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The purpose of this study is to provide quantitative information on the growth of Islamic finance literature. The study focused on publishing trends, countries producing research on Islamic finance, key authors, major contributing organizations, authorship patterns, keywords and articles with the highest citations.

Bibliometric analysis is applied to analyse the growth and publishing trends in Islamic finance literature. The Web of Science (WoS) database was used to extract bibliometric data covering the period 1939–2019 for Islamic finance literature.

The study finds that Islamic finance research has gained remarkable momentum in the literature. However, such growth is largely manifested in Malaysia because of a conducive atmosphere for this type of research. Interestingly, the study finds that the three most productive journals are located in the UK and Malaysia, while Professor M. Kabir Hassan from the University of New Orleans, the USA appears to head the list of authors with 23 publications on Islamic finance.

This study provides up-to-date literature on the current state of Islamic finance in the world; as a result, it supports the development of policies by the Islamic finance industry. The findings of the study also serve as a reference point for Islamic finance training and educational institutions.

Islamic finance is an emerging financial discipline; as such, there is a need for more awareness of this financial system in the world. Muslim-majority countries, especially Saudi Arabia, Turkey, Indonesia, the United Arab Emirates (UAE), Pakistan and Bahrain, have to include Islamic finance in their curriculum and establish research institutions and research journals. In addition, Arabic language journals should be indexed in WoS and/or Scopus to provide a high-quality publication platform. This study provides a more comprehensive bibliometric analysis on the growth of Islamic finance literature (1939–2019) in the WoS database; most of the prior studies have covered relatively few areas of focus and a lower range of years in some cases.

The purpose of this study is to present a conceptual model where consumer electronic sports (eSports) engagement (CeSE) acts a predictor for gamers’ online engagement in eSports-related products/firm either through direct contribution (purchase intention) or indirect contribution (co-production, community engagement, word-of-mouth and recruitment).

Data from 262 eSports consumers aged 18–24 years were collected and analyzed through WarpPLS 8.0.

The findings of this study confirm that CeSE significantly influences all dimensions of the consumption behaviors (purchase intention, co-production, community engagement, word-of-mouth and recruitment).

This study provides empirical support for a conceptual framework developed through the social exchange theory and engagement theory. Besides, hierarchical component model approach is applied to estimate the composite model of CeSE.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.