Forthcoming and Online First Articles

International Journal of Revenue Management

International Journal of Revenue Management (IJRM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Revenue Management (3 papers in press)

Regular Issues

  • Do top ranked ESG score countries provide diversification opportunities with global markets?   Order a copy of this article
    by Sudhi Sharma, Neeraj Aswal, Reepu , Parul Bhatia, Shristi Jain, Vaibhav Aggarwal, Shashank Sharma 
    Abstract: The paper attempts to find diversification opportunities with Nordic countries having high Environmental, Social, and Governance (ESG) scores with world and emerging indices. The study also identifies the resilient diversification opportunities in these countries during pandemic. The study utilizes daily adjusted closing prices of the selected indices from March 11, 2018, to March 11, 2021, divided into pre-COVID and post-COVID windows. The time-varying connectedness-wavelet coherence model and Network Analysis capture the time-varying volatilities and analyze the interconnectedness between the indices. The results found that MSCI Emerging markets diversification opportunities are least impacted by the outbreak of the pandemic and thus, show more resilient diversification in comparison with MSCI World Index. It has been found that Iceland and Switzerland are the two indices that are providing diversification with benchmark indices and captured diversification. Finally, Switzerland is providing resilient diversification opportunities with both benchmark indexes
    Keywords: environmental; social; and governance; ESG scores; sustainable development; economic growth; COVID-19; time-varying connectedness; UN SDG8.
    DOI: 10.1504/IJRM.2025.10069687
     
  • Exploring the moderating role of CEO overconfidence in the CSR-financial performance nexus: a quantile analysis in the banking sector   Order a copy of this article
    by Afef Jarraya, Mouna Abbes Boujelbene  
    Abstract: The present study investigate to what extent CEO overconfidence could moderate the CSR and financial performance binding relationship, concerning a sample of American and European banks, regarding the period spanning from 2013 to 2021. Using simultaneous quantile regression analysis, the empirical results indicate that engaging in socially responsible activities might negatively impact banking financial performance at high financial performance levels., Moreover, CEO overconfidence appeared to positively affect financial performance at all quantile levels. Additionally, CEO overconfidence was liable to exert a rather significant influence on the CSR of banks displaying FP scores approaching the highest performance levels. The findings also highlighted that overconfidence positively moderates the CSR effect on banking financial performance at all quantile levels. This research provides practical implications and insights for managers and policymakers seeking to improve banks' financial performance through highlighting the joint effect of strong commitment to CSR practices and CEO overconfidence.
    Keywords: corporate social responsibility; CSR; behavioural finance; overconfidence; performance; sustainability; COVID-19 pandemic.
    DOI: 10.1504/IJRM.2025.10069963
     
  • Comparative study on classifying overinvestment by machine learning   Order a copy of this article
    by Tam Phan Huy  
    Abstract: This research delves into the prevalent issue of overinvestment in Vietnamese firms, employing a range of machine learning algorithms to classify such behaviour. The study utilises data from Vietnam’s stock exchanges, applying a wide array of machine learning algorithms ranging from Logistic Regression to complex deep learning architectures to identify the most effective tool in classifying overinvestment. The findings from this study reveal that certain machine learning algorithms, particularly ensemble methods like AdaBoost, XGBoost, LightGBM, and CatBoost, demonstrate exceptional proficiency in classifying overinvestment scenarios among Vietnamese firms. These models outperform traditional statistical approaches by effectively capturing the complex, non-linear dynamics that characterise overinvestment behaviour. Notably, the research uncovers that AdaBoost and XGBoost are particularly adept in this context, offering high accuracy and robustness in detecting overinvestment patterns. The research concludes that these machine learning tools are crucial for understanding and mitigating overinvestment risks, providing a significant analytical resource for Vietnam’s financial sector. The study recommends further adoption of machine learning in corporate financial analysis to enhance decision-making and market stability.
    Keywords: overinvestment; machine learning; Vietnam markets.
    DOI: 10.1504/IJRM.2025.10070675
     

 

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