Purpose
For two decades, the Global Entrepreneurship Monitor (GEM) has collected survey and national expert data to better understand entrepreneurial activity and the country context within which this occurs. In this paper, we re-envision GEM’s country groupings, positing a novel approach to more fully understanding the nuances of entrepreneurial activity.

Design/methodology/approach
Using data from the GEM’s Entrepreneurial Framework Conditions (EFCs) (2017–2020), we employ an unsupervised machine learning method (clustering) to classify countries into distinct groups according to country-specific government policies, education, sociocultural, and entrepreneurship infrastructure support. Then, building on the identified two sets of distinct economies (termed as “matured” and “maturing” entrepreneurial economies) and using the GEM’s data on Entrepreneurial Behaviour and Attitudes (EBAs), fuzzy-set Qualitative Comparative Analysis (fsQCA) is applied to highlight the complex nature of entrepreneurial intentions and to identify configurations of possible conditions that confirm intention pathways in entrepreneurial activities in each cluster of economies.

Findings
Our key findings suggest that in “matured” economies, where entrepreneurship is well-supported, people are driven by opportunity and a supportive environment. Moreover, in “maturing” economies, where there is less support, factors like personal status and overcoming fear of failure play a bigger role in their motivations. This new perspective is crucial not just for understanding entrepreneurship but also for shaping policies that truly support budding entrepreneurs. The results suggest that governments should tailor their support for entrepreneurship based on their maturity level.

Originality/value
Historically, GEM grouped countries based on the level of economic development (efficiency, innovation and factor economies) and, more recently, used the level of income (high, medium and low). Both categorisations are essentially outcome factors reflecting economic progress. While it is useful to compare countries based on these dimensions, we propose grouping based on entrepreneurial framework or contextual conditions, enabling scholars and policy makers alike to better understand how context influences entrepreneurial activity.

This paper offers step-by-step guidance and best practices for using fuzzy-set qualitative comparative analysis (FsQCA) in entrepreneurship research. We analyzed publications in which FsQCA was utilized as the primary methodological choice, focusing our scope on 10 top entrepreneurship journals. Our review reveals that inconsistencies exist regarding how to use the method, what processes to follow, and how to interpret results. Specifically, there is a need for an improved understanding regarding (a) considerations for performing direct calibration, (b) performing (necessity) analysis for both the outcome of interest and its negation, and (c) assessing the sensitivity of the resulting configurations to the selected threshold and cutoff values. To illustrate best practices, we conclude by analyzing GEM data from 43 countries to demonstrate how to best use the method, what to consider, and how the findings change if best practices are not followed.

Purpose: Building on construal level theory (CLT), this study explores mental representations of entrepreneurial intentions (EIs) with different foci (i.e. social and commercial) among university students from Generations Y and Z. Design/methodology/approach: Using a sample of university students from the United States contacted through the Entrepreneurship Education Project, this study employs a configurational perspective—fuzzy-set qualitative comparative analysis (fsQCA)—to identify the pathways leading to EIs and social entrepreneurial intentions (SEIs). Findings: Results show that the configurations of conditions leading to the outcomes (EI and SEI) are not disparate but share far more similarities even when considering socially oriented antecedents, supporting the claim that students perceive both EIs with different foci as high-level construals. The results also demonstrate no differences within gender, but there are asymmetries between gender in the configurations leading to EI and SEI. Research limitations/implications: This study contributes to EI literature by providing new insights into understanding how individuals perceive EIs at an early stage of entrepreneurship and by bringing CLT to the EI literature. Practical implications: These results have implications for entrepreneurship education and practice, as it recognizes that students' EIs are psychologically distant, lacking a level of detail and specificity. This would explain why students do not immediately create ventures, but that entrepreneurship has a certain incubation time to create an entrepreneurial mindset. Originality/value: Exploring the configurational approaches can help to uncover the complexity and idiosyncrasies underlying EIs.