This study provides a bibliometric review of 279 studies on the applications of big data and artificial intelligence (AI) in the maritime industry, published in 214 academic outlets, authored by 842 scholars. We extracted bibliographical data from the Web of Science database and analysed it using the Bibliometrix tool in R software. Based on citation analysis metrics, we revealed the most influential articles, journals, authors and institutions. Using the bibliographic coupling methodology, we identified four underlying research clusters: (1) digital transformation in maritime industry, (2) applications of big data from AIS, (3) energy efficiency and (4) predictive analytics. We analysed these clusters in detail and extracted future research questions. Besides, we present research collaboration networks on the institution and author level.

Purpose: Earlier firms were evaluated mostly from their financial performance perspective, but with the increasing attention to sustainability goals, environmental, social and governance (ESG) performance of firms became key concerns to stakeholders. The purpose of this paper is to explore the effects of ESG performance of banks on their financial performance, in the context of emerging markets. Design/methodology/approach: This study employs the generalised method of moments technique for estimation purpose due to the dynamic nature of the data and to correct for endogeneity. This study uses the ESG performance data of 93 emerging market banks from 2015 to 2018, available in Asset4 ESG database of Refinitiv, formerly known as Thompson Reuters. The accounting and financial data are collected from Refinitiv Datastream database. Findings: The findings indicate a positive association of emerging market banks’ environmental and social performance with their financial performance, but governance performance does not influence financial performance. Originality/value: While many studies exist on the association of ESG concerns of an organisation with their financial profitability, the literature on in the context of banking is still limited. To the best of the authors’ knowledge, this is the first study that examines the effect of ESG practices of banks on their financial performance in the context of emerging economies.

The current Coronavirus infection (COVID-19) outbreak has had a substantial impact on many aspects of general life. Although a number of studies have been published on the topic already, there has not been a critical review of studies on the impacts of COVID-19 by and on environmental factors. The current study fills this gap by presenting a critical analysis of 57 studies on the nexus between COVID-19 and the environment, published in nine journals up to May 2020. Majority of the studies in our sample are published in Science of the Total Environment (74%), and studies used mostly descriptive statistics and regression as research methods. We identified four underlying research clusters based on a systematic content analysis of the studies. The clusters are: (1) COVID-19 and environmental degradation, (2) COVID-19 and air pollution, (3) COVID-19 and climate/metrological factors and (4) COVID-19 and temperature. Besides a critical analysis of the studies in each cluster, we propose research questions to guide future research on the relationship between COVID-19 and the environment.

Purpose: Gender diversity in corporate boards is broadly studied in existing corporate governance literature. However, the role of board gender diversity on environmental, social and governance (ESG) performance of the banks is still unaccounted for. Drawing on resource dependence and legitimacy theory, this study addresses this pressing research issue. Moreover, investigation of ESG controversies as a moderator paves the existing corporate governance research to the new avenues. Design/methodology/approach: Data were sourced from Refinitiv database on 37 US banks from the period of 2013 to 2017. This study employs static and dynamic panel regression models that include random effects, fixed effects and dynamic generalised method of moments (GMMs) to test the hypotheses. Furthermore, system GMM is used to reduce the issue of endogeneity, measurement error, omitted variables bias and bank-specific heterogeneity. Findings: We identify a significant positive relationship between board gender diversity and the ESG performance of US banks. However, the result propounds non-significant moderating effect of ESG controversies on the board gender diversity–ESG performance nexus. Originality/value: Literature on board gender diversity and ESG separately and predominantly explains firm/bank's financial performance. This study is one of the pioneering attempts to explain the role of board gender diversity on ESG performance. Although incremental, however, this study also contributes to the literature on ESG in the US context.

This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.