Journal of Entrepreneurship, Management and Innovation (2024)

Volume 20 Issue 4: 5-25

DOI: https://doi.org/10.7341/20242041

JEL Codes: L25, L26, M21

Boris Urban, Professor, Wits Business School, University of Witwatersrand, 2 St Davids Place, Parktown, Johannesburg, South Africa, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Thanusha Govender, Dr, Wits Business School, University of Witwatersrand, 2 St Davids Place, Parktown, Johannesburg, South Africa, email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

PURPOSE: Corporate entrepreneurship (CE) is a multi-faceted phenomenon, and although there is extant research on CE, there are knowledge gaps that warrant a deeper understanding. Indeed, focusing solely on independent variables overlooks the extent to which CE activities are mutually and reciprocally supportive. We align our article with research calls for theory building, which provides a novel understanding of the dynamic complexity of the CE process. METHODOLOGY: In this regard, we formulate and empirically evaluate an integrated CE model that reflects the integrative complexity of the antecedents driving CEA. The study context is the South African banking sector, where primary data (n = 312) is obtained via a structured survey. Four meta-hypotheses and several sub-hypotheses, reflecting the organizational, individual, and environmental level antecedents, are tested using partial least squares structural equation modeling (PLS-SEM). FINDINGS: The main study finding validates that corporate strategy is the bedrock of CEA. The results also reveal that entrepreneurial strategy, entrepreneurial structure, transformational leadership, organizational resources, and an entrepreneurial mindset are significant predictors of CEA. IMPLICATIONS: Practical implications highlight that it is important for managers to consider the configuration of the predictors within the CE model, which function as pathways to entrepreneurial corporate strategy. ORIGINALITY AND VALUE: Our study makes a unique contribution by developing and testing an integrated and comprehensive model reflecting the dynamic complexity of the antecedents driving CEA. It is anticipated that the results will allow researchers to compare and examine comparable antecedents to CEA and their applicability in global country contexts.

Keywords: corporate entrepreneurship, antecedents, entrepreneurial strategy, organizational resources, transformational leadership, entrepreneurial mindset, partial least squares structural equation modelling, PLS-SEM, South Africa.

INTRODUCTION

The domain of corporate entrepreneurship (CE) has steadily evolved over the last 50 years and is widely viewed as contributing to the advancement of a firm’s corporate strategy, enhancing our understanding of the complex and dynamic nature of entrepreneurship within established organizations (Glinyanova et al., 2021; Ireland et al., 2009; Kreiser et al., 2021; Urban, 2021). CE typically varies in firms due to the mix of individual, organizational, and environmental antecedents, which combine together to influence entrepreneurial activity (Kuratko et al., 2014; Lenart-Gansiniec et al., 2023; Weiss et al., 2023). In this regard, scholars have observed that corporate entrepreneurship activity (CEA) is a result of both internal and external triggers of organizational change and have designated a ‘pro-entrepreneurship organizational architecture’ as a crucial element of a CE strategy (Ireland et al., 2009. Kuratko et al., 2014; Suder, 2024; Urbano et al., 2022).

Although extant research is available on CE, there are research gaps that warrant a deeper understanding, particularly as CE has been studied from different fields using diverse theoretical approaches (Kuratko et al., 2021; Urbano et al., 2022). While many studies have identified a spectrum of antecedent sets for CEA, this has mostly been ascertained through isolated research studies underpinned by competing theories and research inconsistencies (Bloodgood et al., 2015). Indeed, as has been noted by scholars, the whole “corporate entrepreneurial ecosystem is materially different from the sum of the atomized parts of which it is constituted off” (Anderson et al., 2012, p. 961). Studying individual elements of CE as independent variables overlooks the extent to which CE activities are mutually and reciprocally supportive (Lampe et al., 2020; Urban, 2021).

Against this backdrop, where much fragmentation limits our understanding of the CE phenomenon, the purpose of our article is to develop an integrated and comprehensive model reflecting the dynamic complexity of the antecedents driving CEA. Secondly, we align our article with research calls for CE theory building, which deepens the academic understanding of the dynamic complexity of the corporate entrepreneurial process (Kreiser et al., 2021; Lenart-Gansiniec et al., 2023; Reyes-Gómez et al., 2024). Following Urbano et al. (2022, p. 545), we adopt a broad definition of CEA formulated as those “firm initiatives that aim at creating and adding new business, or at fostering innovation, change and renewal.” In this regard, we assess and predict the relative importance of three sets of antecedents, namely organizational level factors, individual level factors, and environmental level factors, to ascertain the effect of their interrelationships in relation to CEA, using partial least squares structural equation modeling (PLS-SEM).

Past studies have focused on a number of antecedents driving CEA, which include entrepreneurial strategy, entrepreneurial structure, transformational leadership, organizational resources and capabilities, entrepreneurial mindset, entrepreneurial persona, social networks, entrepreneurial culture, entrepreneurial reward practices, environmental dynamism, and hostility (Covin & Slevin, 1991; Crawford & Kreiser, 2015; Glinyanova et al., 2021; Guth & Ginsburg, 1990; Ireland et al., 2009; Kreiser et al., 2021). Leveraging such prior work and relying on different theoretical models, we make a clear contribution by synthesizing and integrating various antecedents of CEA and examining their direct and moderating effects and interactions across individual, organizational, and environmental levels of analyses. This is the first time, to our knowledge, that such an elaborate array of CE predictors have been treated as an interrelated system, and the proposed model is certainly an expanded original formulation.

Our study takes place in South Africa which has one of Africa’s most sophisticated economies, yet also has one of the highest inequality rates in the world (Dana et al., 2022; Urban & Townsend, 2021). We agree with researchers who note that Africa remains a fertile ground to test CE theory and has the potential to further theory development in this field of research (Dana et al., 2022; Urban, 2021). We also take note that CE scholars have noted that future research could especially consider the service sector (Urban and Townsend, 2021). Banking and the broader financial services sector play a “crucial role in contributing towards Africa’s growth agenda, as the banking industry across Africa is the second fastest growing and most dynamic industry across the continent where it has made significant inroads into the advancement of banking technology, digitization, and innovation” (Urban & Townsend, 2021, p. 5). Moreover, because of COVID-19 it has been cautioned that “African banks cannot afford to leave their performance recovery to chance and if they do, it will inevitably lead to multiple years of returns below the cost of capital” (McKinsey, 2021, p. 6). Consequently, by focusing our study on the South African banking industry sector, it is anticipated that the finalized structural model emerging from our empirical analyses will provide a useful contribution to managers by providing them with a set of reliable and valid predicters, which lays the tracks for the formulation of a successful CEA.

The continuing sections of the article are organized as follows. The next section describes the theoretical background and literature review. The third section refers to the methodology employed to test the hypotheses. Section four displays the results and interprets the statistical findings. Section five discusses the results and integrates the findings with theory. The last section in this article provides a conclusion, offers study implications, and sets out the study limitations and directions for future research.

THEORETICAL BACKGROUND AND LITERATURE REVIEW

The corporate entrepreneurial ecosystem

The overarching theoretical basis for this article was based on the complex dynamic systems theory (Crawford & Kreiser, 2015; Lenart-Gansiniec et al., 2023; Reyes-Gómez et al., 2024), which is leveraged as a framework for conceptualizing antecedents driving CEA (Ireland et al., 2009; Kuratko et al., 2014). Employing this theoretical framework will not only “evaluate dependencies but will equally evaluate interdependencies, as the model is aimed to capture the complexity of social organizational reality and transformational intrapreneurial change” (Kraus et al., 2011, p. 61). Through this theoretical lens, CE is theorized as a multi-dimensional behavioral construct in which the external environment, in the form of dynamism and hostility environmental conditions, triggers intrapreneurial organizational action as a result of employees who have a pro-entrepreneurial disposition, exhibiting entrepreneurial behavior due to the existence of an internal environment conducive to CEA (Glinyanova et al., 2021; Ireland et al., 2009; Kreiser et al., 2021).

The dynamic complex adaptive corporate entrepreneurial ecosystem is made up of three layers, namely, the individual employee layer, the organizational layer, and the environment layer (Lenart-Gansiniec et al., 2023; Urbano et al., 2022). Change induced by the environment, organizational setting, or employee acting as a change agent might result in a wave of change in other parts of the ecosystem. For instance, a recent study demonstrates how through relational interlinks with internal and external stakeholders, firms combine the exploration of new market opportunities with the exploitation of existing core competencies to develop new competitive advantages (Weiss et al., 2023). Consequently, the necessity for an organization to transform depends on the optimal configurational patterns of CE drivers, which can change over time (Anderson et al., 2012; Kraus et al., 2011).

A literature review on the analysis of the antecedents of CEA (Urbano et al., 2022), reveals that at least three distinct types of factors at various levels can influence CE, namely the individual, organizational, and environmental. Within this dynamic complex adaptive corporate entrepreneurial ecosystem, several key frameworks emerge, such as Covin and Selvin’s (1991, p.8) work who regard, “firm performance as a function of both organizational, as well as individual level behavior, moderated by the environmental context and operating paradigm of incumbent firms.” Some theoretical models, such as Zahra et al. (1999), pursue similar approaches by grouping the antecedent factors at these various levels of analysis. Other scholars, including Guth and Ginsberg (1990) and Kuratko et al. (2014), perceive CE as a result of internal and external organizational change triggers. Ireland et al. (2009, p. 24) delineate a “pro- entrepreneurship organizational architecture as elements of a CE strategy shaped by an entrepreneurial vision and dominant logic”. Acknowledging the wide range of research on the antecedents of CEA, we identify and discuss several types of factors at the three observed levels to inform the study hypotheses, namely at the organizational, individual, and environmental levels. See Table 1 for a theoretical domain analysis with core conceptual model deductions and theoretical reasoning provided, which is used in the formulation of the hypotheses.

Table 1. Theoretical domain analysis of the study constructs

Theoretical domain	Core conceptual model deductions	Theoretical rationale
Corporate Entrepreneurship	CEA is regarded as a multi-dimensional behavioral construct, where “firm performance is a function of both organizational, as well as individual level behavior,” and is moderated by the environmental context (Covin and Slevin, 1991, p.8)	CE, like other organizational occurrence is best understood as an open system, as it accounts for the “dynamic complexity of corporate entrepreneurship” as a construct (Miller & Friesen, 1982). It is an “adaptative system of interrelated cogs working together to achieve collaborative goals both of the organization and that of the employee where the outputs generated over time is a result of non-linear effects” (Bloodgood et al., 2015, p.384).
Leadership	Given the different perspectives offered across various models, managerial impact across all levels has been included into the conceptual model as managers across all levels play a critical role within the CE process.	Considering the complexity of “getting things done” and the proximity of influence, the leadership construct is more closely aligned vis-a-vis managerial support, and it has been further narrowed down to ‘transformational leadership,’ which is included as an exogenous construct in the conceptual model given the specific competency profile of transformational leadership in relation to positively motivating entrepreneurship behaviors (Pan et al., 2021; Verma & Mehta, 2020).
Organizational culture	Organizational culture is included as an exogenous construct in the conceptual model that enables an organization to adapt quickly to constant market shifts and display business response flexibility and market leadership through creativity and innovation (Sarros et al., 2008).	A positive organizational culture drives risk-taking, propensity for innovation, and the continuous search and exploitation of new opportunities based on emerging trends, market dynamics and consumer behavioral patterns (Ireland et al., 2009). The organizational culture can shape the entrepreneurial posture of a business and drive organizational level entrepreneurship.
Organizational strategy	Organizational strategy acts as a primary driver of entrepreneurial behavior and entrepreneurial transformation and is a key construct in the model (Ireland et al., 2009).	Organizational strategy is included as an exogenous construct in the conceptual model as a driving force that initiates the entrepreneurial transformation process (Ireland et al., 2009). Strategy is an organizational-level driver of CE as CEA is delivered through the cohesion amongst the vision and strategic intent of a company, its strategic choices and risk appetite of the incumbent, which is brought to life through the decisions and actions of the leadership of the business and the enduring behaviors of the employees of the organization (Covin and Slevin, 1991; Ireland et al., 2009; Kuratko et al., 2014, Miller, 1983).
Organizational structure	Organizational structure is a critical antecedent of corporate entrepreneurial behavior as it provides employees with a sense of empowerment and allows them to collaborate easily across the organization (Kuratko et al., 2014).	The organizational structure of a company denotes the hard wiring of an organization’s ways of working characterized through its facilitation of communication flows throughout the organization, the arrangement of authority and workflow relationships within an incumbent and the allocation of jobs and accountabilities within a company (Covin and Slevin, 1999; Ireland et al., 2009).
Reward	Reward practices are included as an exogenous construct in the conceptual model as reward is pivotal motivational level for employee entrepreneurial behavior.	Reward practices incentivise people to behaviour intrapreneurially, take accountability and ownership (Bloodgood et al., 2015; Kuratko et al., 2014). To exceed an employee’s expectation, the reward system must demonstrate effective coverage across pivotal extrinsic and intrinsic effects, thereby motivating entrepreneurial behavior (Kuratko et al., 2014).
Resources	Organizational resources and capabilities act as a crucial pro-organizational architecture construct.	Resource availability is one of the most crucial organizational antecedents and are associated with the firm’s overall entrepreneurial orientation and strategy (Kreiser et al., 2021; Urbano et al., 2022).
Entrepreneural mindset	The proposed conceptual model will expand the definition of entrepreneurial cognitions posited within the Ireland et al. (2009) study, to include entrepreneurial metacognitions as defined by the study conducted by Haynie et al. (2010).	An entrepreneurial mindset is highly developed ability cognitions that enable employees to engage in entrepreneurial action. Haynie et al.’s (2010) conceptualization of the entrepreneurial mindset is adopted in terms of categorizing metacognition’s five theoretical dimensions, as: (1) goal orientation, (2) metacognitive knowledge, (3) metacognitive experience, (4) metacognitive choice, and (5) monitoring.
Entrepreneurial personality ‘persona’	A collection of traits was obtained through an inductive investigation of the commonly measured personality traits in research on entrepreneurs (Kuratko et al., 2021; Salmony & Kobach, 2022).	Even though some CE studies have highlighted an entrepreneur’s individual characteristics as a foundational driver of entrepreneurial behavior (Kuratko et al., 2021), research has been inconclusive around the detail on which characteristics form the exact premise for entrepreneurial behavior (Kuratko et al., 2004). The themes that cover most of the main theoretical contributions to the entrepreneurial traits literature, are the study focus area in terms of: Creativity, Self-Efficacy, Locus of control, and Achievement motivation.
Social networks	The inclusion of the social network construct within the conceptual model is based on theory insofar as entrepreneurial behaviors depend on the fit of the underlying social capital resources available at different levels in the organization, which combine into complex social capital configurations (Glaser et al., 2021).	Social networks are pivotal in an organizational system, in which organizational information flows and enables a position of enterprise-wide influence. In the context of CE implementation, it significantly improves access to novel and privileged information, access to funds, sponsorship, and idea buy-in, ultimately increasing entrepreneurial effectiveness and impact (Alder and Kwon, 2002; Nahapiet and Ghoshal, 1998).
Environmental dynamism and hostility and entrepreneurial growth momentum	As the study conceptual model aims to be a predictive implementation model, understanding the relational influence of a large spectrum of environmental attributes will dilute the targeted contribution this study aims to deliver. Hence, the environmental variables were narrowed down to dynamism and hostility, as they provide both the ‘yin and yang’ of environmental influence (Choi et al., 2020; Uzkurt et al., 2012; Zahra, 1993). Entrepreneurial momentum refers to an organizational system, processes and beliefs that shape employee behaviors and the decisions and actions within an organization to drive sustainable market growth and outperformance of the market regardless of the market fluctuations and the rapid evolution of market trends (Pan et al., 2021).	Theorists maintain that the relationship between resources and the value of such resources varies according to their environment (Choi et al., 2020). Consequently, many scholars adopt a contingency perspective in organizational studies where environmental impact and involvement have been diagnosed and associated with the performance of organizations across different industries and contexts (e.g., Uzkurt et al. 2012). Equally, since the African banking industry is described as both dynamic and hostile (Urban & Townsend, 2021), these two variables are good descriptors of the environmental context within banking across the African continent. The stimulus-reaction phenomenon between environmental triggers and entrepreneurial momentum can be explained by leveraging retention mechanisms whereby incumbents choose and decode stimuli according to theories of action encrypted by previous strategies, the dominant logic of the company and its DNA. As such, member behavior is constrained by their perception of the “rules of the game” (Kuratko et al., 2004). The current organizational “momentum is a pervasive force” within a firm, and firms evolve and react to external stimuli by continuing to “evolve in the same direction” as dictated by the current organizational inertia. As such, “the transformational trigger provides the impetus to behave entrepreneurially when other conditions are conducive to such behavior” (Kuratko et al., 2004; Pan et al., 2021).

Organizational level antecedents

At the organizational level, for CEA to manifest, firms must develop three foundational elements consisting of an entrepreneurial strategic vision, a pro-entrepreneurship organizational architecture, and entrepreneurial processes and behavior as demonstrated throughout the organization (Ireland et al., 2009; Li et al., 2005). CE strategy is conceptualized as a vision-directed, “organization-wide reliance on entrepreneurial behavior that purposefully and continuously rejuvenates the organization and shapes the scope of its operations through the recognition and exploitation of entrepreneurial opportunity” (Ireland et al., 2009, p. 21). On the other hand, organic structures are inclined to be decentralized organizational designs, with distributed divisions of labor and broader spans of control, which function as an essential catalyst for CEA. Such organic structures provide employees with a sense of empowerment and the room to innovate, fail, grow, and collaborate easily across the organization (Kuratko et al., 2014). Similarly, an effective entrepreneurial culture enables an organization to adapt quickly to constant market shifts, exude market prowess, and display business response flexibility and market leadership through creativity and innovation (Sarros et al., 2008). For instance, an organization with a prevailing entrepreneurial momentum has clear, exacting standards for performance and ambitious growth objectives, which are reinforced through organizational trust that facilitates the discovery of innovative ideas and the creation of new value adding capabilities (Pan et al., 2021).

Recent literature highlights resource availability as one of the most crucial organizational antecedents and explains how governance and ownership systems, access to resources, time availability, and knowledge capabilities are associated with the firm’s overall entrepreneurial orientation and strategy (Kreiser et al., 2021; Urbano et al., 2022). Kuratko et al. (2021) further hypothesized that reward frameworks are, in fact, a primary determinant of intrapreneurial behavior of top-level and middle level managers within organizations. In this regard, an important organizational perspective is the different role of managers at different hierarchical levels where the level of influence and impact varies across the various levels of management. Specifically, from a top management team perspective, managers are thought to have multiple and critical roles in CEA, because of central involvement in both corporate venturing and strategic renewal forms of CE (Kuratko et al., 2021). In addition, the widely used ‘corporate entrepreneurship climate instrument (CECI)’ has shown meaningful relationships in terms of five key organizational areas, namely management support; reward and resource availability, organizational structure, and boundaries, risk-taking, and time availability (Kuratko et al., 2014). Moreover, transformational leaders are able to shape the culture of a firm, by developing empowering opportunities for employees and encouraging shared values while permitting followers themselves to be leaders (Chang et al., 2019; Verma & Mehta, 2020). Research confirms that transformational leaders are a necessary organizational requirement for CEA as they can guide and maneuver ever-changing, uncertain environments while enabling firms to respond to challenges as a workforce collective (Kuratko et al., 2014; Sarros et al., 2008; Urban, 2021). Recognizing that research has converged on a number of organizational antecedents as drivers of CEA and in conjunction with Table 1, the following hypotheses are formulated:

H1: Organizational level antecedents in terms of (a) corporate strategy, (b) distinctive culture archetype, (c) organic

operating structure, (d) reward practices, (e) organizational resources, and (f) transformational leadership, positively

influence CEA.

Individual level antecedents

At the individual level, literature has drawn attention to personality traits, emotional and cognitive factors, including attitudes, values, and beliefs, as playing a decisive role in initiating and sustaining CE activities (Ireland et al., 2009; Lampe et al., 2020; Sarros et al., 2008). Some researchers have focused on personality traits of entrepreneurs and how they compare to other groups in this regard (Kerr et al., 2018), while others have examined the role of capabilities, and entrepreneurial cognition in CE related issues (Kuratko et al., 2014; Urbano et al., 2022). In established disciplines such as psychology, the convergence in personality theory has led to an overarching five factor model of personality (Salmony & Kobach, 2022). This robust model has caused a resurgence of interest in personality traits and their effects on individual behaviors and performance in various occupational settings, including CE (Kuratko et al., 2021). Prior research on entrepreneurial behavior, attitudes, and predispositions is fairly substantial, where efforts to develop a persona of entrepreneurs include the following attributes which are at the forefront of discussions of entrepreneurial persona, namely creativity, self-efficacy, locus of control, and achievement motivation (for a systematic review see, Salmony & Kobach, 2022).

Moreover, prior studies report that the employee value drivers of CE relate to entrepreneurial persona and entrepreneurial mindset (Kuratko et al., 2004; Kuratko et al., 2021). Kuratko et al. (2021, p.133) conceptualize CE as an “environment where the entrepreneurial mindset of individuals is sought after, supported, and nurtured for the purpose of carrying out innovative activities”. We extend this conceptualization further by locating the entrepreneurial mindset within the broader cognitive science domain, specifically within metacognitive theory (Haynie et al. 2010). Haynie et al. (2010, p. 217) describe the entrepreneurial mindset as “metacognitive processing or thinking patterns, where the underpinnings of a mindset are deep-seated in higher-order mental processing that enable the entrepreneur to think beyond or reorganize existing knowledge structures and heuristics, promoting adaptable cognitions in the face of novel and uncertain decision contexts”. For the purpose of our study, we follow Haynie et al.’s (2010) operationalization of the entrepreneurial mindset in terms of categorizing metacognition’s five theoretical dimensions as: (1) goal orientation, (2) metacognitive knowledge, (3) metacognitive experience, (4) metacognitive choice, and (5) monitoring.

Another crucial factor driving CE is social networking, where extensive literature highlights the importance of networks to entrepreneurs as it allows access to opportunities and important resources (e.g., Chang et al., 2019). Social networking is constituted of various elements such as the structural element of the network, the relational strength of the network, as well as the resources that would be made available through the network (Gedajlovic et al., 2013). Particularly important for CEA are ‘social network theories of innovation,’ which explore the link between social network capital and organizational innovation (Alder and Kwon, 2002; Glaser et al., 2021). Increasing research on the social networking-organizational innovation link has advanced beyond examining tangible forms of capital (human and financial), to examining the importance of ‘social ingredients,’ or intangible forms of social capital such as the relational strength of the network, and the cognition or perception of the individual in the network relationship (Alder and Kwon, 2002; Chang et al., 2019; Gedajlovic et al., 2013; Glaser et al., 2021). Consequently, in recognizing the centrality of the entrepreneurial persona, the entrepreneurial mindset, and networks in fostering CEA, we formulate hypotheses for these variables, in conjunction with the theoretical rational provided in Table 1.

H2: Individual level antecedents in terms of (a) entrepreneurial persona relating to creativity, self-efficacy, locus of control,

achievement motivation, and the (b) the entrepreneurial mindset relating to goal orientation, metacognitive knowledge, metacognitive experience, metacognitive choice, and monitoring, positively influence CEA.

Environment level antecedents

At the environment level of analysis, most studies focus on antecedents relating to industry-level variables such as the degree of market dynamism, hostility, and heterogeneity (Zahra, 1993; Zahra et al., 1999). Prior research has shown that the external environmental factors are capable of affecting the failure and success of firms and can serve both as opportunities as well as challenges to firms (Choi et al. 2020). According to Covin and Slevin (1991, p. 11), the “external environmental forces of dynamism and hostility, has a deterministic influence on the existence and effectiveness of entrepreneurial activity,” thus concluding that the external environment and the entrepreneurial process are intertwined. We rely on the environmental dimensions of dynamism and hostility, which are consistent with earlier research and theory building on the nexus between entrepreneurship and the environment, predominantly as these two dimensions have in the past shown modest correlations (Zahra, 1993), signifying that unique aspects of the environment are captured with each dimension.

Research shows that dynamism relates to unpredictable and rapid change, which has the probability to cause uncertainty for firms who operate in such environments, while hostile environments are categorized by heightened competition and environments which stifle the exploitation of opportunity (Uzkurt et al., 2012). The impact of environmental dynamism and hostility has been studied in several different industries and contexts, including CE leadership and top management team capability, new product development, and exporting strategy (Uzkurt et al., 2012). In terms of CEA some researchers find that competitive intensity positively influences innovations “as more than half of product and technological innovations emerge in response to competitive, market or other environmental pressures” (Uzkurt et al., 2012, p. 18). Zahra (1993) posited that even though the environmental context plays a crucial role in CE, the relationship between environmental antecedent variables and CEA is not necessarily a linear relationship. Hence, we consider that dynamism and hostility will also have a positive moderating influence on CEA when firms have an underlying growth momentum or orientation. The notion of entrepreneurial momentum or growth orientation relates to the firm’s adaptability to environmental shifts and shocks, which requires balancing trade-offs to unlock the innovative potential within the firm while simultaneously removing any of the firm constraints (Pan et al., 2021). This compels the firm and employees to tightly manage the incongruous tensions of effectively running its core business and delivering value while being malleable to augment the core business with capabilities and solutions based on an evolving opportunity set shaped by the rapidly changing environmental context (Pan et al., 2021). An organisation with a prevailing entrepreneurial momentum has clear, high standards for performance and ambitious growth objectives for the business. This cascades into stretch and go-getting goals for all employees, which are supported by enabling processes and organizational systems. Hence, the entrepreneurial momentum of a firm enables exponential growth, which is fuelled by innovation, market prowess, and a greater risk appetite for change (Pan et al., 2021). Arguably, the entrepreneurial momentum of a firm accounts for the heterogeneity in the different research findings in terms of environmental hostility and dynamism, which do not always negatively influence CEA, but rather, the direction of that relationship differs depending on the existing entrepreneurial momentum within an organization. Indeed, in dynamic environments within which levels of demand change promptly, opportunities become more ample, and innovations should increase (Pan et al., 2021; Urbano et al., 2022).

H3: The relationship between environment level antecedents in terms of (a) dynamism and, (b) hostility and CEA will be

positively moderated by (c) entrepreneurial momentum

H4: The relationship between individual level antecedents, organizational level antecedents and CEA will be positively

moderated by environment level antecedents in terms of (a) dynamism and, (b) hostility and (c) entrepreneurial

momentum.

Study model

Based on the literature review and relying on the dynamic complex adaptive corporate entrepreneurial ecosystem theory, four hypotheses and subsequent sub-hypotheses were formulated reflecting the organizational, individual, and environmental levels relevant to CEA. All of the hypothesized relationships are illustrated in Figure 1, and their theoretical rationale is explained in Table 1. In Figure 1, the various antecedents at the different levels of analyses may be viewed as a set of interrelated processes that together describe CEA. This perspective reflects the underlying complex dynamic systems theoretical framework of our study where an effective CE ecosystem requires adaptation in terms of the various levels of analyses. As such, the organizational antecedents of CEA were narrowed down to entrepreneurial strategy, entrepreneurial culture, transformational leadership, reward practices, organizational structure, and organizational resources. The individual antecedents of CEA were narrowed down to entrepreneurial persona, network ties, and entrepreneurial mindset. Lastly, the environmental antecedents of CEA were steered towards the external environmental forces of dynamism and hostility, and entrepreneurial momentum. As per the pathways in Figure 1, all the various antecedents at the different levels of analyses represent the causal chain of CEA and are best viewed as an iterative process. While the selection of the antecedents in the formation of the hypotheses is by no means all-inclusive, it is recognized that numerous individual, organizational and environmental antecedents directly or indirectly influence how CEA is established and moderated at the firm level, and that no single set of antecedents can explicitly determine the outcome of the dynamic complex process of CEA (Kraus et al., 2011; Urban, 2021).

Figure 1. Study model showing hypotheses

RESEARCH DESIGN

Sampling and data collection

We assessed our theoretical model by collecting survey data from various levels of management located at different banks in South Africa. The rationale for focusing on the banking sector is that it has become urgent for banks to become more adaptable and innovative, particularly with the move to technologically enabled banking solutions (Urban & Townsend, 2021). To ascertain the population dataset, the total number of banks in South Africa, including local branches of foreign banks, was established using data obtained from the South African Reserve Bank (SARB, 2019). This analysis concluded that the study population is a total of 19 Banks in South Africa, where, according to the South African Reserve Bank’s Prudential Authority, “the banking sector is dominated by five large banks, which collectively hold 90.5 percent of the total banking sector assets as of 31 March 2019” (SARB, 2019, p. 182). These ‘big five’ banks, representing the total population dataset, have more than 10,000 employees and are headquartered in South Africa (SARB, 2019). We choose to target managers of units in the various banks to gather the data to test our hypotheses.

Following the Protection of Information Act (POPI Act) of the Republic of South Africa, the privacy and anonymity of participants are rigorously protected, making access to potential respondents extremely difficult to access. All participants in the sampling frame were assigned a random number via an online survey that could be accessed via a Qualtrics webpage link. We sent follow-up reminders to complete the survey between 2 and 4 weeks after the initial invitation. We kept track of how many invitations to complete the questionnaire were sent out, and our received sample of n=312 from 1170 invitations represented a 18.1% response. This procedure culminated in responses from various managerial grades across the total population dataset, and the sample included (a) employees that are decision-makers within the organization regarding CEA, and (b) hold an average span of control of 10 employees per manager. In this regard, academics have acknowledged the role that management at multiple levels plays in achieving a CE activities (Brown et al., 2001; Ireland et al., 2003; Kuratko et al., 2014; Weiss et al., 2023).

Furthermore, the sample size was verified using the” partial least squares structured equation modeling (PLS-SEM) heuristic statistical tests to identify the minimum number of respondents required, namely the ‘10 times’ PLS-SEM analytic heuristic and the power analyses tests” (Hair et al., 2017). The minimum sample size was calculated at 156, which was considered a fair representation (one percent of the target population) to enable robust data modeling and analysis. As the study was based on voluntary participation, an over-sampling factor of 100 percent was applied to the target sample size, thus increasing the sample size to 312 to account for non-responses. During the data collection phase, the rights and protection of all respondents were upheld to ensure ethical practices were maintained. Participation was purely on a voluntary basis, whereby consent was obtained from all respondents, and anonymity was ensured at all stages of the research process, with no biometric data or personally identifiable data captured or recorded.

Sample characteristics reveal that the profile of managerial levels amongst the sample of respondents showed that the majority of respondents were part of the senior management cohort (66%), followed by middle management (18%), and then as team leader (16%). Initially, we intended to use country as an additional control variable to consider the effect of the bank’s footprint in Africa via their foreign branches, but due to the 99 percent skewness of the data, towards South Africa, this parameter was dropped as a control variable.

Measurement of constructs

Existing measurement scales were used to collect responses. All measures were operationalized based on the theoretical discussions in the literature (the full instrument is available). The survey questionnaire was designed so that participants could not complete the next survey without completing the preceding one. Prior instruments were employed using the Likert scale and Bipolar scale formats. Annexure A identifies all the study variables as per the study model, with columns indicating primary and secondary latent measurement constructs, the research scale, scale type and sources as well as previously established reliability of the scales.

Consistent with previous CE studies, control variables focused on various levels of management, where there is a theoretical basis for expecting these variables to have a relationship with either the IVs or DV, or both. See Annexure A.

Data analyses

PLS-SEM can be used successfully to test complex models where other approaches would fail due to the high number of relationships, constructs, and indicators (Alexandre et al., 2009). The model was employed which is a Type 1 model of a reflective-reflective higher order latent variable model, whereby a disjoint two-stage approach was adopted to estimate the measurement and structural parameters of the high order constructs within the model leveraging SMART PLS 3, as a software package. As such, the measurement (outer) model was analysed in three tiers, namely a) first order construct, stage 1, b) second order construct, stage 2 and c) third order construct, stage 2, whereby a battery of diagnostic tests pertaining to reliability, multi-collinearity and validity were systematically tested for each construct within each tier of the model for inclusion into the confirmed structural model that informs the finalized path model (Hair et al. 2017). A model fit analysis was conducted to ascertain the fit of the structural model for theory examination of CEA. The finalized path model was assessed based on the relevance of the path coefficient and significance of the hypothesized path relationships by employing the bootstrapping technique, which together served as a proxy of the empirical model’s predication capabilities (Hair et al. 2017).

Common method variance was controlled for which can bias results when relationships between variables captured from one single source are examined, a concern in our study (Podsakoff & Organ, 1986). To minimize such bias, the research instrument was carefully crafted to separate each latent variable scale from one another. Equally, social desirability and consistency motif method biases were controlled by ensuring the anonymity of respondents throughout the research cycle. Furthermore, bias was mitigated by evaluating the construct validity of the measurement model using confirmatory factor analysis (CFA), discussed in the following section. Two statistical tests confirmed that the data set does not exhibit common method bias, firstly, convergent validity was established, and secondly, no first order correlations were greater than 0.70, which is significantly below the threshold of 0.90 (Podsakoff & Organ, 1986). Moreover, since our model specification was quite complex, respondents could not have anticipated the results. As mentioned, we ensured the confidentiality of data collected and communicated this clearly to our respondents by encouraging them to answer the questions honestly.

RESULTS

Measurement model assessment

Descriptive statistics were calculated where the mean scores and standard deviations revealed a uniform spread around the construct’s average score and the standard deviation parameters of the first order constructs are within the range of 0-2, representative of the variation in responses away from the construct’s average score. Pearson’s correlation was applied to both the measurement model and the structural model, to determine and eliminate highly correlated manifest and latent variables (Hair et al., 2017). Correlation coefficients for the respective constructs, were positively but mostly moderately correlated with each other, for instance: Reward and CEA (r = 0.38; p < .05), while Strategy and CEA showed a larger correlation coefficient (r = 0.50; p < .01) (See annexure B).

Taking into consideration the scale makeup of all latent variables (Annexure A) and as illustrated in the empirical model, Figure 1, a reflective measurement model is represented. The accuracy of the measurement model is improved through the utilization of multi-item scales for each latent construct, which has been employed to epitomize the varied aspects of the construct, thereby significantly reducing the measurement errors of the latent constructs, due to casual effects of the latent variable that have been omitted from the model (Hair et al., 2017).

The use of PLS-SEM, as a specific category of SEM, was leveraged to holistically evaluate the plausibility of our study model. Due to the complexity of the hypothesized model, it was “operationalized at a higher level of abstraction, thus creating a hierarchical component model (HCM),” that incorporated 22 first order latent constructs, four second order latent constructs, and two third order latent constructs. Based on the relationship between the higher-order components and the lower-order components, the measurement model was a reflective-reflective HCM (Hair et al., 2017). Since the model had third order constructs, stage 1 included one layer, namely the first order model, and stage 2 included two layers, namely the second order and third order models. Hence, a three-tier analysis was undertaken, and the effect of each layer of analysis was statistically validated to determine which layer functioned as a significant value driver of CEA. Both the outer (measurement) and inner (structural) models were subjected to reliability, validity, and collinearity testing, respectively (Hair et al., 2017). See Table 2 for third-order model results.

Convergent validity for indicator variables is established when the ‘outer loadings of the indicator variable is statistically significant and greater than 0.70, thus equalling an indicator reliability that is greater than 0.50, whereas the convergent validity on the construct level is tested by examining the ‘average variance extracted (AVE) and validity is established when the AVE values for each measured construct is greater than 0.50 (Hair et al., 2017). Based on the initial AVE analysis across the first order latent variables, the following indicator variables were deleted from the measurement model as their AVE scores were less than 0.50: A04, A06, A08, A10, LC06, C07, MK03, MK06, MK09, MK10, M04. Moreover, the elimination of specific items was required due to poor loadings on the extracted components as per the initial factor loading analysis. As a result, several latent constructs from the higher order construct level were removed as the factor loadings were significantly below the minimum threshold. Figure 2 shows the measurement model Stage 3 impact illustration of these poor loadings. Nonetheless, the final AVE scores across the first order latent variables, see Table 2 were all greater than the minimum threshold of 0.50, which is an acceptable threshold to conclude convergent validity, as indicator variables scores converge to assess the underlying latent construct (Hair et al., 2017).

A tri-modal discriminant validity approach was employed. The outer-loadings of the construct were evaluated to determine whether the measurements are larger than any of its cross-loadings. The Fornell-Larcker criterion was employed whereby the square root of each construct’s AVE should be greater than its highest correlation with any other construct, and finally, the Heterotrait-Monotrait ratio (HTMT) of the associations was examined whereby, “the true correlation between latent constructs is determined based on the notion that they are perfectly reliable. Validity is established if the HTMT confidence interval does not include 1 (Hair et al., 2017, p.116). The cross-loading analysis for indicator variables were observed to be less than its outer loadings, and thus, discriminant validity was established based on the cross-loadings assessment (Hair et al., 2017).

Figure 2. Measurement model Stage 3 impact illustration

An assessment of the square root of AVE scores across all first order latent constructs, denotes values that are larger than its association with other first order latent constructs, thus discriminant validity is established based on the Fornell and Larcker (1981) principle. The HTMT ratio for first order latent constructs was observed to be less than 0.7, which is significantly below the maximum threshold of 0.9, thus, discriminant validity was established based on the HTMT ratio criterion (Fornell & Larcker, 1981). The standardized root mean square residual (SRMR) assessment was used to determine the model fit as it is deemed as an absolute measure of fit that is defined as the root mean square discrepancy between the observed correlations and the model-implied correlations. Analyzing the model fit data, see Table 3, the structured model exhibits a good fit as both the saturated and estimated model were less than 0.10 (Hair et al., 2017).

In terms of reliability, the ‘Composite Reliability (CR)’ method was employed, considering some of the shortcomings existing on Alpha values when utilizing PLS-SEM (Hair et al., 2017). A lower bound score of 0.7 (on a scale from 0 to 1) was required to ascertain acceptable levels of reliability of a composite variable. The final CR scores across the third order latent variables, remained within the range of 0.71 and 0.94, which stood at an acceptable threshold to conclude internal consistency reliability, see Table 2 (Hair et al., 2017). In summary, as shown in Table 2, the findings demonstrate that measurement models satisfy all the minimum criteria.

Table 2. Confirmatory factor analysis results for third-order latent constructs

Model Index Fit	Acceptable threshold	CEA	Organisational	Individual	Environment	Growth
χ²/df	<=3.00	1.77	1.81	1.91	1.21	0.96
RMSEA	<=0.08	0.06	0.03	0.05	0.06	0.00
IFI	>=0.90	1.00	1.00	1.00	1.00	1.00
TLI	>=0.90	0.99	0.99	1.00	0.99	1.09
CFI	>=0.90	1.00	1.00	1.00	1.00	1.00
SRMS	<=0.10	0.01	0.05	0.05	0.03	0.02
Average Variance Extracted (AVE)	>=0.50	0.63	0.58	0.83	0.51	0.53
Composite Reliability	>=0.70	0.91	0.82	0.91	0.71	0.89
Cronbach Alpha	>=0.50	0.90	0.73	0.80	0.73	0.89

Structural model and hypotheses testing

Similar to the measurement model, the structural model was assessed for collinearity. The collinearity of predictor variables was assessed by calculating the tolerance (TOL) values and the variance inflation factors (VIF) of the predictor variables. If the TOL values are greater than 0.20 and the VIF values are less than 5, the predictor variables will not exhibit any high levels of collinearity. If high levels of collinearity are presented, strategies of creating higher-order constructs, eliminating, or merging indicators will be considered (Hair et al., 2017). To test for collinearity within the structural model, every group of predictor latent construct for each component part of the structural model was analyzed. Based on the VIF analysis (not shown, but available), all predictor variables contained within the inner structural model have a VIF score of less than 5 (range from 2.124 to 1.502), indicating no multi-collinearity issues (Hair et al., 2017).

The relationships within each sub-component part of the structural model represented the hypothesized relationships among the constructs, as displayed in Figure 3. The results of the relationships within each sub-component part of the structural model are reflected in Table 3 to Table 5, which represent the hypothesized relationships among the exogenous constructs and endogenous constructs. The two-tailed bootstrapping testing approach was applied to evaluate the significance of the path coefficients. The evaluation of the relationships between constructs was conducted using the path coefficients (β), p-values (≤ 0.05), and t-values (threshold of 1.96). Bootstrapping confidence intervals were examined to provide supplementary evidence “on the stability of the path coefficients (Hair et al., 2017).

Figure 3. Final structural model

Table 3 shows the path coefficients, which are significant for the relationship between employee and CEA, organization and CEA, and growth orientation and CEA. For H1, the statistical analysis concluded that the path coefficient between organizational-level antecedent constructs and CEA was 0.66, which was statistically significant at p < 0.01. The quantum of the path coefficient is evidence of a direct positive, large linear association and exhibits a significantly large effect (0.82) on CEA within the modeled ecosystem; thus, it is a critical predictor of CEA. It is notable that the size of the path coefficient for the significant hypothesized relationship between organization and CEA is an important positive linear relationship within the ecosystem model, whereby its association is large (0.664). Moreover, the size of the path coefficient for the significant hypothesized relationship between growth and CEA is a negative linear relationship that warrants attention within the ecosystem model, even though its association is small (-0.155). H2 results were found to be significant, albeit the quantum of the path coefficient (0.15) demonstrates a small effect (0.04) on CEA.

Table 3. Path coefficients and significance levels of the outer structural model

Path relationship	Path coefficient	2.5%	97.5%	p-values	t-value
Employee to CEA	0.145	0.026	0.254	0.012	2.514**
Environment to CEA	0.056	-0.046	0.174	0.312	1.012
Organisation to CEA	0.664	0.560	0.776	0.000	12.013***
Growth to CEA	-0.155	-0.140	0.078	0.020	2.326**
Management to CEA	0.061	-0.077	0.199	0.384	0.871
Senior management to CEA	-0.106	-0.257	0.056	0.180	1.341
Top management to CEA	-0.076	-0.198	0.063	0.251	1.149

Note: *p<0.10, **p<0.05, ***p<0.01.

Table 4 shows the path coefficients, for which there are no significant path coefficients within the first order structural model. Table 5 shows the path coefficients, which signify the hypothesized relationships within the second order structural model, and which are significant for the relationship between entrepreneurial mindset and CEA, transformational leadership and CEA, strategy and CEA, entrepreneurial structure, and CEA and, organizational resources to CEA. It is observed that the size of the path coefficient for the significant hypothesized relationship between strategy and CEA is an important positive linear relationship within the ecosystem model, whereby its association is moderate to large (0.427). A summary of the path-analyses results, as per the hypotheses, are available in Annexure C.

Table 4. Path coefficients and significance levels of the first order, inner structural model

Path relationship	Path coefficient	2.5%	97.5%	p-values	t-value
Achievement to CEA	0.065	-0.089	0.202	0.376	0.885
Locus of control to CEA	0.003	-0.136	0.161	0.963	0.046
Creativity to CEA	-0.068	-0.190	0.088	0.334	0.967
Optimism to CEA	0.027	-0.126	0.187	0.727	0.349
Dynamism to CEA	-0.011	-0.137	0.131	0.872	0.162
Hostility to CEA	0.072	-0.055	0.193	0.247	1.157

Note: *p<0.10, **p<0.05, ***p<0.01.

Table 5. Path coefficients and significance levels of the second order, inner structural model

Path relationship	Path coefficient	2.5%	97.5%	p-values	t-value
Entrepreneurial persona to CEA	0.007	-0.143	0.143	0.919	0.101
Entrepreneurial mindset to CEA	0.149	0.035	0.297	0.025	2.249**
Social networks to CEA	-0.059	-0.163	0.074	0.321	0.993
Transformational leadership to CEA	0.147	0.035	0.259	0.011	2.541**
Strategy to CEA	0.427	0.302	0.556	0.000	6.606***
Entrepreneurial culture to CEA	-0.126	-0.252	0.108	0.170	1.373
Entrepreneurial structure to CEA	0.175	0.044	0.307	0.009	2.623***
Entrepreneurial reward practices to CEA	0.035	-0.102	0.159	0.591	0.537
Organizational resources to CEA	0.144	0.034	0.260	0.013	2.492**

Note: *p<0.10, **p<0.05, ***p<0.01.

The theoretical model, illustrated in Figure 1, shows the moderator hypothesized relationships (H4), while Table 6 summarizes the moderation analysis results. The relevant moderator is included in the path model by the inclusion of an interaction term variable, i.e. moderator x exogenous variable. In keeping with the overall estimation approach, i.e. disjoint two-stage methodology for the path model, the two-stage moderation approach was adopted, which enhances the ability of the path model to establish moderation significance (Hair et al., 2017). Table 6 shows that the interaction term of ‘Environment x Employee’ was non-significant; hence, there is no evidence that the proposed moderator variable, environment, has any effect on the relationship between the Employee and CEA. While the interaction between ‘Environment x Organisation’ shows an effect size of the interaction term greater than 0.025, which is evident of a large effect on CEA, the interaction was non-significant. The interaction ‘Organization x Employee exhibits a non significant moderation influence; whereby the effect size of the interaction term is 0.000, which indicates that there is no evidence that the proposed moderator variable, organization, has any effect on the relationship between the Employee and CEA. Similarly, the effect size of the interaction term ‘Growth x Environment’ was non-significant; hence, there is no evidence that the proposed moderator variable has any significant effect on CEA.

Table 6. Summary of moderation analysis

Hypotheses	Interaction term	Strength (Path coefficient)	Significance (t-value)	f2	Supported
H3c: Moderator 3	Growth X Environment	-0.040	0.730	0.004	No moderation effect
H4a: Moderator 1a	Environment X Individual Employee	-0.009	0.151	0.000	No moderation effect
H4b: Moderator 1b	Environment X Organization	-0.115	1.758	0.030*	No moderation effect

Note: *p<0.10, **p<0.05, ***p<0.01.

Recognizing that researchers should routinely consider the assessment of the predictive power of their PLS path models (Shmueli et al., 2019), the empirical model’s predictive power was determined by its R2 value and was symbolic of the combined effects of all the exogenous latent constructs on the endogenous latent construct, CEA. The endogenous latent construct, CEA, had a R2 value of 0.577. The R2adj is 0.548, which compensated for adding several non-significant exogenous constructs within the model, which included the external environment, entrepreneurial persona, achievement, locus of control, creativity, self-efficacy, dynamism, hostility, entrepreneurial reward practices, entrepreneurial culture, and social networks. The difference between the R2 value and R2adj value was not pronounced enough to warrant dropping any additional predictor variables (Hair et al., 2017). The Q2 score for the model was greater than 0; hence, the explanatory predictor constructs displayed predictive relevance for CEA (Hair et al., 2017).

The main finding from this study validates that an entrepreneurial corporate strategy is the bedrock of CEA. Its singular effect on CEA is five times larger than any other predictor within the corporate entrepreneurial ecosystem (f2=0.24), see Annexure B. Employee enablement of the corporate entrepreneurial strategy occurs via the decisions and behaviors of transformational leaders (f2=0.04) and the entrepreneurial mindset (f2=0.03) of employees to develop initiatives and formulate strategic plans that enable the delivery of the CEA. The execution of the corporate entrepreneurial strategy occurs via the implementation of an organic organizational structure (f2=0.05) and the deployment of novel resource recipes (f2=0.03) to build new capabilities and adjacent capabilities to a firm’s core offering. The collective effect, see Figure 4, of the spectrum of predictor variables included within the CE model resulted in a 58 percent explanation of the variation in CEA, which was deemed as showing moderate to substantial predictive power (Hair et al., 2017). The latent exogenous constructs of employee and growth had a small effect on CEA, whereas the organization layer within a corporate ecosystem has a significantly large effect on CEA. The latent exogenous constructs of entrepreneurial mindset, transformational leadership, entrepreneurial structure, and organizational resources, all had a small effect on CEA, whereas an organization’s strategy orientation had a medium to large effect on CEA. In summary, the optimal configuration of the antecedents in driving CEA activity is illustrated in Figure 4.

Figure 4. Model illustration of effects of antecedents toward driving CEA

DISCUSSION

Recognizing that researching individual antecedents of CEA ignores the extent to which CE activities are mutually and reciprocally supportive, a comprehensive CE ecosystem model was framed by delineating core relationships through a structured diagnostic of pattern fits that were empirically validated to be primary value drivers of CEA. H1 was partially supported insofar as the existence of a pro-organizational corporate ecosystem characterized by a firm’s organizational level antecedent variables of corporate strategy, structure, transformational leadership, and organizational resources all positively influence the extent of CEA. Moreover, a direct positive, moderate to large linear association between entrepreneurial strategy and CEA was observed and as such, this finding aligns with the theoretical proposition theorized by Guth and Ginsberg (1990) and Ireland et al. (2009), whereby CE is enabled through the development and adoption of a corporate entrepreneurial strategy that is consistent with the strategic intent and vision of the firm. This relationship holds particularly true in the CE domain of strategic renewal, involving the transformation of large incumbent companies, as CEA, from approval to resource investment and trade-offs does not happen organically but rather it is driven intentionally by the top management team through the endorsement of a CE strategy. This suggests that entrepreneurial strategy acts as a pivotal value driver of CEA within a corporate entrepreneurial ecosystem framework. Similarly, the positive influence of transformational leadership on CEA was observed from the results. This finding concurs with the principle that transformational leaders are visionary and serve as inspirational motivators within organizations (Pan et al., 2021). These leaders are able to derive higher levels of motivation, empowerment, shared commitment, and performance from employees via their leadership, which acts as an important antecedent to driving CEA.

On the other hand, culture as an antecedent in our model did not significantly influence the degree of CEA. Culture is by nature an organizational driver that unlocks performance value over the medium to long-term (Sarros et al., 2008). Hence, it is plausible that as a cross-sectional study, the results may not have captured a realistic portrayal of how respondents could have reached a point of maturity where they would have been expected to exhibit a deep homogenous cultural pattern with respect to CEA. Similarly, reward practices did not significantly influence the degree of CEA. This finding reflects the mixed research findings in relation to reward practices, which could also be a result of the organizational context (Urban & Townsend, 2021). Given that the banking industry was surveyed and as respondents are governed by a stringent set of regulatory standards, the relevance of reward frameworks in CEA under such conditions could have been compromised. Plausibly there is a lack of performance-based rewards systems in the African banking sector, which, in turn, discourages CEA.

H2, in terms of the extent to which an employee’s antecedent qualities of a pro-entrepreneurial cognitive, personality and motivational disposition and network ties are positively related to CEA, was only partially supported insofar as the entrepreneurial mindset was found to be a significant driver of CEA. Hypothesis 2b entailed the evaluation of the impact of an entrepreneurial mindset in driving CEA, which encapsulates an individual’s metacognitions and thinking paradigm as a robust precursor of individual level entrepreneurial behavior (Kuratko et al., 2021). Thus, our article contributes to the study of higher-order cognitive processes where a metacognitively aware individual would engage in the process of identifying alternative strategies that maximize the likelihood of achieving his/her goal (Haynie et al., 2010), which in this case, would mean executing the most appropriate strategic drivers to CEA.

Notwithstanding the positive and significant result obtained for the employee’s entrepreneurial mindset in relation to influencing the degree of CEA, the employee’s antecedent qualities of a pro-entrepreneurial personality, motivational disposition and network ties were found to be non-significant. While previous studies show how pro-entrepreneurial cognitive, personality and motivational disposition enable an individual to navigate the entrepreneurial process, the rigid organizational structure of many large banking institutions with related issues of red-tape, organizational boundaries, and lack of management support may stifle individual personality and motivational disposition making them less predisposed toward CEA.

In terms of H3 and H4, which predicted the extent to which the macro environment, shaped by the intensity of dynamism and hostility within banking environments, influences CEA, it was established through empirical data that H3 and H4 cannot be supported insofar as environmental dynamism and hostility do not play a direct or moderating significant role in CEA in the banking industry in South Africa. Moreover, in terms of H3c, based on the two-stage statistical approach in relation to the moderation analysis, the casual relationship between an underlying growth orientation momentum and CEA was deemed to have a significant weak to moderate relationship. These findings are not altogether surprising given the environmental nuances with respect to the South Africa context where, similar to many countries in Africa, the banking industry is plagued with deep levels of continued market uncertainty, depressed economic conditions, societal and political instabilities, and weakened institutional structures. Perhaps, as prior studies suggest, during such extreme conditions, leaders manage business risk by shifting their priorities towards business survival as a single-minded goal for their firm, thus focusing on effectively running their core business under unprecedented market conditions and not investing in growth and transformation (Pan et al. 2021; Suder, 2024). It is worth remembering that banking institutions are highly regulated companies with a primary focus on managing business risk and thus, a more conservative business approach is applied during times of market instability and uncertainty. Thus, incumbent banks manage market risk uncertainty by maintaining stable business models and acting more cautiously, resulting in them choosing to strategically conserve resources rather than competitively respond by investing in innovation and new business models (Urban & Townsend, 2021). It must also be borne in mind that the influence of the macro environment context in driving CEA in developed countries compared to emerging economies is moderated by less fiscal support and fewer business safety nets in emerging economies, which seems to influence leadership responses in a different manner, making them more risk averse (Dana et al.,2022).

CONCLUSION

Recognizing that prior studies focused on individual elements of CE as independent variables overlook the level to which CE activities are mutually and reciprocally supportive, our study makes a unique contribution by developing and testing an integrated and comprehensive model reflecting the dynamic complexity of the antecedents driving CEA. We have extended the current theoretical domain of CE by predicting and assessing the relative importance of three sets of antecedents to CEA, namely organizational factors, individual level factors and environmental factors, to ascertain the effect of their interrelationships and interdependencies. Based on the study results and from the array of hypothesized predictors of CEA, it was entrepreneurial strategy, entrepreneurial structure, transformational leadership, organizational resources and capabilities, and an entrepreneurial mindset that were proven to be significant predictors of CEA within a corporate South African banking ecosystem.

Contribution

An important contribution of our article has been that through empirical testing, the validity and reliability of different antecedents to CEA have now been established in an under-examined African context, allowing for their theoretical and empirical reach to be advanced. Since mainstream management theories mostly describe the North American and European contexts and are subsequently not always relevant to emerging market environment, without modifications, it is anticipated that the results will allow researchers to compare and examine antecedents to CEA and its applicability in similar country contexts.

Our findings provide a better contextual understanding of CE as well, where South Africa, despite its emerging market status, has a progressive financial services and banking sector, which compares positively with those of industrialized countries (Urban & Townsend, 2021). Such understanding of the leading role of strategy in CEA is highly relevant when considering the entry of Fintech players coupled with intense rivalry, which has subsequently changed the financial services and banking landscape into a more dynamic and competitive industry. Such transformation is further being intensified by digitally driven transformation, threatening the long-term survival of traditional banks. It is an imperative for managers to embrace strategic renewal and the creation of new business models to optimize current revenue streams and create new revenue streams (McKinsey, 2021). The banking industry will need to cultivate and secure a coherent CE strategy and develop entrepreneurial skills and competencies to remain competitive under these circumstances.

Implications

The practical implications of our study highlight that an entrepreneurial corporate strategy is at the center of a corporate entrepreneurial ecosystem. In fact, its singular effect on CEA is circa five times larger than any other predictor within the modeled corporate entrepreneurial ecosystem. Moreover, it is important for managers to consider the configuration of the predictors within the CE model, which function as pathways to entrepreneurial corporate strategy. They need to pay attention to employee enablement via their transformational leadership behaviour and cultivate an entrepreneurial mindset, allowing employees to develop initiatives and formulate strategic plans that enable the delivery of the corporate entrepreneurial strategy. The execution of the corporate entrepreneurial strategy is also only possible through an organic organizational structure and the deployment of novel resources to build new capabilities and adjacent capabilities to enhance a firms’ CE activities. Our study has important implications for organizational leaders and management who can better understand which enablers are useful to manage, leading to the CEA. Managers are advised to grow their organizational knowledge regarding which enablers, as delineated in this article, offer the best pathway towards the development of a more robust CE framework.

Limitations and future research

Study limitations relate to the nature of the cross-sectional design, where a longitudinal study would provide more clarity on whether enduring antecedents influence the CEA in the long term. Sampling was also a challenge; due to the absence of sampling frames, no claim of representativeness to the entire sector can be made, and as non-random sampling was used, this may challenge the generalizability of the findings. Future research could evaluate the antecedents to CEA across diverse types of sub-context organizational environments to isolate the predictors of intrapreneurial behavior within the CE ecosystem. Moreover, supplementing the measures used in this study with additional macro-level variables – pertinent to an African context – could also help explain CEA. In this regard future research could potentially leverage institutional theory to determine to what extent the regulatory and normative environments influence CEA in an African emerging market context.

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Annexure A. Study construct measures, scales, sources, and prior reliabilities scores reported

Latent measurement construct	Scale type	Research scale source and prior reliability scores
Latent measurement construct	Scale type	Research scale source and prior reliability scores
Entrepreneurial Strategy	Seven-point Likert scale [Very untrue of me to Very true of me]	Entrepreneurial strategy-making scale (Miller, 1983 & Covin and Slevin, 1991), Cronbach alpha of 0.75 (Li et al., 2005)
Entrepreneurial Culture	Bipolar rating scale along a 10-point continuum scale	Entrepreneurial Culture: Stevenson scale (Brown et al., 2001): Cronbach alpha of 0.68 (Brown et al., 2001)
Entrepreneurial Structure	Bipolar rating scale along a 10-point continuum scale	Structure: Stevenson Scale (Brown et al., 2001): Cronbach alpha of 0.78 (Brown et al., 2001)
Entrepreneurial Reward	Bipolar rating scale along a 10-point continuum scale	Reward Philosophy: Stevenson scale (Brown et al., 2001): Cronbach alpha of 0.68 (Brown et al., 2001)
Organizational Resources	Bipolar rating scale along a 10-point continuum scale	Resource Orientation: Stevenson scale (Brown et al., 2001): Cronbach alpha of 0.68 (Brown et al., 2001)
Transformational Leadership: Inspirational motivation Intellectual stimulation Individual consideration	Seven-point Likert scale [Never to Every time]	Transformational Leadership (TL): Multifactor Leadership Questionnaire (Bass et al., 2003): Cronbach alpha of 0.81 (Bass et al., 2003)
Entrepreneurial Employee Mindset: Goal orientation Metacognitive knowledge Metacognitive experience Metacognitive choice Monitoring	Seven-point Likert scale [Very untrue of me to Very true of me]	Entrepreneurial Mindset (EM): MAC (Haynie et al., 2010): Cronbach alpha of 0.885 across all 5 dimensions (Haynie et al., 2010)
Entrepreneurial Employee Persona: Creativity; Self-Efficacy Locus of control Achievement	Seven-point Likert scale [Very untrue of me to Very true of me]	Creativity scale (Tierney et al., 1999): Cronbach alpha of 0.96 (Tierney et al., 1999) General Self-efficacy scale (Chen et al., 2001): Cronbach alpha of 0.82 (Chen et al., 2001) Self-monitoring scale (McShane & Von Glinow, 2003): Cronbach alpha of 0.72 (Hmieleski and Corbett, 2006) Achievement Motivation (McClelland, 1965). Cronbach alpha of 0.78 (McShane & Von Glinow, 2003).
Social Networking	Seven-point Likert scale [Strongly disagree to Strongly agree]	Social Capital: Social Networks (Nahapiet and Ghoshal, 1998): Cronbach alpha of 0.86 (Nahapiet and Ghoshal, 1998)
Entrepreneurial momentum: Growth Orientation	Bipolar rating scale along a 10-point continuum scale	Growth Orientation: Stevenson scale (Brown et al., 2001; Pan et al., 2021): Cronbach alpha of 0.71 (Brown et al., 2001)
Dynamism	Seven-point Likert scale [Strongly disagree to Strongly agree]	Environmental Dynamism (Uzkurt et al., 2012; Zahra, 1993). Cronbach alpha of 0.72 (Zahra, 1993)
Hostility	Seven-point Likert scale [Strongly disagree to Strongly agree]	Environmental Hostility (Uzkurt et al., 2012; Zahra, 1993). Cronbach alpha of 0.72 (Zahra, 1993)
CEA	Seven-point Likert scale [Very untrue of me to Very true of me]	CE Index (Miller and Friesen, 1982): Cronbach alpha of 0.75 (Zahra & Covin, 1995)

Annexure B. Correlation matrix

Latent Variable	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22
1. CEA	1
2. EEP: ACH	0.12	1
3. EEP: LOC	-0.03	-0.04	1
4. EEP: CRT	0.16**	0.45***	-0.24***	1
5. EEP: SE	0.19**	0.42***	-0.24***	0.59***	1
6. EEM: GO	0.16**	0.50***	-0.28***	0.46***	0.46***	1
7. EEM: MK	0.20***	0.40***	-0.10	0.34***	0.45***	0.46***	1
8. EEM: ME	0.06	0.47***	-0.20***	0.51***	0.54***	0.56***	0.51***	1
9. EEM: MC	0.23***	0.45***	-0.03	0.36***	0.41***	0.55***	0.56***	0.47***	1
10. EEM: MT	0.19**	0.43***	-0.12	0.51***	0.49***	0.59***	0.66***	0.65***	0.64***	1
11. SN	-0.02	0.11	-0.16**	0.16**	0.18**	0.08	0.15**	0.13*	0.00	0.14*	1
12. ESTRAT	0.50***	0.19***	-0.09	0.20***	0.21***	0.14*	0.16**	0.09	0.15*	0.16**	0.15**	1
13. ECULT	-0.14*	-0.03	0.10	-0.10	-0.13*	-0.12	-0.15**	-0.11	-0.00	-0.07	-0.07	-0.05	1
14. ESRT	0.38***	-0.09	0.05	-0.02	-0.04	-0.08	-0.04	-0.09	0.01	-0.00	0.06	0.27***	0.28***	1
15. ERWRD	0.38***	-0.06	0.02	0.08	0.15*	0.01	-0.01	0.00	0.04	0.00	0.13*	0.39***	0.21***	0.52***	1
16. ORG RS	0.18**	0.09	-0.07	0.09	0.12	0.00	-0.03	0.11	0.04	0.05	0.04	0.26***	0.24***	0.13*	0.22**	1
17. TL:IM	0.38***	0.05	-0.16**	0.13*	0.08	-0.09	0.10	0.00	0.01	0.00	-0.09	0.39***	-0.10	0.15**	0.18**	0.11	1
18. TL: IST	0.32***	-0.00	-0.19**	0.26***	0.14*	-0.02	0.11	0.01	0.05	0.10	-0.08	0.33***	-0.05	0.18**	0.18**	0.11	0.80***	1
19. TL:ICS	0.39***	0.03	-0.09	0.23**	0.13*	-0.03	0.14*	0.05	-0.01	0.09	-0.10	0.32*	-0.09	0.20***	0.20***	0.17**	0.77	0.75***	1
20. GWRTO	-0.09	-0.15**	0.05	-0.06	-0.05	-0.11	-0.10	-0.04	-0.06	-0.07	-0.05	0.06	0.47***	0.33***	0.25***	0.19***	0.04	0.06	0.10	1
21. ENV: DYN	0.17**	0.18**	0.10	0.04	0.04	0.02	0.15**	0.01	0.12	0.12	0.07	0.19**	0.03	0.04	0.13*	0.17**	0.01	0.01	0.02	0.09	1
22. ENV: HOS	0.13*	0.29***	0.12	0.08	0.01	-0.01	0.08	-0.02	0.03	0.02	-0.07	0.11	0.07	0.01	0.06	0.16**	0.10	0.04	0.06	0.02	0.47***	1

Note: *p<0.10, **p<0.05, ***p<0.01. Abbreviations: CEA = corporate entrepreneurship activity; EEP: ACH = Entrepreneurial Employee Persona: Achievement; EEP: LOC = Entrepreneurial Employee Persona: Locus of Control; EEP:CRT = Entrepreneurial Employee Persona: Creativity; EEP: SE = Entrepreneurial Employee Persona: Self-efficacy; EEM: GO = Entrepreneurial Employee Mindset: Goal orientation; EEM: MK = Entrepreneurial Employee Mindset: Metacognition knowledge; EEM: ME = Entrepreneurial Employee Mindset: Metacognition experience; EEM: MC = Entrepreneurial Employee Mindset: Metacognition choice; EEM: MT = Entrepreneurial Employee Mindset: Monitoring; SN = social networks; ESTRAT = Entrepreneurial strategy; ECULT = Entrepreneurial culture; ESRT = Entrepreneurial structure; ERWRD = Entrepreneurial reward; ORG RS = Organizational resources; TL: IM = Transformational leadership: Inspirational motivation; TL: IST = Transformational leadership: Intellectual stimulation; TL: ICS = Transformational leadership: Individual consideration; GWRTO = Growth orientation; ENV: DYN = Environmental dynamism; ENV: HOS = Environmental hostility.

Annexure C. Summary of hypotheses and path relationship results

Hypotheses	Path relationship	Path coefficient	p-value	t-value	f2	Supported
H1	Organization to CEA	0.664	0.000	12.013**	0.818***	Yes
H1a	Strategy to CEA	0.427	0.000	6.606***	0.243***	Yes
H1b	Entrepreneurial culture to CEA	-0.126	0.000	1.373	0.029	No
H1c	Entrepreneurial structure to CEA	0.175	0.009	2.623**	0.045**	Yes
H1d	Entrepreneurial reward practices to CEA	0.035	0.591	0.537	0.002	No
H1e	Transformational leadership to CEA	0.147	0.011	2.541**	0.040**	Yes
H1f	Organizational resources to CEA	0.144	0.013	2.492**	0.034***	Yes
H2	Individual employee to CEA	0.145	0.012	2.514**	0.044**	Yes
H2a	Entrepreneurial persona to CEA	0.007	0.919	0.101	0.000	No
H2ai	Achievement to CEA	0.065	0.367	0.885	0.005	No
H2aii	Locus of control to CEA	0.003	0.963	0.046	0.000	No
H2aiii	Creativity to CEA	-0.068	0.334	0.967	0.005	No
H2aiv	Optimism to CEA	0.027	0.727	0.349	0.001	No
H2b	Entrepreneurial mindset to CEA	0.149	0.025	2.249**	0.029**	Yes
H2c	Social network to CEA	-0.059	0.321	0.993	0.008	No
H3	Environment to CEA	0.056	0.312	1.012	0.006	No
H3a	Dynamism to CEA	-0.011	0.872	0.162	0.000	No
H3b	Hostility to CEA	0.072	0.247	1.157	0.008	No

Note: *p<0.10, **p<0.05, ***p<0.01.

Biographical notes

Boris Urban (Ph.D. UP) is a Professor at the WBS and was the inaugural Chair in Entrepreneurship at University of Witwatersrand. His primary research agenda is to deepen understanding of entrepreneurial behavior within a unified explanatory structure at the individual, organizational, and societal levels. Boris has published over 200 journal articles, case studies, and book chapters. He is the editor of five books published by Oxford University Press, Pearson, and Springer. Most articles appear in a wide range of high-impact, peer-reviewed journals. He serves on several editorial boards, including the Journal of Social Entrepreneurship (UK). He is often invited as a subject expert on TV and radio shows and, occasionally writes for newspapers (Business Day) and has featured as a keynote speaker at seminars such as the SA Innovation Summit, Sunday Times Directors Event, SME Summit, LeaderEx and the Association of African Business Schools.

Thanusha Govender (Ph.D. WITS) is the CEO and Executive Director at Xlink, responsible for leading the company’s overall business, ensuring shareholder returns and shared value are delivered to all our stakeholders. Previously she was the Group Head of Corporate Strategy and Planning and Divisional COO at a major bank, where she led the design of a cohesive group strategy, ensuring that the cluster's strategies were fully integrated and the corporate strategy was executed across the group. Before joining the banking industry, she was a seasoned management consultant with a global consulting firm. She holds a BSc in Chemical Engineering, a Masters in Management Entrepreneurship and New Value Creation obtained Cum laude, and has completed the International Executive Leadership Programme IEDP at Wits Business School and London Business School.

Authorship contribution statement

Boris Urban: Supervision, Conceptualization, Writing Article, Writing – Review and Editing, Revisions. Thanusha Govender: Writing – Original Draft Preparation, Data Collection, Methodology, Data Analysis, Software Application.

Conflicts of interest

The authors declare no conflict of interest.

Citation (APA Style)

Urban, B., & Govender, T. (2024). An integrated PLS-SEM model on the interplay of antecedents and moderators driving corporate entrepreneurship activity in South Africa. Journal of Entrepreneurship, Management and Innovation, 20(4), 5-25. https://doi.org/10.7341/20242041

Received 3 January 2024; Revised 8 April 2024, 20 May 2024; Accepted 4 June 2024.

This is an open access paper under the CC BY license (https://creativecommons.org/licenses/by/4.0/legalcode).