Key failure factors of start-up women owned SMEs in service sector in Kigali: a principal component analysis approach

Purpose This study aims to develop and test a framework for studying the failure of new women entrepreneurs in the small- and medium-sized enterprises (SMEs) sector. Design/methodology/approach Using a sample of 114 unsuccessful entrepreneurs in Kigali, Rwanda, this study aimed to identify key failure factors of women-owned SMEs. This study used mixed methods where quantitative data were analysed using the principal component approach with Varimax rotation to reduce the variables to only three clusters. Findings The study findings revealed that the failure of women-owned SMEs results from the entrepreneur’s inability followed by the enterprise incompetence, which are both internally controllable factors and the inauspicious business environment. These findings contribute to the validity of the dynamic capability theory by explaining how well internal and external factors must stay glued together to avoid failure among women-owned SMEs, something that was not yet previously well documented so far. Originality/value New SMEs are considered a noteworthy constituent of Rwandan development. Unfortunately, most new SMEs, in general, do not grow; their failure rate is high (70%), which raised many worries for both researchers and policymakers as to why this occurs at this stage of business growth. Therefore, to the best of the authors’ knowledge this paper is the first to analyse the reasons for the failure of Rwandan women-owned SMEs in the service sector. These findings are important because they suggest that policies designed to reduce the incidence of SMEs’ failure should take account of the two main factors influencing failure among women entrepreneurs.

Applying a hierarchical clustering on principal components approach to identify different patterns of the SARS-CoV-2 epidemic across Italian regions

Italy has experienced the epidemic of Severe Acute Respiratory Syndrome Coronavirus 2, which spread at different times and with different intensities throughout its territory. We aimed to identify clusters with similar epidemic patterns across Italian regions. To do that, we defined a set of regional indicators reflecting different domains and employed a hierarchical clustering on principal component approach to obtain an optimal cluster solution. As of 24 April 2020, Lombardy was the worst hit Italian region and entirely separated from all the others. Sensitivity analysis—by excluding data from Lombardy—partitioned the remaining regions into four clusters. Although cluster 1 (i.e. Veneto) and 2 (i.e. Piedmont and Emilia-Romagna) included the most hit regions beyond Lombardy, this partition reflected differences in the efficacy of restrictions and testing strategies. Cluster 3 was heterogeneous and comprised regions where the epidemic started later and/or where it spread with the lowest intensity. Regions within cluster 4 were those where the epidemic started slightly after Veneto, Emilia-Romagna and Piedmont, favoring timely adoption of control measures. Our findings provide policymakers with a snapshot of the epidemic in Italy, which might help guiding the adoption of countermeasures in accordance with the situation at regional level.

Different Patterns of the SARS-CoV-2 Epidemic across Italian Regions: a Hierarchical Clustering on Principal Components approach

Italy has experienced the epidemic of severe acute respiratory syndrome coronavirus 2, which spread at different times and with different intensities throughout its territory. We aimed to identify clusters with similar epidemic patterns across Italian regions. To do that, we defined a set of regional indicators reflecting different domains and employed a hierarchical clustering on principal component approach to obtain an optimal cluster solution. As of 24 April 2020, Lombardy was the worst hit Italian region and entirely separated from all the others. Sensitivity analysis - by excluding data from Lombardy - partitioned the remaining regions into four clusters. Although cluster 1 (i.e. Veneto) and 2 (i.e. Piedmont and Emilia-Romagna) included the most hit regions beyond Lombardy, this partition reflected differences in the efficacy of restrictions and testing strategies. Cluster 3 was heterogeneous and comprised regions where the epidemic started later and/or where it spread with the lowest intensity. Regions within cluster 4 were those where the epidemic started slightly after Veneto, Emilia-Romagna and Piedmont, favoring timely adoption of control measures. Our findings provide policymakers with a snapshot of the epidemic in Italy, which might help guiding the adoption of countermeasures in accordance with the situation at regional level.

The Evolution of Financial Integration on Selected European Stock Markets: a Dynamic Principal Component Approach

The goal of this paper is to recognize the dynamics of financial integration across the European stock markets over the last two decades. We investigate two groups of markets: (1) three developed European markets in the U.K., France, and Germany; and (2) three emerging Central and Eastern European markets in Poland, the Czech Republic, and Hungary (CEE–3). The evolution of the integration process is analyzed using a dynamic principal component approach. The index of integration serves as a robust measure of integration. The empirical results reveal that the dynamics of integration across the whole group of markets increased significantly following the CEEC–3’s accession to the European Union. An inverted U‑shape in the index of integration has been found in this case. Moreover, the average index of integration was significantly different during the Global Financial Crisis compared to the pre‑crisis period.