PurposeThis paper seeks to explore the role that boards can play in the contribution of organizational performance and to empirically examine the relationship between the chairman, key board composition variables and firm performance.Design/methodology/approachThe data presented are drawn from a sample of 27 chairmen of Greek corporations listed in the Athens Stock Exchange (ASE). The authors examine hypothesized links between the demographic characteristics of the chairmen of the board, board composition variables, and organizational performance.FindingsThe authors found a positive relationship between age and competitive positioning which indicates that, the older the chairperson, the better the competitive positioning. Amazingly, the relationship between age and overall firm performance was found to be negative, indicating that even if the chairperson is older and more experienced the possibly efficient performance of the firm does not depend on that.Research limitations/implicationsThe authors managed to collect data from 27 questionnaires; given the small size of the sample, the authors cannot generalize the findings.Practical implicationsThe research indicates that understanding the variables that influence top management team and firm performance enhances value creation to investors and shareholders.Originality/valueThe choice of the sample was based on a fundamental principle of corporate governance board leadership structure: the chairman of the board is a different entity from the chief executive officer (CEO). This is one of very few studies concerning corporate governance in Greece focusing on the perception of Greek chairpersons.
During the time of the coronavirus, strict prevention policies, social distancing, and limited contact with others were enforced in Greece. As a result, Twitter and other social media became an important place of interaction, and conversation became online. The aim of this study is to examine Twitter discussions around COVID-19 in Greece. Twitter was chosen because of the critical role it played during the global health crisis. Tweets were recorded over four time periods. NodeXL Pro was used to identify word pairs, create semantic networks, and analyze them. A lexicon-based sentiment analysis was also performed. The main topics of conversation were extracted. “New cases” are heavily discussed throughout, showing fear of transmission of the virus in the community. Mood analysis showed fluctuations in mood over time. Positive emotions weakened and negative emotions increased. Fear is the dominant sentiment. Timely knowledge of people’s sentiment can be valuable for government agencies to develop efficient strategies to better manage the situation and use efficient communication guidelines in Twitter to disseminate accurate, reliable information and control panic.
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