Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2021 Vol: 20 Issue: 2

Strategic Leadership Capabilities and Sustainable Competitive Advantage in Private Universities

Omar Rabeea Mahdi, Applied Science University

Islam A. Nassar, Applied Science University

Mahmoud Khalid Almsafirc, Design for Scientific Renaissance (DSR) Jalan Ampang Putra

Abstract

Purpose: Strategic Leadership (SL) has been established to be significant to organizations. However, the theories and researches in SL were not able to contribute to establishing academic strategic leadership ASL’s importance in the achievement of sustainable competitive advantage (SCA) in higher education. Also, there is insufficient curiosity on the effect of human and social capital development to sustain competitive advantage. This study aims to determine the effect of strategic leadership capabilities (SLC) in achieving SCA from a strategic perspective in private universities.

Design/Methodology/Approach: Literature has supported the need for the formulation of a hypothesis to achieve a specific objective. In this study, the methodology used was a quantitative survey design. A deductive approach was used to be able to utilize SEM in examining the relationship among the study variables. 44 private universities composed in Iraq the statistical population of this study. The respondents are 525 academic leaders from various positions.

Findings: There is a significant relationship that can be observed between SLC and SCA, from the results of the statistical analysis of this study. To be specific, results showed that there is a need for private universities to utilize, maintain, and develop the human and social capital of their respective universities to produce greater SCA.

Research Limitations/Implications- This study may provide future researchers a basis for further investigations and studies regarding subjects that are related to this study, for it added substantial evidence and framework for great group’s view of strategic leadership and resource-based view (RBV). The results of this study also established the effect of RBV as a subordinate theory that links the study variables, SLC and SCA, to each other.

Practical Implications: This study may provide awareness to the heads of organizations about the implementation of SL not just in a local setting, but also internationally, regardless of the environment, whether general or academic.

Originality/Value: There is an abundance of studies on this topic, but only on a qualitative approach. This study may contribute to solving the problem of scarcity of SL and SCA literature that is quantitative.

Keywords

Strategic Leadership Capabilities, Human Capital, Social Capital, Competitiveness, Core Competence, Resource-Based View (RBV).

Introduction

It is a complicated task that must be supported with reliable knowledge, to achieve and increase the global economic value of organizations in the 21st century (Halawi et al., 2005). The global economy, more than any other factor, has created the need for the top management team to effectively exercise strategic leadership in organizations (Ireland & Hitt, 2005). Several challenges are being confronted by the academic world such as limited resources, increased global competition between universities, high-quality predictions, many conflicting demands both internal and external and the right balance between education and research. These are driving forces that demand the academic environment to change due to internal and external causes (Moore & Diamond, 2000) and for the organizations to survive the said environment, staying competitive is one of the ways (Ainasoja et al., 2012). Consequently, the measurement of a country’s competitiveness in higher education has become a focus on variated competitiveness indices and introduced to rank countries, such as the global competitiveness index by the World Economic Forum (WEF) (Sala-i-Martín et al., 2014) and the world competitiveness ranking by the Institute for Management Development (IMD) World Competitiveness Centre (Garelli, 2014). Simultaneously, several researchers attempted to create regional indices based on the idea that each region within a country can have different characteristics and competitiveness levels (Charles & Zegarra, 2014; Huggins & Izushi, 2008). In Iraq, private universities as educational institutions like other institutions seek to survive and grow in the market, and are working to develop their strategic resources to ensure the achievement of their goals. However, universities are increasingly facing new challenges, including financial and non-financial challenges, local and international competition, and the pressures of the diverse and changing labor market requirements (Almassoudi, 2007). In the changing world of today, private universities are influenced by some radical changes in the political, social, and economic aspects. They have embarked on a heated competition among themselves, between themselves, and also with the public universities (Taka, 2010). Private universities are facing complex challenges in attaining their objectives as well as achieving sustainable competitive advantages. To achieve SCA, strategic resources and capabilities are few of its possible sources (El Shafeey & Trott, 2014). A theoretical framework of the RBV of an organization was formulated by Barney (1991 & 1995); Barney & Arikan (2001); Barney & Clark (2007) to explain sources of SCA (El Shafeey &Trott, 2014). According to the said resources, there are four (4) key elements in achieving SCA (El Shafeey & Trott, 2014). First, there must be two (2) assumptions on the nature of resources of an organization, namely heterogeneous and immobile. This is under the premise that there are resources that can be very costly or inelastic in supply (Barney, 2007). Second, the firm is an organization composed of tangible and intangible resources which it controls, for the said resources are necessary for the formulation and implementation of strategies that will develop the efficiency and effectiveness of the organization’s performance (Barney & Clark, 2007). However, intangible assets like a channelling of intellectual capital are now more significant in modern business communities, while tangible assets and capitals no longer have a huge contribution in producing competitive advantage (Halawi et al., 2005). Third, an organization must acknowledge that there are certain sets of skills and resources that bring upon SCA (Amit &Schoemaker, 1993; Barney, 1986; Mahoney & Pandian, 1992; Rumelt, 2003; Wernerfelt, 1984). Three (3) schools of thought that are different but closely related to each other are being tapped also by RBV, namely: RBV of the firm; the dynamic capability based view of the firm; and the competence view of the firm. Regardless of their differences, some researchers believe that the said schools of thought may be viewed as a single or one school thought, for they share the same fundamental theoretical structure (El Shafeey & Trott, 2014). In this regard, it is the characteristics of the resources that must be considered if they can be a source of SCA, and not the resources themselves (Barney & Clark, 2007). According to Barney & Clark (2007), there are different types of resources. These resources may have different effects to organizations as well, which may imply that not all firms have the potential of achieving SCA. To acquire potential, an organization resource must possess the following four (4) attributes namely: valuable (V); rare (R); imperfectly imitable (I); and organization (O). Together, these attributes are known as the VRIO framework (Mahdi et al., 2019).

The fourth and final element in achieving SCA is the competitive position of an organization in its product market. The ability to produce more impact in the economy than a marginal (breakeven) competitor is a manifestation that the organization has a competitive advantage (Peteraf & Barney, 2003). According to Porter (1985), unhealthy competition in the market may be the result if organizations will focus on improving operational effectiveness than its strategic positioning. Operational effectiveness pertains to the ability to execute tasks more efficiently than its competitors while strategic positioning refers to the execution of tasks in a different way than their competitor does, or it may also refer to the performance of various activities than their competitors (Porter, 1996). Currently, it is more appropriate to increase the performance of the organization to achieve a competitive advantage position (Raduan et al., 2009).

To achieve strategic competitiveness in an extremely unpredictable environment have now, effective SL practices must be performed and implemented (Ireland & Hitt, 2005). The leaders in the educational environment need to create and develop a new vision for the universities’ future. There is a great difference between the structure of business organizations and a university, for the latter possesses quite independent departments within it (Ainasoja et al., 2012).

Presently, all sorts of activities are being tested and made by the institutions to provide their managers with comprehensive guidelines of SL in the modern context (Ireland & Hitt, 2005). According to Hitt et al. (2010a), one must consider the fact that the global economy has become more complex and dynamic, and while it promises opportunities, it also brings threats to the survival of organizations. Also, Hitt et al. (2010a) believes that there must be an emphasis on the need for effective SL practices, not just for the development of its performance alone, but to ensure its maintenance and survival as well. Luthans & Slocum (2004) likewise believed that the dynamic nature of the economic, technological, socio-political, and moral/ethical atmospheres necessitates an updated perspective, leadership theories, and practices. Approximately twenty years ago, a transfer of focus from “supervisory” leadership to “strategic” leadership was demonstrated by management scholars to adapt to the ever-changing environment of organizations (Boal & Hooijberg, 2001; House & Aditya, 1997; Narayanan & Zane, 2009). Considering this, further substantial research on strategic leadership is indeed needed.

It was emphasized by Hitt & Ireland (2002) that the management of human and social capital is the core of SL. Human capital is the repository of knowledge and skills of the organization while social capital makes critical resources accessible to the organization. From a strategic viewpoint of RBV (Barney, 1986; Haanes & Fjeldstad, 2000; Prahalad & Hamel, 1990), organizations are heterogeneous institutions that are distinct for their unique resource base, where human and social capital is the strategic assets of the organization. Clearly, it is an established knowledge that developing human capital (DHC) and developing social capital (DHC) are the organization’s strategic assets (Hitt & Ireland, 2002). In order to develop human capital (HC) and social capital (SC), SL as the capability of the organization, will develop the said capital through capability-based approach (Teece et al., 1997). This study attempts to examine the relationship between SLC and SCA in Iraqi private universities.

Strategic Leadership

Various works in strategic management have established and instituted the use of the term “strategic leadership” (SL). Several works as well have indicated the necessity of an organization’s preparation and mechanisms to adapt to the dynamic nature of the business environment, while maintaining the competitive advantages of the organization. This further supports the concept that SL is needed in the management of an organization, for it to be effective and progressive in competitive business environments (Sosik et al., 2005).

Possessing a full grasp on the essence of SL includes an emphasis on the activities that effective leaders do to generate a strategy-focused organization (Rumsey, 2013). According to Elenkov et al. (2005), SL is a theory where the leaders are viewed as individuals who have the ability to strategically envision, anticipate, innovate, adapt, and mobilize people to be versatile and be responsive to the demands of the dynamic environment of the organization. From the processes’ perspective, Sosik et al. (2005) see SL as a set of procedures that will result in the determination of the level of performance in which the organizations will be able to establish a network of people, technology, work processes, and business opportunities. This network aspires to contribute to the social, economic, and intellectual capital of the stakeholders, society, and employees. From the perspective of “educational strategic leadership”, Hamidi (2009) brought in a definition that states that employee empowerment, creation of common vision, teamwork development, dissemination of creativity and innovation, and creation of strategic change and cultural development are what consists strategic leadership. Another perspective is the creating value, in which SL is the one that guide, impact, enable and develop HC. According to Memon et al. (2009), those actions create value for the organization. Based on the RBV, strategic leaders need to have the vital resources, capabilities, and/or competencies as its center, for these are possible factors that bring upon SCA and sustained future success. Some scholars have supported this perspective like Hitt & Ireland (2002) who made a good opinion that SL is about possessing important resources, including but not limited to connections and partnership with different organizations (social capital) and working up great teams (human capital).

Still, under the same view, Crossan et al. (2008); Hitt et al. (2010a) believes that SL is the capability of a leader to predict, foresee and cause the organization to remain in its successful state and versatility to promote strategic change that is responsive to its current situation. Besides, SL was defined by Boal (2004) as the one that improves, centers, and empowers the human and social capital and capabilities of an organization to reach the actual time it takes to acquire opportunities and threats. Moreover, according to Hirschi & Jones (2009), SL is the preparedness of the leader to every future circumstance and the capability of the leader to manage the organization’s vital resources in order to achieve SCA. Under the same circumstance, Jooste & Fourie (2009) believe that SL requires the adaptation and integration of the internal and external business environment of an organization. Managing and being exposed to complex information processing is included also. SL also is the wisdom and vision of the leader in building and formulating plans and constructing decisions in the dynamic and confusing environment of an organization (Guillot, 2003).

A new element has been added in the definition of SL by Davies & Davies (2004) that states that inspiration and support to others are not just the factors in achieving a vision for the organization, but also the mobilization of employees and the organization to implement plans and strategies. An SL definition was also introduced by Hinterhuber & Friedrich (2002) of having a relationship with the golden rule that states “Lead others as you would like to be led”, in which SL is the ability of the person to construct a vision and to be a model which may increase the value of the organization. It is deemed as the combination of science, art, and the heart. Under this basis, this definition was adopted in a present study that was presented by Hitt & Ireland (2002); Ireland & Hitt (2005); Mahdi & Almsafir (2014) where SL is believed to be the ability to predict, plan, adapt and cooperate with others to develop the human and social capital to achieve SCA in favor of the organization.

Strategic Leadership Capabilities

Effective SLC is necessary for the new competitive landscape expected for the 21st century and was emphasized by the strategic literature review (Hitt et al., 1995; Hitt et al., 2010b; Ireland & Hitt, 2005). Exploration and maintenance of distinct core competencies, DHC and DSC, has been integrated by Hitt et al. (2010b) to jointly and effectively manage the organization’s resources. To attain a competitive advantage, strategic leaders must constitute the organization to utilize its capabilities, which are organized from the resources of an organization, and develop those resources and implement strategies that will provide an advantage, in favor of the organization (Sirmon et al., 2007). To be specific, strategic leaders must utilize and retain at the same time, the core competencies and improve and maintain the human and social capital of the organization (Hitt et al., 2010b). Nevertheless, SL’s nature is the management of human and social capital (Hitt & Ireland, 2002). As a result, SLC centers on DHC and DSC. The characteristics of human capital include individual-level knowledge, skills, and capabilities. On the other hand, social capital’s characteristics are composed of social awareness and self-management, cooperation, coordination, commitment, or networks as well as a relational ability for engaging others (McCallum & O’Connell, 2009). Also, HC and SC were recognized by McCallum & O’Connell (2009) as partially dependent on each other. This is the reason why some leadership capabilities can be implied as both HC and SC. Thus, for the requirements of this study, DHC and DSC have been chosen in preference to be both an SLC according to Hitt & Ireland (2002).

Developing Human Capital

A known definition of HC states that it is the “knowledge and skills of a firm’s entire workforce” Hitt et al. (2010b). As mentioned earlier, it is considered as a vital resource of an organization that corresponds to the employee’s knowledge, skills, capabilities, commitment, know-how and ideas, and health, which increases the value of an organization in the market (Birasnav et al., 2011; Ulrich et al., 1999). The relationship between HC variables and career success has been affirmed by Ballout (2007). According to him, the theoretical foundation for the realization of the individual approach to career success attainment has been provided by HC. From another point of view, HC also refers to activities and procedure concerning skills, values, socialization, and intellectual development of an employee, which may result in employees’ satisfaction and performance which will also lead to the improvement of the overall performance of the organization (Dizgah et al., 2011).

Under RBV, Wright et al. (1994) contend that SCA can be obtained from a “pool of human capital”. Competitiveness depends on tacit knowledge than explicit knowledge. Therefore, it is important to utilize workers’ knowledge to attain internally-developed core competencies that are valuable, rare, inimitable, and non-transferable (Barney, 1991).

HC has been established to be a contributor to economic development and individual well-being at both organizational and national levels, in business practice and modern-day studies (Au et al., 2008). In relation to this, policymakers recognize the investment in human resources as a key driver of competitiveness (Akpinar et al., 2017). Based on Porter’s diamond model, human resources along with knowledge resources are identified as important factor conditions of spatial competitiveness, innovation, and growth (Brosnan et al., 2016; Porter, 1990). The importance of investing in DHC has been emphasized by Hitt et al. (2010b); Hitt et al. (2010a); Hitt & Ireland (2002). From the HC’s point of view, employees are capital resources that need to be invested upon. Researches have proved that organizations that provide development and utilize DHC achieve a competitive advantage and acquire increased levels of organizational performance. Also, these investments in HC have been proven to produce an increased return, leaders, high-level project opportunities, and promotion (Bontis & Serenko, 2009; Harley, 1999; Hitt & Ireland, 2002; Motley, 2007; Ulrich et al., 1999). These benefits are inferred by the employees who are related to the creation of HC (Birasnav et al., 2011).

From another perspective, SL is an essential component of DHC as human competence is extremely significant in determining the level of excellence. In this regard, it is equally important to dig into HC’s importance and dwell also on processes that will further develop it to be able to increase the value of the organization and establish SCA (Seemann et al., 2000). Further, competitiveness is impelled by the creation of HC, which is now a source of competitive advantage in the knowledge economy. For this reason, it was suggested by Ireland & Hitt (2005) that it is necessary to continuously value, develop, and organize HC with other characteristics.

Developing Social Capital

According to Balkundi & Kilduff (2006), SC exists as an essential component in the sharpness of an individual’s formation of socialization as a concept and its structure. Also, SC has been described by Prusak & Cohen (2001) to be present in the association among people, where confidence, shared knowledge and behaviors bind the members of teams, which promotes cooperation. Adler & Kwon (2002) state the definition of SC as follows: “social capital is the goodwill available to individuals or groups. Its source lies in the structure and content of the actor’s social relations. Its effects flow from the information, influence, and solidarity it makes available to the actor”. Also, the internal and external relationships are being considered in the achievement of the tasks of the organization. Since SC is a value of an organization, there must be reconciliation between the SC within and outside the organization, facilitated by a strategic leader (Adler & Kwon, 2002; Hitt &Ireland, 2002; Prusak & Cohen, 2001). Another literature accompaniment, Hitt et al. (2003) refers to Internal SC as those about the relationship among strategic leaders and the people they lead while the external SC refers to the facilitated relationship among organization’s work units. Proper handling of DSC by strategic leaders results in desirable business outcomes (McCallum & O’Connell, 2009).

Sustainable Competitive Advantage

The term “competitive advantage” has been defined using the concepts of resources and the characteristics of an organization which is the vital factors in the achievement of an organization’s desirable performance in the market according to Christensen & Fahey (1984), Kay (1995), and Porter (1985). It has been supported by Barney (1986), Peteraf (1993), and Teece et al. (1997) that competitive advantage is sourced from the characteristic resources that are distinct and inimitable. According to Li & Zhou (2010), the competitive advantage also offers tangible (securing resources) and intangible benefits (support from dominant institutions) to the organization.

On the other hand, four conditions that need to be met to attain SCA were distinguished by Peteraf (1993): superior resources (heterogeneity within an industry), ex-post limits to competition, imperfect resource mobility, and extant limits to competition. A definition as well was developed by Barney (1995) which states that SCA is the resources and capabilities in the organization that are diverse and immobile, which also uses four attributes or empirical indicators as its basis, as follows: value (V), rareness (R), imperfectly imitability (I), and organized (O) to be captivated and utilized as a source of SCA. A comparison was conducted by Hunt & Morgan (1995) between neoclassical theory and comparative advantage theory of the firm. It is believed that competitive advantage in the marketplace is the result of comparative advantage in resources. SCA has been negatively defined by Barney & Hesterly (2009) also who believes that it cannot be achieved if there is inability of an organization to imitate the source of advantage or to produce a better offering. Therefore, through all the definition, the one that will be used in this study is according to Barney (1995), Barney & Clark (2007), and Mahdi et al. (2019) where SCA is the resources, capabilities, competencies, core competencies that are critically heterogeneous and immobile depending on four attributes or empirical indicators of the value (V), rareness (R), imperfectly imitability (I), and organized (O) to capture and exploit value of them

Process of Sustainable Competitive Advantage

The SCA process is a dynamic set of procedures that addresses the present needs of an organization without compromising the ability of the organization to meet future needs. In this study, four distinguishable points can be deduced to SCA process according to Mahdi et al. (2019) as follows: subjects, media, objective, and updating.

Subject

The first component of the process is the subject, which are the organizational resources (RES). It is the source of SCA once it satisfies the five attributes needed, valuable, rare, costly to imitate, and organized (Barney & Clark, 2007). Hafeez et al. (2002) defined resources as anything “tangible” as well as “intangible” owned or acquired by a firm; also, they classified resources into three subcategories of physical, intellectual, and cultural assets. Each organization has a bundle of resources, but not every firm can put its resources to the best use. Organizations vary in how they leverage their resources (Javidan, 1998).

Media

The second component of the SCA process is the media, a sub-process that converts resources and skills from the subject phase to capabilities, competencies, and core competencies. The ability of a corporation to utilize its resources is referred to as the Capabilities (CAP). It is characterized through its specific function (e.g. human resource management capabilities) and contributes to the generation of high-profit margins and are the clear market winners in securing market share (Hafeez et al., 2002). By reviewing previous empirical studies, it can be observed that there are three types of capabilities; specific or individual, processes, and organizational capabilities (Galbreath, 2005; Tuan & Yoshi, 2010). Another classification of capabilities lies with contemporary research which is, operational or dynamic (Helfat & Peteraf, 2003). According to Savory (2006) and Javidan (1998), dynamic capabilities are in a higher hierarchical order than operational capabilities.

Competencies (COM.) are the cross-functional integration and coordination of capabilities (Javidan, 1998). It has been established by almost all of the authors that inside elements such as unspecific isolated components and specific integrated idiosyncratic components are sources of competitive advantages (Jüttner & Wehrli, 1994). Three categories of competencies were introduced by Heikkilä & Cordon (2002) as follows: distinctive competencies, which are the most important in a company; necessary competencies which is the same as what the competitors have but which are needed for operational reasons; and protected competencies, which if improperly utilized can pose damage to the organization.

Referred to as one of the popular strategic management concepts, Core Competence (CC.) was used by Prahalad & Hamel (1990) to manage the capabilities within the broadened organization under the competency-based perspective. Most studies revolve around the notion that the main source of competitive advantage is the core competencies, which is a collection of skills and technologies. It is also strategically and continuously versatile, which is a characteristic responsive to the dynamic environment of organizations (Javidan, 1998). This dynamic characteristic is identified using two elements: resource redeployment and routines reorganization. These elements aid the organization’s performance in its competition within the market (Ljungquist, 2008).

Objective

The objective is the third element of the SCA process. It is referred to as a secondary process that is intended to be inimitable to the organization’s competitors. It is insufficient to maintain the important assets of the organization, resources must be valuable, rare, costly to imitate, and non-substitutable to sustain competitive advantage, according to Barney (1991), under RBV. However, this was further improved by Barney (1995) himself and converted VRIN to VRIO to ensure that the resource may be able to address the question, “is a company organized to exploit these resources?” Barney & Clark (2007) believe that VRIN is the improved framework of the RBV, which was also adopted by Rothaermel (2013).

Updating

The fourth element of the SCA is the process is updating. This phase reviews the SCA process in its dynamic nature. It was suggested by Hitt et al. (2010a) that attainment of SCA entails survival in the wild environment while formulating competitive advantage, for the SCA process exists in a continuum where the anticipation of the declination of a present competitive advantage is made through the achievement of a new one. It is a never-ending process.

Hypotheses Development

Based on the theoretical framework, the hypothesis was developed to examine the relationship between SLC and SCA amongst private universities in Iraq. The study suggested that an appropriate theoretical framework is needed that should be developed to reflect the environment within which the study was conducted. The theoretical framework in this study emphasizes the importance of the influence of SLC in SCA as shown in Figure 1.

Figure 1 Theoretical Framework

A study was conducted to examine the critical components of SL by Hagen et al. (1998), which was further improved and framed by Hitt et al. (1995). The findings of the said study determined the coordination of critical components that are introduced by the American chief executive officers (CEOs) to the existing model of SL of the organization based on the great group's view of SL (Ireland & Hitt, 2005). According to Hitt et al. (1995), the ranking of critical components is acceptable except for the idea of prioritizing HC development over the exploitation and maintenance of the core competencies of the organization for the 21st century. This is supported by a theoretical study of Hitt & Ireland (2002) which affirmed that both HC and SC create value for the organization and are vital to the achievement and sustenance of competitive advantage. Under RBV, (Barney, 1986; Prahalad & Hamel, 1990), said sustain competitive advantage has been tried by many organizations already through the utilization of resources and capabilities. It is widely assumed that development of HC and SC to produce tacit knowledge, which does not improve SCA like explicit knowledge’s performance, according to the great groups view of SL (Ireland & Hitt, 2005), RBV (Barney, 1991, 1986; Haanes & Fjeldstad, 2000; Prahalad & Hamel, 1990; Wernerfelt, 1984). However, it is the development of HC and SC which affects positively the creation of SCA (Hirschi &Jones, 2009; Hitt & Ireland, 2002).

The implementation of the mechanisms of the organization is important in achieving competitive advantage, which is why the assurance that the gathered HC who will implement the said mechanisms has the ideal combinations of resources is very significant (Seemann et al., 2000). HC is a special resource for its uniqueness. In this regard, HC has a clear effect on the achievement of competitive advantage and economic growth through the participation of decision-making process venture capitalists in the investments in entrepreneurial ventures (Hitt & Ireland, 2002).

Under RBV (Barney, 1991; Barney & Wright, 1998; Coff, 1997; Teece et al., 1997; Wernerfelt, 1984), the distinctness of sources of SCA must be established. In relation to HC, the detainment of employees that are the source of distinctness produces “human capital advantage”, which has been supported by Boxall (1996) on intellectual capital and its components. Further, HC may be a strategic asset of the organization when knowledge and learning’s associated to them are valuable and unique, which generates competitiveness and more profit (Subramaniam & Youndt, 2005). Also, the variations in the knowledge, skills, and capabilities of humans increase the value of HC as a source of competitive advantage (Dizgah et al., 2011). According to this point, intangible resources or capabilities that are valuable, rare, costly to imitate, and difficult to substitute or organized are distinct as sources of SCA of organizations. Hence, there is a positive relationship between HC and SCA.

It is important to possess all vital resources that an organization needs to survive in the international arena; however, some resources are inaccessible (Hitt et al., 2001). A solution to this is the establishment of linkages with organizations that possess for the development of SC, regardless of its formal or informal connection (Ireland et al., 2001). Engagement in these types of networks is usual in the organizational setting (Gulati et al., 2000), which sometimes provides opportunities to interact with the organization’s rivals in the market (Ireland et al., 2002). This relationship makes knowledge, technology, and all other capabilities that are helpful to the organizations in achieving SCA, available (Ireland et al., 2001). Aside from the attainment of SCA, it may also support the survival and maintenance of the organization’s SCA.

Different mechanisms on SC are created to achieve competitive advantage and to increase the quality of organizational performance. Effectively utilizing the competition values and interaction with other organizations increases the potential return of financial investments (McCallum & O’Connell, 2009). Under RBV, developing SC may also imply the development of a strategic resource. Also, the uniqueness of SC may affect all sides of organizational performance, which makes its development a priority in the achievement of SCA (Ellinger et al., 2011). However, there have been few attempts to establish the relationship between SLC (HC, SC) and SCA. As strengthened by the literature mentioned in this study, it is therefore hypothesized that SLC affects SCA significantly, as follows:

H1 Strategic leadership capabilities have a significant relationship with the sustainable competitive advantage of private universities.

Method of Study

This study uses quantitative methods through deduction, under the scientific/positivistic paradigm. This study applied structured survey questions that allowed for the collection of data that is easier to quantify as the same questions were asked of all respondents. The standardization of the questions posed to respondents eliminates bias (Settle & Alreck, 1984). The use of a survey method was believed to help the researcher to obtain a sufficient number of respondents within the budget and resource constraints and reduced non-response bias.

Structural equation modeling, or SEM, was the statistical analysis method used in this study to examine the interactions between SLC and SCA. The data were inputted and analyzed using a statistical package for the social sciences (SPSS) version 21 and the analysis of moments structures (AMOS) version 21 software. Participant assessment is included in the process of screening procedures and preparation of data. During the preliminary analysis, factor analysis tests were used to determine the reliability of data. This was conducted to ensure the inclusion of items into the proper variable. The single method used in dealing with missing data in AMOS represents a direct approach based on full information maximum likelihood estimation (MLE). It uses for determining parameter estimates which have been found to have the least bias (Byrne, 2001).

The statistical population of this study comprised 44 private universities in Iraq. The census method was used due to the few numbers of private universities. In the current study, the respondents are the academic leaders working at higher organizational levels with a leadership position in a private university.

There were a total of 540 questionnaires given out, with 462 of those being collected. Out of those 462, 418 usable questionnaires were used and 44 (8%) cases provided incomplete questionnaires were dropped; thus the response rate of (418/540) 100 = 77%. The missing data include 11 cases deleted from the dataset for processing the missing data. In addition, 13 cases are deleted from the dataset for processing the outlier. Thus, the remaining data are 394 cases. The most important consequence of a low response rate is the non-response bias, caused by people refusing to participate. This non-response bias can be reduced through the implementation of a pilot study (Settle & Alreck, 1984), which is useful in helping to provide indications of the direction of the bias. However, in this study, the response rate was sufficient.

Measurements of Study

All theoretical variables are operationalized using previously developed multi-item scales or based on theoretical concepts from related research. The measure of SLC is categorized into two constructs: developing human capital and developing social capital. In this study, there was a total of 40 items used to develop a measure of SLC adapted from Mahdi & Almsafir (2014) according to Hitt & Ireland (2002), Ireland & Hitt (2005), Hitt et al. (2010b). Of these, 20 items were related to measure the DHC, and also 20 items to measure the DSC. The respondents were all the academic leaders to measure strategic leadership capabilities in all the private universities. The measure of SCA is categorized into four constructs: RES., CAP., COM., and CC. This study attempted to measure SCA using 16 items, where 4 items are for each construct and was used to develop the measurement of SCA adopted from Mahdi et al. (2019). The respondents were asked to rate their universities in comparison to their top competitors in the same area from the educational environment on measures of SCA. In this study, all these constructs and items were measured using a five-point Likert-scale and were based on the respondent’s perspective. Furthermore, the survey questions used for this study conformed to three types of validity: content validity, criterion validity, and construct validity according to DeVellis (1991), Holmbeck (1997), Malhotra (1997), Punch (2005), and Zikmund et al. (2013). Typically, in SEM construct validity focuses on the extent to which data exhibits evidence of convergent validity and discriminant validity (Byrne, 2010; Hair et al., 2010a; Kline, 2011).

Results

The 44 private universities were chosen by the study sampling where there are a total of 540 academic leaders as the respondents. 86% of the total respondents or 462 respondents’ questionnaires were collected. After the screening of the responses, where questionnaires with missing data, outliers, and partially completed answers were omitted from the final set, only 394 cases remained. The gender ratio was 85% to 15%, for males and females, respectively. With regard to the age of the respondents, 1% is between 26 - 30 years and between 31 - 35 years, 9% were between 36 - 40 years, 31% were 41-45 years, and finally, 58% were more than 45. All (100%) of the respondents have a Ph.D. In terms of the position distribution of the academic leaders, 6 (2%) are chancellor, 12 (3%) are vice-chancellor, 58 (15%) are deans, 111 (28%) are deputy dean, 198 (50%) are head of the department, 4 (1%) are heads of divisions, and 5 are heads of centers (1%). Furthermore, in terms of their working experience, 7 (2%) of the academic leaders had less than 5 years of experience, 42 (11%) had between 5 and10 years of experience, 160 (41%) had between 11 and 15 years of experience, and the remaining 185 (47%) had more than 15 years of experience.

Goodness of Measures

Factor analyses and reliability tests are used in a preliminary analysis to determine the goodness of data. The reliability measures show that the Cronbach's alpha coefficient for this study is 0.98 for SLC with 40 items and 0.93 for SCA with 16 items. The closer the Cronbach's alpha is to 1, the higher the internal consistency reliability (Sekaran, 2003). Each variable shows Cronbach's alpha readings of acceptable values of above 0.70 (Hair et al., 2006). It demonstrates that all the variables used in the study are reliable and can be used for further analysis. Furthermore in SEM, along with Cronbach's alpha, the reliability of the survey questionnaire could be measured by using construct or composite reliability (CR) and average variance extracted (AVE) (Awang, 2012; Fornell & Larcker, 1981).

The 40 items were factor-analyzed to examine the factor analysis to measure the exogenous variable of SLC. An initial factor analysis was conducted on 40 response items that measure the exogenous variable of SLC. The result indicates that the factor loading of the SLC constructs has a value higher than .50, implying the significance of overall correlations within the correlation matrix and indicating that all the forty response items are loaded in the exogenous variable of SLC (Hair et al., 2010b). Thus, the study concludes that all constructs are valid.

The results of the factor analysis for the SCA have shown that 4 factors were extracted from 16 items of this variable. The result indicates that the factor loading of SCA constructs has a value higher than .50, indicating the significance of overall correlations within the correlation matrix and indicating that all the 16 response items are loaded in the endogenous variable of SCA (Hair et al., 2010a). Thus, the study concludes that all constructs are valid.

Correlation Analysis among Variables

Firstly, the descriptive statistics are computed for all data prior to the stage of conducting the detailed statistical analyses. The mean, standard deviation, minimum values, and maximum values are computed (Table 1). The mean value for SLC is 3.42, while the mean value for the SCA 3.62. The standard deviation value for SLC is 0.74 and 0.73 for SCA. The minimum and the maximum values portray that all the studied items are within the Likert-scale of 1 to 5 as used in the questionnaire. Secondly, the results of the coefficient correlation and Cronbach's alpha for each measure are presented in Table 1. The Pearson product-moment coefficient correlation is used to explain the relationship between the variables under study. The correlation results indicated that all the variables have a positive significant relationship between the exogenous variable of SLC and the endogenous variable of SCA with r = 0.54, p< 0.01. All the values of coefficient correlation are below 0.85, which shows that there is no multicollinearity in the study variables.

Table 1 Inter-Correlation Coefficient and Cronbach's Alpha
Variables Mean Minimum Maximum Std. Deviation 1 2
Exogenous: Strategic Leadership
Capabilities (SLC)
3.42 1.98 4.78 0.74 (0.98)  
Endogenous: Sustainable
Competitive Advantage (SCA)
3.53 1.56 4.94 0.77 0.55** (0.93)

Cronbach's alpha coefficient shown in the bracket in diagonal parenthesis

Measurement Model

Primarily, the hypothesized model of variables has not a good fit with the goodness of fit indices. To achieve an acceptable level of a good fit for the hypothesized model, the CFA and measurement models were conducted. The CFA is conducted in two groups to assess the unidimensionality of the measurement model for each and the overall measurement model. It is also conducted to assess the psychometric properties of measures in terms of testing the convergent and discriminant validity, and the reliability properties of the measures to identify the internal consistency and the adequate fit of the scale items.

The CFA is conducted simultaneously on two latent exogenous first-order constructs included DHC and DSC, which are under the exogenous variable of SLC (second-order construct) (Figure 2a). Based on the CFA, the revised model eliminated 15 items (DHC2, DHC7, DHC11, DHC12, DHC16, DHC18, DSC1, DSC2, DSC3, DSC7, DSC11, DSC15, DSC16, DSC17, and DSC18) were below the recommended value of the cut-off. 50. The CFA is conducted by constraining 25 items as the SLC construct to achieve a good model fit. The factor loadings or standardized regression weight (SRW) of the latent to observed variable should be greater than or equal to .50 for adequate individual item reliability (Byrne, 2010; Hair et al., 2010a), which provides support for convergent validity (Bagozzi & Yi, 1988). The SRW for all values is (≥ 0.50) ranged from 0.50 to 0.98 and it is statistically significant. Squared multiple correlations (SMC) show the contribution of each item to the variable should be greater than, or equal to, the values of 0.10 (Falk & Miller, 1992). The results of SMC range from .25 to .96 and are concluded as being significant. This indicates that the first-order construct and response items are valid. The goodness of fit indices shows that the data fit of the measurement model fit adequately. The Chi-square was 500.46 at p-value=0.000<0.001. Although the Chi-square goodness of fit is still significant due to the large sample size (N=394). According to Hair et al. (2010b) and Jöreskog & Sörbom (1993), the researcher can ignore the absolute fit index of minimum discrepancy Chi-square if the sample size obtained for the study is greater than 200. The df was 274 ≥ 0. The Chi-square/df was 1.83 less than 5.0. The values of GFI and CFI were .91 and .97 respectively, which were above the cut-off 0.90. The RMSEA was .05 less than the threshold value of 0.08. This indicates that all the constructs conform to the construct validity test according to Hair et al. (2010b). The study concludes that all first order construct, second order construct, and response items are valid, indicating a good model fit of SLC variable.

The CFA is conducted on 16 items that were constrained to the endogenous variable of SCA as illustrated in Figure 2b The endogenous variable is the second-order construct which has four first-order latent constructs that include RES, CAP, COM, and CC. Based on the CFA, the results indicated that the SRW was above the cut-off 0.50 for all values response items of SCA which consists of 16 items, ranged from 0.53 to 0.90. It is statistically significant as recommended by Byrne (2010); Hair et al. (2010b) for adequate individual item reliability, which is providing support for convergent validity (Bagozzi & Yi, 1988). Therefore, no item was removed from the model of SCA, since there was no insufficient factor loading. The results of SMC were (≥ 0.10) as recommended by Falk & Miller (1992), where they range from 0.28 to 0.77 and are concluded as being significant. This shows that the first order construct and response items are valid. The goodness of fit indices shows the data fit of the measurement model fit adequately. The Chi-square was 136.21 at p value=0.009. According to Hair et al. (2010b) and Jöreskog & Sörbom (1993), the researcher can ignore the absolute fit index of minimum discrepancy Chi-square if the sample size obtained for the study is greater than 200.The df was 100 ≥ 0. The Chi-square/df was 1.36 less than 5.0. The values of GFI and CFI were 0.96 and 0.99 respectively, which were above the cut-off 0.90. The RMSEA was .03 less than cut-off value 0.08. This indicates that all the constructs conform to the construct validity test according to Hair et al. (2010b). All measures of the measurement model are within adequate levels, indicating the data have fitted the model very well. In other words, a good model fit of the SCA variable has been observed. The study concludes that all first order construct, second order construct, and response items are valid.

Figure 2 (a) Measurement Model of Strategic Leadership Capabilities; (b) Measurement Model of Sustainable Competitive Advantage

Composite Reliability and Convergent Validity

The assessments for the reliability of a measurement model was made using internal reliability, composite or construct reliability (CR), and average variance extracted (AVE) (Awang, 2012; Fornell & Larcker, 1981). The validity was assessed using construct validity, convergent validity, and discriminant validity for each measurement model (Byrne, 2010; Hair et al., 2010a; Kline, 2011). The composite reliability and convergent validity of exogenous and endogenous variables are shown in Table 2. The CR of all latent variables is with values greater than 0.60 recommended by Bagozzi & Yi (1988), which demonstrates adequate CR. The convergent validity could also be verified through AVE. The value of AVE was above cut-off 0.50, indicated a high convergent validity as a recommended value by Hair et al. (2006). This indicates that each construct accounts for 50% or more of the variance of its items.

Table 2 Composite Reliability and Average Variance Extracted
Second Order Construct First Order Construct Number of Items SRW α (Above
0.70)
C.R
(Above 0.60)
AVE
(Above 0.50)
Strategic
Leadership Capabilities SLC
Developing Human Capital
DHC
14 0.98 0.98 0.87 0.78
Developing social capital DSC 11 0.77
Sustainable Competitive Advantage SCA Recourses RES. 4 0.84 0.93 0.91 0.72
Capabilities CAP. 4 0.88
Competence COM. 4 0.81
Core competence CC 4 0.87

Table 3 shows the result of the square root of the AVE to support the discriminant validity of constructs. The diagonal values (in bold) are the square root of AVE while other values are the correlation between the respective constructs. The discriminate validity is achieved when a diagonal value in bold is higher than the values in its row and column. According to Fornell and Larcker (1981), the AVE should be more than the correlation squared of the two constructs to support the discriminant validity. Each square root of the AVE value is found to be more than the correlation, thus discriminant validity is supported or multicollinearity absent.

Table 3 Discriminant Validity
Indices SLC SCA
SLC (0.88)  
SCA 0.62 (0.84)

The CFA was conducted on the overall measurement models of exogenous, endogenous. The adequacy of the measurement model can be evaluated on the criteria of the convergent validity, discriminant validity, composite reliability, and the overall model fit to the data. The overall measurement model was run with 41 response items under 6 first-order latent constructs to assess 2 second-order latent constructs which included the exogenous variable of SLC and the endogenous variable of SCA. Since the overall measurement model did not achieve a model fit, therefore, the explanation of the overall measurement model result was based on the revised model (Figure 3). Based on the overall measurement model, the revised model has eliminated 2 items (DHC5, DHC9), and constrained 39 items to remain to achieve the significant model of the overall measurement model. In addition, set e2 and e5; e16 and e17; and e36 and e38 as free parameter estimates seek to solve the redundant items DHC3 and DSC8; DSC6 and DSC6; and COM2 and COM4 to achieve the significant model fit of the overall measurement model. The overall measurement model was evaluated on the criteria of overall fit. The overall measurement model was evaluated on the criteria of the overall fit. As evidence of convergent validity, the SRW of the latent constructs or items should be (≥ 0.50) (Byrne, 2010; Hair et al., 2010a). All items are loaded significantly on their pre-specified first-order latent constructs and all estimated loadings are either large or equal to 0.50 and are statistically significant. The SRW for all values is adequate ranged from 0.92 to 0.51. The results of SMC were (≥ 0.10) as recommended by Falk & Miller (1992), where they range from 0.26 to 0.85 and are concluded as being significant. This indicates that the first-order construct and response items in the overall measurement model are valid, which in turn confirms to the construct validity test mentioned by Hair et al. (2010a).

Figure 3 Overall Measurement Model of Variables Simultaneously

The CFA of constructs has produced a relatively good fit as indicated by the goodness of fit indices. The Chi-square=940.35, p=0.000. According to Hair et al. (2010b) and Jöreskog & Sörbom (1993), the researcher can ignore the absolute fit index of minimum discrepancy Chi-square if the sample size obtained for the study is greater than 200. The df was 963 more than the cut-off 0. The Chi-square\df was 1.36, less than the threshold value of 5.0. Further, the GFI was 0.90, which is equal to the cut-off value of 0.90. The CFI was 0.96, which was above the cut-off 0.90. Moreover, the RMSEA was 0.03 less than the threshold of 0.08. This indicates that all the constructs conform to the construct validity test according to Hair et al. (2010b). Each exogenous and endogenous construct was allowed to correlate with one another. Furthermore, the overall measurement model of the constructs had confirmed the non-multicollinearity issue (covariance<0.85). Therefore, the study concludes that SLC and SCA variables are totally heterogeneous and valid for further hypothesis testing (Awang, 2012). Table 4 summarizes the goodness of fit indexes of measurement models.

Table 4 Goodness of Fit Indexes of Measurement Models (N = 394)
  Indices Recommended Value Measurement Model
(Exogenous)
Measurement Model
(Endogenous)
Overall Measurement Model
SLC SCA
Items Remain   25 16 39
Chi-square - 500.46 136.21 940.35
p-value > 0.05 0.000 0.009 0.000
Df > 0 274 100 963
Chi-square/df < 5 1.83 1.36 1.36
GFI ≥ 0.90 0.91 0.96 0.90
CFI ≥0.90 0.97 0.99 0.96
RMSEA < 0.08 0.05 0.03 0.03

Structural Model and Hypothesis Testing

The purpose of testing the structural model was to assess the fitness of the model data and the hypothesized relationships between theoretical variables, as shown in Figure 4.

Figure 4 Re-Specified Model of the Study Variables

The final SRW and SMC results of final the model-data fit is shown in Table 5. As evidence of convergent validity, the SRW of the latent constructs or items should be (≥ 0.50) (Byrne, 2010; Hair et al., 2010a). All items were loaded significantly on their pre-specified first-order latent constructs and all estimated loadings are larger than 0.50 and are statistically significant. The study observed that the factor loadings of all items are adequate ranging from 0.51 to 0.92. In addition, the SMC for the exogenous and endogenous variables was achieved and confirmed based on the important criteria to achieve a model fit. The results of SMC were (≥ 0.10) as recommended by Falk & Miller (1992), where they range from 0.28 to 0.85 and are concluded as being significant. This indicates that all the variables and their constructs conform to the construct validity test. The CFA of the model data fit had a good fit with the data as indicated by the goodness of fit indices such as (Chi-square=940.35, p= 0.000, df=693, Chi-square\df=1.36, GFI=0.90, CFI=0.98, and RMSEA=0.03) as recommended by Hair et al. (2010a). This indicates that all the variables and their constructs conform to the construct validity test. The remaining number of items for each variable is as follows: SLC includes 23 items, 12 items for DHC, and 11 items for DSC; meanwhile, SCA has 16 items, resources, capabilities, competencies, and core competencies 4 items each.

Table 5 SRW and SMC of RE-Specified Model
Variable Constructs Path SRW SMC S.E. C.R. p-value
SLC SCA SLC SCA 0.64 0.41 0.11 7.78 ***
SLC DHC SLC DHC 0.91 0.82      
DSC SLC DSC 0.84 0.70 0.13 9.00 ***
  SCA RES SCA RES 0.83 0.69      
CAP SCA CAP 0.89 0.79 0.08 12.35 ***
COM SCA COM 0.81 0.66 0.07 12.03 ***
CC SCA CC 0.87 0.76 0.07 13.77 ***
Ratio; *** p ≤ 0.001

Table 6 illustrates the results of hypothesis testing. The hypothesis states that there is a significant relationship between SLC and SCA. The explanation of the relationship hypothesis was based on the re-specified Model. The Beta estimate of the SLCs on SCA is 0.64. In other words, the Beta estimate for SLCs in the prediction of SCA is significantly different from zero as 0.64 at the .001 level (two-tailed). Clearly, when the exogenous variable of SLCs goes up by 1 standard deviation, the endogenous variable of SCA goes up by 0.64 standard deviation. The estimate R2 was significant (≥ 0.10) as recommended by Falk & Miller (1992) and significant (≥ 0.30) as given by Quaddus & Hofmeyer (2007). R2 has estimated that the predictors of the SCA explain 0.41% of its variance. In other words, the error variance of SCA is approximately .59% of the variance of SCA itself. The probability of getting C.R. as large, at 7.78 in an absolute value is less than 0.001. Hence, the hypothesis is asserted at (β = 0.64; R2 ≥ 0.10; C.R.=7.78 >1.96; p < 0.001. Therefore, the study hypothesis is accepted. The results were consistent with expectation and they were statistically significant in the expected direction. In conclusion, the positive relationship between SLC and SCA is supported by the data.

Table 6 The Results of Hypothesis Testing Based on Path Analysis
Path Beta Estimate R2 S.E. C.R. p-value Hypothesis Result
SLC SCA 0.64 0.41 0.07 7.78 *** Supported

Discussion and Conclusion

The objective of this study was to examine the relationship between SLC and SCA in private universities. The findings reveal that a significantly positive effect is present in this relationship, indicating that an SCA can be developed when SLC is applied in private universities. This study’s results are consistent with that of Ireland & Hitt (2005), who pointed out the role of SL in achieving and maintaining strategic competitiveness based on great groups’ view of strategic leadership. This finding also agrees with other studies on strategic leadership using RBV (Barney, 1986; Hirschi & Jones, 2008, 2009). The purpose of SLC in developing value and uniqueness in terms of human capital is to achieve competitiveness (Perez & Pablos, 2003; Seemann et al., 2000). As such, strategic leadership seeks to achieve a sustainable competitive advantage by investing in social capital. This is for the reason that social capital creates a strategic utility and an environment conducive for the nurturing of mutually beneficial relationships between employees and they are which contributes to the organizations’ competitiveness (Ellinger et al., 2011). In relation to strategic leadership, Thomas & Thomas (2011) posit it as a process that emphasizes the mechanism of leaders to summon and utilize resources in a system of interrelationships. Strategies are being depicted to affect the present circumstances, to arrive at the outcome they desire. This also shows the responsiveness quality that leaders provide throughout the stages of their leadership life.

This study further affirmed the association between strategic leadership and the human and social capital in the private universities, which may improve the sustainable competitive advantage based on the fundamental theories utilized in this study, such as but not limited to the great groups’ view of strategic leadership and RBV. Hence, the researchers may safely pronounce, that the generation of SCA may be sourced from effective strategic leadership capabilities that develop effective and valuable human and social capital. This study provides a clear and deep comprehensive perception to practitioners and business owners on the application of strategic leadership in an international context, in general, and in private universities, in particular. It significantly contributed to the demonstration that sustainable competitive advantage is a continuously changing process in the academic strategic leadership in private universities. This will provide private universities to anticipate and predict potential competition or challenges, which is critical in the survival and betterment of the status of an organization in the international academic environment. In this way, the organization may establish a climate conducive for the development and maintenance of the human and social capital, and rich in opportunities, knowledge, and ideas. Academic strategic leadership capabilities can increase private universities’ ability to create, share and utilize knowledge. Thereafter, SCA will be the result of effective management of such knowledge assets.

References

Adler, P.S., & Kwon, S.W. (2002). Social capital: Prospects for a new concept. Academy of management review, 27(1), 17-40.

Ainasoja, J.A., Kenmogne, K., Hardy, P., Menet, W., Plouffe, C., & Saboktakin, P. (2012). Visionary leadership and organizational change in an academic environment: Case Linköping University.

Akpinar, M., Can, Ö., & Mermercioglu, M. (2017). Assessing the sources of competitiveness of the US states.

Competitiveness Review: An International Business Journal.

Amit, R., & Schoemaker, P.J.H. (1993). Strategic assets and organizational rent. Strategic Management Journal, 14(1), 33-46.

Au, A., Altman, Y., & Roussel, J. (2008). Employee training needs and perceived value of training in the Pearl River Delta of China: A human capital development approach. Journal of European Industrial Training, 32(1), 19-31.

Awang, Z. (2012). Structural Equation Modeling Using Amos Graphic. Penerbit Press, Malaysia.

Bagozzi, R.P., & Yi, Y. (1988). On the evaluation  of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

Balkundi, P., & Kilduff, M. (2006). The ties that lead: A social network approach to leadership. The Leadership Quarterly, 17(4), 419-439.

Ballout, H. (2007). Career success: The effects of human capital, person-environment fit and organizational support.

Journal of Managerial Psychology, 22 (8), 741-765.

Barney, J. (1991). Firm Resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. Barney, J.B. (1995). Looking inside for competitive advantage. Academy of Management Perspectives, 9(4), 49-61. Barney, J., & Clark, D. (2007). Resource-based theory: Creating and sustaining competitive advantage. OUP Oxford.

Barney, J., & Hesterly, W. (2009). Strategic management and competitive advantage: Concepts and cases, 3rd ed., Prentice Hall.

Barney, J., & Wright, P. (1998). On becoming a strategic partner: The role of human resources in gaining competitive advantage. Human Resource Management, 37(1), 31-46.

Barney, J.B. (1986). Strategic factor markets expectations, luck, and business strategy. Management Science, 32(10), 123-41.

Barney, J.B. (2007). Gaining and sustaining competitive advantage. Pearson Prentice-Hall, New Jersey.

Barney, J.B., & Arikan, A.M. (2001). The resource-based view: Origins and implications. The Blackwell handbook of strategic management, 5, 124-188.

Birasnav, M., Rangnekar, S., & Dalpati, A. (2011). Transformational leadership and human capital benefits: The role of knowledge management. Leadership & Organization Development Journal, 32(2), 106-126.

Boal, K., & Hooijberg, R. (2001). Strategic leadership research: Moving on. The Leadership Quarterly, 11(4), 515- 549.

Boal, K.B. (2004). Strategic leadership, in Goethals, G.R., Sorenson, G.J., & Burns, J.M.G. (Eds.), Encyclopedia of Leadership, 3, 1497-1504.

Bontis, N., Bart, C., & Serenko, A. (2009). A causal model of human capital antecedents and consequents in the financial services industry. Journal of intellectual capital.

Boxall, P. (1996). The strategic HRM debate and the resource‐based view of the firm. Human resource management journal, 6(3), 59-75.

Brosnan, S., Doyle, E., & O’Connor, S. (2016). From marshall’s triad to porter’s diamond: Added value?. Competitiveness Review.

Byrne, B.M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International journal of testing, 1(1), 55-86.

Byrne, B.M. (2010). Structural equation modeling with AMOS: basic concepts, applications, and programming (multivariate applications series). New York: Taylor & Francis Group, 396, 7384.

Charles, V., & Zegarra, L.F. (2014). Measuring regional competitiveness through Data Envelopment Analysis: A Peruvian case. Expert Systems with Applications, 41(11), 5371-5381.

Christensen, K., & Fahey, L. (1984). Building distinctive competencies into competitive advantage. Strategic Planning Management, 2,113-123.

Coff, R.W. (1997). Human assets and management dilemmas: Coping with hazards on the road to resource-based theory. Academy of Management Review, 22(2), 374-402.

Crossan, M., Vera, D., & Nanjad, L. (2008). Transcendent leadership: Strategic leadership in dynamic environments. The Leadership Quarterly, 19(5), 569-581.

Davies, B.J., & Davies, B. (2004). Strategic leadership. School Leadership & Management, 24(1), 29-38. DeVellis, R.F. (1991), Scale Development: Theory and Applications. Sage, California.

Dizgah, M.R., Alipour, H.R., & Chegini, M.G. (2011). Human capital characteristics and organizational performance, Australian Journal of Basic & Applied Sciences, 5(8), 803-809.

Elenkov, D.S., Judge, W., & Wright, P. (2005), Strategic leadership and executive innovation influence: An international multi-cluster comparative study. Strategic Management Journal, 26(7), 665-682.

Ellinger, A.E., Baş, A.B.E., Ellinger, A.D., Wang, Y.l., & Bachrach, D.G. (2011). Measurement of organizational investments in social capital: The service employee perspective. Journal of Business Research, 64(6), 572- 578.

El Shafeey, T., & Trott, P. (2014). Resource-based competition: Three schools of thought and thirteen criticisms. European Business Review, 26(2), 122-148.

Falk, R.F., & Miller, N.B. (1992). A primer for soft modeling. University of Akron Press, Akron, Ohio.

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 93-50.

Galbreath, J. (2005). Which resources matter the most to firm success? An exploratory study of resource-based theory. Technovation, 25(9), 979-987.

Garelli, S. (2014). The fundamentals and history of competitiveness, IMD world competitiveness yearbook 2014.

IMD World Competitiveness Centre, Lausanne.

Guillot, M. (2003). Strategic leadership: Defining the challenge. Air and Space Power Journal, 17(4), 67-75. Gulati, R., Nohria, N., & Zaheer, A. (2000). Strategic networks. Strategic Management Journal, 21(3), 203-216.

Haanes, K., & Fjeldstad, O. (2000). Linking intangible resources and competition. European Management Journal, 18(1), 52-62.

Hafeez, K., Zhang, Y., & Malak, N. (2002). Core competence for sustainable competitive advantage: A structured methodology for identifying core competence. Engineering Management, 49(1), 28-35.

Hagen, A., Hassan, M.T., & Amin, S.G. (1998). Critical strategic leadership components: An empirical investigation. SAM Advanced Management Journal, 63(3), 39-44.

Hair, J., Black, W., Barry, J., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis. Prentice Hall, New Jersey.

Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis. Prentice Hall, New Yorke.

Halawi, L.A., Aronson, J.E., & McCarthy, R.V.  (2005). Resource-based view of knowledge management  for competitive advantage. Journal of Knowledge Management, 3(2), 75-86.

Hamidi, Y. (2009). Strategic leadership for effectiveness of quality managers in medical sciences universities : What skills is necessary. Employee Relations, 3(3), 2563-569.

Harley, B. (1999). The myth of empowerment: Work organisation, hierarchy and employee autonomy in contemporary Australian workplaces. Work, Employment & Society, 13(1), 41-66.

Heikkilä, J., & Cordon, C. (2002). Outsourcing: A core or non core strategic management decision?, Strategic Change, 11(4),183-193.

Helfat, C., & Peteraf, M. (2003). The dynamic resource based view: Capability lifecycles. Strategic Management Journal, 24(10), 997-1010.

Hinterhuber, H.H., & Friedrich, S.A. (2002). The technology dimension of strategic leadership: The leadership challenge for production economists. International Journal of Production Economics, 77(3), 191-203.

Hirschi, G., & Jones, M. (2008). Strategic leadership: A resourced based view. MIBES Transactions, 60-74.

Hirschi, G., & Jones, M. (2009). Affects of strategic leadership on business success: A cross Cultural analysis from a resource based view. MIBES Transactions, 3(1), 1-18.

Hitt, I., Ireland, R., & Hoskisson, R.E. (1995). Strategic management: Competitiveness and globalization: concepts. West Publishing Company, Ohio.

Hitt, M.A., Haynes, K.T., & Serpa, O. (2010a). Strategic leadership for the 21st century. Business Horizons, 53(5), 437-444.

Hitt, M.A., & Ireland, R.D. (2002). The essence of strategic leadership : Managing human and social capital. Journal of Leadership & Organizational Studies, 9(1), 3-14.

Hitt, M.A., Ireland, R.D., Camp, S.M., & Sexton, D.L. (2001). Strategic entrepreneurship: Entrepreneurial strategies for wealth creation. Strategic Management Journal, 22(6-7), 479-491.

Hitt, M.A., Ireland, R.D., & Hoskisson, R.E. (2010b). Strategic management: Concepts and cases competitiveness and globalization. South-Western, Canada.

Hitt, M.A., Keats, B.W., & Yucel, E. (2003). Strategic leadership in global business organizations: Building trust and social capital. Advances in Global Leadership, 3(3), 9-35.

Holmbeck, G.N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child clinical and pediatric psychology literatures, Journal of Consulting and Cinical Psychology, 65(4), 599-610.

House, R., & Aditya, R.N. (1997). The social scientific study of leadership: Quo vadis. Journal of Management, 2(23), 409-473.

Huggins, R., & Izushi, H. (2008). Benchmarking the knowledge competitiveness of the globe's high‐performing regions: A review of the World Knowledge Competitiveness Index. Competitiveness Review: An International Business Journal.

Hunt, S.D., & Morgan, R.M. (1995). The comparative advantage theory of competition. The Journal of Marketing, 59(2), 1-15.

Ireland, R., Hitt, M., Camp, S., & Sexton, D.L. (2001). Integrating entrepreneurship and strategic management actions to create firm wealth. The Academy of Management, 15(1), 49-63.

Ireland, R.D., & Hitt, M.A. (2005). Achieving and maintaining strategic competitiveness in the 21 st century : The role of strategic leadership. Management, 19(4), 63-77.

Ireland, R.D., Hitt, M.A., & Vaidyanath, D. (2002). Alliance management as a source of competitive advantage.

Journal of Management, 28(3), 413-441.

Javidan, M. (1998). Core competence: what does it mean in practice. Long Range Planning, 3(1)1, 60-71.

Jooste, C., & Fourie, B. (2009). The role of strategic leadership in effective strategy implementation : Perceptions of south african strategic leaders. Southern African Business Review, 13(3), 51-68.

Jöreskog, K.G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS Command Language, 1st ed., Scientific Software International, Chicago.

Jüttner, U., & Wehrli, H.P. (1994). Competitive advantage merging marketing and the competence-based perspective. Journal of Business & Industrial Marketing, 9(4), 42-53.

Kay, J. (1995). Why firms succeed. Oxford University Press, USA.

Kline, R. (2011). Principles and Practice of Structural Equation Modeling, 3rd ed., Guilford Press, New Yorke.

Li, J.J., & Zhou, K.Z. (2010). How foreign firms achieve competitive advantage in the Chinese emerging economy: Managerial ties and market orientation. Journal of Business Research, 63(8), 856-862.

Ljungquist, U. (2008). Specification of core competence and associated components: A proposed model and a case illustration. European Business Review, 20(1), 73-90.

Luthans, F., & Slocum, J. (2004). New leadership for a new time. Organizational Dynamics, 33(3), 227.

Mahdi, O.R., & Almsafir, M.K. (2014). The role of  strategic leadership in building sustainable competitive advantage in the academic environment. Procedia - Social and Behavioral Sciences, 129, 289-296.

Mahdi, O.R., Nassar, I.A., & Almsafir, M.K. (2019). Knowledge management processes and sustainable competitive advantage: An empirical examination in private universities. Journal of Business Research, 94, 320-334.

Mahoney, J., & Pandian, J. (1992). The resource based view within the conversation of strategic management. Strategic Management Journal, 13(5), 363-380.

Malhotra, Y. (1997). Process models vs. variance models: What is the difference. Retrieved from http://www.brint.com/wwwboard/messages/842.html

McCallum, S., & O’Connell, D. (2009). Social capital and leadership development: Building stronger leadership through enhanced relational skills. Leadership & Organization Development Journal, 30(2), 152-166.

Memon, M., Mangi, R., & Rohra, C. (2009). Human capital a source of competitive advantage, ideas for strategic leadership. Australian Journal of Basic & Applied Sciences, 3(4), 4182-189.

Moore, M.R., & Diamond, M.A. (2000). Academic Leadership: Turning Vision into Reality. Ernst & Young Foundation, London.

Motley, A. (2007). Leadership for the Long Haul. Business Officer Magazine.

Narayanan, V., & Zane, L.J. (2009). Inventing a future for strategic leadership: Phenomenal variety and epistemic opportunities. Journal of Strategy and Management, 2(4), 380-404.

Perez, J.R., & Pablos, P.O. (2003). Knowledge management and organizational competitiveness: A framework for human capital analysis. Journal of Knowledge Management, 7(3), 82-91.

Peteraf, M. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179-191.

Peteraf, M.A., & Barney, J.B. (2003). Unraveling the resource-based tangle. Managerial and Decision Economics, 24(4), 309-323.

Porter, M. (1985). Competitive advantage: Creating and sustaining superior performance with a new introduction. Free Press, New York.

Porter, M. (1996). What is strategy? Published November, 74(6), 61-78.

Porter, M.E. (1990). The Competitive Advantage of Nations, Free Press, New York.

Prahalad, C., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 68(3), 79-91. Prusak, L., & Cohen, D. (2001). How to invest in social capital. Harvard Business Review, 79(6), 86-93.

Punch, K.F. (2005). Introduction to Social Research: Quantitative and Qualitative Approaches. SAGE Publications, London.

Quaddus, M., & Hofmeyer, G. (2007). An investigation into the factors influencing the adoption of B2B trading exchanges in small businesses. European Journal of Information Systems, 16(3), 202-215.

Raduan, C., Jegak, U., Haslinda, A., & Alimin, I. (2009). A conceptual framework of the relationship between organizational resources, capabilities, systems, competitive advantage and performance. Research Journal of Internat.Onal Studes, 12(12), 45-58.

Rothaermel, F. (2013). Strategic Management: Concepts and Cases, 1st ed., McGraw-Hill Irwin, New York. Rumelt, R. (2003). Towards a strategic theory of the firm. Resources, Firms, and Strategies: A Reader in the Resource-Based Perspective, NJ: Prentice-Hall, Englewood Cliffs.

Rumsey, M.G. (2013), The oxford handbook of leadership. Oxford University Press, New Yorke.

Sala-i-Martín, X., Bilbao-Osorio, B., Blanke, J., Hanouz, M.D., Geiger, T., & Ko, C. (2014). The global competitiveness index 2014-2015: accelerating a robust recovery to create productive jobs and support inclusive growth. The Global Competitiveness Report 2014-2015, Full Data., World Economic Forum, Geneva.

Savory, C. (2006). Translating knowledge to build technological competence. Management Decision, 4(8), 1052- 1075.

Seemann, P., DeLong, D., Stucky, S., & Guthrie, E. (2000). Building intangible assets: a strategic framework for investing in intellectual capital. Knowledge management: Classic and contemporary works, 85-98.

Sekaran, U. (2003). Research Methods for Business: A Skill Building Approach. John Wiley & Sons Inc., New Yorke.

Settle, R.B., & Alreck, P.L. (1984). Survey research handbook.

Sirmon, D.G., Hitt, M.A., & Ireland, R.D. (2007). Managing firm resources in dynamic environments to create value: Looking inside the black box. Academy of Management Review, 32(1), 273-292.

Sosik, J.J., Jung, D., Berson, Y., Dionne, S.D., & Jaussi, K.S. (2005). Making all the right connections: The strategic leadership of top Executives in High-Tech organizations, Organizational Dynamics, 34(1), 47-61.

Subramaniam, M., & Youndt, M. (2005). The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48(3), 450-464.

Taka, M. (2010). Paths of private higher education in Iraq for the next five years. Journal of Baghdad College of Economic Sciences University, 25, 1-15.

Teece, D.J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.

Thomas, H., & Thomas, L. (2011). Perspectives on leadership in business schools. Journal of Management Development, 30(5), 526-540.

Tuan, N.P., & Yoshi, T. (2010). Organisational capabilities, competitive advantage and performance in supporting industries in Vietnam. Asian Academy of Management Journal, 15(1), 1-21.

Ulrich, D., Zenger, J.H., & Smallwood, W.N. (1999). Results-based leadership. Harvard Business School Press, Boston.

Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171-80.

Wright, P.M., McMahan, G.C., & McWilliams, A. (1994). Human resources and sustained competitive advantage: a resource-based perspective. International Journal of Human Resource Management, 5(2), 301-326.

Zikmund, W.G., Babin, B.J., Carr, J.C., & Griffin, M. (2013). Business Research Methods, 9th ed., South-Western, Cengage Learning, Ohio.

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