Institutional-driven dimensions and the capacity to start a business
基于制度理论，分析葡萄牙和安哥拉两国的创业框架条件对创业能力的协同效应，发现多数制度驱动关系在两国样本中显著，但跨国比较差异不显著。
Purpose Drawing insights from institutional theory, the purpose of this paper is to analyse the synergistic effects of a range of entrepreneurial framework conditions (EFCs) on the capacity to start a business within different types of economies. Design/methodology/approach This is a preliminary study that uses data from the National Expert Survey-Global Entrepreneurship Monitor (NES-GEM). Specifically, the data were gathered through the application of a questionnaire to National Entrepreneurship Experts in a cross-cultural context. Two countries – Portugal and Angola – are analysed. Findings Among the five structural relationships involving institutional drivers analysed, four are found to be statistically significant in the Portuguese sample. Three are found to be statistically significant in the Angolan sample. The results from the multigroup analysis did not support most of the proposed relationship between the two countries. Research limitations/implications This study is limited by the number and type of countries selected and by the fact that each sub-sample covers several years. It also relies on the perceptions of national experts on entrepreneurship covering several areas. Another limitation is based on the fact that this study emphasises mainly a macro perspective. Therefore, interpretation of these findings and their generalisation should be made with caution. Originality/value First, this study addresses an area of the GEM model that is believed to be under-researched (NES). Second, the model presented is based on latent variables and analysed through a variance-based method, PLS-structural equation modelling. Third, this study compares the proposed relationships between two sub-sample data sets that represent a factor-driven economy and an innovation-driven economy. Fourth, and most importantly, this study responds to the call for the need to use a new procedure for measurement invariance assessment for composite modelling.