Using field and quasi experiments and text-based analysis to advance international business theory

Recently, financial institutions and investors have placed an increasing emphasis on ESG (environmental, social, and governance) as a principal indicator for the evaluation of companies. However, the current ESG scoring systems lack uniformity and are often subjective. It is of great importance to be able to make accurate predictions regarding the ESG scores of corporations. A Stacked Generalization Model that employs Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as base learners, with Bayesian Ridge Regression (BRR) as the meta-model for integrating the predictions of these diverse models is proposed. The goal is to develop an ESG score prediction model for Chinese companies. The experimental data set encompasses Chinese A-share listed companies from 2012 to 2020. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) are employed for model evaluation and are compared with seven benchmark models. The results demonstrate that SGM-BRR reduces the RMSE by 18.4%, 17.3%, 13.7%, and 76.1%, the MAE by 15.4%, 18.4%, 15.8%, and 68.4%, and increases the R2 by 2%, 1.4%, 2%, and 6% for ESG, E, S, and G scores, respectively. Furthermore, the model’s performance is validated across different industries, with SGM-BRR exhibiting the most optimal performance of RMSE, MAE, and R2 in 27, 25, and 27 groups, respectively. Consequently, the model demonstrates broad applicability and stability performance in ESG score prediction.

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From Insight to Impact: The Power of Experimentation in Multinational Corporations

Cerar

2024

AIB Insights

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Multinational corporations increasingly rely on experimentation for data-driven decision-making, yet business schools have failed to prepare managers with these skills. This article introduces a master’s course bridging this gap. Through case studies, lab visits, and hands-on projects, students embed experimentation in their strategic toolkit. This allows companies to recruit individuals who can immediately contribute to experimental projects and foster innovation and agility within MNCs. The article provides practical guidance for educators on integrating experimental methods into their curricula and demonstrates an engaging and accessible approach to teaching research methods.

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Using field and quasi experiments and text-based analysis to advance international business theory

Cited by 4 publications

References 88 publications

Reconsidering, refashioning, and reconceptualizing research methodology in international business

Reconsidering, refashioning, and reconceptualizing research methodology in international business

A Novel Stacked Generalization Ensemble-Based Hybrid SGM-BRR Model for ESG Score Prediction

From Insight to Impact: The Power of Experimentation in Multinational Corporations

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