To perform or not to perform? How strategic orientations influence the performance of Social Entrepreneurship Organizations

Abstract

Social Entrepreneurship Organizations (SEOs) aim to solve social, environmental or societal problems even as they strive to work profitably. The achievement of the social mission also requires economic viability and differentiation from the competition. Acting in contested markets, SEOs must, therefore, adopt a competitive strategy to deliver services and products. While literature concerning achieving competitive advantages through a strategic concept has a long tradition in business management, little is known about how SEOs can use different strategic orientations (SO) to achieve superior performance. Based on a global sample of social entrepreneurs (n = 130), this study assessed the impact of market orientation (MO), entrepreneurial orientation (EO), and brand orientation (BO) on SEO performance. The findings indicate that MO and EO help SEOs to foster social and market performance. Using partial least squares (PLS) path modelling, in conjunction with fuzzy set Qualitative Comparative Analysis (fsQCA), this study also provides new insights into the interplay of SO, illustrating that MO and BO are complementary approaches that contribute to economic performance.

Keywords:

• Social Entrepreneurship Organization
• brand orientation
• entrepreneurial orientation
• market orientation
• performance measurement
• partial least squares modelling
• fuzzy set qualitative comparative analysis

PUBLIC INTEREST STATEMENT

The right choice of strategic direction is one of the key success factors for businesses to achieve competitive advantage. While literature, which deals with achieving competitive advantages through a strategic concept, has a long tradition in business management and, therefore, provides managers with valuable information on the explanation of success, there remains a great need for research in the field of SEOs. Within this study, we have deepened and expanded our understanding of how the concept of strategic orientation can be applied to SEOs. We analyzed the effect of three SO on different performance indicators, namely; MO, EO, and BO. To sum up, this study provides valuable information for social entrepreneurs who are involved in strategic planning. By revealing the effect sizes of SO that can enhance performance, the findings equip social entrepreneurs with relevant knowledge to use resources more effectively and, not least, to contribute to a higher level of social well-being.

1. Introduction

Social entrepreneurship has attracted increasing attention in the last few decades (Austin, Stevenson, & Wei-Skillern, 2006; Mair & Martí, 2006; Nicholls, 2006). Social Entrepreneurship Organizations (SEOs) set social priorities without excluding well-known business principles. In doing so, they generate innovative ideas and solutions to resolve well-known social problems (e.g., Dacin, Dacin, & Matear, 2010). Globally, a new business model has emerged, led by a new generation of social entrepreneurs.

An example of these visionaries is Kelly Peeler, listed in the 2018 edition of the Forbes “30 under 30 social entrepreneurs” and founder of NextGenVest (www.nextgenvest.com), a mission-centered start-up providing Generation Z to better navigate the financial aid and student loan process via text message. Customers have saved more than $39 million through increasing access to the more than $2.7 billion in financial aid that goes unclaimed each year. Besides NextGenVest, there are other examples of successful and well-known SEOs, such as DripTech (www.driptech.com), Dialogue in the Dark (www.dialogue-in-the-dark.com) or This Bar Saves Lives (www.thisbarsaveslives.com).

Given their hybrid concept (Billis, 2010), SEOs are challenged by issues of social mission and economic rationale (Jäger, 2010). According to Santos (2012), a central difference between commercial and social entrepreneurship is that social entrepreneurs are driven primarily by a motivation to create value for society, not for themselves. In doing so, SEOs rely on legitimacy and resources from stakeholders, but they are also challenged by marketing issues, such as “Reason to Buy”. Since market-based actions are elementary corner pillars of their organizational model (Huybrechts & Nicholls, 2012), there is a common interface with their for-profit peers. Both forms of organizations pursue revenue-generating strategies to maximize the creation of social or economic value. It follows that SEOs react in a similar way to for-profits in competitive environments (e.g., Davis, Morris, & Allen, 1991; Weerawardena & Mort, 2006), in which the achievement of the social mission also requires economic viability and differentiation from the competition.

To achieve a competitive advantage, organizations must meet future challenges and adopt aspects of their environment for a more favorable alignment (Kickul & Walters, 2002). Thus, organizations must pay attention to strategic thinking for effective strategic management (Al-zu’bi, 2014). Strategic orientations (SO) are the guiding principles that influence the strategy-making and concrete behavior of an organization (Noble, Sinha, & Kumar, 2002). According to Hakala (2011, p. 199) SO are “principles that direct and influence the activities of a firm and generate the behaviors intended to ensure its viability and performance”, which, as described above, is crucial for SEOs.

While literature, which deals with achieving competitive advantages through a strategic concept (e.g., market orientation (MO) or entrepreneurial orientation (EO) or production orientation), has a long tradition in business management and, therefore, provides managers with valuable information on the explanation of success, there remains a great need for research in the field of SEOs. Only a few studies exist in SEO literature that illustrates the effect of SO on performance (Glaveli & Geormas, 2018; Liu, Takeda, & Ko, 2012; Ma, Kim, Heo, & Jang, 2012). In this context, there are tentative indications that MO and EO assist SEOs to foster superior performance (e.g., customer satisfaction, profitability). MO of an SEO refers to the mindset of the organization and to concrete behaviors that pertain to the actual and latent needs and wants of individual customers (Schmidt, Baumgarth, Wiedmann, & Lückenbach, 2015). It provides SEOs with a better understanding of their environment and customers’ needs (Glaveli & Geormas, 2018). EO of an SEO refers to the organization’s mindset that becomes manifest in the concrete behavior of its members (Schmidt et al., 2015) and it enables SEOs to find new market opportunities (Voss, Voss, & Moorman, 2005), offer innovative solutions (Chell, Nicolopoulou, & Karataş-Özkan, 2010), and gain access to resources from contributers (Liu et al., 2012).

Besides MO and EO, two of the most fundamental and widely discussed SO in the marketing literature, this article investigates a further SO, which can be useful in the context of SEOs, namely: BO. In contrast to MO, an “outside-in approach”, that considers brand image as a fundamental concept, BO takes a primarily “inside-out approach”, with brand identity as a key concept (Urde, Baumgarth, & Merrilees, 2013). Researchers describe the concept of BO as an approach that focuses on brands as resources and strategic hubs (Melin, 1997; Urde, 1994, 1999). In the social sector, a strong brand functions as a source of efficiency that signals the organizational core values and principles to external stakeholders (Boenigk & Becker, 2016). It further indicates that an SEO is well known by a multitude of stakeholders and that these different stakeholder groups believe in the organization’s trustworthiness and credibility, which in turn attracts more customers (Liston–Heyes & Liu, 2010; Napoli, 2006). This raises the question of whether BO could help SEOs to reach their goals more effectively and/or more efficiently.

In addition to the lack of knowledge of individual SO and their contribution to success in the SEO sector, there is little understanding of how those SO interrelate. In line with Barney’s (2014) arguments, that firm’s specific arrangement of resources is crucial for success, the benefit of SO is how these strategic assets are calibrated to achieve performance and competitive advantage (Ziggers & Henseler, 2016). According to Noble et al.’s (2002) suggestion pursuing a configurational approach to determine the relative combinations of various SO that lead to performance, there is a growing body of literature that uses fuzzy set Qualitative Comparative Analysis (fsQCA), which is a configurational, so-called causes-to-effects approach (Mahoney & Goertz, 2006) when assessing interaction effects of SO (e.g., Ho, Plewa, & Lu, 2016; Leischnig, Geigenmueller, & Lohmann, 2014; Ziggers & Henseler, 2016).

Given these research gaps, this study makes three contributions. First, the findings identify SO that enable SEOs to achieve superior performance. In doing so, the study integrates MO, BO, and EO in a single framework and develops the foundation for an empirical test based on a common and comparable conceptualization. Thereby, it answers the call from scholars regarding the need to include both cultural and behavioral dimensions of SO when assessing the impact on SEO performance (Liu et al., 2012). Second, this study investigates, for the first time, the impact of BO on SEO performance illustrating that MO and BO are complementary approaches that contribute to economic performance. Using fsQCA, it, thirdly, provides new insights into the interplay of SO and provides conceptual and empirical evidence for previously understudied combinations in SEO research, as well as overall research that cover SO.

2. Literature review

2.1. Social Entrepreneurship Organizations

The phenomenon of social entrepreneurship is an innovative field of scientific research, which is becoming recognized as a dominant discourse within entrepreneurship research (Kraus, Filser, O’Dwyer, & Shaw, 2014). However, there seems to be some confusion about what exactly a social entrepreneur is, and does. Dacin et al. (2010) cite 37 definitions of social entrepreneurship and social entrepreneur. This lack of a common concept raises questions regarding, which social or profit-making activities fall within the spectrum of social entrepreneurship (Abu-Saifan, 2012). The literature provides three perspectives that seem to dominate social entrepreneurship discussions, namely: the striving for both social and financial outcomes; the obligation of an innovative spirit; and the adoption of commercial activity to generate revenue.

The first perspective refers to the primary purpose and aims of SEOs. Martin and Osberg (2007, p. 34) state that “the social entrepreneur aims for value in the form of large-scale, transformational benefit that accrues either to a significant segment of society or to society at large”. Cho (2006, p. 36) states that social entrepreneurship is “a set of institutional practices combining the pursuit of financial objectives with the pursuit and promotion of substantive and terminal values.” Abu-Saifan (2012, p. 25) views the social entrepreneur as a “mission-driven individual who uses a set of entrepreneurial behaviors to deliver a social value to the less privileged, all through an entrepreneurially oriented entity that is financially independent, self-sufficient, or sustainable.”

The second perspective focusses on the innovative manner in which most SEOs approach their goals. According to Yunus (2008, p. 32), “any innovative initiative to help people may be described as social entrepreneurship. The initiative may be economic or non-economic, for-profit or not-for-profit.” Zahra, Gedajlovic, Neubaum, and Shulman (2009, p. 5) assert that social entrepreneurship “encompasses the activities and processes undertaken to discover, define and exploit opportunities to enhance social wealth by creating new ventures or managing existing organizations in an innovative manner.”

In line with the third perspective, several authors also emphasize that social entrepreneurs distribute their socially innovative models via market-oriented action (e.g., scaling up their initiatives in other contexts by forming alliances and partnerships) to reach broader and more sustainable outcomes (Huybrechts & Nicholls, 2012). SEOs’ strategies to generate revenue from commercial activity share some overlap with organizations in the private and public sectors (Wallace, 1999), but should be conceptually distinguished from traditional non-profit organizations that rely on grants and donations (Doherty, Haugh, & Lyon, 2014).

Referring to this broad-spectrum about the definition of social entrepreneurship, Santos (2012) developed an analytical framework that sheds new light on the phenomenon of social entrepreneurship. In his article “A Positive Theory of Social Entrepreneurship” he pleads for the elaboration of sharper theories of social entrepreneurship that can then compete for attention and validation. Highlighting the trade-off between value creation and value capture, Santos defines social entrepreneurs as “‘economic agents who, due to their motivation to create value without concern for the amount they capture, will enter areas of activity where the more severe market and government failures occur […] these are usually areas with neglected positive externalities affecting disadvantaged populations’” (Santos, 2012, p. 344). This perspective, however, requires a move to the level of the system, away from the organization as a unit of analysis (Agafonow, 2014).

2.2. Concepts of strategic orientations

SO are “principles that direct and influence the activities of a firm and generate the behaviors intended to ensure its viability and performance” (Hakala, 2011, p. 199). The concept of SO integrates the idea that a strategy is not always the explicit choice of management, but can also include the pattern of decisions, or the results of organizational learning (Mintzberg, 1989). The literature offers a wide variety of different SO, for example, market or customer orientation (e.g., Homburg & Pflesser, 2000; Jaworski & Kohli, 1993; Narver & Slater, 1990), BO (e.g., Baumgarth, 2010; Urde, 1994, 1999; Wong & Merrilees, 2008), innovation or technology orientation (e.g., Gatignon & Xuereb, 1997), EO (e.g., Zhou, Yim, & Tse, 2005), and learning orientation (e.g., Baker & Sinkula, 1999), to mention a few. Although some studies include the effect of MO and other SO on performance (Baker & Sinkula, 1999; Gatignon & Xuereb, 1997; Urde et al., 2013; Zhou et al., 2005), fewer consider the interplay of different SO (e.g., Ho et al., 2016; Merrilees & Baumgarth, 2016; Ziggers & Henseler, 2016).

Previous research has extended the focus on classical companies to include non-profit organizations (e.g., Hankinson, 2002; Napoli, 2006). However, there remains a dearth of research in the field of SEOs. Ma et al. (2012) and Liu et al. (2012) have analyzed the effect of MO and EO, using the SEO context. Their empirical studies confirm that MO and EO have a positive effect on different facets of social and commercial performance (e.g., customer satisfaction, job creation). A current study by Glaveli and Geormas (2018) investigates customer, competitor, and technology/product orientations of social enterprises in the Greek context. Their findings demonstrate that pursuing a clear and shared vision and customer orientation play the most vital role in enhancing the effectiveness and profitability of a social enterprise.

The SO literature, especially in the non-profit and social business sector, identifies MO and EO as relevant SO to achieve performance. Drawing on the argument by Napoli (2006) that branding is equally relevant to any type of organizations and can lead to notable improvements in terms of performance, this study examines BO, as a further relevant SO for SEOs. In the social sector, a strong brand functions as a source of efficiency that signals the organizational core values and principles to external stakeholders (Boenigk & Becker, 2016), which generate trustworthiness and credibility for the organization. This is important to ensure economic viability and to fulfill the social mission. Table 1 provides a brief overview of each SO, including a definition, cultural and behavioral dimensions, and previous findings on the impact on performance.

Table 1. Strategic orientations definitions and previous findings

	Market orientation (MO)	Entrepreneurial oriantation (EO)	Brand orientation (BO)
Definition	The extent to which firms focus on satisfying customer needs and creating superior value for them (Narver & Slater, 1990).	The extent to which firms emphasize identifying and seizing new market opportunities, being proactive in scanning the environment, and taking risks (Covin & Slevin, 1989).	The extent to which organizations focus on brands as resources and strategic hubs (Urde, 1994)
Cultural perspective	A market oriented culture (Homburg & Pflesser, 2000; Narver & Slater, 1990) fosters certain cultural norms such as a quick response to negative customer satisfaction information (Pelham & Wilson, 1996).	An entreprreneurial culture is described as one, which is willing to take risks (Stewart & Roth, 2001), is proactive (Lumpkin & Dess, 2001), alerts to opportunities (Baron, 2007), and fosters creativity (Poon et al., 2006).	A brand oriented culture can be conceptualized as brand-building attitude, brand core values, brand norms, and brand (symbolic) artefacts (e.g., Baumgarth, 2010; Wong & Merrilees, 2008)
Behavioral perspective	A market oriented behavior focuses on the satisfaction of individual and changing customer needs and wants such as conducting customer satisfaction surveys and calculating customer lifetime value or customer equity (Kohli & Jaworski, 1990; Shapiro, 1988)	Behaviors that are strongly linked to an EO are, making fast decisions, driving innovation, seeking new markets and entering them (Lumpkin & Dees, 1996), discovering and exploiting new opportunities (Wiklund & Shepherd, 2003), and experimenting with promising new technologies (Lumpkin & Dees, 1996).	A brand oriented bahavior ensures the internal anchorage of the brand identity (mission, vision, and values) such as integrated marketing communication (e.g., Ewing & Napoli, 2005), measurement of brand equity (e.g., Keller, 1993, Keller, 2013), and employer branding (Barrow & Mosley, 2005).
Impact on performance	Studies examine the impact of MO on corporate performance (e.g., Homburg & Pflesser, 2000; Jaworski & Kohli, 1993; Narver & Slater, 1990) and social performance (e.g, Liu et al., 2012).	Studies reveal a positive effect of market orientation on business performance (e.g., Covin & Slevin, 1991; Lumpkin & Dees, 1996; McGrath et al., 1996) and social performance (e.g., Baron, 2007; Morris et al., 2011; Liu et al., 2012).	Studies demonstrate a positive relationship between BO and corporate performance (e.g., Baumgarth, 2009, 2010; Napoli, 2006; Wong & Merrilees, 2008).

2.3. Hypotheses development

As shown in Table 1, it is possible to distinguish cultural and behavioral layers for MO, EO, and BO. According to Schmidt et al.’s (2015) conceptual model, which builds on Baumgarth’s (2010) approach, the cultural perspective is the “background variable” and it takes a more organizational view of the process. According to Schein’s (2004) corporate culture model, it covers values, norms, and symbols. Values are defined as deeply embedded, taken-for-granted, largely unconscious behaviors. They form the core of culture and determine what people think ought to be done. Norms (e.g., conscious strategies, goals, philosophies) represent the explicit and implicit rules of behavior. In an organization, they determine how the members represent the organization both to themselves and to others. Symbols or artifacts are the most apparent element of culture. They include any tangible, overt or verbally identifiable element in an organization (e.g., furniture, dress code, stories, jokes). The behavioral perspective measures the manifestation of the respective orientation. The classical management and marketing concept distinguishes between behaviors involving analysis and activity. Analysis comprises approaches like market research and controlling including key performance indicators, while activity includes strategic decisions and the marketing mix.

This general logic, which describes a causal chain from the abstract cultural layer to the concrete behavior layer, is consistent with frameworks found in the literature of organizational behavior (Katz & Kahn, 1978), attitude theory (Ajzen & Fishbein, 1980), and MO (Homburg & Pflesser, 2000). Empirically, Homburg and Pflesser (2000) show that a market-oriented culture has a positive impact on behavior. In Baumgarth (2009, 2010) proves the positive influence of corporate culture on corporate behavior for the BO construct. This led to the following hypotheses:

H_1a: In an SEO, a brand-oriented culture has a positive influence on brand-oriented behavior.

H_1b: In an SEO, a market-oriented culture has a positive influence on market-oriented behavior.

H_1c: In an SEO, an entrepreneurial culture has a positive influence on entrepreneurial behavior.

Generally, SO is a well-regarded and much-used concept in business literature, which is concerned with firm performance (Kumar, Boesso, Favotto, & Menini, 2012). In the case of MO, several studies have found a consistent positive link with economic performance (e.g., Kohli, Jaworski & Kumar 1993; Narver & Slater, 1990) and market performance (e.g., Houston, 1986; Pelham & Wilson, 1996). Researchers in the non-profit sector additionally highlight the positive link of MO on fundraising success (Kara, Spillan, & DeShields, 2004), members’ satisfaction (Chan & Chau, 1998), and growth in resources and reputation (Padanyi & Gainer, 2004). MO also plays is an important role in social entrepreneurship (Huybrechts & Nicholls, 2012), particularly in the for-profit social enterprise form, which generates profits to reinvest in their social mission (Alter, 2006). In the SEO literature (Glaveli & Geormas, 2018; Liu et al., 2012; Ma et al., 2012), there are first indications that market or customer orientation promotes the achievement of socially oriented goals such as job creation and distributing welfare, but also commercial objectives. It can be argued that market or customer orientation provide SEOs with a better understanding of their environment and customers’ needs (Glaveli & Geormas, 2018), which ultimately results in greater customer satisfaction and higher profits.

Similar to the concept of MO, EO is an essential feature of high performing companies (Covin & Slevin, 1991; Lumpkin & Dees, 1996). The conceptual arguments of previous research have added to the idea that firms benefit from highlighting newness, responsiveness, and a degree of boldness (Rauch, Wiklund, Lumpkin, & Frese, 2009). Besides this empirical evidence on the impact of EO on the economic and market performance, several studies in the non-profit sector (e.g., Davis, Marino, Aaron, & Tolbert, 2011; Morris, Webb, & Franklin, 2011) support the positive link between EO and the achievement of social and financial performance. Building on a strong culture of innovation and openness (Abu-Saifan, 2012), entrepreneurial behaviors enable SEOs to find new market opportunities (Voss et al., 2005), offer innovative solutions (Chell et al., 2010), and gain access to resources from contributers (Liu et al., 2012), leading thus to enhanced attainment of social and financial value.

The construct of BO is a relatively new concept. Therefore, its impact on different performance levels has only recently been more widely discussed. For example, Baumgarth (2009, 2010) empirically shows that BO has a direct positive effect on market performance and an indirect effect on economic performance. Equally, Schmidt, Mason, Steenkamp, and Mugobu (2017), in their study of retail companies, demonstrate that brand-oriented behavior contributes significantly to market performance. According to the results of Wong and Merrilees (2008), BO has a direct influence on brand performance, which lays down the foundation for higher brand loyalty and good image building (Ahmad & Iqbal, 2013). Previous studies in the non-profit sector additionally support the causal link between BO and non-profit performance in terms of achieving short-term and long-term objectives and serving stakeholders (e.g., Napoli, 2006). A strong brand can also be vital for SEOs (Cahine, 2016), more precisely, a high level of social brand value indicates that an SEO is well known by a multitude of stakeholders, and that these different stakeholder groups believe in the organization’s trustworthiness and credibility, which in turn attracts more customers (Liston–Heyes & Liu, 2010; Napoli, 2006) and generate high market and economic performance.

Since SEOs have multiple stakeholders with diverging views on the effectiveness of the organization, Bagnoli and Megali (2011) suggest a multiple performance measurement system. Besides economic and social success factors, the authors recommend integrating institutional legitimacy, verifying that the SEO has respected its self-imposed rules (e.g., statute, mission, program of action) and legal norms that apply to its institutional context (Bagnoli & Megali, 2011). What is still lacking is empirical evidence regarding the relationship between SO and institutional legitimacy. However, Bagnoli and Megali (2011) conceptual paper discusses some drivers and instruments to secure institutional legitimacy. These drivers can connect partially with MO, BO, and EO (Schmidt et al., 2015).

In the literature, it is argued that emerging firms can derive advantage from market-oriented behaviors to generate legitimacy (e.g., by customer-driven activities that aim to educate customers about the benefits of the organization’s offer) (Neuenburg, 2010). MO also opens the possibility for SEOs to gain legitimacy for its organization. Regular exchange processes with customers and other stakeholders, as typical behavioral characteristics of market-oriented organizations, can be used to convince the target groups of the products and/or services, respectively, to explain how the social mission can be achieved. This dialogue inspires trust and credibility. Since legitimacy has an impact on how individuals understand the organization, which includes subjective judgments from individuals objectified at the collective level (Bitektine, 2011), it also can be connected with BO. Justifying the existence through high legitimacy and trust is often expressed in terms of a strong brand (Dahlqvist & Melin, 2010). Entrepreneurial behaviors can also be considered as drivers for institutional legitimacy. Their relationship has not received any attention in the literature so far, but the literature reports that the capability of innovation, as a central part of EO, drives the organization’s reputation (Gupta & Malhotra, 2013), a concept that has similarities to organizational legitimacy in view of antecedents, social construction processes and consequences (Deephouse & Carter, 2005).

We, therefore, expect MO, EO, and BO to have a positive impact on economic performance, market performance, social effectiveness, and institutional legitimacy (see Figure 1). Therefore, it is hypothesized that:

H₂: In an SEO, market-oriented behavior has a positive influence on economic performance (H_2a), market performance (H_2b), social effectiveness (H_2c) and institutional legitimacy (H_2d).

H₃: In an SEO, brand-oriented behavior has a positive influence on economic performance (H_3a), market performance (H_3b), social effectiveness (H_3c) and institutional legitimacy (H_3d).

H₄: In an SEO, entrepreneurial behavior of an SEO has a positive influence on economic performance (H_4a), market performance (H_4b), social effectiveness (H_4c) and institutional legitimacy (H_4d).

Figure 1. The conceptual model and hypotheses.

3. Research methodology

3.1. Context and sample

This study used international SEO networks as a sampling frame (e.g., Ashoka, BWM Foundation, Social Enterprise UK). Because this study sought to investigate SEO strategies and operations, the sampling frame comprised founders, CEOs, members of senior management and members of middle management who are likely to have the relevant knowledge to complete the questionnaire. From an initial list of 1,575 social entrepreneurs, the final data set comprised 144 responses owing to a 9.14% response rate. Following the removal of missing data and outliers, 130 responses remained for further data analysis. To increase the generalizability of our dataset, we followed a strict selection process of study participants. As one of the key criteria for selection, the reviewed organizations should pursue revenue-generating strategies. Organizations who indicated that they only rely on grants to fund their operations were, therefore, excluded from the questionnaire, leading thus to a relatively low effective response rate. Non-response bias was assessed by comparing responses in the questionnaires between the early and late returns based on the assumption that late respondents’ opinions are representative of non-respondents’ opinions (Armstrong & Overton, 1977). A Multivariate Analysis of Variance (MANOVA), using the construct indicators as dependent variables and the time of response (early/late) as the independent variable, yielded no significant difference between the two groups (p > 0.05). Therefore, a non-response bias did not pose a serious problem in the data set.

3.2. Measurement

In the past, most papers have considered only one of the three SO in a framework. Also, the conceptualization and the measurement of the three types are different in unrelated papers and empirical studies. Consequently, comparing or integrating the three SO in a single framework is not informative. Most of the existing papers have developed complex conceptualizations for a single SO and implemented exhaustive measurement scales, which is appropriate, as these papers only consider a single SO (Schmidt et al., 2015). In contrast to the measurement of a single SO, the idea of this approach is to integrate MO, BO, and EO in one common framework, which refers to the “MBE-O framework” introduced by Schmidt et al. (2015).

To develop a reliable and valid scale for the measurement of MO, BO, and EO we followed the expert-based scale development process recommended by Anderson and Gerbing (1988). Due to the innovative character of the conceptual model, the proposed items are derived from our own ideas, but have strongly been influenced by the works of Kohli and Jaworski (1990), Narver and Slater (1990), Cadogan, Diamantopoulos, and De Mortanges (1999) and Kohli, Jaworski, and Kumar (1993) (MO), from the works of Baumgarth (2010), Hankinson (2012) and Gromark and Melin (2011) (BO), and finally from the works of Covin and Slevin (1989), Stewart and Roth (2001), Lumpkin and Dess (2001) and Poon, Ainuddin, and Junit (2006) (EO).

The next step consisted of the examination of the initial pool of 84 items by questioning two groups of marketing students. Initially, the first group (N₁ = 25) sorted the items (presented in random order) into one of the five dimensions (values, norms, symbols, analysis, and activities) and assigned them to one of the three SO. Subsequently, this information was analyzed by using two indices of substantive validity: the proportion of substantive agreement (p_SA) and the substantive-validity coefficient (c_SV). After revising the unaccepted items and adapting definitions of key terms, the second group (N₂ = 21) validated the unaccepted items (the procedure was left unchanged). Overall, this study used 45 items to measure the SO constructs (see Appendix A).

To enable a meaningful comparison between MO, EO, and BO, this study drew on a consistent conceptual approach. We measured all considered SO by five dimensions (values, norms, symbols, analysis, and activities). The individual dimensions (e.g. “values”) were measured by three items that were formulated similarly for all three SO (e.g., for the measurement of MO: “Our founders understand our customers.”; for the measurement of BO: “It is important to our founders what we stand for.”; for the measurement of EO: “Our founders are true ‘men of action’ and entrepreneurs.”).

To develop a reliable and valid scale to measure the performance categories, eight experts in the field of SEOs assessed the selected items from relevant literature (e.g., Bagnoli & Megali, 2011; Emerson & Twersky, 1996). They evaluated 29 proposed items to determine if they were appropriate to measure the performance (1 = “very well suited”; 3 = “not suited”). Taking into account the qualitative responses, this study used ten items to measure the performance indicators (see Appendix B). To measure the performance based on the described performance categories and by considering other research success factors (Baumgarth & Schmidt, 2010; Wiedmann & Schmidt, 1999), this study used a goal-oriented approach. In this regard, the respondents rated the importance that they believe their organization assigns to the achievement of each goal and indicated how well they think the organization achieves those goals. As a result, the model of this study includes an index for each performance category. All indicators relied on a 5-point rating scale, with 1 representing the lowest level and 5 the highest level.

3.3. Method

To estimate the model coefficients and to test the hypotheses, this study used PLS, as implemented in ADANCO 2.0 (Henseler & Dijkstra, 2015). In particular, it drew on a composite measurement model (Henseler, 2017), which is also referred to as the composite factor model (Henseler et al., 2014), or the composite-formative model (Bollen & Diamantopoulos, 2015). According to Henseler (2017), composite measurement assumes a definitorial relationship between a construct and its indicators. This means that “the relationships between the indicators and the construct are not cause-effect relationships, but rather a prescription of how the ingredients should be arranged to form a new entity” (Henseler, 2017, p. 3). Analogous to this, this study regarded the SO constructs as composites, which comprise of its indicators. The cultural layer is defined by its values, norms, and symbols, while the behavioral layer is “designed” by its components of activities and analysis. In addition, this study used a single indicator measurement (Diamantopoulos, Sarstedt, Fuchs, Wilczynski, & Kaiser, 2012) for the performance indicators (see 3.2), as well as for the control variables (firm size, age).

According to Henseler (2017), the composite measurement model does not require any assumptions about the correlations between its indicators. Consequently, the correlations between indicators will not be indicative for any sort of quality. Instead, composites should be assessed concerning nomological validity that concerns how well the research findings fit with existing theory. Thus, the path model, which is sufficiently well known through prior research, should be strong and significant (Cronbach & Meehl, 1955). The model shows highly significant path coefficients between the cultural and behavioral layers for all SO (β = 0.58–0.77, p < 0.001). The squared multiple correlation coefficient of the behavior layers also assumes satisfied values (R² = 0.34–0.60). Therefore, the measures of the cultural layers demonstrate adequate nomological validity. Given the path coefficients between the behavioral layers and the considered performance constructs (see Table 2) and their squared multiple correlation coefficients (R² = 0.09–0.27), the nomological validity of the behavioral measures is partially supported. In addition, this study ensured content validity and face validity. Regarding the scale development process (see 3.2), this study used measures that were adapted from the relevant literature and were assessed via expert validation (Anderson & Gerbing, 1988).

Table 2. Model results

Effect	Coefficient	R^2	Sig.	Cohen’s f^2	Percentile bootstrap quantiles
					2.5%	97.5%
Economic performance		0.09
Market oriented behavior -> Economic performance	0.136		0.094	0.014	−0.059	0.329
Brand oriented behavior -> Economic performance	0.124		0.150	0.011	−0.102	0.357
Entrepr. oriented behavior -> Economic performance	−0.071		0.265	0.004	−0.307	0.143
Firm size -> Economic performance	0.127		0.085	0.017	−0.046	0.308
Firm age -> Economic performance	0.146		0.047	0.019	−0.050	0.301
Market performance		0.272
Market oriented behavior -> Market performance	0.095		0.164	0.008	−0.076	0.285
Brand oriented behavior -> Market performance	0.036		0.342	0.001	−0.120	0.220
Entrepreneurial behavior -> Market performance	0.428		0.000	0.178	0.259	0.565
Firm size -> Market performance	−0.029		0.349	0.001	−0.179	0.114
Firm age -> Market performance	0.228		0.000	0.058	0.085	0.353
Social effectiveness		0.172
Market oriented behavior -> Social effectiveness	0.345		0.000	0.098	0.139	0.543
Brand oriented behavior -> Social effectiveness	0.046		0.322	0.002	−0.118	0.263
Entrepreneurial behavior -> Social effectiveness	0.037		0.371	0.001	−0.196	0.255
Firm size -> Social effectiveness	0.093		0.128	0.010	−0.066	0.258
Firm age -> Social effectiveness	0.022		0.396	0.000	−0.168	0.166
Institutional legitimacy		0.265
Market oriented behavior -> Institutional legitimacy	0.450		0.000	0.187	0.264	0.623
Brand oriented behavior -> Institutional legitimacy	−0.040		0.326	0.001	−0.213	0.138
Entrepreneurial behavior -> Institutional legitimacy	0.046		0.292	0.002	−0.121	0.216
Firm size -> Institutional legitimacy	0.148		0.029	0.028	0.002	0.297
Firm age -> Institutional legitimacy	0.041		0.296	0.002	−0.129	0.167
Market oriented behavior		0.573
Market oriented culture -> Market oriented behavior	0.757		0.000	1.340	0.694	0.832
Brand oriented behavior		0.336
Brand oriented culture -> Brand oriented behavior	0.580		0.000	0.507	0.509	0.693
Entrepreneurial behavior		0.598
Entrepreneurial culture -> Entrepreneurial behavior	0.773		0.000	1.489	0.694	0.849

In addition to validity measures, it is also recommendable to test the variance inflation factor (VIF) of the indicators (Henseler, 2017). Since the VIF ranged between 1.22 and 2.10, multicollinearity was not a serious problem in this study. Finally, the good model fit was supported by a standardized square residual (SRMR) of 0.095, and a geodesic discrepancy (d_G) of 3.66 (estimate). If one regards the indicator correlations as informative in terms of the amount of random error involved, then this study would obtain the following composite 375 reliabilities: the values range between 0.73 and 0.85, which is well above the criterion of 0.7 (Hair, Anderson, Tatham, & Black, 1998). In addition, we provide the construct correlations in Appendix C.

In line with Barney’s (2014) arguments, that firm’s specific arrangement of resources is crucial for success, this study also tested the complementarity of MO, EO, and BO. Although multiple regression analysis (MRA) has dominated complementarity studies in recent years (e.g., Boso, Cadogan, & Story, 2013; Thoumrungroje & Racela, 2013), scholars argue that averages can produce misleading results and stimulate further research that goes beyond the MRA logic (e.g. Woodside, 2013). In this regard, it is argued that conventional statistical methods demonstrate their limitations in handling multifaceted interdependencies (Leischnig et al., 2014). To identify all combinations of causal conditions (including all necessary and sufficient conditions) that contribute to an outcome (Greckhamer, Misangyi, & Fiss, 2013), this study, therefore, used fsQCA, which is a configurational, so-called causes-to-effects approach (Mahoney & Goertz, 2006). This technique is an analysis of set relationships. A set can be a group of elements or, in the case of fsQCA, a group of values (Leischnig et al., 2014). In contrast with net effects analyses, fsQCA identifies different configurations of conditions that predict both the presence and the absence of an outcome. It, therefore, considers causal asymmetry, which implies that solutions for the presence of an outcome can differ substantially from solutions for the absence of the same outcome (Fiss, 2011; Ragin, 2008). Referring to the approach of explanation, a major advantage of fsQCA is the consideration of equifinality (Fiss, 2011), which means that “a system can reach the same final state from different initial conditions and by a variety of different paths” (Katz & Kahn, 1978, p. 30). The concept plays an important role in the management literature (e.g., Marlin, Ketchen, & Lamont, 2007; Payne, 2006) and provides organizations with optional design choices to achieve the desired outcome (Fiss, 2011). Based on Ragin (2008) and Fiss’s (2011) recommendations, the fsQCA proceeds in three stages. The first step includes the calibration of the construct measures. After creating and refining the so-called truth table, it follows the analysis of the truth table.

4. Results

4.1. PLS analysis

The model’s first finding refers to the consistency of the reviewed SO. In all cases, this study demonstrates a significant effect of the cultural layer on the behavioral layer (market-oriented culture on market-oriented behavior: β = 0.76, p < 0.001; brand-oriented culture on brand-oriented behavior: β = 0.58, p < 0.001; entrepreneurial culture on entrepreneurial-behavior: β = 0.77, p < 0.001). These findings support H_1a-1c. The PLS results further show significant net effects of MO on social effectiveness (β = 0.35, p < 0.001) and institutional legitimacy (β = 0.45, p < 0.001). Therefore, the results support H_2c and H_2d. In addition, the findings illustrate that EO drives market performance (β = 0.43, p < 0.001), which supports H_4b. Regarding H3_a-d, the findings do not support any impact of BO on the considered performance categories. Overall, the model explains 27.2% of the variance in market performance, 26.5% in institutional legitimacy, 17% of the variance in social effectiveness, and 9% in economic performance (see Table 2).

4.2. fsQCA calibration and analysis

Following Fiss (2011), this study used parsimonious and intermediate solutions to distinguish between core and peripheral conditions. A core condition appears both in parsimonious and intermediate solutions and has a strong causal relationship with the outcome. A peripheral condition appears only in the intermediate solutions and has a weaker causal relationship with the outcome. Following Ragin’s (2008) recommendation, this study applied a direct calibration method using three anchors to structure a fuzzy set. The anchors included thresholds for full membership (.95), full non-membership (.05), and the cross-over point (.5). The SO variables and performance measures were adjusted using the 40th, 60th, and 80th percentiles as thresholds. The data analysis was conducted by using the software fsQCA 2.0, which based on the fuzzy truth table algorithm. This study used MO, EO, and BO as the causal conditions and economic performance, market performance, social effectiveness, and institutional legitimacy as the outcome variables.

After calibration of all construct measures, this study constructed the truth table (see Appendix D), which illustrates all possible combinations of causal conditions and their degree of empirical representation (Fiss, 2011). To refine the truth table, this study used frequency and consistency scores (Ragin, 2008). Frequency indicates the distribution of empirical cases across the rows of the truth table. The literature recommends for small- and medium-sized samples frequency thresholds of 1 or 2, while for large-scale samples frequency cut-offs should be set higher (Greckhamer, Misangyi, & Fiss, 2013). Consistency outlines the extent to which a causal combination leads to an outcome. The literature suggests here a minimum acceptable consistency level of 0.8 (Ragin, 2008).

In the first fsQCA analysis, the outcome variable was economic performance. This analysis used a frequency cut-off of 4 and a consistency cut-off of 0.8. The solution consistency and the solution coverage were 0.79 and 0.76., which represent appropriate values for both indicators (Woodside, 2013). The causal combinations account for 76% of the total membership in economic performance (see Table 3).

Table 3. Configurations for achieving high economic performance

	Economic performance
	a	b	c
Market orientation	●	●
Brand orientation	●		●
Entrepreneurial orientation		Ɵ	Ɵ
Consistency	0.79	0.82	0.80
Raw coverage	0.66	0.29	0.25
Unique coverage	0.44	0.07	0.03
Solution consistency	0.79
Solution coverage	0.76
Notes: ● indicates the presence of a core condition; Ө indicates the absence of a core condition.

Overall, the fsQCA results suggest three configurations of SO (all core conditions) that lead to economic performance. Solution 1 indicates that SEOs can combine market and BO to achieve economic performance (raw coverage: 0.66; unique coverage: 0.44). With lower coverage scores and, therefore, less empirical importance, Solutions 2 and 3 imply that MO (raw coverage: 0.29; unique coverage: 0.07) and BO (raw coverage: 0.25; unique coverage: 0.03) contribute to economic performance only owing to the absence of EO.

In addition, this study tested the combined effects of MO, EO, and BO on market performance, social effectiveness, and institutional legitimacy. Using the same calibration, as well as frequency and consistency cut-offs, the results do not support any interaction effects; however, the findings identify MO, EO, and BO as core conditions that lead to market performance. In view of the coverage scores, MO has the strongest causal relationship (raw coverage: 0.81; unique coverage: 0.07) with market performance in comparison with EO (raw coverage: 0.76; unique coverage: 0.04), and BO (raw coverage: 0.71; unique coverage: 0.02). Regarding social effectiveness and institutional legitimacy, the findings also identify MO as the main driver, followed by EO and BO; however, appearing only in the intermediate solutions, the considered SO are peripheral conditions with a weak causal relationship with the outcomes. Therefore, the findings are not specified any further.

5. Discussion

5.1. Theoretical and managerial implications

The objective of this research was to provide insights into how SO affect the performance of SEOs. Thus, this study answers the call from scholars regarding the need to conduct a quantitative analysis in this domain (e.g., Dacin, Dacin, & Tracey, 2011) and underlines the relevance of MO, BO, and EO to foster superior social and economic performance. The first implication of this study is the empirical testing of the relationship between MO, social effectiveness, and institutional legitimacy. Hence, SEOs’ inclination towards focusing on the satisfaction of individual stakeholders has the highest impact on the attainment of social objectives (e.g., acceptance within the target group and fulfillment of the social mission). To achieve their social objectives and to gain institutional legitimacy, SEO managers need to pay attention to the internal implementation of the MO concept within the organization. Managers and their staff, at all levels, should internalize the market-oriented values, in particular focusing on the satisfaction of individual and changing customer needs and wants. This also concerns other groups, such as investors, beneficiaries, volunteers, media, politics, and brand communities since SEOs heavily rely on resources and legitimacy from stakeholders. Starting from a strong market-oriented culture, consisting of values, norms, and symbols, SEOs can develop market-oriented behaviors, such as regular exchange processes with customers and other stakeholders, which in turn can be used to convince the target groups of the products and/or services and to prove trustworthiness. As a first step, social entrepreneurs should formulate and communicate market-oriented values and norms and integrate market-oriented symbols (e.g., stories about how customer requirements were satisfied in the past). Then, they should implement market-oriented behaviors among all members of the organization, for instance, control procedures or effectiveness measurement regarding customer satisfaction.

The second implication of this study is the significant influence of EO on market performance. Thus, SEOs’ inclination towards characteristics such as making fast decisions, driving innovation and seeking new markets, have the greatest effect on market-oriented goals (e.g., image among stakeholders and their pioneering role in competitive market contexts). To achieve market-oriented goals, SEO managers should focus on the implementation of the EO concept within the organization. Also here is the starting point building an entrepreneurial culture, which is influenced by values, norms, and symbols (e.g., claiming that innovations are not the task of a few masterminds, but of all employees and departments). The implementation of entrepreneurial behaviors (e.g., to develop new ideas, the organization focuses on creativity techniques and agile project strategies) should follow.

Using fsQCA, the third implication of our study refers to the complementarity between MO and BO. The findings of this study illustrate that SEOs can combine both MO and BO to achieve economic performance. Referring to the approach by Urde et al. (2011) and adapting it to the SEO contexts, brand-oriented SEOs can add a market focus to their BO to maintain the relevance of the brand to customers and other stakeholders. In contrast, market-oriented SEOs can add a strong dose of branding to their MO to achieve coherence and generate a greater degree of difference (Urde et al., 2011).

Our results are consistent with existing studies in the SEO domain, illustrating the effect of MO and EO on social and commercial objectives (Glaveli & Geormas, 2018; Liu et al., 2012; Ma et al., 2012). Using fsQCA, the findings further contribute to SEO research and overall research on SO, by illustrating complementarity between MO and BO. Thus far, little research exists on how BO works together with other SO (Reijonen, Hirvonen, Nagy, Laukkanen, & Gabrielsson, 2015). Empirical studies in the business sector (e.g., Merrilees & Baumgarth, 2016) demonstrate the significant and positive interaction effects of MO and BO on specific performance metrics (e.g., sales and market shares). These results are consistent with the findings of this study, illustrating that SEOs can combine BO and MO to achieve economic performance. In contrast, this study does not support the significant and positive interaction effects of BO and EO. Furthermore, this study does not support the interaction of MO and EO, which contrasts with the findings of previous studies (e.g., Atuahene-Gima & Ko, 2001). However, this study supports the results of Liu et al. (2012), who found that positive interaction effects between MO and EO in the SEO context were not established. This raises the question of whether the relationship between these strategic concepts is transferable from classical companies to SEOs.

To sum up, this study provides valuable information for social entrepreneurs who are involved in strategic planning. By revealing the effect sizes of SO that can enhance performance, the findings equip social entrepreneurs with relevant knowledge to use resources more effectively. To gain the trust of stakeholders, SEO managers should pay more attention to market forces, whereas characteristics such as risk-taking or driving innovation are necessary to achieve market success. Considering economic performance, social entrepreneurs should pay attention to brand building activities and customer satisfaction, simultaneously.

5.2. Limitations and directions for future research

Despite this study’s contributions, several limitations exist. First, this paper includes three SO, which are considered to be the most important within the context of SEOs. Alternative SO exist in the literature that could also have been included in the model (e.g., Baker & Sinkula, 1999; Gatignon & Xuereb, 1997; Noble et al., 2002). Second, using data from international SEO networks, this study does not evaluate the influence of any country-specific factors (e.g., government policies) on the relationship between SO and SEO performance. The influence of these context specifics may be considered in future research. Third, this study uses theories and variables from a limited number of previous studies on SEOs. Hence, it would be useful to better understand the nature of different SO in the context of SEOs. For example, BO in an SEO context could mean something completely different from BO in the context of for profit-businesses. This may also be the case with market and EO. More exploratory research is, therefore, required in this field. Fourth, the measurement of performance indicators is based on self-assessments. In this scenario, the same respondent provides the measures of both the independent and dependent variables (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Although the literature provides some recommendations for the reduction of this potential bias (e.g., including several managers of an organization), it is acknowledged that demands on time and budgets typically prevent their practical implementation in academic research projects (e.g., Baumgarth, 2010).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Florian Lückenbach

Florian Lückenbach is Head of the Competence Development Department at Koblenz University of Applied Sciences and PhD candidate at University of Twente. His research interests include brand management and social entrepreneurship.

Carsten Baumgarth

Carsten Baumgarth is Professor of Brand Management at HWR Berlin. His work has been published in Industrial Marketing Management, Journal of Business Research, European Journal of Marketing, Journal of Product & Brand Management and International Journal of Arts Management among others.

Holger J. Schmidt

Holger J. Schmidt is Professor of Marketing at Koblenz University of Applied Sciences. As a brand researcher, one of his main interests is the interface between brand management and social enterprises.

Jörg Henseler

Jörg Henseler is Professor of Product-Market Relations at University of Twente and visiting professor at Nova IMS, Universidade Nova de Lisboa. His research focuses on SEM, marketing, and design. He is a highly cited author according to Web of Science.

APPENDIX

Appendix A. Measures (strategic orientations)

Dimension	Market orientation	pSA	cSV	Brand orientation	pSA	cSV	Entrepreneurial orientation	pSA	cSV
Values	Our founders understand our customers.	0.50	0.36	It is important to our founders what we stand for.	0.86	0.81	Our founders are true ‘men of action’ and entrepreneurs.	0.62	0.52
	We are convinced that our customer´s satisfaction is a large part of the success of our social business.	0.68	0.52	We are convinced that our brand is a large part of the success of our social business.	0.68	0.59	We are convinced that innovations are a large part of the success of our social business.	0.72	0.56
	The employees of our social business understand that the satisfaction of the customer needs is not only a task for marketing, but a task for all employees and departments.	0.71	0.48	The employees of our social business understand that our brand is not only the task for marketing, but a task for all employees and departments.	0.64	0.48	The employees of our social business understand that innovations are not the task of a few masterminds, but of all employees and departments.	0.68	0.44
Norms	We have defined in writing customer orientation as a central factor.	0.78	0.65	We have defined in writing for all employees our philosophy, our mission and/or our brand positioning.	0.57	0.30	We have defined in writing for all our employees our target ’innovation’ as a central factor.	0.70	0.50
	In our social business there are explicit and comprehensive rules and guidelines how to deal with customers.	0.65	0.42	In our social business there are explicit and comprehensive rules and guidelines for compliance of the brand positioning and corporate designs.	0.71	0.58	In our social business there are explicit and comprehensive rules and guidelines for innovation management.	0.75	0.63
	Our social business is organised in a way that ensures that information about customers (e.g., complaints, requirements) will be forwarded to all departments.	0.29	0.14	Our social business is organised in a way that ensures that the brand is complied with regards to form and content.	0.65	0.52	Our social business is organised in a way that ensures that innovations and impulses can be generated by all departments.	0.43	0.14
Symbols	In our social business lots of stories are being told how we satisfied customer requirements.	0.57	0.48	In our social business one likes to tell stories of the development and importance of our brand.	0.67	0.43	In our social business lots of stories are being told about risky decisions and the development of highly innovative solutions.	0.62	0.52
	Our buildings are designed in a way that visitors and customers quickly find their way.	0.58	0.42	Our buildings are designed in a way that everyone immediately feels the philosophy of our social business.	0.78	0.65	Our buildings are designed in a way that there is a creative and innovative atmosphere.	0.56	0.32
	The employees of our social business are wearing clearly visible name badges so that customers can approach them individually.	0.62	0.33	The employees of our social business can be clearly identified as staff members by their uniforms, dress style, emblems or similar.	0.52	0.19	One can recognise the employees of our social business by being equipped with the newest technological gear.	0.40	0.00
Analysis	We meet regularly with our customers to analyse how they evaluate our supply.	0.90	0.81	We meet regularly internally to analyse and discuss status and development of our brand.	0.79	0.71	We meet regularly with lead users, external experts and lateral thinkers to scrutinise our ideas and solutions.	0.71	0.62
	We regularly conduct customer satisfaction surveys.	0.77	0.65	We analyse regularly, supported by market research, the popularity and image of our social business.	0.58	0.27	We conduct regular studies of the newest technologies and innovative business models, even outside our own markets.	0.52	0.29
	Customer satisfaction is a central controlling and control quantity in our social business.	0.89	0.81	The brand strength is a central controlling and control quantity in our social business.	0.91	0.86	The number of new products and innovative service offerings are central controlling and control quantities in our social business.	0.59	0.44
Activities	We adapt our communication to different target groups. In extreme examples we change our communication for a single customer.	0.86	0.77	We follow an integrated communication. Our communication is monolithic.	0.57	0.30	We mainly communicate our social innovations.	0.77	0.64
	We have implemented a professional CRM-system for handling our customers.	0.67	0.38	We have professionally developed our brand name and logo and had it registered as a trademark.	0.57	0.38	To develop new ideas, we deliberately focus on creativity techniques and agile project strategies.	0.57	0.34
	When we learn that our customer is unsatisfied with our performance, we immediately change our offerings.	0.65	0.50	We practise systematic communication to enhance the brand awareness and to improve our image.	0.80	0.64	We regularly implement new solutions.	0.68	0.45

Appendix B. Measures (performance indicators)

Construct	Measurement items
Economic performance	We are financially independent.
	Our social business is profitable.
	Our social business works efficiently.
Market performance	We enjoy a good image among our stakeholders.
	Other organizations orient themselves on what we do.
Social effectiveness	We reach our target group.
	Our target group accepts our products/services.
	We fulfill our social mission.
Institutional legitimacy	Our social business fulfills needs and interests of our stakeholders.
	There is a high correspondence among activities undertaken, the results achieved,and the goals established by our social business.

Appendix C. Construct correlations

Construct	1	2	3	4	5	6	7	8	9	10	11	12
1. Brand oriented culture	1
2. Entrepreneurial culture	0.497	1
3. Market oriented culture	0.595	0.413	1
4. Market oriented behavior	0.482	0.392	0.757	1
5. Brand oriented behavior	0.580	0.506	0.425	0.408	1
6. Entrepreneurial behavior	0.420	0.773	0.342	0.378	0.496	1
7. Economic performance	0.167	0.071	0.252	0.216	0.122	0.025	1
8. Market performance	0.302	0.419	0.314	0.317	0.239	0.451	0.131	1
9. Social effectiveness	0.394	0.194	0.384	0.400	0.206	0.189	0.294	0.384	1
10. Institutional legitimacy	0.381	0.278	0.452	0.488	0.167	0.194	0.281	0.329	0.505	1
11. Size	0.035	0.015	0.258	0.185	0.059	0.019	0.187	0.045	0.164	0.238	1
12. Age	0.023	0.070	0.166	0.223	0.206	0.133	0.185	0.179	0.104	0.173	0.199	1

Appendix D. Truth table

Market orientation	Brand orientation	Entrepreneurial orientation	Number	raw consistency
1	1	1	64	0.81-0.99
1	0	1	17	0.72-0.99
1	1	0	11	0.83-0.99
0	0	0	11	0.73-0.99
1	0	0	10	0.83-0.99
0	1	1	6	0.80-0.96
0	0	1	5	0.73-0.99
0	1	0	4	0.82-0.99