focus of this research is on the different kinds of stakeholders within a group that make it easier for competing logics to coexist in social enterprises. Based on intra-stakeholder heterogeneity and competing institutional logics, we identify key sub-categories among market stakeholders such as investors, customers, and employees. We tested our hypotheses using survey data collected from 190 social enterprises in Korea. Our research shows that the hybridity of competing logics is better when there are more ethical investors in the investor stakeholder group and cross-sector employees in the employee stakeholder group. However, impure altruistic buyers do not have much of an impact on the hybridity of competing logics among consumer stakeholder groups. Our study’s analysis of intra-stakeholder heterogeneity provides theoretical insight into the hybridity of institutional logics in social entrepreneurship. This study also makes the practical suggestion that in order to achieve hybridity, managers of social enterprises should put in a lot of time and effort to understand the different institutional logics of within-group stakeholders.

1. Introduction

As social enterprises try to help society and make money at the same time [1,2,3,4], they have to meet a wide range of stakeholder demands based on institutional logics such as social welfare and business logics [5]. This is because stakeholders from different institutional logics can affect how a social enterprise responds to institutional logic [6,7]. For example, non-market stakeholders such as the government or communities may ask a focal organization to put social welfare logic first. On the other hand, people who have a stake in the market, such as investors, can put commercial logic ahead of social welfare logic.
Scholars have argued that the ability to manage a diverse set of stakeholders and their expectations is critical to the long-term growth of social enterprises [8,9]. Prior research indicates that appropriate governance [10], legitimation exercise [8], mapping the salience of stakeholders [11], and communication strategy [12] are effective solutions for addressing stakeholders’ preferences and interests. Furthermore, it means that social enterprises achieve hybridity, defined here as a “combination of competing institutional logics [13,14],” when they recognize and incorporate diverse values from various stakeholder groups.
Despite these observations, previous research has assumed that different stakeholder groups—such as investors, customers, or employees—have different sets of objectives. They may, however, have shared preferences among the same group of stakeholders [15,16,17]. Other studies, however, discovered differences among the same stakeholders. In other words, many stakeholders who are recognized as belonging to the same group of stakeholders have diverse values and interests [18,19]. For example, investors’ attitudes toward financial returns in a social enterprise may differ even if they are listed as the same stakeholders. As a result, we are still unsure how the heterogeneity of within-group stakeholders affects the hybridity of social businesses.
To fill this knowledge gap, our research focuses on the unique aspects of intra-stakeholder heterogeneity that contribute to social enterprises’ hybrid nature. We concentrate on the demographics of members of stakeholder groups that are linked to institutional logics. We contend that social enterprises can achieve a higher level of hybridity if the majority of within-group stakeholder members are associated with institutional logics that are balanced between social and commercial logics.
The structure of this study is as follows. We begin with a review of the literature on stakeholder perspective and intra-stakeholder heterogeneity. Then, we formulate testable hypotheses concerning the characteristics of three market stakeholders: shareholders, customers, and employees. The next section describes the methodological approaches, including the empirical setup, data collection, operationalization of variables, and analytic method. We conclude with an interpretation of the data and a discussion of their theoretical and practical implications.

2. Theory and Hypotheses

2.1. Stakeholder Perspective on Social Entrepreneurship

Scholars define stakeholders differently [16], but anyone who has a vested interest in or a stake in an organization’s success is considered a stakeholder [20]. Stakeholder theorists view an organization as the locus of its relationships with stakeholders and as a nexus of contracts [21,22,23]. Furthermore, stakeholder theory assumes that an organization can maximize its welfare by effectively managing complicated relationships with stakeholders [22,24,25]. Thus, organizational responsiveness can be seen as how an organization responds to specific requests from stakeholders [26,27,28].
The stakeholder perspective is also relevant in social entrepreneurship. First, stakeholders provide resources to a social enterprise, such as funding, labor, and expertise, and the social enterprise uses these resources to provide solutions for social needs and create value for stakeholders involved in social enterprise projects [29,30,31]. The social enterprise’s success is inextricably linked to the resources it receives from its stakeholders and the effective management and use of these resources [32,33].
Second, stakeholders are the most influential determinants of dominant institutional logic within a social enterprise. In other words, social entrepreneurship scholars argue that external and internal actors convey institutional demands to a social enterprise [34]. Because organizations are complex entities made up of multiple groups advocating distinct values, purposes, and interests, an organization’s internal and external stakeholders can shape how a social enterprise responds to competing logics [34,35].
Third, a social enterprise has more diverse stakeholders than a purely commercial firm [36,37]. As a result, less powerful stakeholders for commercial firms become salient for social enterprises. For example, commercial firms may regard the community where they operate as a powerless stakeholder, but social enterprises must also attract community support [38]. Furthermore, satisfying the varying demands from many equally relevant stakeholders requires social enterprises to carefully understand the complexity of the stakeholder relationship [39]. By taking into account the needs and concerns of different groups, social entrepreneurs can ensure that their projects are long-lasting, have broad support, and help everyone involved.
Forth, in addition to diversity between stakeholder groups, there is diversity within stakeholder groups; therefore, heterogeneous preferences or motivations may be present within the same stakeholder group. This diversity is known as intra-stakeholder heterogeneity [18,40]. For example, not all employee stakeholders are motivated by monetary incentives. Instead, there are different employees whose interests are diverse. Paid employees seeking pay, volunteers with non-monetary motivations, beneficiaries receiving services, and general customers paying for services in social entrepreneurial organizations comprise the employee stakeholder group [41].

2.2. Intra-Stakeholder Heterogeneity and Hybridity of Competing Institutional Logics

There is not much new research on intra-stakeholder perspective because it is a newer field of study. However, some research has been conducted to investigate how heterogeneity within-group stakeholder in an organization affects decision making and outcomes [18,19]. Intra-stakeholder heterogeneity refers to differences in common attribute X, such as demographics or preferences, within stakeholder groups [18,42]. There are meaningful differences within each stakeholder group. Furthermore, intra-group stakeholder differences are most likely associated with power differentials in organizational management. Kassinis and Vafeas [18] demonstrated, for example, that diverse environmental preferences among community stakeholders result in varying outcomes and levels of toxic emissions from firms. Specifically, plants associated with more environmentally conscious community stakeholders reduce toxic emissions more than those associated with less environmentally conscious community stakeholders. This illustrates that organizational responses can result from intra-stakeholder pressure.
We can therefore use the same justification to comprehend the hybridity of social enterprises. Scholars have described organizational hybridity in terms of “mixed origin” [43], “the integration of logics that normally do not go together” [13], and “functional solidarity” [44]. While members may belong to the same stakeholder group, they have different attachments to institutional logic. Additionally, different members immersed in various stakeholder groups’ logics are linked to their preferences for social entrepreneurship, which has an impact on the hybridity of social welfare and commercial logic. For instance, reciprocators are eager to cooperate freely, in contrast to self-regarding stakeholders who are only concerned with their own gains and costs [45]. On the other hand, if a particular stakeholder group is made up entirely of selfless individuals, pressure from this group should push a social enterprise to follow social welfare logic rather than commercial logic.
We contend that intra-stakeholder heterogeneity is crucial to the hybridity of competitive institutional logics. Specifically, we focus on intra-stakeholder heterogeneity among market stakeholders. Scholars distinguish between market and non-market stakeholders [46]. Market stakeholders are groups whose activities are linked to firms through economic transactions [47]. Shareholders, employees, customers, and even competitors are market stakeholders, whereas regulatory agencies, governments, and special interest groups are non-market stakeholders. Non-market stakeholders are treated less urgently due to their lack of economic power because economic purpose does not guide their interactions with firms [46]. Market stakeholders, on the other hand, are regarded as just as important as primary stakeholders due to their provision of critical resources [48].
Social enterprises face pressure to prioritize commercial logic from the market’s stakeholders, who tend to be economically self-interested, individualistic, and profit-driven [49,50]. Based on this, we expect that social enterprises with sizable in-subgroups that render commercial pressure neutral among market stakeholders would show a rise in hybridity of competing logics. We examine significant sub-categories within the three most important market stakeholders: (1) investors, (2) customers, and (3) workers.

2.2.1. Ethical Investors among Investor Stakeholders

Investors and shareholders are crucial market stakeholders [51]. Investors, as business owners, have a significant influence on the firm’s actions and strategies by distributing money and pressuring managers. When investors are dissatisfied with their companies’ management practices, they are more likely to sell shares [52], jeopardizing the firm’s survival because they are its most visible stakeholders. Thus, firms have strong incentives to understand shareholder interests based on ownership [26,53]. Understanding investors and funders is significant for social enterprises, as they rely heavily on funding from diverse investors with varying motivations and expectations [38]. There are, however, few systematic studies of the relationship between investor expectations and the hybridity of competing logics within social enterprises.
According to the classic view of economics, the primary objective of shareholders is to maximize profits; corporations are responsible for safeguarding and advancing their financial interests [54]. Consequently, managers must make decisions to maximize the value of the firm’s future cash flow [55]. However, behavioral finance scholars have called into question the conventional belief that investments should increase wealth. Beal, Goyen, and Philips [56] assert that some investments are ethical or socially responsible. For example, socially responsible investors held 11% of the assets in the United States and 17% of assets in Europe in 2012 [57]. This will increase to 33.2% in the United States and 37.9% in Europe by 2020 [58].
This type of investor is considered an ethical or socially conscious investor [59,60]. Aside from wealth maximization, ethical or socially conscious investors gain other benefits from an investment [61]. The utility of ethical investment can be maximized by incorporating various factors such as (1) the enjoyment of participation, (2) the formation of an ethical identity, and (3) the flow of pleasure [56]. Ethical investors contribute to the hybridity of competing logics in social enterprises. First, ethical or socially conscious investors are more likely to accept below-market-rate returns than traditional investors. Ethical investors are less likely to be concerned about negative returns when investing in ethical funds [62]. Similarly, experimental studies show that investors with expressive decision frames are more likely to accept lower financial returns from socially responsible investment choices [63]. Accepting low financial returns may help social enterprises become less reliant on commercial activities unrelated to their core social values.
Second, as the current finance literature on social entrepreneurship has illustrated, ethical investors with low expectations of financial return are more likely to prioritize an operation’s scalability [64]. Ethical investors believe that creating social value and profit are not mutually exclusive. Instead, scaling the business is thought to result in significant societal benefits, such as creating jobs for the disabled or supplying low-cost products/services to underdeveloped communities [65]. As a result, they encourage organizations they support to achieve substantial growth and spread positive social impact by combining social value and commercial success. Considering the prior discussion, we hypothesize:
Hypothesis 1.
When there is a greater proportion of ethical investors within an investor stakeholder group, hybridity for competing institutional logics will be higher.

2.2.2. Impure Altruistic Customers within Customer Stakeholders

Customers have the ability to persuade firms to act [66]. Like other economic stakeholders, customers are essential to firm survival because they provide cash flow [67]. Moreover, firms that satisfy customers may increase customer loyalty, lower transaction costs, and lower price sensitivities, positively related to economic returns [68]. Therefore, scholars have proposed enhancing the knowledge of customer stakeholders [69].
From the standpoint of social entrepreneurship, many customers are willing to pay more for things they perceive are ethical [70,71]. According to ethical consumerism research, U.K. consumers spent GBP 25.8 billion per year on ethical goods in 2004, which increased to GBP 36 billion in 2009. The market value of ethically labeled foods has increased lately, from USD 794 billion in 2015 to USD 900 billion in 2021 [72]. Fair trade, defined as “a social movement that attempts to set fair pricing for products, decrease poverty, and assist producers,” has also received much attention [73]. The global market for fair trade products has expanded from USD 2.5 billion in 2007 to USD 5.5 billion in 2015 and USD 9.8 billion in 2018 [72,74]. In a Belgian study, De Pelsmacker et al. [75] discovered that consumers are willing to pay 10% more for fair trade products. Customers in Italy are likewise willing to spend an extra 9% on fair trade items [76].
Researchers have noticed that customers influence the allocation of social companies’ resources to social or commercial activities that produce value for their core customers [77]. For instance, social enterprises are more likely to prioritize resource utilization for commercial operations if they serve economic customers or primarily target individuals who can pay for goods and services [77]. This implies that the majority of customers who adhere to particular institutional logic impact the hybridity of social enterprises.
We argue that “impure altruism,” defined as possessing both altruistic and self-serving motives, within the consumer stakeholder group may strengthen the hybridity of social and commercial logics. In other words, consumers who are neither fully selfless nor completely altruistic may place a great deal of pressure on social enterprises to employ both social and commercial rationale. According to Andreoni [78], people who engage in public good are not motivated solely by charity or egoism. People may engage in prosocial behavior because they desire to help others (an altruistic motive) and because it benefits them (an egoistic purpose) [79].
Similar evidence has been found in the field of social entrepreneurship. Buyers who purchase The Big Issue, a street magazine that allows only homeless people the option to become vendors, demonstrate both the utilitarian value of the magazine and the altruistic aspect of the transaction [80]. According to the study’s findings, impure altruistic consumers who engage in social entrepreneurship may see ideal social enterprises as companies or firms that match customers’ desires to help others while still delivering economically qualified goods and services. Thus, we expect that as the number of impure altruistic customers grows, the chance that social companies will strengthen both social and economic activities without choosing one over the other increases.
Hypothesis 2.
When there is a greater proportion of impure altruistic customers among customer stakeholders, hybridity for competing institutional logics will be higher.

2.2.3. Cross-Sector Workers within Employee Stakeholders

Employees are significant stakeholders [15] and impact organizational responsiveness to institutional logics because they are carriers of institutional logics into organizations [81]. Social enterprises have two categories of employees: beneficiaries and regular employees. Beneficiaries are people who are disabled or other marginalized workers, such as the long-term jobless or people in vulnerable positions [82,83]. Work integration social enterprises (WISE) often provide beneficiaries with fixed-term contracts and training to help them thrive in the primary labor market [82]. On the other hand, regular employees are permanent and responsible for supporting beneficiaries and completing administrative tasks. This study focuses exclusively on heterogeneity among permanent employees rather than beneficiaries mainly because permanent employees (hereafter, employees) are competitive human resources for both social mission achievement and economic performance in social enterprises. Although there are many characteristics of employee diversity, such as age, ethnicity, and education, this study focuses on employees’ past work history since it is connected to employee commitment to diverse institutional logics, either social welfare or commercial logic.
Because of their past jobs, employees may have been exposed to either a social welfare logic or a business logic. This can show how embedded they are in a particular institutional logic, which can be translated into the focal organization. This idea is similar to most research on employees’ functional diversity. Their past work experience can shape their beliefs and lead to different preferences and ways of thinking about strategic issues [84]. Employees keep using the same logic they were taught before [85]. Additionally, they pressure the organization to follow a certain institutional logic that makes sense to them based on their experience. So, employees’ past work experience seems to be a substantial factor that affects how social enterprises respond to competing logics [81].
We predict in this study that cross-sector workers with expertise in both the non-profit and the for-profit sectors help social enterprises achieve hybridity of competing logics for the following reasons. First, workers’ job values vary by sector [86] and are connected to their embeddedness in various institutional logics [87]. For instance, employees in the not-for-profit sector place a greater emphasis on social service than employees in the for-profit sector, but they place less emphasis on professional progression [86]. On the other hand, cross-sector workers are more likely to have varied work values and interests, allowing social entrepreneurs to avoid the problem of one logic overwhelming another within organizations.
Second, another attribute of cross-sector workers is that they have more diversified skills and resources, such as social relationships, outside knowledge, and information [88]. Because an employee’s prior work experience in different institutional logics could be used as a source of information, this could give a social enterprise more ways to deal with competing logics. Thus, we propose the following hypothesis:
Hypothesis 3.
When there is a greater proportion of cross-sector workers among employee stakeholders, hybridity for competing institutional logics will be higher.

3. Methods

3.1. Sample and Data Collection

To test our hypotheses, we analyzed South Korean social enterprises. In 2007, the Korean government enacted the Social Enterprise Promotion Act (SEPA) and began using the accreditation system to identify and clarify social enterprises. The Act defines a social enterprise as an organization that engages in economic activities, such as manufacturing and selling products and services while pursuing a social purpose of improving the quality of life of local residents by providing social services and creating employment for the poor [89].
Using Korean samples was helpful for this study. First, Korea’s accreditation system helps us avoid self-identification problems and find suitable social entrepreneurial organizations. Prior studies used samples that intentionally identified themselves as social enterprises [90]. This practice may have made it difficult to conduct a large sample study without bias. Second, because social and commercial logics in Korean society are fiercely divided, Korean social enterprises give a great context for testing our hypotheses [14].
We compiled a list of 1012 social companies accredited by the Korea Social Enterprise Promotion Agency (KSEPA) as of February 2014 and included them in the study sample. Following Brislin [91], we first created the survey questionnaires in English and then translated them into Korean. Then, using a web-first survey design, we contacted the social enterprises by email, informing them of the study’s goal and providing a code for the online survey [92]. After three reminder emails and phone calls to encourage participants to join our research, we received 281 responses from CEOs.
Following the suggestion to use multiple survey informants [93], we contacted social enterprises that completed the first survey a second time and asked middle managers one additional question regarding topics that were discussed at the top levels of the organization. This extra approach led to 190 usable responses, giving a total usable response rate of 18.96%. To evaluate the potential for nonresponse bias, we compared the respondents’ answers to questions on the firm’s age, size, number of board members, and debt ratio with those of companies that declined to participate or could not be reached. ANOVA revealed no statistically significant group differences (Fs 0.10, p > 0.10).

3.2. Dependent Variable

The dependent variable in this study is hybridity of competing logics. Following previous research [14], the dependent variable is measured in two phases. First, we operationalized a social welfare and commercial logic based on top management’s attention to social and commercial concerns since institutional logic guides critical strategic issues that decision makers in an organization should respond to [94]. Social issues represent social welfare logic, whereas commercial issues represent commercial logic [95]. Then, we captured the hybridity of competing logics as a dependent variable by calculating differential weights for top management’s attention to social and commercial issues [14].
Following previous research [96], we asked middle managers to assess how often top managers discussed four social issues and three financial issues (detailed topics are available in Appendix A). Responses were scored on a scale ranging from 1 for “never” to 7 for “very frequently”. Cronbach’s alpha = 0.90 for attention to social issues and 0.83 for commercial issues. We conducted CFA to assess validity with desirable results (χ2 (12) = 26.46 < 0.01; CFI = 0.982; TLI = 0.968; RMSEA = 0.078).
Second, we operationalized the hybridity of competing logics by using the average scores of attention to social and commercial issues. Adopting the Janis–Fadner (JF) coefficient of imbalance formula [97,98], we calculated relative weights of attention to social and commercial issues using the following formula:
C o e f f i c i e n t = S 2 − S C T 2   i f   S > C ; 0   i f   F = S ;   S C − C 2 T 2   i f   C > S
where S represents attention to social issues, C to commercial issues, and T to total attention. The range is −1 to 1, where 1 represents “only social issues attended” and −1 means “only commercial issues attended.” Zero indicates balanced attention to each issue, which in turn represents the maximum level of hybridity of competing institutional logics. To simplify the assessment, we used absolute values and reversed them by multiplying by −100 so that zero represents the highest level of hybridity of competing logics. On the other hand, negative scores indicate that social enterprises prioritize one logic over another.

3.3. Independent Variable

3.3.1. Proportion of Ethical Investors

Ethical investors are broadly understood as those who make investments without an expectation of financial return [99]. Adopting the categories developed by previous research [100], we identified six categories: (1) investment from inside investors without expectation of financial return, (2) investment from inside investors with reduced expectation of financial return, (3) investment from inside investors with expectation of market-rate financial return, (4) investment from outside investors without expectation of financial return, (5) investment from outside investors with reduced expectation of financial return, and (6) investment from outside investors with market-rate expectation of financial return. We asked respondents to estimate the proportion of investments falling into their investment classifications. In this study, the proportion of ethical investors was calculated as the ratio of internal and external investments without expectations of financial return to the total investment in social enterprises. Higher scores represent greater proportions of ethical investors.

3.3.2. Proportions of Impure Altruistic Customers

We captured four customer group categories based on initial motivations for buying the products of social enterprises [80]. We asked respondents to estimate the proportion of buyers falling into the following classifications: (1) beneficiaries, (2) economic customers, (3) impure altruistic customers, and (4) pure altruistic customers. The proportion of impure altruistic customers was measured by the ratio of impure altruistic customers to total customers in the social enterprises. Higher scores represent greater proportions of impure altruistic customers.

3.3.3. Proportion of Cross-Sector Workers

To construct the proportion of cross-sector workers, we classified employees into four different groups using their prior work experience: (1) social sector, (2) commercial sector, (3) both social and commercial sectors, and (4) non-experience. Then, we measured the proportion of cross-sector workers using the ratio of employees having experience in both social and commercial sectors to the total employees. The higher scores represent the greater proportion of cross-sector workers.

3.3.4. Control Variables

As other variables influence hybridity, we accounted for many control variables. Specifically, we controlled for total attention to issues, legal status, prior performance, attainment discrepancy, firm age, debt ratio, industry, type, diversity of the board of directors, and CEO duality. We calculated total attention to issues by the sum of attention to social and commercial issues. We controlled for legal status by using a dummy variable, coded “1” when a social enterprise has a for-profit legal status and “0” if it has a non-profit legal status. We controlled for prior performance using eight items on a 7-point Likert scale validated and used in previous research [101]. Attainment discrepancy, or a firm’s relative performance compared to past performance [102], was measured by a dummy variable coded “1” when the current performance was expected to be greater than past performance, and “0” if it was not. We controlled for the age of firms by subtracting the date of founding from 2014, which is consistent with prior research [103]. We log-transformed values for normality. We included the ratio of debt, measured as the ratio of long-term debt to total assets [104]. We used seven dummy variables to control for industry: (1) arts and culture, (2) civil and human rights, (3) economic development, (4) education, (5) environment, (6) health/healthcare, (7) public service, and (8) others. Dummy variables were used to control for types of social enterprises: (1) Social service, (2) work integration social enterprises (WISEs), (3) mixture of social service and WISEs, (4) community-based, and (5) others. We measured the diversity of the board of directors by experience: (1) social sector, (2) commercial sector, (3) both social and commercial sectors, and (4) non-experience. CEO duality was measured by the dichotomous variable of “1” if the CEO is the chairperson of the board and “0” otherwise.

3.4. Analysis

To assess the effects of the proposed variables on the hybridity of competing logics, we ran five regression models adopting a hierarchical regression approach in which we included independent variables in different stages. Before running the hierarchical regression models, assumptions were tested by examining normal probability plots of residual and scattered spots of residuals versus predicted residuals. It was found that the residuals did not violate any assumptions of normality, linearity, or homoscedasticity. In order to test for multicollinearity, we employed the variation inflation factor (VIF). Except for the highest VIF, which was 3.02, all of the VIFs were below 2.0. Because of this, there was no problem with multicollinearity because all of the VIFs were less than 10.

4. Results

Table 1 contains the means, standard deviations, and correlations for all variables. Table 2 reports the results of five regression models explaining the hybridity of competing logics. These models are as follows: F(19, 171) = 2.179, p = 0.005 < 0.01 in Model 1, F(20, 170) = 2.394, p = 0.001 < 0.01 in Model 2, F(20, 170) = 2.188, p = 0.004 < 0.01 in Model 3, F(20, 170) = 2.347, p = 0.002 < 0.01 in Model 4, F(22, 168) = 2.597, p = 0.000 < 0.001 in Model 5, respectively.
Model 1 in Table 2 contains only the control variables. R2 for Model 1 is 0.195, which means the control variables predict 19.5% of the variance in the hybridity of competing logics. None of the control variables were significant except for the total attention to issues (p < 0.001).
Model 2 in Table 2 includes the proportion of ethical investors. The R2 for Model 2 is 0.220, which means the ratios of ethical investors predicted 22.0% of the variance in the hybridity of competing logics to total investors.
When the percentage of ethical investors is added to the model, it explains a lot more of the difference from the previous stage (∆R2 = 0.025, p < 0.05). Model 2 shows that the coefficient of the proportion of ethical investors on the hybridity of competing logic is positive and statistically significant (β = 0.165, p = 0.021 < 0.05). This indicates strong support for Hypothesis 1, which proposed that a greater proportion of ethical investors would be positively associated with the hybridity of competing logics.
To test Hypothesis 2 concerning the effects of the internal heterogeneity of customers on the hybridity of competing logics, Model 3 included the proportion of impure altruistic customers. Although model fit is statistically significant (F(20, 170) = 2.188, p = 0.004 < 0.01), Model 3 does not represent a significant improvement over Model 1 (∆R2 = 0.010, p > 0.10). The proportion of impure altruistic customers was not a significant determinant of differential weights for commitments to competing issues (β = 0.190, p = 0.15). Therefore, Hypothesis 2 was not supported.
Hypothesis 3 predicted that an increase in cross-sector workers would enhance the hybridity of competing logics in social enterprises. Model 4 in Table 2 reports the effect of the proportion of cross-sector workers, which yields a significant model fit for the data at the 0.001 level and a statistically significant improvement from Model 1 (∆R2 = 0.021, p < 0.05). In Model 4, the main effect of the proportion of cross-sector workers is positive and significant (β = 0.157, p = 0.033 < 0.05). This provides strong support for Hypothesis 3.
In the final step, Model 5 included all variables in this study. We found consistent support for Hypothesis 1 (β = 0.178, p = 0.012 < 0.05) and Hypothesis 3 ((β = 0.163, p = 0.024 < 0.05). Model 5 still does not support Hypothesis 2 (β = 0.110, p = 0.138 > 0.1).

5. Robustness Check

We conducted several additional analyses as robustness checks. There are four main issues to be considered: (1) the prior level of hybridity of competing logics, (2) potential endogeneity bias, (3) possible non-normal distribution for the proportion variables, and (4) diversities within each stakeholder group. First, the previous level of hybridity of competing logics can determine the current hybridity of competing logics. Thus, we need to control for the prior hybridity of competing logics, which is measured by labeling claims and social enterprises’ self-categorization as social or commercial. Since social enterprises can categorize themselves as either a “social organization” or as a “commercial company,” we asked respondents to assess the extent to which their social enterprises used a social organization or commercial company label in the organization’s name.
We used self-reported measures of each item to assess each label (social organization or commercial company) on a scale ranging from 1 (strongly disagree) to 7 (strongly agree). Then, we also calculated the prior hybridity of competing logics using Janis–Fadner’s [98] coefficient of imbalance.
C o e f f i c i e n t = S 2 − S C L 2   i f   S > C ; 0   i f   C = S ;   S C − C 2 L 2   i f   C > S
where S is the “social” label, C is the “commercial” label, and L is the total label. The range is −1 to 1, where 1 represents “100% social organization” and −1 means “100% commercial company.” Zero represents the maximum hybridity of competing institutional logics. We also used absolute values and reversed them by multiplying by −100.
We added the coefficient and total labeling as additional control variables in Model 6 (Table 3). The significance levels of main effects remained the same.
To check for potential endogeneity, we used three instrumental variables: (1) the existence of a blockholder, (2) the proportion of the largest customer’s sales, and (3) the number of employees, all of which were regressed on differential weights for competing issues. Instrumental variables should be correlated with endogenous variables in the first stage but not related to dependent variables [105].
The existence of a blockholder may determine the proportion of ethical investors because the ownership structure may not be dispersed evenly and is often concentrated [106]. We controlled for blockholders as a dummy variable coded “1” if an investor owned or was expected to own 50% or more of the organization and “0” if there is no investor with more than 50% ownership.
Consumer power can derive from sales. The sales of the focal organization may be concentrated around a few large customers and directly associated with the proportion of certain customer segments. Hence, we asked respondents to gauge the relative proportions of their largest customers among total sales. The proportion of cross-sector workers can be correlated with the number of employees. We calculated the number of employees as the logarithm of the number of employees.
Then, we used two-stage least squares (2SLS) regressions with the ivregress command in STATA 13.0. In this additional analysis, the coefficients of the residual variable were not significant. We also conducted the Durbin–Wu–Hausman test, which suggested that endogeneity is not a concern in this study (F(3163) = 0.147571; p = 0.9311). In addition to the test for endogeneity bias, we treated these three instrumental variables as control variables. Then, we regressed these variables on the main equation. The results of the new regression are shown in Model 7 in Table 3. All significance levels of independent variables remained the same.
Third, the proportion variables may violate the normality assumption of OLS regression. Following previous research [107], we added a constant of one to all three independent variables. Then, we transformed them into natural logs. We replaced all previous independent variables with the new log proportions of ethical investors, impure altruistic customers, and cross-sector workers in Model 9 in Table 4. However, Model 9 did not differ from previous results.
Lastly, it is possible that the hybridity of competing logics was influenced by diversity within each stakeholder group, rather than by the proportion of certain characteristics of the intra-stakeholder groups. To control for this alternative explanation, we calculated different independent variables. To capture the degree to which investments are dispersed in a social enterprise, we used the Hirschman–Herfindahl index (HHI). Following the Herfindahl Index approach [108] and adopting the categories developed in previous research [100], we created a measure of investor diversity. The formula is 1 − ∑ i = 1 n P i 2 , where P is the share of investment category i of a firm and N is the total number of investment categories. To assess the diversity of customers, we identified five categories using objective sales sources: (1) government, (2) for-profit organizations, (3) non-profit organizations, (4) other social enterprises, and (5) individual customers. Next, we calculated the diversity of customer groups using the Herfindahl Index.
To capture the diversity of employees, we used the same categories as employee backgrounds. The formula of 1 − ∑ i = 1 n P i 2 , where P is the proportion of employees with an experience category i and N is the total number of experience categories, was used. The new diversities of investors, customers, and employees replaced all previous independent variables in Model 10 in Table 4. None of the variables were significant.

6. Discussion

Integrating institutional logic with intra-stakeholder heterogeneity, we hypothesized that intra-group stakeholders’ diverse values and interests contribute to the hybridization of social enterprises. We offered a novel perspective on the relationship between particular subcategories within-group stakeholder and hybridity in social enterprises. The results of this study show that subgroups within each market stakeholder that bring together social and commercial logics play a role in making social enterprises more hybrid. Consistent with intra-stakeholder perspective, support was obtained for Hypothesis 1 that higher proportions of ethical investors were positively associated with hybridity of competing logics. We also found similar evidence for Hypothesis 3 that social enterprises with a higher proportion of cross-sector workers were more likely to have greater hybridity of competing logics. However, the results do not support the effects of altruistic buyers among consumer stakeholders. Overall, the study’s findings make a number of theoretical contributions to the literature on social entrepreneurship.

6.1. Theoretical Implications

Our study contributes to the literature on the institutional logic perspective in social entrepreneurship. From the standpoint of institutional logics, social enterprises have recently been seen as “born hybrids” because they have to combine social and commercial logics, which conflict with each other [95,109]. Furthermore, prior research has figured out why some social enterprises are better at achieving hybridity than others [81,110]. Our findings contribute to this line of study by indicating that stakeholders’ attachments to institutional logics are significant drivers of social businesses’ hybridity of competing institutional logics.
Second, our theoretical framework broadens the stakeholder viewpoint in social entrepreneurship by emphasizing intra-stakeholder heterogeneity associated with various values and interests. Our study reveals that the existence of ethical investors among investor stakeholders and cross-workers among employee stakeholders promotes the hybridity of competing logics. We believe this is an innovative approach to the study of stakeholder management in social entrepreneurship [8,9].
Third, we contribute to the literature on sustainability by demonstrating how various institutional logics within-group stakeholders affect sustainable social enterprises. Since sustainability and social entrepreneurship are closely related, social entrepreneurial practices and processes may help other organizations’ long-term growth, and vice versa [111]. For this reason, new calls for further research into the long-term viability of social organizations have emerged from academics in social entrepreneurship [31,32]. To the best of our knowledge, this is one of the first studies to look at the dynamics of within-group stakeholders and their influence on how well social enterprises sustain financial viability and social value creation simultaneously.
Lastly, we provide empirical evidence by collecting and quantitatively analyzing large-scale data. Using the STrengthening the Reporting of OBservational research in Epidemiology tool (STROBE) [112], we could determine whether or not our empirical study was validated. Although social entrepreneurship has received much scholarly attention, relatively few studies have been devoted to the analytic, detailed examination of social entrepreneurship hypotheses using quantitative data. Thus, more empirical studies have been called for [113,114]. Additionally, using hand-collected data from the survey of both non-for-profit and for-profit social enterprises, we developed and tested a number of hypotheses regarding the direct effects of characteristics of intra-stakeholders on the hybridity of competing logics.

6.2. Practical Implications

We offer several implications for practitioners. First, the results of this study suggest that social entrepreneurs can learn to maintain institutional logics by understanding and keeping an eye on the characteristics of each stakeholder group. This makes their social enterprises sustainable. In social entrepreneurship, one institution’s logic usually precedes another’s at the core of the organization’s work [85]. This can result in “mission drift,” which is the deviation from a social goal [3], or “philanthropic amateurism” as a result of excessive reliance on volunteers or the provision of mediocre goods/services [115]. These problems can be avoided by stakeholder comprehension.
Second, our empirical findings demonstrate that investors’ financial return expectations and employees’ historical experiences are important factors. When investors have low financial return expectations and workers have work experience from both the social and commercial sides, social companies may better accept competing logics. This conclusion can help social entrepreneurs recognize that attracting such investors and employees to organizations may benefit from incorporating contradictory logics.
Third, policymakers and programmers who collaborate with social entrepreneurs may find our results helpful and put them to use. When evaluating the efficiency of social companies, one may make predictions about their long-term viability based on our empirical research findings. For instance, if social company A has more ethical investors or cross-sector workers than social enterprise B, we may anticipate that social company A will be more successful in managing the complexity that is anticipated to increase.

6.3. Limitations and Future Research

It is important to point out a few limitations in order to pave the way for more study in the future. In this study, we primarily used the cross-sectional survey design. Therefore, any conclusions concerning causality are limited. For example, there is a possibility that the high degree of hybridity of competing logics at time 1 might attract additional investors whose financial expectations are low at time 2, which results in concerns about reverse causality. Although we used one-year lagged variables by asking respondents to provide both current and one-year lagged information, future research should include longitudinal designs to explore potential causal relationships.
In addition to investors, customers, and staff, social entrepreneurship has many more stakeholders. Community [114], government [116], and fellowship organizations such as the Ashoka Foundation are examples of influential stakeholders who might influence the hybridity of conflicting logics. We suggest that future research can explore the specific sub-categories within non-market stakeholder groups.
Third, this study was conducted in a single country. Thus, it is possible that the results of this research are only valid in the Korean context of government-driven policy. Future research could establish the generalizability of our findings by replicating the proposed models in other national contexts. In addition, future research should compare and contrast results from other countries to identify more attributes related to sub-group heterogeneity.

Author Contributions

Conceptualization, T.J.B.; Methodology, T.J.B.; Investigation, J.O.F.; Data curation, T.J.B.; Writing—original draft, T.J.B.; Writing—review & editing, J.O.F.; Supervision, J.O.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of University of Louisville.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data are not publicly available due to privacy issues.

Acknowledgments

We would like to thank the guest editors and anonymous reviewers of this journal.

Conflicts of Interest

The authors have no conflict of interest to declare.

Appendix A. Top Management’s Discussion Topics (for DV)

Seeking the good of society (1 = never, 7 = very frequently), the company’s role in society (1 = never, 7 = very frequently), improving social conditions (1 = never, 7 = very frequently), efforts for beneficiaries (1 = never, 7 = very frequently), financial performance (1 = never, 7 = very frequently), strategy and planning (1 = never, 7 = very frequently), productivity and efficiency (1 = never, 7 = very frequently).

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Table
Table 1. Descriptive statistics and correlations.
Table 1. Descriptive statistics and correlations.
−VariablesMeanSD12345678910
1Hybridity of logics−4.615.41
2Total attention10.721.860.35 **
3Legality a0.650.480.030.03
4Firm age (ln)1.610.62−0.18 *−0.13−0.27 **
5Prior performance4.311.120.20 **0.32 **−0.13−0.05
6Attainment Discrepancy a0.140.35−0.12−0.10−0.040.14 *−0.01
7Debt ratio0.001.00−0.07−0.020.15 *−0.01−0.07−0.05
8Diversity of BOD 0.310.28−0.06−0.04−0.01−0.01−0.070.17 *−0.08
9Duality a0.770.42−0.02−0.080.24 **−0.15 *−0.040.010.060.03
10P. of ethical investors b0.520.430.120.01−0.110.100.010.09−0.030.03−0.12
11P. of p.a. customers b0.110.220.01−0.120.020.13−0.140.020.080.040.080.01
12P. of cross-sector workers b0.230.380.14 *−0.04−0.02−0.04−0.12−0.080.02−0.25 **−0.03−0.07
13Arts and culture a0.160.37−0.07−0.02−0.28 **0.16 *0.00−0.09−0.040.060.040.03
14Civil and human rights a0.000.07−0.040.03−0.100.080.00−0.03−0.050.11−0.030.08
15Development a0.010.12−0.00−0.010.000.070.020.07−0.030.09−0.030.04
16Education a0.080.270.130.050.06−0.13−0.06−0.06−0.07−0.04−0.110.08
17Environment a0.180.38−0.01−0.040.26 **0.080.020.000.11−0.020.03−0.03
18Health/healthcare a0.050.230.100.040.04−0.06−0.020.030.030.040.08−0.09
19Public service a0.080.270.030.000.060.050.020.040.10−0.070.020.11
20Social service type a0.050.22−0.09−0.03−0.17 *0.09−0.15 *0.110.050.09−0.10−0.02
21Work integration type a0.680.460.090.030.21 **−0.120.080.020.04−0.010.05−0.12
22Combination type a0.100.31−0.05−0.010.010.07−0.050.050.02−0.080.03−0.04
23Community-based type a0.030.17−0.030.03−0.060.010.00−0.07−0.030.02−0.050.03
Variables111213141516171819202122
12P. of cross-sector workers0.00
13Arts and culture a˗0.030.03
14Civil and human rights a−0.04−0.04−0.04
15Development a0.14 *−0.05−0.05−0.01
16Education a−0.020.01−0.05−0.02−0.04
17Environment a0.060.05−0.19 **−0.03−0.06−0.14
18Health/healthcare a0.01−0.02−0.05−0.02−0.03−0.07−0.11
19Public service a−0.19 **−0.01−0.05−0.02−0.04−0.09−0.14−0.07
20Social service type a−0.020.080.05−0.02−0.030.10−0.050.05−0.07
21Work integration type a−0.090.07−0.19 **−0.010.08−0.20 **0.17 *−0.070.12−0.34 **
22Combination type a0.12−0.11−0.050.21 **−0.14−0.14−0.030.06−0.04−0.08−0.51 **
23Community-based type a−0.08−0.010.01−0.01−0.02−0.02−0.08−0.040.060.04−0.26 **−0.06
* p < 0.05; ** p < 0.01; Note: a dummy variable; b proportion.
Table
Table 2. Results of hierarchical regression models.
Table 2. Results of hierarchical regression models.
VariablesDV: Hybridity of Competing Institutional Logics
Model 1Model 2Model 3Model 4Model 5
Intercept−14.08(3.25) ***−15.22(3.25) ***−14.85(3.28) ***−15.36(3.27) ***−17.42(3.29) ***
Control variables
Total attention0.80(0.21) ***0.78(0.21) ***0.82(0.21) ***0.81(0.21) ***0.80(0.21) ***
Legality a−0.41(0.91)−0.28(0.90)−0.36(0.91)−0.34(0.90)−0.15(0.89)
Firm age (ln)−0.66(0.66)−0.70(0.65)−0.74(0.66)−0.59(0.66)−0.70(0.65)
Prior performance0.44(0.36)0.43(0.36)0.52(0.37)0.54(0.36)0.61(0.36) †
Attainment discrepancy a−0.90(1.11)−1.19(1.11)−0.80(1.11)−0.77(1.10)−0.97(1.09)
Debt ratio−0.51(0.42)−0.53(0.42)−0.56(0.42)−0.51(0.42)−0.57(0.41)
Diversity of BOD−1.27(1.42)−1.37(1.40)−1.49(1.42)−0.42(1.46)−0.72(1.44)
Duality a0.19(0.95)0.31(0.94)0.13(0.95)0.22(0.94)0.30(0.93)
Arts and culture a−0.14(1.30)−0.08(1.29)−0.02(1.30)−0.33(1.29)−0.15(1.27)
Civil and human rights a−1.38(5.43)−2.38(5.38)−0.77(5.43)−1.59(5.37)−2.08(5.30)
Economic development a−1.74(3.70)−1.66(3.65)−2.72(3.75)−1.48(3.66)−2.36(3.66)
Education a2.73(1.50)2.42(1.49)2.77(1.50) †2.68(1.49) †2.39(1.47)
Environment a0.62(1.09)0.49(1.08)0.47(1.10)0.46(1.09)0.16(1.07)
Health/healthcare a2.86(1.75)3.08(1.74)2.81(1.75)2.77(1.74)2.95(1.71)
Public service a0.78(1.47)0.32(1.46)0.22(1.51)0.79(1.45)−0.27(1.49)
Social service type a−1.24(2.02)−1.15(2.00)−0.96(2.03)−1.68(2.01)−1.30(1.99)
Work integration type a0.48(1.34)0.93(1.34)0.76(1.35)0.25(1.33)1.01(1.34)
Combination type a−0.93(1.66)−0.39(1.66)−0.97(1.66)−0.72(1.65)−0.18(1.63)
Community-based type a2.16(2.62)2.53(0.88)2.70(2.64)1.93(2.60)2.87(2.58)
Main Effects
P. of ethical investors b 1.81(0.89) * 1.21(0.89) *
P. of pure altruistic customers b 2.48(1.71) 2.49(1.67)
P. of cross-sector workers b 2.27(1.05) *2.36(1.04) *
F value2.19 **2.39 **2.18 **2.35 **2.60 **
R20.1950.2200.2050.2160.254
∆R2 0.025 **0.0100.021*0.059
Mean VIF1.351.351.351.351.35
Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001; unstandardized coefficient and standard error; a dummy variable; b proportion.
Table
Table 3. Supplementary analysis: adding prior hybridity of competing logics and other controls.
Table 3. Supplementary analysis: adding prior hybridity of competing logics and other controls.
VariablesDV: Hybridity of Competing Institutional Logics
Model 6Model 7Model 8
Intercept−14.39(3.47) ***−17.83(3.87) ***−15.10(3.99) ***
Control Variables---
Additional Controls
Prior hybridity0.06(0.03) † 0.06(0.03) †
Total name claims−0.36(0.17) * −0.37(0.17) *
Block holder a −0.08(0.91)0.16(0.91)
Major customer share −0.01(0.01)0.01(0.01)
Number of employees (ln) −0.20(0.46)0.11(0.46)
Main Effects
P. of ethical investors b2.11(0.86) *2.16(0.86) *2.04(0.88) *
P. of pure altruistic customers b2.24(1.65)2.55(1.71)2.36(1.70)
P. of cross-sector workers b2.48(1.07) *2.37(1.06) *2.52(1.10) *
F value2.71 **2.27 **2.27 **
R20.2800.2600.280
Mean VIF1.3801.3601.390
Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001; unstandardized coefficient and standard error; a dummy variable; b proportion.
Table
Table 4. Supplementary analysis: change in IVs.
Table 4. Supplementary analysis: change in IVs.
VariablesDV: Hybridity of Competing Institutional Logics
Model 9Model 10
Intercept−15.02(4.00) ***−10.98(3.90) **
Control Variables
Additional Controls
Prior hybridity0.06(0.04) †0.07(0.04) †
Total name claims−0.37(0.17) *−0.32(0.17) †
Block holder a0.15(0.92) −0.23(0.90)
Major customer share0.01(0.01)0.00(0.01)
Number of employees (ln)0.10(0.46) −0.02(0.47)
Alternative Independent Variables
Log percentage of ethical investors3.05(1.27) *
Log percentage of reciprocal customers2.82(2.30)
Log percentage of cross-sector workers3.57(1.57) *
Alternative Independent Variables
Diversity of investors 1.30(1.38)
Diversity of customers −2.86(1.75)
Diversity of workers −1.47(1.71)
F value2.37 **1.93 **
R20.2800.240
Mean VIF1.3801.400
Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001; unstandardized coefficient and standard error; a dummy variable.
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Bae, T.J.; Fiet, J.O. Intra-Stakeholder Heterogeneity Perspective on the Hybridity of Competing Institutional Logics for Social Enterprises. Sustainability 2023, 15, 3215. https://doi.org/10.3390/su15043215

AMA Style

Bae TJ, Fiet JO. Intra-Stakeholder Heterogeneity Perspective on the Hybridity of Competing Institutional Logics for Social Enterprises. Sustainability. 2023; 15(4):3215. https://doi.org/10.3390/su15043215

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Bae, Tae Jun, and James O. Fiet. 2023. "Intra-Stakeholder Heterogeneity Perspective on the Hybridity of Competing Institutional Logics for Social Enterprises" Sustainability 15, no. 4: 3215. https://doi.org/10.3390/su15043215

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