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This work aims to analyze and observe the trends in the literature on corporate governance and disclosure. The study presents bibliometric analyses from the Scopus database for the period 1991–2020.

A bibliometric analysis is conducted on 1,697 studies on corporate governance and disclosure across several countries. The articles were assessed and visualized with Vosviewer based on the authors, sources and countries with the highest publication rate, journals with the most published research and highly cited articles and authors.

The analyses provide a comprehensive outlook of the field, and the results show the dominance of documents on corporate governance and disclosure in 2020. The results have been discussed with avenues for further research.

This paper focuses on corporate governance and disclosure research from the Scopus database to highlight the extensive and somewhat ignored areas in extant literature. This would aid upcoming researchers in identifying scholars in the field when exploring future research avenues to close ensuing gaps.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

The activities carried out in ports are disruptive to the environment where they are located. Therefore, the objective of this work is to analyze the presence of corporate social responsibility (CSR) in the missions and visions of the main ports of Latin America.

A qualitative technique of content analysis was applied to the missions and visions of 72 ports in Latin America. First, the missions and visions of the ports were collected (72). Second, it was assigned a value 1 if the mission had any evidence of CSR, 0 if it had no evidence and “-” if the mission was not found. The same procedure was performed with the visions.

Results indicate that 20.83% of the ports allude to CSR in their missions, 34.72% of the ports allude to it in their missions and visions and 13.88% only allude to it in their visions (22 ports did not mention it in their missions or their visions). So, the main findings indicate that in Latin America the majority of ports do not consider elements of CSR in their missions and visions.

This paper is mainly focused on covering two gaps in the literature: first, to increase knowledge about the strategic bases of ports in Latin America through their missions and visions; and second, to visualize the coherence of the missions and visions with the activities of CSR.