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Market Orientation, Social Entrepreneurial Orientation, and Organizational Performance: The Mediating Role of Learning Orientation.

Introduction

There is a growing concern that non-profit sector has come under great pressure due to the intense competition for volunteers, finances, and employees ( Salamon, Sokolowski, Megan, & Tice, 2013) as well as the increased demand for performance by stakeholders (Dees, 2001; Herman & Renz, 2008). This situation could not be handled through conventional means, and scholars suggest that managers must think more strategically and non-profit organizations can and should be based on corporate knowledge and practices--correspondingly called managerialism approach (Hvenmark, 2013; Prugsamatz, 2010). Managerialism can be achieved through different means or concepts like corporatization, venture philanthropy, professionalization, etc. However, Marketization and Social Entrepreneurial Orientation have received good support in the previous research works. Hence, the principal objective of this research is to provide an insight on how different strategic orientations can be utilized for non-profit sector organizational performance, and to empirically test the relationship of different strategic orientations with Non-Profit Organizations' (NPOs) performance. This study will try to answer the questions that why Market, Social Entrepreneurial, and Learning orientations are vital for non-profit sector, and whether Market, Social Entrepreneurial, and Learning orientations can enhance the performance of non-profit organizations as alternatives or in complementarity mode (Chad, Kyriazis, & Motion, 2013; Schweiger, Stettler, Baldauf, & Zamudio, 2019)?

In the related literature, different Strategic Orientations like product orientation, market orientation, customer orientation, and technological orientation have been discussed in large, but still most of these studies prevail in commercial sector ( Deutscher, Zapkau, Schwens, Baum, & Kabst, 2016; Hakala, 2011;Tajeddini, 2016) while very limited studies can be found to be on the non-profit sector (Alarifi, Robson, & Kromidha, 2019; Glaveli & Geormas, 2018; Luckenbach, Baumgarth, Schmidt, & Henseler, 2019; Shin, 2018). This study will cover this gap by finding a mutual effect of two strategic orientations: Market Orientation (MKTO) and Social Entrepreneurial Orientation (SEO) on the Non-Profit Organizations' (NPO) Performance (Perf) along with Learning Orientation (LOR) as a mediator. Market Orientation is considered quite helpful in the third sector to explore not only well-identified demands but also to understand the implicit requirements of all stakeholders. However, relying too much on MKTO may lead to imitation; therefore, scholars recommend adopting SEO, as it relies more on an innovative and proactive approach. Being intangible resources, MKTO and SEO could not perform well unless an organization does not have the capability like LOR to utilize such resources to achieve competitive advantage (Baker & Sinkula, 1999). Scholars discourage relying on a single orientation and suggest that the introduction of different orientations in combination may be helpful to generate more an advanced organizational culture that enables organizations to perform better than competitors (Deutscher et al., 2016; Grinstein, 2008; Schmidt, Baumgarth, Wiedmann, & Luckenbach, 2015).

To conclude, this study will have significant contributions by covering different gaps in the strategic orientation and its association with performance literature. Firstly, the study would be helpful to bridge the literature gap through the empirical analysis of the associations of MKTO, SEO, and LOR and their effect on organizational performance from the non-profit sector perspective (Alarifi et al., 2019; Luckenbach et al., 2019). Secondly, Learning Orientation (LOR) for the first time been introduced as a strong mediator to study its relationship with proposed strategic orientations (Grinstein, 2008; Rupcic, 2016). Finally, this research will provide a good framework to study MKTO, SEO, and LOR under Resource Based Theory (RBT) and dynamic capability theory. Last but not least, this research will provide a good basis to academicians in extending business strategies to other fields and will help to find an answer to the question that whether strategic orientations become more effective when implemented in complementarity mode or in alternative mode (Schweiger et al., 2019).

Literature Review

Resource Based View Theory

Resource Based Theory (RBT) suggests that an organization can achieve sustainable competitive advantage if it is capable to leverage its internal resources against competitors or external market forces that may affect its performance negatively. These resources may relate to organizational processes, assets and capabilities or information and knowledge (Barney, 1991). Market Orientation, as an intangible resource that is recognized as the skill to understand the business atmosphere and use this information to provide an appropriate course of action is considered a decisive factor for an organization success (Corte, D'Andrea, & Del Gaudio, 2018). Similarly, Social Entrepreneurship is also considered a strategic resource and an organization with propensity to take high risk and to adopt innovation will also be able to create more social and economic value for stakeholders (Day & Jean-Denis, 2016). However, most of the studies unanimously agree that to widen and to better understand RBT, more empirical works and developing interactive framework with other fields and theories like dynamic capability theory proposed by Teece, Pisano, and Shuen (1997) is required. Therefore, Learning Orientation has been introduced as a strong capability in this model to equip organization with learning culture. The information generation and dissemination through Market Orientation and innovativeness, proactiveness, and risk-taking behavior under Entrepreneurial Orientation would be meaningless if there is no such kind of learning culture where it becomes difficult to raise questions about old values, policies, and procedures.

Market Orientation (MKTO)

Market Orientation represents such kind of corporate culture that places customers in the center of a firm's operation and how marketing principles can be applied practically (Kohli & Jaworski, 1990; Narver & Slater, 1990). It is ensured through organizational routine processes that emphasize the customer, value of information, more coordination across the departments, and the better responsive behavior. In commercial sector, Market Orientation has been widely discussed (Deutscher et al., 2016; Kharabsheh, Ensour, & Bogolybov, 2017; Tajeddini, Trueman, & Larsen, 2006); however, it has been recently introduced in the non-profit sector and very limited research has been conducted in this domain (Glaveli & Geormas, 2018; Modi, 2012).

The literature fully supports the argument that many tools and techniques applicable in commercial marketing practices are indeed applicable to non-profit sector but the difference only lies in their application ethos (Chad, Kyriazis, & Motion, 2014; Hyojin, 2002).This is why no consensus could be found in the literature on one scale and results are so fragmented. This requires more empirical works to get a refined scale that can be generalized. In this research, Modi's (2012), Market Orientation Non-Profit Organization (MONPO) scale will be used which has been adapted from Narver and Slater's (1990) MAKTOR scale.

Social Entrepreneurial Orientation (SEO)

The issues of poverty, climate change, and social inequality increasingly put pressure on organizations to identify novel approaches. To address these challenges, Social Entrepreneurship has evolved as a new paradigm. Therefore, over the years, a variety of definitions have been suggested that appear to share three aspects: (1) the basic motive of social entrepreneurial behavior is the development of social value; (2) the salient feature of entrepreneurship is innovation, and (3) social entrepreneurship achieves social mission by utilizing entrepreneurial behavior and activities (Alarifi et al., 2019; Syrja, Puumalainen, Sjogren, Soininen, & Durst, 2019). This research follows the innovative spirit perspective, and argues that corporate entrepreneurial constructs like innovativeness, proactiveness, and risk taking are also relevant for non-profit sector as emphasized by (Andersson & Helm, 2012; Hu & Pang, 2013; Syrja et al., 2019). They asserted that the central purpose of non-profit organizations to serve social purpose could be achieved only as long as it remains financially viable. Furthermore, for an organization to become competitive and financially independent, a non-profit organization must develop entrepreneurial posture. Therefore, SEO is known as a process to establish Social Values (SV) in order to discover alternative solutions through innovation to address societal problems. It includes collecting resources strategically, leveraging opportunities to encourage societal progress, fulfilling social needs, and designing innovative community goods and services.

Organization Learning Orientation (LOR)

Neither for-profit nor non-profit organizations can learn without interacting with their environment, either internally and externally. Organizational learning is generally described as an organization's explorative and exploitative capability to make an ideal utilization of information that is accessible inside and outside the organization so as to influence organizational performance (Mahmoud & Yusif, 2012).

Modern organizations as well as non-profit sector rely heavily on a learning orientation that is comprised of four components i.e. commitment to learning, shared vision, open-mindedness, and knowledge sharing or knowledge exchange inter- and intra-organizationally to develop a competitive advantage (Alegre, & Chiva, 2013).

Organizational Performance (Perf)

Non-profit organizations are largely characterized by a lack of interest in profit making and may pursue many goals simultaneously (Hansmann, 1987). Therefore, it is quite difficult to use a one-size-fits-all solution to assess how such goals are met.

For the better part of a century, measuring viability and effectiveness largely remained a generous source of confrontation. The performance of NPOs is multidimensional and entails many social and organizational aspects ( Short, Moss, & Lumpkin, 2009). After reviewing non-profit performance literature, Richard, McMillan-Capehart, Bhuian, and Taylor (2009) recommended that non- profit performance usually involves four broad categories of concerns: a) monetary performance (e.g., year-long contributions, public funding), b) output for stakeholders (e.g., volunteer satisfaction, donor commitment, identity of stakeholders), (c) market performance (e.g., non-profit image, brand repute, standard of service), and (d) mission performance (accomplishing organization mission).

Hypothesis Development

Market Orientation and Organizational Performance

Shoham, Ruvio, Vigoda-Gadot, and Schwabsky (2006) conducted a thorough review of literature on the impact of Market Orientation on the performance of NPOs. Their study concluded that the causal effect of Market Orientation on non-profit organization performance is not only positive but also even stronger than its effect on the for-profit sector. Moreover, the investigation of NPOs in Spain by Vazquez, Alvarez, and Santos (2002) showed that organizations that adopt market-orientation can address the requirements of beneficiaries as well as the expectations of donors and effectively achieve NPO missions. Similarly, Glaveli and Geormas (2018) accepted the role of MKTO in the performance of social entrepreneurial organizations. They believe that in order to consistently deliver beyond-average performance, an organization should design a superior value for customers. This only becomes feasible by grasping the market dynamics and customers and creating a culture of value. Therefore, it can be hypothesized that:

H1: Market Orientation has a significant positive effect on the Non-Profit Organization overall Performance

Social Entrepreneurship and Organizational Performance

As no consensus so far exists on a unique definition for Social Entrepreneurship that can be applied to non-profit sector, empirical work has been very limited and with mixed results (Short et al., 2009). However, there is a general agreement on two things. It is unfair to restrict or associate Social Entrepreneurship to any particular sector; rather, it may be implemented within a sector and across it. Secondly, the excellent incentives for social entrepreneurship scholars to evaluate perspectives and inferences from theories exist in entrepreneurship framework (with entrepreneurship as core area). Therefore, most of the empirical studies in this regard have used entrepreneurial constructs to understand non-profit organizations' entrepreneurial posture (Andersson & Helm, 2012; Morris, Webb, & Franklin, 2011).

Pearce, Fritz, and Davis. (2010) found a favorable association between entrepreneurial conduct and performance by measuring the rise in church participation and contributions by church individuals. Morris et al. (2007) found no connection between Entrepreneurial Orientation in the non-profit organizations and their different financial performance indicators (total revenue and net asset). Andersson (2011) concludes that entrepreneurial behavior in non-profit organizations will probably lead to higher fund generation capacity and furthermore, less entrepreneurial-oriented organization will focus more on efficiency management and short-term goals rather than growth and long-term goals. In contrast, more socially entrepreneurial organization will be able to accomplish social goals and can achieve economic efficiencies. Therefore, the second hypothesis can be suggested as follows:

H2: Social Entrepreneurship has a significant positive effect on the overall performance of the Non-Profit Organization.

Market Orientation and Organizational Learning Orientation

Market Orientation and Learning Orientation are considered routine processes that create superior value to customers. Market Orientation influences scope related to market dynamics, while Learning Orientation (LOR) challenges the nature of market activities. In other words, LOR scope is broader than MKTO because it deals with both external and internal issues (Baker & Sinkula, 1999). As against profit sector, where the attraction and allocation of resources is mostly similar, the non-profit sector needs more customized resources allocation and attraction due to target publics and their needs. This is only possible if an organization has a good mechanism to learn about its beneficiaries. A more market-oriented organization will put more pressure on higher management to respond by developing learning-oriented culture. Lonial and Crum (2011) explained the same stance and assert that the market consists of a structure that is very dynamic and difficult to predict, one which demands the company to adapt itself according to whatever changes been identified and whatever improvements been introduced. Such responsive market-oriented organizations need to heavily access and rely on their organizational learning capabilities, as this learning capability provides tools and techniques to collect timely information that can be used to execute strategies effectively ( Zainul, Astuti, Arifin, & Utami, 2016). This also leads to the conclusion that Market Orientation as a strategic resource could only enhance the performance of an organization when it is supported by good learning culture (Baba, 2015; Baker & Sinkula, 1999). It may therefore be concluded that:

H3: Market Orientation has a significant positive effect on the Organizational Learning Orientation.

Social Entrepreneurship and Organizational Learning Orientation

Social Entrepreneurial Orientation is also based on innovativeness, proactiveness, and risk-taking factors. The salient feature of innovativeness is that it favors new ideas and changes, while proactiveness relies on future opportunities and working on prospective changes as well as being a pioneer in the introduction of new products and processes. Risk taking helps with taking bold decisions to explore the unknown. All these factors help acquire best and updated information about environment and competitors in a proactive way. Such attributes will ultimately help an organization to develop SEO as a strategic resource, which is imperfectly imitable and leads to a competitive advantage (Baker & Sinkula, 2009; Lisboa, Skarmeas, & Lages, 2011). Similarly, Wang (2008) believes that a firm with more entrepreneurial attitude will be more proactively and aggressively engaged to keep eyes on the environmental changes which will help it to a large extent to collect information and share it strategically among all stakeholders. This SEO and LOR relationship is also of great importance in that entrepreneurial attitude helps with introducing ideas that challenge the accepted assumptions and cognitive structures. When a less entrepreneurial organization faces a problem, it mostly relies on previous knowledge for a solution. This leads to complementary knowledge rather novel and double loop learning (Siren, Hakala, Wincent, & Grichnik, 2017). Thus, the fourth hypothesis of this study can be formulated as follows:

H4: Social Entrepreneurial Orientation has a significant positive effect on the Organizational Learning Orientation.

Organizational Learning Orientation and Organizational Performance

The connection between Learning Orientation and performance has by and large been observed to be positive and LOR has been proposed as one of the most valuable resources to compete globally (Kharabsheh, et al., 2017; Tajeddini, 2009). That is the reason organizations are constantly searching for approaches to build their learning capability. Tajeddini (2016) even proposed that an organization which values openness, knowledge sharing, and commitment to learning will be able to better predict organizational outcomes and future orders. This would help in reducing the impact of such sudden changes and ultimately would help running routine business operations smoothly.

With regard to the non-profit sector, different scholars also have tried to establish the importance of learning orientation for improving the organization performance (Choi, 2014). A good learning-oriented organization will be able to improve the worker's competence that would help in executing the programs more effectively, which will ultimately increase stakeholders' satisfaction. A highly satisfied stakeholder means good cash flows, as a satisfied donor or beneficiary will spread good word of mouth and will motivate other donors for funding another project (Baba, 2015). Therefore, the fifth hypothesis of this study can be put forth as follows:

H5: Organizational Learning Orientation has a significant positive effect on the overall performance of the Non-Profit Organization.

Organizational Learning Orientation as a Mediator

Although the previous studies found a useful effect of Learning Orientation on performance (Baker & Sinkula, 1999; Lopez, Peon, & Ordas, 2005; Tajeddini, 2016), scholars are divided on the role of Market Orientation and Learning Orientation in the performance of organization. Baker and Sinkula (1999) found that without a solid LOR, MKTO is less inclined to enhance performance altogether in respect to market rivals. Sinkula (1994) explored the relationship between Market Orientation and Organizational Learning in line with Resource Based Theory, and deemed that MKTO as a strategic resource alone may not get the desired superior performance. This affiliation has also been recognized by scholars such as Jaworski & Kohli (1996) who completely supported the critical role of organizational learning (capabilities) in propagating market-oriented thought and behavior in an organization. This is then said to lead to derived benefits, e.g. predominant performance. Morgan and Strong (1998) were similarly of the same view, defied the idea of Market Orientation (MKTO) as sole player in the performance of an organization, and recommended that MKTO is just the first principle.

Despite the fact that the earlier works primarily addressed the direct effects of strategic orientations on the organization performance, the literature supporting the intervening impact of LOR on SEO and Performance relationship is exceptionally restricted and no empirical work been carried out in non-profit sector. The importance of learning orientation has also been discussed by Slater and Narver (1995) who argued that LOR reinforces firms' self-revitalization capability and establishes a platform that steers nonstop progress in entrepreneurial activities and performance. Altinay, Madanoglu, De Vita, Arasli, and Ekinci (2016) unfolded a research model in which SEO positively affects LOR, which ultimately affects firm performance positively. Lisboa et al. (2011), adopting the Resource Based View theory, proposed that the possession of entrepreneurial orientation is an important but insufficient prerequisite for value delivery as long as it is supported by a capability like explorative and exploitative one. Wang (2008) inferred that SEO positively affects learning orientation and thus is helpful for the organizational performance. Accordingly, the following hypothesis can be suggested in this regard:

H6a: Organizational Learning Orientation would positively mediate the Market Orientation and Non-Profit Organization overall performance relationship.

H6b: Organizational Learning Orientation would positively mediate the Social Entrepreneurial Orientation (SEO) and Non-Profit Organization overall performance relationship.

Conceptual Framework

Methodology

Data Collection

This study used the quantitative approach, and the data collection was done using survey questionnaires from employees at a good managerial position of non-profit organizations registered under Pakistan Center for Philanthropy (PCP) and Societies Registration Act, 1860. There are 687 active non-profit organizations on PCP website and the list is updated after the submission of the audit report. A list of 30 organizations with good reputation, size, and working records in Pakistan since last past 5 years from each province (total four provinces) of Pakistan was prepared. Out of these 120 organizations, 50 organizations were randomly selected through excel sheet RND command. However, the employee's information was not available due to the turnover and security issues. Therefore, the snowball sampling method was used to distribute the questionnaires and get the maximum responses.

To confirm the minimum sample size, the table proposed by Hair, Hult, Ringle, Christian and Sarstedt (2017) proposed was used. As in this structural model, the number of highest arrows that lead to a variable is three. Therefore, as per the Hair table, a sample size of 130 was found to be sufficient to determine the lowest [R.sup.2] value of 0.10 for any variable at a significance level of 5%. Since 319 usable responses had been collected, the lowest sample size condition was met. An alternative method has been proposed by Chin, Marcelin, and Newsted (2003) for PLS for the times the model is reflective. In this method, out of the three independent variables, any construct with the highest number of structural paths will be multiplied ten times. This method indicates 10 x 16= 160 as an adequate sample size.

In sum, 500 questionnaires were distributed either directly or through a focal person, mostly through the human resource departments. Around 147 questionnaires were either not returned or misplaced, while 34 were not properly filled out and excluded from the data. Therefore, the response rate was around 64%. Thus, 319 responses in total were found suitable for data analysis. All ethical parameters were strictly observed during the data collection procedure.

Instruments and Measures

The fallowing measurement scales on a 5-point likert scale questionnaire were used for different variables used in the model. To measure Market Orientation, Modi (2012) MONPO scale of 14 items was used. The Social Entrepreneurial Orientation construct was measured using Hu and Pang (2013) scale with 11 items. The Organizational Learning Orientation was measured by adapting 14 items of Jerez-Gomez, Cespedes-Lorente, & Valle-Cabrera (2005). Performance was taken as the dependent variable and was measured by 11 subjective statements under Non-Economic, Economic, and Social Effectiveness dimensions, which have been used in the past studies.

Data Analysis

To complete the data analysis and estimation for present study, a two-step approach was utilized using Smart PLS v.3.2.7. Smart PLS is a useful analytical method because the data does not have to be normally distributed, while in case of CB-SEM, it is often impossible to measure the complex models with many latent variables and/or indicators. PLS-SEM can also handle both the reflective and the formative models without any additional constraints. PLS is also useful when the relationships among theoretical constructs have not been explored well before (Hair et al., 2017). To confirm the reliability and validity of the measures, the measurement model (also called the outer model) was first evaluated along with reflective measures. After the confirmation of all basic thresholds, the evaluation of structural model (also known as inner model) was carried out in the next phase.

Common Method Variance

To avoid Common Method Variance (CMV), procedural measures were taken by developing simple questions, communicating the purpose of the research and the set of instructions to the participants, and labeling every point on the response scale. CMV was also examined statistically. The manifestation of a VIF more than 3.3 is supposed to suggest that an excessive collinearity exists, which implies that a model may be compromised by any specific method bias (Kock, 2015). The VIF values for this study were found below 3.3, which shows that the common method bias, in terms of the collected data, does not appear to be the part of any severe issue.

Preliminary Analysis

The male and female respondents were 235 and 84, respectively, and the respondents' highest percentage of education was master's degree, which was around 80%. The descriptive analysis for this study shows that the means of variables ranges from 3.79 to 4.17, whereas standard deviations range from 0.43 to 0.79. Skewness and Kurtosis values are within the standard limit (-3 to +3) as per the recommendation of Ghasemi and Zahediasl (2012). The correlations among the constructs are also quite lower than the threshold value of 0.90. Therefore, there is no issue of multicollinearity in this study ( Tabachnick, Fidell, & Osterlind, 2001).

Measurement Model

Generally, the measurement model (a.k.a. outer model) represents the link between primary construct and its relevant indicators. This association could be reflective or formative, depending upon the theoretical background (Hair et al., 2017). There were fifteen latent variables (DO, IC, PO, BFO, INN, PRO, RISK, REC, MC, SP, OPEX, KTR, PerfNEC, PerfEC, PerfSEF) and Confirmatory Factor Analysis (CFA) was carried out prior to hypothesis testing to validate the measurement model.

The composite reliability (CR) and Cronbach's [alpha] are the most extensively used methods to assess the internal consistency. All values of CR are higher than the cut-off value of 0.70 as proposed by Hair et al. (2019), which reflects that the group of items have measured the main construct very well. Average Variance Extracted (AVE) and factor loadings are used to examine convergent validity. The results show that no value is below the cut off values i.e. 0.50 and 0.70 for AVE and factor loading, respectively (Hair et al., 2019). Only AVE of one item, PerfNEC2 was below 0.50, causing a low value for PerfNEC1, which was deleted; this helped increase the PerfNEC1 value from 0.48 to 0.55. The figures exhibited in Table 1 endorse all these statements.

Discriminant Validity

Discriminant validity helps test whether concepts and measurements that are theoretically supposed to be distinct from each other are actually unrelated or not? Higher numbers indicate that the variable is rare and holds such kind of phenomena, which cannot be reflected by other constructs present in the model. As per Fornell and Larcker's (1981) criterion, if there exists an issue of discriminant validity, then there is a greater chance that variables will less likely correlate within parent factor variables and correlate more with outside parent factor variables. It is evident from Table 2 that there is no issue of any such discriminant validity and variables are well distinct.

However, recently scholars such as Henseler, Ringle, and Sarstedt (2014) have shown the reservation on Fornell-Larcker criterion and have proposed heterotrait-monotrait ratio of the correlations (HTMT). It is measured by taking the average of the correlations of indicators across constructs that measure different concepts relative to the average correlations of the items measuring the same variable. As two submatrices are used, the geometric mean of their average correlation is also calculated. For conceptually similar constructs, the discriminant validity problem exists when threshold value is more than 0.90, while for the distinct constructs, the threshold value should be less than 0.85 (Henseler et al., 2014). The values for HTMT are presented in Table 3.

Structural Model

The [R.sup.2] value measures the structural model. It reflects the statistical accuracy and represents the amount of variation it brings into the endogenous variable explained by all exogenous constructs linked to it. As shown in Figure 3 below, the [R.sup.2] values estimated for Learning Orientation (the mediating variable) and Performance are 0.19 and 0.154, respectively. This suggests that 19% and 15.4% of change occurred due to an exogenous variable. To conclude, it can be presumed that the model is quite acceptable and meaningful.

The t-values are determined using the bootstrapping method to test the recommended hypotheses. A re-sampling of 1000 bootstraps has been used to accurately measure t-tests. Bootstrapping is known as one of the modern techniques to establish the significance of path coefficient through t-values (Hair et al., 2019). Figure 3 illustrates both regression weights and t-values. The assessment of the effects of the path coefficient indicates that MKTO significantly and positively affects organizational performance (b = 0.150; t = 2.70; p < 0.05). The findings confirm the acceptance of Hypothesis 1.

Direct and Indirect Path Analysis

Bootstrapping method is used in smart PLS to measure the indirect effect and its significance in order to determine the mediation effect. In the first PLS test run, the path coefficient for direct relationship of Market Orientation with performance is found to be 0.15, with the t value of 2.70, which signifies a positive relation and supports H1. However, the relationship between Social Entrepreneurial Orientation and performance is 0.10 with a t value of 1.50, which reflects an insignificant relation and does not support H2.

However, after introducing Learning Orientation (LOR) as a mediator, the path coefficient for Market Orientation (MKTO) becomes 0.066 with a t value of 2.96, which reflects that the mediator affected the relationship and a partial mediation power. However, in the case of SEO relationship with performance, the latter actually improved with the presence of LOR because the path coefficient is now 0.080 while the t value becomes 2.87. This reflects that LOR works as a strong mediator and H6a and H6b are confirmed, too. These results also support the theoretical framework that merely acquiring resources is not sufficient, and an organization should also develop the capability to utilize resources like MKTO and SEO to achieve the sustainable competitive advantage in the long run.

Discussion

This study aimed at assessing the mediating effect of Learning Orientation (LOR) on the relationship between MKTO, SEO, and organizational performance (Perf). A positive significant relationship was found between MKTO and Perf with the path coefficient 0.15 and the t value of 2.70 as well as LOR with the beta value of 0.25 and the t value 4.0. This confirms that MKTO is a strong strategic orientation. However, MKTO and Perf positive relationship changed significantly in the presence of LOR as the path coefficient value weakens from 0.15 (*) to 0.066 (*) while the t value increases from 2.70 to 2.96. The second model is, however, quite opposite to the previous studies (Stecker, 2014; Hu & Pang, 2013) who proposed Social Entrepreneurship as a key player in the performance of non-profit organizations as no causal effect was found between SEO and Perf. As the path coefficient was 0.10 with the t-value 1.50, this relationship becomes significant in the presence of LOR as a mediator as the path coefficient became 0.080 with the t value 2.87. The results also signify that Market Orientation and Social Entrepreneurship can better facilitate an organization to develop a more proactive learning culture.

This model also reflects that LOR is a strong mediator which supports the argument of Baker and Sinkula (1999) and Wang (2008) that MKTO and SEO form a vital strategic orientation but are not adequate resources, because an organization would perform better when it does not rely on a single orientation. Therefore, LOR as a strategic capability would better utilize these resources to achieve a sustainable performance with a better competitive advantage. Results also positively answer the questions that if these strategic resources (MKTO, SEO, and LOR) are also useful to study the non-profit sector and can be borrowed from commercial sector but with adaptation. Hence, these strategic resources play a more effective role in the complementary mode rather than as alternatives.

Conclusions

The present study is quite helpful in answering the research question that why Market Orientation (MKTO) and Social Entrepreneurial Orientation (SEO) are useful strategic intangible assets for non-profit organizations. The study demonstrates that the Market Oriented nonprofit organization can build good relationship with stakeholders and can better engage them through marketing activities. Market Orientation is good for non-profit organizations to understand environment, to achieve social mission, to increase revenue, and even can make donors loyal through excellent services. Similarly, a less entrepreneurial non-profit organization will not be able to solve social problems with innovation, will be less market savvy, and may not be able to achieve social goals effectively.

This research also shows the significance of Learning Orientation (LOR) as a strong capability mediator of an organization. As an organizational capability, it can effectively utilize Market and Social Entrepreneurial resources to transform them into imperfectly imitable resources so that a competitive advantage can be achieved. The work thus contributes considerably to the strategic orientation literature by providing the empirical evidence for a causal relationship between intangible resources (MKTO and SEO) and organization performance. The strength of this causal effect changes in the presence of Learning Orientation as a mediator. Such observations are consistent with Deutscher et al. (2016) and Schweiger et al. (2019) directions that there is a need to study strategic orientations in complementary mode rather than as alternatives. The non-economic, economic, and social effectiveness performance goals of an organization would improve significantly when an organization simultaneously focuses and implements more than one strategic orientation.

Practical Implications

The present study is helpful for non-profit sector managers to realize the importance of borrowing the management skill from commercial sector. Market Orientation (MKTO) could help them better understand both explicit and implicit needs of all stakeholders (donors, beneficiaries, government). They should realize that marketing is not all about public relations and advertising but a business philosophy to work together with all stakeholders so as to better serve customers. MKTO is a good strategic tool that can help this sector to exploit market intelligence and utilize this information for providing customized products and services that can ultimately help satisfy all stakeholders (donors, beneficiaries, employees). The top management of non-profit sector should also focus on the entrepreneurial orientation to effectively attract and utilize resources and to fulfill the social mission. An organization with entrepreneurial mindset will be able to achieve all performance goals and can solve the social problems effectively and innovatively. It is also worth mentioning that an organization with a good learning culture always acquires new knowledge and questions the existing beliefs; this helps respond proactively to the environmental changes. LOR also helps with developing imperfectly imitable resources so that sustainable advantages can be achieved. Academicians should also work together with non-profit sector policy makers for sharing these ideas and strategic orientations for mutual benefits and for the introduction of a new culture.

Limitations and Future Directions

This study tried to cover maximum sectors to see the general trend. However, the research is not without limitations. This research is a purely quantitative study; therefore, a qualitative research in future would help understand the non-profit organizational culture from top management perspective and the kinds of barriers they may face to introduce such strategic orientations into the non-profit sector. Another limitation is that small data was utilized; however, in future a large sample from all provinces of Pakistan would be really helpful to get a better representation of the population and to improve its generalizability. Similarly, mediators such as political power and leadership style as well as moderators such as organizational structure could be interesting variables for future studies. It would be also interesting to examine the impact of other orientations such as the Brand and Employee orientations on the non-profit organization performance.

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Imran Khan (*), Taqadus Bashir

Department of Management Sciences, Bahria University, Islamabad Campus, Islamabad, Pakistan

(Received: September 29, 2019; Revised: March 19, 2020; Accepted: April 10, 2020)

(*) Corresponding Author, Email: imrankomsat11@gmail.com

DOI: 10.22059/ijms.2020.289467.673800
Table 1. Measurement model (outer loadings, Cronbach [alpha], Composite
Reliability (CR), Average Variance Extracted (AVE) and VIF)

First Order         Second Order                       Factor
Constructs          Constructs       Indicators        Loading

                                     DO1               0.8
                                     DO2               0.8
Donor Orientation                    DO3               0.8
                                     DO4               0.7
                                     IC1               0.8
Inter Departmental                   IC2               0.8
Coordination                         IC3               0.8
                                     PO1               0.8
                                     PO2               0.8
Peer Orientation                     PO3               0.8
                                     PO4               0.8
                                     BFO1              0.8
Beneficiary                          BFO2              0.9
Orientation                          BFO3              0.8
                                     Donor             0.8
                                     Orientation
                                     Inter             0.6
                    Market           Departmental
                    Orientation      Coordination
                                     Peer Orientation  0.7
                                     Beneficiary
                                     Orientation       0.7
                                     INN1              0.9
                                     INN2              0.9
Innovation                           INN3              0.9
                                     INN4              0.8
                                     PRO1              0.9
Proactiveness                        PRO2              0.9
                                     RISK1             0.8
Risk                                 RISK2             0.8
                                     RISK3             0.8
                                     REC1              0.9
Reciprocal                           REC2              0.9
                                     Innovation        0.8
                    Social           Proactiveness     0.6
                    Entrepreneurial  Risk              0.7
                    Orientation      Reciprocal        0.5
                                     OLMC1             0.7
                                     OLMC2             0.7
Managerial                           OLMC3             0.7
Commitment                           OLMC4             0.8
                                     OLMC5             0.7
                                     SP1               0.8
System                               SP2               0.8
Perspective                          SP3               0.8
                                     OPEX1             0.7
                                     OPEX2             0.8
Openness and                         OPEX3             0.8
Experiment                           OPEX4             0.7
                                     KTR1              0.8
Knowledge                            KTR2              0.9
Transfer and                         KTR3              0.7
Integration                          KTR4              0.6
                                     Managerial
                                     Commitment
                                     System            0.6
                    Organizational   Perspective
                    Learning         Openness and      0.8
                    Capability       Experiment        0.8
                                     Knowledge
                                     Transfer and      0.7
                                     Integration
                                     PERNEC2           0.5
                                     PerfNEC3          0.7
                                     PerfNEC4          0.8
Non-Economic                         PerfNEC5          0.8
Performance                          PerfNEC6          0.8
                                     PerfNEC7          0.8
                                     PerfNEC8          0.7
                                     PerfEC1           0.8
Economic                             PerfEC2           0.9
Performance                          PerfEC3           0.8
                                     PerfSEF1          0.9
Social                               PerfSEF2          0.9
Effectiveness                        PerfSEF3          0.9
                                     Non-Economic
                                     Performance       0.9
                                     Economic
                    Performance      Performance       0.6
                                     Social
                                     Effectiveness     0.7

First Order          Cronbach        CR         AVE      VIF
Constructs           [alpha] > 0.70  > 0.70     > 0.50   < 3

Donor Orientation    0.778            0.857      0.6     YES
Inter Departmental   0.713            0.838      0.633   YES
Coordination
Peer Orientation     0.799            0.869      0.624   YES
Beneficiary          0.774            0.869      0.689   YES
Orientation
                     0.82             0.857      0.634   YES
Innovation           0.871            0.912      0.722   YES
Proactiveness        0.737            0.883      0.791   YES
Risk                 0.682            0.825      0.611   YES
Reciprocal           0.771            0.897      0.813   YES
                     0.807            0.852      0.71    YES
Managerial           0.772            0.842      0.517   YES
Commitment
System               0.74             0.852      0.658   YES
Perspective
Openness and         0.748            0.841      0.571   YES
Experiment
Knowledge
Transfer and         0.764            0.848      0.587   YES
Integration
                     0.826            0.858      0.57    YES
Non-Economic         0.841            0.881      0.518   YES
Performance
Economic             0.784            0.874      0.698   YES
Performance
Social               0.837            0.902      0.754   YES
Effectiveness
                                      0.877
                     0.85                        0.59    YES

Table 3. Discriminant validity test (HTMT)

      BFO    DO     EC     IC     INN    KTR    LOR    MC

BFO
DO    0.442
EC    0.036  0.107
IC    0.345  0.536  0.097
INN   0.190  0.143  0.175  0.188
KTR   0.265  0.294  0.210  0.257  0.195
LOR   0.296  0.290  0.278  0.266  0.305  0.875
MC    0.128  0.193  0.155  0.178  0.210  0.278  0.812
MKTO  0.852  0.935  0.167  0.825  0.233  0.363  0.403  0.236
NEC   0.200  0.269  0.386  0.158  0.146  0.302  0.369  0.189
OPEX  0.213  0.110  0.249  0.179  0.220  0.516  0.950  0.290
PO    0.415  0.326  0.208  0.236  0.152  0.218  0.289  0.166
PRO   0.089  0.122  0.114  0.129  0.405  0.132  0.201  0.137
Perf  0.235  0.258  0.779  0.192  0.219  0.312  0.420  0.224
REC   0.097  0.062  0.118  0.089  0.156  0.182  0.285  0.222
RISK  0.163  0.226  0.177  0.187  0.412  0.302  0.416  0.193
SEF   0.276  0.135  0.434  0.174  0.212  0.139  0.271  0.153
SEO   0.209  0.204  0.218  0.224  0.918  0.294  0.439  0.277
SP    0.282  0.254  0.203  0.150  0.275  0.460  0.890  0.286

      MKTO   NEC    OPEX   PO     PRO    Perf   REC    RISK   SEF

BFO
DO
EC
IC
INN
KTR
LOR
MC
MKTO
NEC   0.277
OPEX  0.260  0.350
PO    0.838  0.151  0.239
PRO   0.181  0.113  0.152  0.165
Perf  0.339  1.035  0.402  0.262  0.137
REC   0.122  0.096  0.301  0.101  0.230  0.161
RISK  0.289  0.184  0.384  0.231  0.556  0.255  0.471
SEF   0.316  0.363  0.281  0.307  0.083  0.756  0.191  0.242
SEO   0.306  0.198  0.372  0.232  0.831  0.286  0.660  0.995  0.272
SP    0.325  0.240  0.707  0.227  0.167  0.297  0.099  0.365  0.230

      SEO    SP

BFO
DO
EC
IC
INN
KTR
LOR
MC
MKTO
NEC
OPEX
PO
PRO
Perf
REC
RISK
SEF
SEO
SP    0.344

Table 4. Direct and indirect effects

                      Direct     Indirect
Hypothesis            Effects    Effects    t -Values

MKTO ___ PERF         0.15 (*)              2.70
SEO___ PERF           0.10                  1.50
LOR __ PERF           0.27 (*)              4.30
MKTO____ LOR          0.25 (*)              4.0
SEO ____ LOR          0.303 (*)             3.73
MKTO --LOR -- - PERF             0.066 (*)  2.96
SEO -- LOR -- PERF               0.080 (*)  2.87

                      Bias Corrected
                      Confidence Interval
Hypothesis            Lower    Upper  Results
                      Level    level

MKTO ___ PERF          0.035   0.255  H1: YES
SEO___ PERF           -0.061   0.216  H2: NO
LOR __ PERF            0.153   0.377  H5: YES
MKTO____ LOR           0.123   0.361  H3: YES
SEO ____ LOR           0.136   0.446  H4: YES
MKTO --LOR -- - PERF   0.026   0.108  H6a: Partial Mediation
SEO -- LOR -- PERF     0.039   0.146  H6b: Indirect Effect

Note: MKTO = Market Orientation, SEO = Social Entrepreneurial
Orientation, PERF = Performance, LOR = Learning Orientation, (*) = P <
0.05
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