PurposeThe purpose of this paper is to emphasize that the growing of competitiveness at any level may be possible through more responsibility (business ethics) on the one hand and less corruption (as lack of business ethics) on the other.Design/methodology/approachThe objective of the paper is to identify the double‐way relationships between competitiveness and the responsible (beyond ethics) behaviour. In order to do this, the authors used correlation indexes CORREL and R2 and the graphic representation able to illustrate the above‐mentioned interrelations.FindingsThe authors observed that there is a strong and direct correlation between GCI, RCI and CPI – at national level, and six possible situations which reflect the interrelations between NP and FGP.Research limitations/implicationsThe paper may be continued with specific behavioural models of MNEs in different host countries – integrating different approaches of business ethics.Practical implicationsThe practical implications of the paper consist in offering some guidelines/starting points for firms in the search of global competitiveness through responsible/ethical conduct.Originality/valueThe paper develops a new conceptual framework, which integrates two “obsessions” of nowadays (competitiveness and responsibility) into the concept of global performance – national and firm related.
Over the last few decades Big Data has impetuously penetrated almost every domain of human interest/action and it has (more or less consciously) become a ubiquitous presence of day to day life. The main questions this exploratory paper seeks to address (throughout its two parts) are the following: What is the (actual) impact of Big Data on Business & Management and How can businesses (through their management) leverage the potential of Big Data to their benefit? A gradual, step by step approach (based on literature review and a variety of secondary data) will guide the paper in search for answers to the abovementioned questions: starting with a concise history of the topic Big Data as reflected in academia and a critical content analysis of the Big Data concept, the paper will then continue by emphasizing some of the most significant realities and trends that characterize the supply-side of the big data industry; the second part of the paper is dedicated to the investigation of the demand-side of the big data industry – by highlighting some evidences (and projections) on the impact of big data analytics on Business & Management (both at aggregate and granular level) and exploring what companies could and should do (through their management) in order to best capitalize on the opportunities of big data and avoid/minimize the impact of its threats.
Every company has a different structure of balance sheet. Some of the companies have more liabilities than equity. Considering the industry or debt-to-equity ratio, the balance sheet structure affects the company profitability measured by DuPont system. The main objective of the paper is to analyze the structure of balance sheet and to identify some optimal levels in order to increase company profitability. The DuPont returns like ROA (return on assets) and ROE (return on equity) will be used to measure the company profitability, while the debt-to-equity ratio will be used as a measure (reflection) of capital structure. The samples consist on the most profitable non-financial companies ranked in Fortune Global 500. The companies will be grouped in clusters (based on industry or debt-to-equity ratio) in order to identify the signification of the correlation between the profit and the balance sheet structure. The main results of the paper refer to the company profitability that can be increased by using an optimal structure of liabilities and equity.
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