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The purpose of this article is to analyze resilient female leadership as a sustainable promoter of business excellence in small and medium-sized Wayuu handicraft marketing enterprises. The present study uses a quantitative methodology with a non-experimental cross-sectional field design, with an analysis and interpretation of the data provided by the surveyed subjects. A 33-item questionnaire with multiple response options is applied. The population consists of 110.012 eradicated women. A probabilistic sampling technique is applied with a margin of error of 5% and a confidence level of 95%, for a total of 383 Wayuu women entrepreneurs in the Department of La Guajira, Colombia. Our findings explain that female leadership transcends the boundaries of business management, being present in both small and medium enterprises (SMEs). This study confirms the positive relationship between sustainability and resilience in the Wayuu handicrafts market, being women who turn their actions into success factors by working with women who show technical, conceptual, and human skills.
Ángel Acevedo-Duque; Romel Gonzalez-Diaz; Elena Vargas; Anherys Paz-Marcano; Sheyla Muller-Pérez; Guido Salazar-Sepúlveda; Giulia Caruso; Idiano D’Adamo. Resilience, Leadership and Female Entrepreneurship within the Context of SMEs: Evidence from Latin America. Sustainability 2021, 13, 8129 .
AMA StyleÁngel Acevedo-Duque, Romel Gonzalez-Diaz, Elena Vargas, Anherys Paz-Marcano, Sheyla Muller-Pérez, Guido Salazar-Sepúlveda, Giulia Caruso, Idiano D’Adamo. Resilience, Leadership and Female Entrepreneurship within the Context of SMEs: Evidence from Latin America. Sustainability. 2021; 13 (15):8129.
Chicago/Turabian StyleÁngel Acevedo-Duque; Romel Gonzalez-Diaz; Elena Vargas; Anherys Paz-Marcano; Sheyla Muller-Pérez; Guido Salazar-Sepúlveda; Giulia Caruso; Idiano D’Adamo. 2021. "Resilience, Leadership and Female Entrepreneurship within the Context of SMEs: Evidence from Latin America." Sustainability 13, no. 15: 8129.
Internet search engines have become a popular and readily accessible source of information. Google Trends, by means of analyzing the popularity of search queries in Google Search, allows to provide deep insights into population behavior. Interestingly, Google is increasingly being used also to obtain health-related information, as well as to self-prescribe one’s dietary intake. In particular, we analysed the search traffic related to the keywords Mediterranean diet since it has always been very popular. More specifically, we propose to use Google Trends data as proxies for the interest in Mediterranean diet and to analyze them through the functional data analysis (FDA) approach.
G. Caruso; F. Fortuna. Mediterranean Diet Patterns in the Italian Population: A Functional Data Analysis of Google Trends. Inventive Computation and Information Technologies 2021, 63 -72.
AMA StyleG. Caruso, F. Fortuna. Mediterranean Diet Patterns in the Italian Population: A Functional Data Analysis of Google Trends. Inventive Computation and Information Technologies. 2021; ():63-72.
Chicago/Turabian StyleG. Caruso; F. Fortuna. 2021. "Mediterranean Diet Patterns in the Italian Population: A Functional Data Analysis of Google Trends." Inventive Computation and Information Technologies , no. : 63-72.
Cluster analysis has for long been an effective tool for analysing data. Thus, several disciplines, such as marketing, psychology and computer sciences, just to mention a few, did take advantage from its contribution over time. Traditionally, this kind of algorithm concentrates only on numerical or categorical data at a time. In this work, instead, we analyse a dataset composed of mixed data, namely both numerical than categorical ones. More precisely, we focus on profiling visitors of the National Park of Majella in the Abruzzo region of Italy, which observations are characterized by variables such as gender, age, profession, expectations and satisfaction rate on park services. Applying a standard clustering procedure would be wholly inappropriate in this case. Therefore, we hereby propose an unsupervised classification of mixed data, a specific procedure capable of processing both numerical than categorical variables simultaneously, releasing truly precious information. In conclusion, our application therefore emphasizes how cluster analysis for mixed data can lead to discover particularly informative patterns, allowing to lay the groundwork for an accurate customers profiling, starting point for a detailed marketing analysis.
Giulia Caruso; Adelia Evangelista; Stefano Antonio Gattone. Profiling visitors of a national park in Italy through unsupervised classification of mixed data. Proceedings e report 2021, 127, 135 -140.
AMA StyleGiulia Caruso, Adelia Evangelista, Stefano Antonio Gattone. Profiling visitors of a national park in Italy through unsupervised classification of mixed data. Proceedings e report. 2021; 127 ():135-140.
Chicago/Turabian StyleGiulia Caruso; Adelia Evangelista; Stefano Antonio Gattone. 2021. "Profiling visitors of a national park in Italy through unsupervised classification of mixed data." Proceedings e report 127, no. : 135-140.
One of the key indicators of a population’s well-being and the economic development of a country is represented by health, the main proxy for which is life expectancy at birth. Some factors, such as industrialization and modernization, have allowed this to improve considerably. On the other hand, along with high global population growth, the factor which may jeopardize human health the most is environmental degradation, which can be tackled through the transition to renewable energy. The main purpose of our study is to investigate the relationship between renewable energy consumption, social factors, and health, using a Panel Vector Auto Regression (PVAR) technique. We explore the link between some proxy variables for renewable energy consumption, government policy, general public awareness, the market, lobbying activity, the energy dependence on third countries, and health, spanning the period from 1990 to 2015, for a cluster of 12 European countries characterized by common features. Specifically, our analysis shows the importance of having a stringent policy for the development of renewable energy consumption and its influence over other social factors, rather than the existence of causal relationships between health and renewable energy consumption for the analyzed countries. This kind of analysis has a great potential for policy-makers. Further, a deeper understanding of these relationships can create a more effective decision-making process.
Giulia Caruso; Emiliano Colantonio; Stefano Antonio Gattone. Relationships between Renewable Energy Consumption, Social Factors, and Health: A Panel Vector Auto Regression Analysis of a Cluster of 12 EU Countries. Sustainability 2020, 12, 2915 .
AMA StyleGiulia Caruso, Emiliano Colantonio, Stefano Antonio Gattone. Relationships between Renewable Energy Consumption, Social Factors, and Health: A Panel Vector Auto Regression Analysis of a Cluster of 12 EU Countries. Sustainability. 2020; 12 (7):2915.
Chicago/Turabian StyleGiulia Caruso; Emiliano Colantonio; Stefano Antonio Gattone. 2020. "Relationships between Renewable Energy Consumption, Social Factors, and Health: A Panel Vector Auto Regression Analysis of a Cluster of 12 EU Countries." Sustainability 12, no. 7: 2915.
This paper addresses regional studies at a micro-local level from both the perspective of institutional governance and economic analysis, by examining a case study in Central Italy. Our analysis concludes that the past policy approach in the area under investigation offers a potential pathway for academics to work with policy-makers in moving towards the realization of local growth policies. Such kind of analysis has a great potential for local institutions as well as a deeper understanding at a local scale can enhance a more effective decision-making process also at a global scale.
Giulia Caruso; Tonio Di Battista; Stefano Antonio Gattone. A Micro-level Analysis of Regional Economic Activity Through a PCA Approach. Advances in Intelligent Systems and Computing 2020, 227 -234.
AMA StyleGiulia Caruso, Tonio Di Battista, Stefano Antonio Gattone. A Micro-level Analysis of Regional Economic Activity Through a PCA Approach. Advances in Intelligent Systems and Computing. 2020; ():227-234.
Chicago/Turabian StyleGiulia Caruso; Tonio Di Battista; Stefano Antonio Gattone. 2020. "A Micro-level Analysis of Regional Economic Activity Through a PCA Approach." Advances in Intelligent Systems and Computing , no. : 227-234.
The increase in global population and the improvement of living standards in developing countries has resulted in higher solid waste generation. Solid waste management increasingly represents a challenge, but it might also be an opportunity for the municipal authorities of these countries. To this end, the awareness of a variety of factors related to waste management and an efficacious in-depth analysis of them might prove to be particularly significant. For this purpose, and since data are both qualitative and quantitative, a cluster analysis specific for mixed data has been implemented on the dataset. The analysis allows us to distinguish two well-defined groups. The first one is poorer, less developed, and urbanized, with a consequent lower life expectancy of inhabitants. Consequently, it registers lower waste generation and lower C O 2 emissions. Surprisingly, it is more engaged in recycling and in awareness campaigns related to it. Since the cluster discrimination between the two groups is well defined, the second cluster registers the opposite tendency for all the analyzed variables. In conclusion, this kind of analysis offers a potential pathway for academics to work with policy-makers in moving toward the realization of waste management policies tailored to the local context.
Giulia Caruso; Stefano Antonio Gattone. Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data. Social Sciences 2019, 8, 186 .
AMA StyleGiulia Caruso, Stefano Antonio Gattone. Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data. Social Sciences. 2019; 8 (6):186.
Chicago/Turabian StyleGiulia Caruso; Stefano Antonio Gattone. 2019. "Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data." Social Sciences 8, no. 6: 186.
When you dispose of multivariate data it is crucial to summarize them, so as to extract appropriate and useful information, and consequently, to make proper decisions accordingly. Cluster analysis fully meets this requirement; it groups data into meaningful groups such that both the similarity within a cluster and the dissimilarity between groups are maximized. Thanks to its great usefulness, clustering is used in a broad variety of contexts; this explains its huge appeal in many disciplines. Most of the existing clustering approaches are limited to numerical or categorical data only. However, since data sets composed of mixed types of attributes are very common in real life applications, it is absolutely worth to perform clustering on them. In this paper therefore we stress the importance of this approach, by implementing an application on a real world mixed-type data set.
G. Caruso; S. A. Gattone; A. Balzanella; T. Di Battista. Cluster Analysis: An Application to a Real Mixed-Type Data Set. Developments in Advanced Control and Intelligent Automation for Complex Systems 2018, 525 -533.
AMA StyleG. Caruso, S. A. Gattone, A. Balzanella, T. Di Battista. Cluster Analysis: An Application to a Real Mixed-Type Data Set. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2018; ():525-533.
Chicago/Turabian StyleG. Caruso; S. A. Gattone; A. Balzanella; T. Di Battista. 2018. "Cluster Analysis: An Application to a Real Mixed-Type Data Set." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 525-533.
Cluster analysis has long played an important role in a broad variety of areas, such as psychology, biology, computer sciences. It has established as a precious tool for marketing and business areas, thanks to its capability to help in decision-making processes. Traditionally, clustering approaches concentrate on purely numerical or categorical data only. An important area of cluster analysis deals with mixed data, composed by both numerical and categorical attributes. Clustering mixed data is not simple, because there is a strong gap between the similarity metrics for these two kind of data. In this review we provide some technical details about the kind of distances that could be used with mixed-data types. Finally, we emphasize as in most applications of cluster analysis practitioners focus either on numeric or categorical variables, lessening the effectiveness of the method as a tool of decision-making.
Giulia Caruso; Stefano Antonio Gattone; Francesca Fortuna; Tonio Di Battista. Cluster Analysis as a Decision-Making Tool: A Methodological Review. Advances in Intelligent Systems and Computing 2017, 618, 48 -55.
AMA StyleGiulia Caruso, Stefano Antonio Gattone, Francesca Fortuna, Tonio Di Battista. Cluster Analysis as a Decision-Making Tool: A Methodological Review. Advances in Intelligent Systems and Computing. 2017; 618 ():48-55.
Chicago/Turabian StyleGiulia Caruso; Stefano Antonio Gattone; Francesca Fortuna; Tonio Di Battista. 2017. "Cluster Analysis as a Decision-Making Tool: A Methodological Review." Advances in Intelligent Systems and Computing 618, no. : 48-55.