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This paper aims to review 69 studies related to Muslim consumer behavior and determine the relationship between these topics and Islamic rationality. In addition, this paper elaborates on Al-Ghazali’s Islamic rationality model.

A text analytics approach is used to map 69 studies on Muslim consumer behavior. In addition, the historical-critical and inductive approach is used to identify Muslim scholars’ concepts and opinions regarding Islamic rationality, especially Al-Ghazali.

This study confirms that Muslim consumer behavior is in line with the concept of Islamic rationality proposed by Al-Ghazali. This is evidenced by a strong awareness of Islamic morals and values, which fosters a high commitment to halal products.

The findings of this study will provide essential benefits in the development of Islamic rationality theory, which can then be used as an alternative in explaining Muslim consumer behavior and also can be used as a reference for stakeholders in the industry to mainstream halalfication on products offered in the Muslim market.

The value of originality in this study lies in identifying the relation between Islamic rationality and Muslim consumer behavior, and this effort was confirmed through 69 selected studies related to Muslim consumer behavior.

By combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity.

We used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms.

Our findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions.

The insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm’s adoption of corporate actions (ESG practices).

To the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms.