The role of entrepreneurial education: A consolidated empirical framework for academic entrepreneurship

John Liu
National Taiwan University of Science and Technology

Mei Hsiu-Ching Ho
National Taiwan University of Science and Technology

Chun-Ping Yeh
National Taiwan University of Science and Technology

The trend for universities to engage with industry especially through creating new firms to commercialize their technologies is growing rapidly. Following the trend, universities either provide incentives to faculties, establish technology transfer offices, set up incubation centres, or take any actions that can facilitate the technology commercialization. The phenomenon attracts scholars around the world to study it thus gradually created academic entrepreneurship (AE) research area. In addition, universities see the necessity of offering entrepreneurship education programs, which also aggregate to become a research stream. This research stream is usually labelled entrepreneurship education (EE). Up until recently, research in AE and EE seems to be running on parallel lines. They do not exchange ideas much.
The role of EE in AE, nevertheless, should not be overlooked. EE raises students’ entrepreneurial intention and prepared them with knowledge needed to start up a business. This study examines the existing knowledge interchange between AE and EE research, analyse significant articles in both AE and EE, and in the end proposes a consolidated AE empirical framework that considers the role of EE.

Research Methodology and Data
The main methodology is content analysis and main path analysis (MPA). We read article contents and code the factors, variables, parameters that are considered to be antecedents or outcomes of AE and EE. Reading the complete AE and EE literature is a prohibitive task. In contrast to the traditional approach that select articles from the prestigious journals or highly cited articles, we use MPA to select a reasonable number of significant articles prior to conduct the content analysis. MPA is a citation-based method, which is designed to identify the most significant citation chains (paths) from the citation network of a scientific field. The key-route approach of MPA further allows MPA to include articles of secondary significance (Liu & Lu, 2012). Articles along the main paths are considered to be the most significant ones in the development of the target research area. The method was first proposed by (Hummon & Doreian, 1989) and is now widely used to review academic literature (Liu et al., 2013) or trace technological development trajectory (Martinelli, 2012).
We first search AE and EE articles from the Web of Science database using a query string that includes ‘entrepreneurship education’, ‘entrepreneurial university’, ‘university entrepreneurship’, ‘academic entrepreneurship’, and many other related terms. The search results are retrieved in October, 2019. We further screened the dataset and reduced the number of articles to 1,074 articles. From citations among these articles, we create a citation network, which is then divided into two main and few insignificant sub-networks through edge betweenness clustering algorithm. The two main sub-networks represent AE and EE, respectively. We apply MPA to each sub-network and obtain main paths for AE and EE. The articles on the main paths are used as the bases for content analysis.

A cross-citation check between the AE and EE sub-network shows that there are only 373 cross-links (4.25%) among the total 8,778 citation links, which indicates that AE and EE literature are lightly connected in citation, if not totally independent. The fact that they do not reference each other much implies that there are very few knowledge exchanges between the two research areas.
MPA on AE identifies 65 articles while that on EE finds 35 articles. These articles exhibit wide diversity with their publishing year ranging from 1986 to 2019 and citation counts stretching from 0 to as high as 684. The factors, variables, parameters and the like used in each article for both AE and EE are pooled together and categorized into four conceptual components: Environmental context, University Inputs, Entrepreneurial outputs, and Economic/societal impacts. Each consists of factors that are involved in the AE discussion. Environmental context includes factors that support AE, such as governmental policies/funding, science parks, regional conditions, etc. The University inputs component contains elements that are controllable by the universities, including faculties, technology transfer offices, departments, internationalization, entrepreneurial activities, and the educational aspects that were mostly neglected in the prior AE studies, for example, entrepreneurial programs, coursed, and pedagogy. Entrepreneurial outputs refer to tangibles such as papers and patents as well as intangibles including entrepreneurial culture, students’ entrepreneurial intention, attitude, and knowledge, skill, etc. Economic/societal impacts refers to licensing incomes, spinouts, offshoots, jobs created, revenues, profits, etc.
The proposed framework deviates from the existing ones in two aspects. First, the role of EE is added into the framework through the University component, addressing the need to take EE into consideration. Second, the generation of AE outcomes or performances are separated into two components: Outputs and Economic/societal impacts. In parallel to production concept, universities first produce outputs (technologies and well-prepared students), and these outputs subsequently generate economics and societal values.

We show from cross-citation analysis that current AE research considers too lightly the role of EE, which supports the comment in Siegel and Wright (2015)“ … the debate regarding universities and academic entrepreneurship has relied too much on the research–third mission nexus and insufficient focus on the teaching/education–third mission nexus informed by research.” In this study, we respond to the comment by proposing a consolidated AE empirical framework that strengthens the role of EE. In addition, the framework distinguishes the outputs and impacts, which will deepen the discussions on the AE performance.