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.

In order to understand the antecedents of costumer satisfaction, businesses can analytically utilise the growing amount of customer's information. Unstructured text data can be used to uncover important information owing to developments in Natural Language Processing and text analytics approaches. In this paper, we focus on customer reviews posted on e-commerce shopping platforms. We perform manual data annotation to determine the sentiment of the review with respect to the most important aspects of the customer journey. The 14 extracted aspects are grouped into three constructs that correspond to the stages of the customer's interaction with the e-commerce platform. We make use of a configurational approach, Fuzzy-set Qualitative Comparative Analysis, to understand how the sentiment with respect to the three stages combine to achieve positive customer satisfaction. The outcomes of the analysis show that all the three stages of customer journey play important roles in determining the final evaluation of a customer, leading to a positive or a negative sentiment. The theoretical and practical implications are discussed.

Entrepreneurship education is critical for developing the entrepreneurial skills of tomorrow’s entrepreneurs and leaders. In this paper, we aim to identify factors influencing entrepreneurship educators’ pedagogical choices, in particular, factors influencing their preferences to become either a teacher-centric or a student-centric educator. Our analysis includes job satisfaction, self-efficacy, and department support as the antecedent variables influencing the outcome. The data are collected from 289 global entrepreneurship educators, and fuzzy-set comparative qualitative analysis (fsQCA) was used to obtain multiple configurations of conditions leading to either a teacher-centric or student-centric model. The fsQCA analysis reveals that for teacher-centric educators, job satisfaction and more than 10 years of teaching experience are the most important factors, whereas for student-centric educators, teaching experience is not important factor, but self-efficacy and entrepreneurship teaching training are influential factors. In the article we discuss the important theoretical and practical contributions resulting from the analysis.

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.

In this article, we focus on mobile wellness and health-related applications from the perspective of the level of imprecision present in the data used in the recommendation systems. We propose a general fuzzy optimization model based on chanced constrained optimization to design recommendation systems that can take into consideration (i) the imprecision in the data and (ii) the imprecision by which one can estimate the effect of a recommendation on the user of the system. Our proposal is one of the first to use fuzzy optimization models in health-related decision making problems and the first to define a chance constrained optimization problem for interval-valued fuzzy numbers. The proposed approach identifies a set of actions to be taken by the users in order to optimize general health-related and/or wellness condition of the user from various perspectives. The model is illustrated through the example of walking speed optimization, with an additional numerical experiment offering a comparison with traditional methods.

This paper focuses on multi-attribute design problems in which several quantitative and/or qualitative attributes aresimultaneously involved. Preference elicitation methods dealing with decision-makers’preferences are often employed tosolve such problems. The present paper proposes a conceptual framework that considers preference elicitation as a processshaped by the interaction of different factors including the researcher, methodology, questionnaire and respondent in a givencontext, at the same time. The aim of the framework is to assist in choosing a decision support tool among various multi-attribute decision-making methods, as well as in evaluating and understanding preference elicitation results. To illustrateand exemplify the use of the framework, it is applied to a real-world case of end user preferences of mobile serviceplatforms. We make use of two methods that are suitable for such a setting: analytic hierarchy process and conjointanalysis. Albeit with considerable discrepancies, the results show that both methods provide insightful information onthe principle. Nonetheless, to a large degree, these methods perform differently in the case of non-complex tasks andunknowledgeable respondents, which the process view aids in evaluating and understanding. Copyright © 2015 JohnWiley & Sons, Ltd.

The rapid and widespread development of innovations in mobile services is changing societies and improving lives around the world. Due to lagging adoption, many of these new innovations have yet failed to generate revenue that was expected by mobile network operators, application and content developers. There are several factors which are affecting the service adoption by consumers. This paper aims to provide practitioners and academics, an insight on what consumers’ preferences are by using an Analytic Hierarchy Approach (AHP). The objective of this paper is to identify factors influencing the adoption of the mobile services. In this study we have considered Payment Mode, Functionality, Added Value and PQCP (perceived quality, cost and performance) as the main service adoption factors. The survey results indicate that Functionality is the most important influencing factor for the respondents, followed by Added Value, PQCP and Payment Mode.