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 Articles will not be published until the final proofs are validated by their authors.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 (2 papers in press)

Regular Issues

  • Forecasting natural rubber prices using commodity market indicators: a machine learning approach   Order a copy of this article
    by Precious Nyondo, Roshna Varghese 
    Abstract: Natural rubber is an essential raw material in sectors such as automotive, construction, healthcare and manufacturing. Volatility in natural rubber prices can have a long run impact on rubber growers and rubber-based industries. This study develops and compares different forecasting models of natural rubber prices based on machine learning algorithms - support vector machine, random forest, decision tree, artificial neural networks and k-nearest neighbours. Natural rubber price forecasting models are developed using a set of explanatory commodity market indicators encompassing macroeconomic factors, demand and supply factors and price of related commodities. Based on our results, we propose a forecasting model of natural rubber prices employing the random forest algorithm which outperformed the other machine learning algorithms in its predictive capabilities. This paper makes substantial contributions to policymakers, businesses and rubber growers in making informed decisions and managing price risk in the natural rubber sector.
    Keywords: natural rubber; commodity market indicators; price forecasting; machine learning; support vector machine; SVM; artificial neural networks; ANN; k-nearest neighbours; KNN; random forest; decision tree; ARIMAX.
    DOI: 10.1504/IJRM.2024.10064126
  • Tax systems in India: potency of GST on work contract and real estate sector   Order a copy of this article
    by Shahwar Khan, Vikas Singh 
    Abstract: Research originality: In India, the real estate sector is prosperous but due to multiple taxes, business in this sector is becoming problematic. Research purpose: This study shows the GST impact on the work contract and real estate sector in India which is still unexplored due to advanced development of GST. Methods: This research examine the complexity of tax and construction costs before and after the implementation of GST. This study is based on secondary data and different descriptive statistics have been used for analysis. Empirical Result: The study revealed that the construction capital cost has been mostly affected by land acquisition and building materials by the implementation of GST, which led to increased property prices. It is concluded that the GST has a positive impact on the work contract and real estate sector in India. Implications: The simplification of the tax structure has led to a reduction in the cost of properties.
    Keywords: GST; goods and services tax; real estate sector; work contract; tax base; Indian economy.
    DOI: 10.1504/AJFA.2024.10064296