Role of Institutional Ecosystem in Entrepreneurship Education: An Empirical Reiteration

Abstract

This study examines the influence of higher education institutions’ (HEIs’) ecosystem on entrepreneurship education (EE) and attempts to map the complex relationship between both. It also captures the actual practice of EE in HEIs. A two-stage empirical approach was used in the research design. Drawing upon literature, a conceptual framework was developed to relate HEIs’ ecosystem and EE in the first stage. This framework was tested with the data obtained from 264 academicians. The findings yielded eight factors of HEIs’ ecosystem and six factors of EE. In the second stage, opinion of 15 experts in the area of EE was sought to develop cause–effect relationships between the two constructs. The decision-making trial and evaluation laboratory (DEMATEL) approach was used to examine the cause–effect relationship. Findings indicate that entrepreneurship promotion activity and institutions’ attitude towards entrepreneurship is important causal factors leading to effective EE. The research contributes to literature by fusing two theoretical paradigms. The outcomes of the study have a strong implication for HEIs to build a conducive educational ecosystem for entrepreneurship development.

Keywords

Entrepreneurship education institutional ecosystem DEMATEL entrepreneurship promotion higher education institutions

The contribution of EE, in nurturing entrepreneurship, has been well documented in scholarly writings (Matlay, 2008; Nabi, Holden, & Walmsley, 2006; Trivedi, 2016; Young, 1997). In this regard, EE has grabbed significant attention in modern higher education (HE) (Gibb & Hannon, 2006; Pittaway & Cope, 2007). The EE adequately props up students in evaluating opportunities and managing risks (Solesvik, Westhead, Matlay, & Parsyak, 2013), raising awareness on the entrepreneurial process (Hynes, 1996) and bringing forth economic and social changes in the society as a whole (Kanter, 1983). The EE fosters the students’ entrepreneurial skills and influences the performance of the new venture (Dickson, Solomon, & Weaver, 2008; Galloway & Brown, 2002). This has resulted in the development of focussed courses and pedagogy to instil entrepreneurial spirits and to facilitate the realisation of entrepreneurial aspirations. Fayolle (2013) has observed the dearth of a strong theoretical and conceptual foundation explaining EE and negligence of proper reflection on the practice followed in EE. This is mainly due to the underdeveloped knowledge on EE from the perspective of higher education institutions (HEIs), educators and actual practices followed in EE. In this backdrop, we aim to seek answers to two principal research questions. What are the significant factors influencing EE? And, how does HEIs’ ecosystem influence EE?

Recent systematic reviews (Liñán & Fayolle, 2015; Pittaway & Cope, 2007) and co-citation analysis (Loi, Castriotta, & Di Guardo, 2016) have indicated the multidimensional attributes of EE research. However, the majority of research exercises have confined themselves in evaluating the impact of EE through students’ entrepreneurial intention (Liñán & Fayolle, 2015). The intention-focussed studies capture entrepreneurial intention among students; but, neither explains the process of transformation from intention to practice, nor does answer how the intentions or student entrepreneurial outcome can be enhanced (Varamäki, Joensuu, Tornikoski, & Viljamaa, 2015). Students, being the focal point among the majority of EE researchers (Liñán & Fayolle, 2015), the role of other stakeholders in EE is partially neglected. A few studies have noticed that the academicians and institutions extend ample scaffolding in realising overall entrepreneurial outcome (Mukesh, Rao, & Pillai, 2018; Walter & Block, 2016). The HEIs facilitate entrepreneurial activity among students through incubation and training (Fenton & Barry, 2014). The contribution of academia, institutions and other stakeholders, collectively, has significant influence on instilling entrepreneurial skills to the aspirants (Leffler & Näsström, 2014; Matlay, 2011). It is also evident that entrepreneurial orientation among academicians serves as a significant success factor for effective EE (Gustafsson-Pesonen & Remes, 2012). The academicians’ exposure to EE and the pedagogic practices are critical success factors of EE in HEIs (Lepistö & Ronkko, 2013). EE and HEIs play a critical role in shaping students’ entrepreneurial intention and process (Fayolle & Liñán, 2014). Matlay (2008) and Isenberg (2010) argue that a wide range of other stakeholders, from government to business organisations, contribute to EE, supplementing the efforts of academicians and institutions. Researchers (Fayolle, 2013; Wright, Siegel, & Mustar, 2017) have noted a knowledge vacuum in the domain of EE research, despite its apparent prominence. Wright et al. (2017) have noticed the lack of conceptual framework that relates HEIs’ ecosystem and EE. Moreover, the complexity and uneven terrain of EE among HEIs have added methodological challenges as well (Fayolle, Verzat, & Wapshott, 2016). These methodological issues and regional variations embedded in the past studies (Bergman, Hundt, & Sternberg, 2016; Nabi, Liñán, Fayolle, Krueger, & Walmsley, 2017) have led to the fragmentation of EE research and failed to provide a comprehensive picture. An integrated view of EE and HE has been captured in a couple of recent studies (Guerrero & Urbano, 2012; Guerrero, Urbano, Cunningham, & Organ, 2014). In this context, it is essential to have broader context and its relevant sub-context to get a holistic view.

In view of getting a broader context-based evidence from different studies, we conceptualised that HEIs form an ecosystem in which EE is hosted. This ecosystem significantly differs in nature, structure and operations from region to region. It is also evident that there exists a gap between what is practiced and what we know about the relationship of HEIs, EE and the role of different stakeholders (Matlay, 2011). This strongly justifies the need for a fresh investigation. The study also explores the complexity of HE ecosystem and helps understanding its influence on EE. This basic understanding of the nature and structure of relationship adds to the body of knowledge as well as act as a policy guide for strengthening EE from the perspective of HEIs. Furthermore, the study also brings new methodological approach to resolve the methodological issues raised by previous studies (Fayolle et al., 2016) and will act as a benchmark for similar studies.

Understanding the nature and complexity of the relationship between HEIs’ ecosystem and EE is an essential precondition for reckoning the student entrepreneurship formation. The HEIs’ ecosystem has a complex interrelationship with EE. The relationship between HEIs’ ecosystem and EE is multidimensional and heterogeneous (Guerrero & Urbano, 2012; Isenberg, 2010), and displays a complex relationship with different stakeholders (Wright et al., 2017). The complexity also lies within the EE with respect to pedagogical objectives and the expected outcome (Fayolle et al., 2016). Thus, the relation between HEIs’ ecosystem and EE has juxtaposed and complicated cause and effect pattern. Developing an appropriate methodology for such situation is challenging. Conventional tools, such as multiple-regression and structural equation modelling, will be appropriate when there is an asymmetric relationship among cause and effect factors (Woodside, 2013) and where different cause and effect factors are known. However, these conventional tools will be inappropriate to analyse social phenomena with causal complexity (Ragin, 2008). In similar research work, Patzelt and Shepherd (2009) used conjoint analysis for understanding the interrelationship and factors influencing academic entrepreneurship and environment. Based on the nature of the research question, we established a conceptual framework grounded in the existing literature and developed a structured instrument to capture the actual practices of HEIs in delivering EE. Furthermore, to understand the complexity and relationship among HEIs’ ecosystem and EE, we used multi-criteria decision-making (MCDM) approach through decision-making trial and evaluation laboratory (DEMATEL) technique (Raghuvanshi, Agrawal, & Ghosh, 2017; Si, You, Liu, & Zhang, 2018; Tzeng, Chiang, & Li, 2007). Decision-making trial and evaluation laboratory is appropriate in understanding a complex problem with a cluster of the intertwined problems (Tzeng et al., 2007). It helps in unwinding the complex problem into a workable hierarchical structure as well as it presents a cause and effect diagram (digraph) showing the relationship among different elements of the system (Wu, 2008).

The main contribution of our article is the adoption of an integrated approach for understanding HEIs’ ecosystem and EE. In a similar vein, our study attempted to test and validate the confluence of two conceptual frameworks to explain the relationship between HEIs’ ecosystem and EE. Even though EE and HEIs are closely associated, past studies have failed to offer a coherent picture. Seldom does the extant literature address the gap existing in the body of knowledge and actual practice. Our study considers actual practice as a basis for presenting our analysis and interpretation. We also endeavoured to validate a rarely used, but robust methodological approach in understanding the complex interrelationship among HEIs’ ecosystem and EE.

The rest of the manuscript has been organised as follows: We present a brief review of the literature in the next section. It is followed by the conceptual framework of the study and detailed research methodology. Findings of the study have been presented in results and analysis. This section is followed by a discussion and concluding comments.

Overview of Entrepreneurship Education (EE) Literature

Defining Entrepreneurship Education

In this section, we present multiple contexts of the EE definition. The EE research, as a nascent area, has picked up pace in the recent time (De Faoite, Henry, Johnston, & Van der Sijde, 2003; Hannon, 2005; Kuratko, 2005; Solomon, 2007). The researchers in the area of EE were unable to find consensus with reference to its definition. The formal definition of EE manifests explicit dichotomy, that is, EE as a delivery system to enhance students’ entrepreneurial intention (Fayolle et al., 2016; Lautenschläger & Haase, 2011; Pittaway & Cope, 2007) and as a tool to develop entrepreneurial skills, knowledge and attitude (Matlay, 2008). Jones and English (2004) define EE as ‘…….. the process which includes, instruction in opportunity recognition, commercializing a concept, marshaling resources in the face of risk, and initiating a business venture’ (p. 416). Bae, Qian, Miao, and Fiet (2014) have recognised EE as a delivery system to develop ‘desires to own or start a business’ focussing on enhancing the entrepreneurial intentions and potential. The most comprehensible definition was given by Fayolle and Gailly (2009) as ‘the activities aiming to foster entrepreneurial mindsets, attitudes and skills and covering a range of aspects, such as idea generation, start-up, growth, and innovation’. In contention, Stamboulis and Barlas (2014) advocate that EE is not just for entrepreneurship development; it is a tool for enriching student attitudes and attributes that are needed to face uncertainty in the entrepreneurial endurance. Collectively in a broader sense, the role of EE is to prepare students to reckon entrepreneurship as a long journey in life (Welsh, Tullar, & Nemati, 2016). Thus, HEIs are an integral part of EE, and it is essential to understand HEIs in the context of EE (Chalmers & Shaw, 2017; Chlosta, 2016).

HEIs in the Context of EE

The HEIs have been evolved from their predominant role of teaching and research towards creating economic and social impact (Neck, Meyer, Cohen, & Corbett, 2004). Fayolle and Redford (2014) argue that HEIs and universities play a critical role in developing and implementing EE. Fenton and Barry’s (2014) observation is a reinforcement of the contribution of HEIs. They content that HEIs can enhance entrepreneurial activity through EE, incubations, academic spin-offs, knowledge transfers, research and development, and training (Fenton & Barry, 2014). However, Gibb and Hannon (2006) noticed that HEIs are more oriented towards preparing graduates for employment. On the contrary, Ferrandiz, Fidel, and Conchado (2018) have argued that HEIs should promote entrepreneurship through developing an entrepreneurial culture among staff and students. Studies have also focussed on understanding the structure and actual practices of different stakeholders in EE (Fayolle & Redford, 2014; Redford & Fayolle, 2014). Literature has so far explored that EE is hosted within the HEIs through training and incubation (Fenton & Barry, 2014), and it involves different stakeholders, such as universities, academicians, government institutions and other business organisations (Matlay, 2008). However, it is also evident that the HEIs’ ecosystem differs from country to country and is heterogonous (Bergman et al., 2016). An integrated view of EE and HE has been captured in some of the recent studies (Guerrero & Urbano, 2012; Guerrero et al., 2014; Pittaway & Cope, 2007; Wright et al., 2017). These studies are regionally specific and address ecosystem in the context of developed economies. We attempted to replicate these studies in the Indian context, which is explained in the conceptual framework.

Conceptual Framework

A conceptual framework was developed to link and understand the interrelationship between HEIs’ ecosystem and EE based on the existing literature. Wright et al. (2017) argues the need for a conceptual framework to understand the interrelationship between various elements of HEIs and EE. The present conceptual framework, which is shown in Figure 1, is drawn upon Pittaway and Cope’s (2007) thematic framework for EE and Guerrero and Urbano’s (2012) model of the entrepreneurial university. Both these models are regarded as foundational studies in the domain of EE and its ecosystem. However, they are built on separate theoretical grounds.

Figure 1.

Source: The authors.

The Pittaway and Cope’s (2007) thematic framework of EE presents the picture of EE and its surrounding environment. Its framework consists of three levels: general policy environment for EE, university enterprise context and programme context of EE. The general policy environment for EE comprises of government and quasi-government institutions in implementing and promoting EE (Mortimer, 1995; Mowery & Sampat, 2004; Newby, 1998). The university enterprise context constitutes institutional entrepreneurial ecosystem and its role in promoting graduate entrepreneurship. It is linked to the HEIs’ structure, leadership (Sotirakou, 2004), strategies (Poole & Robertson, 2003), infrastructure (Grigg, 1994) and the degree of R&D commercialisation (Etzkowitz, 2003). The university’s enterprise context is critical in fuelling entrepreneurship-friendly setup for effective EE (Poole & Robertson, 2003). The programme context is more specific and includes EE as a core component. The programme context contains—actual pedagogic practices of EE, extra-curricular activities related to EE, department philosophy on EE, students’ entrepreneurial propensity and student capability. Thus, collectively programme content is specifying the elements of EE from the perspective of HEIs.

We considered all these five elements of programme context as factors of EE with minor refinement. We replaced student capability with student mentoring as the factor of EE. We argue that the former fails to be an element of EE, as it is an individual disposition. Further to support our argument, we propose that handholding is an important component of EE; the students need to be mentored in the initial part of venture creation (Edelman, Manolova, & Brush, 2008; Stewart & Knowles, 2003; Truell, Webster, & Davidson, 1998). Stevenson and Lundström (2001), in their ‘framework of entrepreneurship policy measures’, consider mentoring as a pertinent factor influencing EE and entrepreneurial outcome.

The Guerrero and Urbano’s (2012) model of the entrepreneurial university is grounded in resource-based view of an institution. Guerrero and Urbano’s (2012) model presumes entrepreneurial university as a single system. It presents the influence of HE ecosystem on the entrepreneurial outcome within a university/HEI setup. The model highlights the combined influence of HEIs’ ecosystem and its internal factors on the entrepreneurial outcome through teaching, research and entrepreneurial activity. The HEIs’ ecosystem includes structure, leadership and governance of HEIs, support measure for entrepreneurship, attitude towards entrepreneurship, EE, role model and reward system. These elements are essential components of HEIs’ ecosystem, which seek to develop student entrepreneurship (Clark, 1998). In addition, the HEIs’ internal factors, such as human capital, financial capital, social capital and its status and prestige, do have a significant influence on student entrepreneurship (Sporn, 2001). The HEIs’ internal factors are the resources and capabilities, which are acquired and developed to facilitate student entrepreneurship (Poole & Robertson, 2003; Wright et al., 2017).

Nevertheless, the two models used in the study are theoretically strong and closely associated with HEIs and EE, we found neither is fully relevant to the context of our study. However, based on the merits of these studies, we decided to consider the elements of the HEIs’ ecosystem from Guerrero and Urbano (2012) and those of EE from Pittaway and Cope (2007). Yet, we refined this framework as part of contextualisation. Hence, our study has merged two theories to develop an integrated framework, which explains the relationship between HEIs’ ecosystem and EE (refer to Annexure 1 for details).

Research Methodology

In the current study, we used a two-stage empirical approach to the topic of inquiry. As we proceeded to answer the two research questions, we designed the first stage to capture the actual practices of EE followed by HEIs. Furthermore, based on the results from the first stage, we designed another study to understand the complex relationships among HEIs’ ecosystem and EE through the DEMATEL approach. The two stages are detailed below.

Stage 1

Sampling

In the first stage, we developed a structured instrument to capture the actual practices of EE in HEIs, based on the proposed conceptual framework. The participants were academicians and administrators from management and engineering institutes/colleges. The first stage of survey was conducted in Karnataka state, India, which houses the second highest number of management (220) and engineering (238) colleges in the country, comprising of a total of 4,380 academicians (UGC, 2015). The research team contacted 400 academicians through email questionnaires from 20 management and 25 engineering colleges, which have been operational for 10 years preceding to the survey, on a judgemental sampling basis. We received usable responses from 264 academicians, with a response rate of 66 per cent. The judgemental sampling design was so chosen keeping the focus of the study and the specialised nature of the prospective respondents (Neuman, 2006) in mind.

Measures

The instrument development was based on the conceptual framework drawn upon the literature (Guerrero & Urbano, 2012; Pittaway & Cope, 2007), refer Annexure 1 for details. The items were measured on a 5-point Likert-type scale, where one and five represented very unimportant and very important, respectively.

Tools of Analysis

The data obtained form 264 academicians were subjected to exploratory factor analysis, using principal component analysis (PCA) for dimension reduction (Malhotra & Dash, 2011) and to evaluate the theoretical rigor of the conceptual framework. The PCA eliminated one dimension, that is, status and prestige, and the scale recorded a Cronbach’s alpha value of 0.921 to attest internal consistency of the items (Hair, Black, Babin, Anderson, & Tatham, 2011). The Kaiser–Meyer–Olkin (KMO) value was found to be 0.851 (refer Annexure 2 for details), well above the threshold value (0.7) to ensure sampling adequacy (Hair et al., 2011). The data analysis helped reduce the number of factors to 14, which contained eight factors of HE ecosystem and six factors of EE. The first stage of study culminated in finalising the instrument for the main survey.

Stage 2

Sampling

In the second stage, the prospective respondents were experts in EE, those who handled entrepreneurship-related courses and those who were in charge of entrepreneurship development cells/student incubation cell of the respective institutes. A total of 17 experts were issued the questionnaire, out of which 15 responses were received back with a return rate of 88.22 per cent. The experts possessed an average experience of 17.2 years in teaching and mentoring entrepreneurs.

Measures

A DEMATEL questionnaire was designed to obtain the pair-wise opinion of eight factors of HE ecosystem and six factors of EE. The respondents were asked to rate their perception on how far do they think one factor influences the other, on a five-point scale, where zero indicates no influence four indicates high influence, in a pairwise comparison format. The researchers carried out sufficient prior briefing as how to respond to items in the questionnaire to ward off any potential ambiguities and resultant response errors.

Tool of Analysis (DEMATEL)

The data were analysed using DEMATEL (Gabus & Fontela, 1972). Decision-making trial and evaluation laboratory was originally developed at Battelle Memorial Institute of Geneva Research Centre in the 1970s (Gabus & Fontela, 1972). It is a MCDM approach based on graph theory, used to study and analyse complex and intertwined problem groups. It produces a visualised model of a complex causal relationship in the form of digraph, and it also projects the contextual relations among elements of a system (Raghuvanshi et al., 2017; Si et al., 2018). Furthermore, it explores the critical factors influencing the complex system with an impact relationship. The tool offers a great deal of advantages and flexibility in solving the complex problems in different disciplines, and many researches have applied this approach in addressing complicated situations (Raghuvanshi et al., 2017; Si et al., 2018). The procedure of DEMATEL method is presented as follows (Figure 2).

Figure 2.

Source: The authors.

Step 1: Collecting Expert Opinion and Computing Average Matrix. The measuring criteria or factors influencing the complex system were presented to a group of experts. Individual experts were asked to evaluate the degree of influence between two factors, by an integrated score ranging from 0 to 4 (0—no influence, 1—very low influence, 2—low influence, 3—high influence and 4—very high influence) on a pair-wise comparison format. The degree to which factor i affects factor j is represented as x_ij. From each expert a n × n non-negative matrix, where all principal diagonal elements were equal to zero was developed known as $X^{k} = [x_{i j}^{k}]$ , where n is number of factors, k is number of experts evaluated the factors with 1 ≤ k ≤ H. The group of H experts evaluation was thus presented in the form of X¹, X², X³, … … … …X^H matrix. All the experts’ responses were transformed into an average matrix A = [a_ij] can be constructed as

a_{i j} = \frac{1}{H} \sum_{k = 1}^{H} x_{i j}^{k}

(1)

Step 2: Computing the Normalised Initial Direct-relation Matrix. The normalised matrix was obtained by D =A × S. The value of all the elements in D matrix falls within 0–1.

S = \frac{1}{\max 1 \leq i \leq n \sum_{j = 1}^{n} x_{i j}^{k}}

(2)

Step 3: Total Relationship Matrix. The total relation matrix T is defined as T = D(I – D)^–1. Where I is an n × n identity matrix. In matrix T each t_ijindicates both direct and indirect effects by factors i to the other factors. Thus, matrix T presents the total relationship among each pair of factors in the complex system.

Step 4: Establishing Cause and Effect Relationship. From T matrix, the sum of rows r_i and sum of columns c_i are denoted as vector r and c. The sum of vectors (r_i + c_i) indicates the total effect given and received by factor i, given j = i. The vector (r_i + c_i) also represents the degree of importance each factor has on the entire system. The difference of vector (r_i – c_i) indicates the total contribution by factor i on the entire system. The positive (r_i – c_i) represents net cause, and negative (r_i – c_i) represents net receiver (effect).

Step 5: Constructing the Cause and Effect Relationship Digraph. From the T matrix, a threshold value (α) was calculated. The threshold value was the average of the elements N, that is, n × n. The threshold value eliminates the small or negligible degree of effect on the other factors and helps indicate only significant effects. The threshold value (α) is computed by.

α = \frac{\sum_{i = 1}^{n} \sum_{j = 1}^{n} [t_{i j}]}{N}

(3)

A digraph was constructed by mapping the coordinates of [(r_i + c_i), (r_i – c_i)] to visualise the cause and effect relation. It unwinds the entire complex system. Only the factors whose t_ij is more than α is represented in the digraph.

Results Analysis

The responses from 15 experts in the area of EE were collected and reorganised into pair-wise comparison matrix of 14 × 14 each. The average matrix A was calculated using Equation (1). The average matrix A is presented in Table 1. Than, the A matrix was subjected to normalisation through D =A × S, where S was computed using Equation (2). Normalised initial direct-relation matrix. The final total relationship matrix was computed using T = D(I – D)^–1 where I is a 14 × 14 identity matrix. Total relationship matrix T is presented in Table 2. The threshold value (α) is equal to 0.9589, and was computed by Equation (3), a slightly higher roundup value 1.0000 is considered a threshold value to eliminate any negligible effect (Raghuvanshi et al., 2017; Si et al., 2018). The sum of row vectors r_i and the sum of columns c_i was computed, the cause and effect relationship is presented in Table 3. The digraph of 14 factors was constructed based on the coordinates of [(r_i + c_i), (r_i – c_i)], and is presented in Figure 3.

Based on the value of (r_i + c_i) the significance of 14 factors was prioritised as EPAH > DEEC > HATE > SOE > DPE > ETM > HACSI > MCPE > EARE > HTS > HSE > HPIF > HFSE > HGS out of which entrepreneurship promotional activities by HEIs (EPAH) was the most significant factor with the highest (r_i + c_i) value of 28.0707 and HEIs’ governance, and structure is least significant with the (r_i + c_i) value of 24.5122.

The (r_i – c_i) values indicate cause and effect factors. The positive values of (r_i – c_i) indicate the causal factors and the negative values indicate effect factors (Raghuvanshi et al., 2017; Si et al., 2018). The hierarchy of causal factors was as follows: HGS > HFSE > HPIF > HATE > HSE > EPAH > HTS > DPE. The individual cause factors and their effects are as follows: higher education institutions and governance structure (HGS) was the major cause factor which affects HATE, EPAH, HTS, HACSI, degree of entrepreneurial education in curriculum (DEEC), entrepreneurial teaching methodologies (ETM), EARE, DPE, SOE and MCPE. The HEIs’ financial support for entrepreneurship (HFSE) was the second major causal factor which affects DEEC, ETM, EARE, DPE, SOC and MCPE. HEI’s physical infrastructure and facilities (HPIF) was the third major causal factor which influences DEEC, ETM, EARE, SOE and MCPE. The results also indicate that HGS, HFSE and HPIF are independent causal factors and were not influenced by any other factors.

The HEIs’ attitude towards entrepreneurship (HATE) was the fourth major causal factor, which influences HSE, HATE, EPAH, HTS, HACSI, DEEC, ETM, EARE, DPE, SOE and MCPE. The HEIs’ support for entrepreneurship (HSE) was the fifth major causal factor, which impacts EPAH, DEEC, ETM, EARE, SOE and MCPE. Entrepreneurship promotion activity by HEIs was the sixth major causal factor, which influences HATE, HTS, HACSI, DEEC, ETM, EARE, DPE, SOE and MCPE. The HEIs’ teaching and staff (HTS) was the seventh major causal factor, which influences EPAH, DEEC, ETM, EARE, DPE, SOE and MCPE. The department philosophy on entrepreneurship (DPE) was the last causal factor, which affects EPAH, HACSI, DEEC, ETM, EARE, SOE and MCPE. The factors with negative values of (r_i – c_i) EARE, ETM, MCPE, SOE, DEEC and HACSI were the net receivers or the outcomes.

Table 1.

Average Matrix A

	HGS	HSE	HATE	EPAH	HTS	HACSI	HFSE	HPIF	DEEC	ETM	EARE	DPE	SOE	MCPE
HGS	0.0000	2.6667	3.2000	2.6000	2.8667	2.8667	2.4000	2.8667	2.9333	3.0000	2.6667	2.6667	2.7333	2.8000
HSE	2.2667	0.0000	2.6667	2.9333	2.6667	2.6000	2.3333	2.4000	3.0667	2.5333	2.8000	2.8667	2.8000	2.8667
HATE	3.0000	2.9333	0.0000	3.0000	2.7333	2.7333	2.5333	2.3333	2.9333	3.0667	2.8667	2.7333	2.8667	3.0000
EPAH	2.1333	2.4667	2.9333	0.0000	2.8667	2.7333	2.6667	2.4667	3.1333	2.9333	3.0000	2.8667	3.0667	3.1333
HTS	2.0667	2.4000	2.8667	2.7333	0.0000	2.7333	1.9333	2.0667	3.1333	3.1333	2.8667	2.8000	3.0000	3.1333
HACSI	2.2667	2.6000	2.5333	2.7333	2.5333	0.0000	2.4000	2.3333	2.8667	2.8000	2.9333	2.6000	2.8667	3.0000
HFSE	1.8667	2.5333	2.6667	2.6000	2.2000	2.2667	0.0000	2.7333	2.6000	2.7333	3.0000	2.7333	2.8667	2.8667
HPIF	1.7333	2.5333	2.6667	2.8667	2.2667	2.6000	2.5333	0.0000	2.5333	2.6000	2.9333	2.7333	2.6667	2.7333
DEEC	1.8667	2.8667	2.8000	2.9333	2.4667	2.8667	2.0667	2.1333	0.0000	3.0000	2.8667	2.8667	2.7333	2.8667
ETM	1.4667	2.2667	2.4667	2.4667	2.5333	2.6000	2.3333	2.2667	3.0000	0.0000	2.8000	2.3333	2.7333	2.8000
EARE	1.3333	2.2000	2.2000	2.5333	2.4000	2.8667	1.8667	2.0000	2.7333	2.8667	0.0000	2.8000	2.7333	2.8000
DPE	2.0667	2.8000	2.6667	2.8000	2.9333	2.3333	2.3333	2.4000	3.0667	3.3333	3.0667	0.0000	2.9333	2.6667
SOE	1.9333	2.5333	2.7333	2.8000	2.8667	2.9333	2.2667	2.4667	2.4667	2.7333	3.0667	2.3333	0.0000	2.5333
MCPE	1.9333	2.3333	2.2667	2.4000	2.5333	2.6667	2.0000	2.3333	2.6000	2.8000	2.8000	2.4000	3.0000	0.0000

Source: The authors.

Table 2.

Total Relationship Matrix T

	HGS	HSE	HATE	EPAH	HTS	HACSI	HFSE	HPIF	DEEC	ETM	EARE	DPE	SOE	MCPE
HGS	0.7292	0.9867	1.0358	1.0450	1.0124	1.0378	0.8886	0.9268	1.0961	1.1102	1.1060	1.0281	1.0903	1.0963
HSE	0.7592	0.8839	0.9867	1.0155	0.9719	0.9946	0.8553	0.8829	1.0602	1.0598	1.0696	0.9963	1.0531	1.0587
HATE	0.8131	1.0036	0.9667	1.0657	1.0204	1.0459	0.9014	0.9242	1.1081	1.1238	1.1228	1.0409	1.1055	1.1129
EPAH	0.7847	0.9831	1.0305	0.9801	1.0136	1.0359	0.8958	0.9182	1.1020	1.1101	1.1154	1.0341	1.0997	1.1055
HTS	0.7545	0.9453	0.9914	1.0109	0.9045	0.9981	0.8458	0.8750	1.0620	1.0742	1.0714	0.9946	1.0580	1.0652
HACSI	0.7515	0.9404	0.9735	1.0006	0.9590	0.9186	0.8483	0.8726	1.0449	1.0553	1.0620	0.9800	1.0441	1.0512
HFSE	0.7261	0.9191	0.9560	0.9765	0.9307	0.9559	0.7694	0.8638	1.0165	1.0314	1.0414	0.9625	1.0222	1.0260
HPIF	0.7188	0.9136	0.9504	0.9770	0.9268	0.9581	0.8290	0.7894	1.0090	1.0222	1.0337	0.9569	1.0114	1.0169
DEEC	0.7402	0.9443	0.9772	1.0028	0.9551	0.9884	0.8383	0.8654	0.9701	1.0574	1.0578	0.9837	1.0383	1.0455
ETM	0.6863	0.8746	0.9116	0.9327	0.8996	0.9239	0.7945	0.8169	0.9836	0.9193	0.9936	0.9132	0.9767	0.9820
EARE	0.6693	0.8556	0.8872	0.9158	0.8789	0.9120	0.7678	0.7944	0.9582	0.9723	0.9032	0.9060	0.9575	0.9626
DPE	0.7639	0.9669	0.9995	1.0255	0.9908	1.0014	0.8664	0.8943	1.0741	1.0925	1.0899	0.9368	1.0699	1.0678
SOE	0.7289	0.9202	0.9590	0.9825	0.9478	0.9732	0.8285	0.8584	1.0150	1.0328	1.0443	0.9544	0.9511	1.0197
MCPE	0.6976	0.8763	0.9072	0.9313	0.9000	0.9259	0.7868	0.8189	0.9745	0.9904	0.9938	0.9149	0.9832	0.9113

Source: The authors.

Table 3.

Cause and Effect Relationship

Factors	Sum of Rows r_i	Sum of Columns c_i	(r_i + c_i)	(r_i – c_i)
HGS	14.1892	10.3231	24.5122	3.8661
HSE	13.6474	13.0137	26.6611	0.6338
HATE	14.3548	13.5327	27.8875	0.8220
EPAH	14.2088	13.8619	28.0707	0.3469
HTS	13.6509	13.3116	26.9625	0.3393
HACSI	13.5021	13.6697	27.1718	−0.1677
HFSE	13.1976	11.7159	24.9135	1.4817
HPIF	13.1133	12.1014	25.2147	1.0120
DEEC	13.4646	14.4743	27.9388	−1.0097
ETM	12.6085	14.6518	27.2603	−2.0433
EARE	12.3408	14.7048	27.0456	−2.3639
DPE	13.8397	13.6023	27.4420	0.2374
SOE	13.2157	14.4611	27.6768	−1.2454
MCPE	12.6123	14.5215	27.1338	−1.9092

Source: The authors.

To offer more clarity and to obtain a better understanding of complex relationships among the factors, we introduced a new add-on indicator to the existing DEMATEL analysis through separating factors into four quadrants based on importance and causal relationship. The coordinates of X-axis indicate low importance and high importance and Y-axis indicates cause and effect factors as indicated in the Figure 3. Thus, it classified the outcome into four major categories as follows: A1 (Area 1)—high importance and cause factors, A2—low importance and cause factors, A3—low importance and effect factors, A4—high importance and effect factors. The details of factors falling under each category are presented in Table 4 and Figure 3.

Figure 3.

Source: The authors.

Area A1 represents high importance and cause factors, which consist of EPAH, HATE, DEP, HTS and HSE. These factors have a high degree of importance on HEIs’ ecosystem and are identified as causal factors.

Area A2 represents low importance and cause factors, which include HGS, HFSE and HPIF. These factors are the causes but with low significance on the entire system. In other words, HEIs’ ecosystem is having less influence by HGS, HFSE and HPIF.

Area A3 represents low importance and effect factors. There are no factors falling under this category.

Area A4 represents high important and effect factors. The factors falling under this category are DEEC, SOE, ETM, HACSI, MCPE and EARE. This indicates that all the factors register significant influence on EE and are the major effect factors. The factors falling under A4 are influenced by factors from A1 and A2.

Discussion

The study has contributed to the advancement of literature in the domain of HE ecosystem in the context of EE. The past studies have focussed on conceptualising the HE entrepreneurial ecosystem (Pittaway & Cope, 2007; Wright et al., 2017), studying the factors influencing academic entrepreneurship (Patzelt & Shepherd, 2009) and understanding the nature of entrepreneurial university (Guerrero & Urbano, 2012; Matlay, 2008). However, these studies lack empirical rigour in understanding the complexity of HEIs’ ecosystem. Thus, our study offers a unique insight on the complexity and interrelationship among different factors of HEIs’ ecosystem and EE. The DEMATEL analysis has segregated all 14 factors into three major categories—high and low important causal factors and high important effect factors. Collectively the cause factors were from HEIs’ ecosystem, and the effect factors were from EE with mild overlapping. This finding indicates strong influence of HEIs’ ecosystem on EE. Accordingly, the DEMATEL analysis has validated our conceptual framework with empirical legitimacy. It also offers the interrelationship of independent factors with cause and effect mapping. Thus, our study offers a robust conceptual framework relating to HEIs’ ecosystem and EE, and addressing the Wright et al. (2017) call for developing an overall framework that showcases student entrepreneurship. With the confluence of ‘thematic framework for EE’ (Pittaway & Cope, 2007) and ‘model of the entrepreneurial university’ (Guerrero & Urbano, 2012), we have contributed a hybrid and robust theoretical model to the literature.

Table 4.

Classification of Cause and Effect Relation on the Basis of Importance

Area in Digraph	Category of Factors	Factors falling in the category
Area 1 – A1	High importance and Cause factors	Entrepreneurship Promotion activity by HEIs (EPAH)
		HEIs attitude towards entrepreneurship (HATE)
		Department philosophy on entrepreneurship (DPE)
		HEIs teachers and staff (HTS)
		HEIs support for entrepreneurship (HSE)
Area 2 – A2	Low importance and Cause factors	HEIs physical infrastructure and facilities (HPIF)
		HEIs financial support for entrepreneurship (HFSE)
		Higher Education Institution’s (HEIs) & Governance structure (HGS)
Area 3 – A3	Low importance and effect factors	No factors in this category
Area 4 – A4	High importance and effect factors	Degree of entrepreneurial education in curriculum (DEEC)
		Student orientation on entrepreneurship (SOE)
		Entrepreneurial Teaching Methodologies (ETM)
		HEIs ability to connect startup’s with industry (HACSI)
		Mentoring and coaching programs for entrepreneurs (MCPE)
		Extra-curricular activity relating to entrepreneurship (EARE)

Source: The authors.

Most of the previous studies on HEIs’ ecosystem and EE are conceptual with less emphasis on actual practices of academicians (Guerrero & Urbano, 2012). Our study presents the actual practices of EE in HEI’s in twofold. In the first level, we obtained inputs from a large sample of academicians to validate the elements of the theory, and in the second level, we established the relationship between different elements from experts. Thus, our approach was more reliable in capturing the actual practices in congruence with some of the past studies (Guerrero & Urbano, 2012; Matlay, 2011).

Our study also used a new methodological approach in understanding the complex interrelationship among the different factors of HEIs’ ecosystem and EE. It is evident in the past literature that such complex systems pose difficulty in developing an appropriate methodology (Fayolle et al., 2016). In this regard, we adopted a rare and robust methodological approach to address the research problem. This will further open the doors for studying similar complex systems relevant to entrepreneurship research.

We also established the roles of different stakeholders influencing the HEIs’ ecosystem and EE. Furthermore, it also offers the degree of influence of different stakeholders in the entire system. Accordingly, our findings suggest that the academicians, staff and HEIs’ leadership play a significant role in the effective implementation of EE. Previous studies have also highlighted the importance of different stakeholders in enhancing student entrepreneurship (Gustafsson-Pesonen & Remes, 2012; Leffler & Näsström, 2014; Matlay 2011).

Extending on the previous studies, our study has emphasised the significance of HEIs’ ecosystem through EPAH (Kirby, 2006; Laviolette, Radu Lefebvre, & Brunel, 2012; Rosique-Blasco, Madrid-Guijarro, & García-Pérez-de-Lema, 2016), HEIs attitude towards entrepreneurship, DPE (Krueger, Reilly, & Carsrud, 2000; Robinson, Neergaard, Tanggaard, & Krueger, 2016), HEIs’ teachers and staff (Krueger, Hansen, Michl, & Welsh, 2011) and HEIs support for entrepreneurship (Link & Scott, 2005). These factors are regarded as important causal factors, which influence EE. While we perceived significance of HEIs’ ecosystem, we found that HPIF, HFSE and HGS are regarded as low important causal factors of EE. The key insight is that the soft factors, such as promotion, attitude of HEIs, academicians and orientation of different academic departments, play a significant role in shaping a conducive ecosystem towards EE. However, hard factors, such as finance, infrastructures and structure of HEIs, play a negligible role in EE.

Our model was successful in redefining the elements of EE, which incorporated the degree of entrepreneurial education in the curriculum, student orientation on entrepreneurship (SOE), entrepreneurial teaching methodologies, HEIs’ ability to connect startups with industry (HACSI), mentoring and coaching programmes for entrepreneurs (MCPE) and extra-curricular activity relating to entrepreneurship (EARE). Some of the recent studies have reiterated the significance of pedagogy, teaching methods, mentoring and extra-curricular activities in EE (Fretschner & Weber, 2013; Mukesh, Pillai, & Mamman, 2019; Pittaway, Gazzard, Shore, & Williamson, 2015).

Practical Implications

The study draws significant relationship of HEIs’ ecosystem and EE as well as it presents individual cause and effect factors among them. The findings of the study act as a guideline for policy development and implementation of EE effectively through HE ecosystem. The study also acts as a guideline for designing effective HE entrepreneurial ecosystem with multiple stakeholders’ engagement. The study can, further, be ignition for developing various types of entrepreneurship promotion activity within HEIs.

Limitations and Avenues for Future Research

Our study has confined itself only to 14 factors influencing HEIs’ ecosystem and EE. As HEIs’ ecosystem and EE are large and heterogeneous in nature, there may be other critical factors as well. Exploring such new factors can offer avenues for future research. We captured the HEIs’ ecosystem and EE from the perspective of academicians. Although they are suitable and competent to judge, the opinion from other stakeholders can also be taken into consideration to get a holistic picture. The outcome of the study is based on 15 experts’ opinion. Perhaps, a broader study, with large samples from different disciplines and geographies, may ensure more robustness in the validity of outcome and get a broader realm of HEIs’ ecosystem and EE. The MCDM approach offers other different tools, such as AHP, TOPSIS and VIKOR and DEMATEL also has different enhanced variants, such as fuzzy DEMATEL and grey DEMATEL. A comparative analysis can be conducted to test the validity of the outcome in future.

Conclusion

In the absence of reliable model, explaining the influence of HEIs’ ecosystem on EE, our study contributed to bridging the knowledge vacuum. We adopted a unique methodology to address the complex relationship among HEIs’ ecosystem and EE. A conceptual model of HEIs’ ecosystem and EE was developed based on two different theoretical paradigms and further synthesised into eight factors of HEIs’ ecosystem and six factors of EE. These factors were categorised and mapped through the DEMATEL approach. The study was successful in developing a causal relationship among the different factors of HEIs’ ecosystem and EE. The study supports the ongoing debate of significant relationship of EE through explaining the influence of the HEIs’ ecosystem. It also contributes to the existing theory development process in the areas of the entrepreneurial university, HE ecosystem and EE.

Footnotes

Acknowledgements

The authors would like to thank the anonymous referees and the Editor Prof. Sasi Misra for their valuable comments to improve the structure and content of the manuscript. They would also like to thank Ms. Jahnavi for her valuable assistance on DEMATEL and Scilab software.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

Annexure 2.

Details of PCA and KMO Test

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin measure of sampling adequacy.		0.851
	Approx. Chi-square	4160.394
Bartlett’s test of sphericity	df	91
	Sig.	0.000

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	7.344	52.460	52.460	7.344	52.460	52.460	4.234	30.246	30.246
2	1.663	11.877	64.337	1.663	11.877	64.337	3.266	23.329	53.576
3	1.393	9.947	74.284	1.393	9.947	74.284	2.899	20.709	74.284
4	0.827	5.909	80.193
5	0.645	4.609	84.802
6	0.516	3.684	88.486
7	0.418	2.988	91.474
8	0.384	2.743	94.216
9	0.314	2.243	96.459
10	0.247	1.765	98.224
11	0.112	0.799	99.023
12	0.075	0.539	99.562
13	0.058	0.414	99.976
14	0.003	0.024	100.000
Extraction Method: PCA

Component Matrix^a
	Component
	1	2	3
14Student orientation/propensity	0.832
4EPAH	0.784
3HEIs attitude towards entrepreneurship	0.776
12Extra-curricular activity relating to EE	0.775
1HEIs and governance structure	0.758
11Pedagogy in the curriculum/entrepreneurial teaching methodologies	0.756
13DPE	0.750
10DEEC	0.707
6HEIs ability to connect startups with other stockholders	0.706	0.560
7HEIs financial support for entrepreneurship	0.677
2HEIs support for entrepreneurship (Technology transfer and start-up promotion)	0.669	0.561
15Mentoring and coaching programmes for entrepreneurs	0.660	−0.510
5HEIs teachers and staff	0.657	−0.513
8HPIF	0.595
Extraction Method: PCA

Source: The authors.

Note: ^aThree components extracted.

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