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While recent years have seen a remarkable relaxation of the antitrust laws, these laws are not yet dead. Moreover, what is permitted in the United States might well be illegal in the European Economic Community. American business managers therefore should proceed carefully in taking advantage of the trend to relax antitrust laws.

The entrepreneur is the main decision-maker in small and medium-sized enterprises and is the principal force in the implementation of a firm's international strategy. Research has paid limited attention to the intangible aspects of human capital, namely behavioural competencies that may have an impact on the entrepreneur's ability to take advantage of international opportunities. This study addresses this gap, identifying the behavioural competencies that distinguish entrepreneurs who pursue a stronger internationalisation expansion beyond the European market.

A competency modelling process is implemented for a sample of Italian entrepreneurs. Data on behavioural competencies are obtained through the coding of behavioural event interviews administrated to the entrepreneurs. Export intensity is adopted as a performance criterion to classify the entrepreneurs.

Three behavioural competencies (change catalyst, teamwork and organisational learning orientation) emerged as more significantly activated by entrepreneurs who show a higher export intensity in the global market.

The exploratory nature of the study, conducted in a small sample and in a specific geographical area, may reduce the generalisability of the findings.

Entrepreneurs can become aware of the behavioural competencies needed for the implementation of internationalisation processes. Additionally, training programmes can be designed to promote the development of these behaviours.

Bridging the literature on international entrepreneurship, cross-cultural studies and competency-based research, this study highlights the role of behavioural competencies in the internationalisation process from a micro level of analysis. This article proposes a competency framework that can be adopted to assess a broader portfolio of entrepreneurs' behaviours.

This paper seeks to identify factors potentially conditioning firms’ financial internationalization. Companies often internationalize their financial areas as part of their larger internationalization strategy. In other words, such an initiative is associated with the internationalization of non‐financial business areas. However, the move to financial internationalization may also obey a specific strategy designed to take advantage of the opportunities offered by increasingly global financial milieus and markets. Then again, of course, it may respond to a combination of the two, in which case all the factors mentioned are likely to exercise some influence.

To test these propositions, a sample of 461 firms located in the Basque Country (Northern Spain), were analyzed between 16 June and 13 July 2004. Primary data were collected by telephone surveys, with a specially designed questionnaire tested previously with a number of pilot businesses. The sample represents a confidence level of 95 percent and 4.25 percent as a maximum level of error. This sample was divided by company size and the sector each business worked in, maintaining, approximately, proportionality in each stratum with respect to the population. Mann‐Whitney and Kruskal‐Wallis tests and logit analysis were used, among others.

Companies most likely to go into debt abroad are larger and more internationalized commercially and in production. First are large exporting companies with one or more production facilities abroad (PFA), which are followed by: medium enterprises that export and which have at least one PFA and large companies that export but which have no PFA. The profile of firms with foreign shareholders begins with manufacturing companies that import, followed by commercial businesses that also import. One interesting feature is the low number of companies in the construction industry and the services sector, particularly the ones that neither export nor import.

A sample of 461 firms located in the Basque Country (Northern Spain) were analyzed and thus the sample might be geographically limited. Also, the degree of financial internationalization of these firms is relatively low. A sample which covers a greater amount of financially internationalized firms, might have led to more solid conclusions.

The most noteworthy practical implication of the paper is the confirmation that Basque firms still do not clearly perceive opportunities for financial internationalization. The barriers and risks to be faced beyond geographical borders weigh heavily. In other words, the threats companies are exposed to outweigh potential opportunities in international markets, or the conditions for financing and domestic financial investment available are in general more favourable than the conditions obtainable abroad.

Within the Basque firms, even if the commercial, supply and production internationalization has been analyzed at length, the financial internationalization has not. Moreover, the profiles of financially internationalized firms have not been analyzed previously on the basis of a different sample.

The purpose of this paper is to explore the factors affecting the intention of social networking sites (SNS) users to comply with government policy during the COVID-19 pandemic.

Based on the theory of appraisal and coping, the research model is tested using survey data collected from 326 SNS users. Structural equation modeling is used to test the research model.

The results show that social support has a positive effect on outbreak self-efficacy but has no significant effect on perceived avoidability. Government information transparency positively affects outbreak self-efficacy and perceived avoidability. Outbreak self-efficacy and perceived avoidability have a strong positive impact on policy compliance intention through problem-focused coping.

The results suggest that both government and policymakers could deliver reliable pandemic information to the citizens via social media.

This study brings novel insights into citizen coping behavior, showing that policy compliance intention is driven by the ability to cope with problems. Moreover, this study enhances the theoretical understanding of the role of social support, outbreak self-efficacy and problem-focused coping.

According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease.

A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases.

The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6.

The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(n^k+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.