Forthcoming 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 Articles will not be published until the final proofs are validated by their authors.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

  • Prediction of abnormal returns on bidding firms in mergers and acquisitions   Order a copy of this article
    by Jianyu Ma, Yun Chu, John C. Stewart 
    Abstract: This study investigates the predictability of abnormal returns for bidding firms following M&A announcements in China, focusing on deal characteristics and firm size. Using 1,253 transactions from 2010-2018, we apply a K-nearest neighbours (KNN) model to classify whether post-announcement abnormal returns are positive or negative across Day +1, Day +2, and CAR (1, 2) windows. Predictions draw on four variables: form of acquisition, payment method, industry-relatedness, and firm size. Results show strong predictability for small acquirers those in the bottom 15%-30% by assets achieving over 70% accuracy across windows. Predictive power declines with larger firms and approaches randomness for the full sample. The findings indicate that abnormal returns are more systematically predictable when involving smaller bidders and clear structural deal features, offering insights for investors and managerial decision-making in short-term market reactions.
    Keywords: merger and acquisition; abnormal return prediction; K-nearest neighbours; KNN algorithm; machine learning; China Stock Market; firm size effect.
    DOI: 10.1504/IJRM.2026.10078142
     
  • Unravelling the research landscape of dynamic pricing and learning: insights and future research directions   Order a copy of this article
    by Ariit Sengupta, Himanshu Rathore, Suresh K. Jakhar 
    Abstract: This study is undertaken in an attempt to consolidate the existing literature on dynamic pricing and learning and offer a conceptual framework encapsulating its knowledge. The relevant articles were identified using pertinent keywords from Scopus database. Articles were screened using the PRISMA model, yielding 482 articles which were analysed employing a bibliometric analysis facilitated by VOSviewer and Biblioshiny (Bibliometrix). Further, a thematic content analysis was carried out to identify the underlying themes and the state-of-the-art of the literature. Citations and co-citation analysis revealed the intellectual structure of the field. The thematic content analysis unveiled four major themes that synthesised into a conceptual framework leveraging the interlinkages. Dynamic pricing and learning have been of immense practical relevance to pricing managers and researchers, owing to their effectiveness in enhancing revenues. This study attempts to consolidate the existing literature on dynamic pricing and learning.
    Keywords: dynamic pricing; demand learning; revenue management; exploration-exploitation trade-off; behavioural pricing; machine learning; pricing strategy; network revenue management; bibliometric analysis; consumer fairness.
    DOI: 10.1504/IJRM.2026.10078143
     

 

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