International Journal of Revenue Management (5 papers in press)

Regular Issues

• Prediction of abnormal returns on bidding firms in mergers and acquisitions  
  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  
  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
   
  
• Enhancing surplus reservation management using artificial intelligence  
  by Bessem Dammak, Soumaya Yacout, Antoine Saucier 
  Abstract: Frequent flyer programs play a central role in airline revenue management by supporting customer loyalty and utilising surplus seat capacity. Airlines periodically allocate to these programs surplus seats that remain unreserved through standard commercial channels. A key operational challenge is anticipating whether these seats will be reserved in the short term, as limited visibility on reservation probability constrains allocation and marketing decisions. This research estimates the probability of surplus seat reservation across multiple flights and markets using classification models based on historical reservation data and flight characteristics. Several machine learning methods are evaluated, with LightGBM identified as the most effective. Three modelling strategies are implemented to enhance predictive performance, and the effects of key explanatory variables are analysed using partial dependence. The best strategy achieves accuracy, recall, and precision above 80%. The resulting probabilities provide short-term decision signals to improve surplus allocation efficiency and profitability.
  Keywords: frequent flyer programs; FFPs; surplus allocations; predictive modelling; machine learning; reservation probability; machine learning; LightGBM; imbalanced classification; decision support systems.
  DOI: 10.1504/IJRM.2026.10079320
   
  
• Consumer resistance to green banking: examining inhibiting factors and implications for revenue management  
  by G. H. Kerinab Beenu, M.J. Sumaiah Shaheen , M. Deepa , Jenifer Arokia Selvi , M. Karthikeyan  
  Abstract: This empirical study investigates the key factors contributing to consumer resistance to green banking, with a particular focus on green perceived risk (GPR), green confusion (GC), and attitude (ATT) and their influence on consumer resistance (CR). Green banking, which integrates environmentally sustainable practices into the financial sector, is increasingly important as the world transitions towards sustainability. The study uses a purposive sampling methodology, with data collected from 326 respondents across major metropolitan areas in India, focusing on their resistance to green banking. The research employs partial least squares structural equation modelling (PLS-SEM) to analyse the direct and indirect effects of the identified variables. The results reveal that both GPR and GC significantly influence CR, with GC having a more substantial effect. Attitudes play a mediating role in the relationship between GPR, GC, and CR, though attitudes alone do not directly impact CR. The findings emphasise the importance of reducing confusion through clear communication and addressing perceived risks to enhance consumer engagement with green banking. This study contributes to the literature on green consumer behaviour and provides practical recommendations for financial institutions to foster greater adoption of sustainable banking practices.
  Keywords: green banking; green perceived risk; GPR; green confusion; attitude; consumer resistance.
  DOI: 10.1504/IJRM.2026.10079373
   
  
• Decoding supply chain finance for small and medium enterprises: a TCM-ADO synthesis of barriers, trends and future pathways  
  by Nibedita Tulsiyan, Manvendra Pratap Singh, Vikas Thakur 
  Abstract: The study systematically maps supply chain finance (SCF) research in the small and medium enterprises (SMEs) context. Integrating bibliometric analysis of 352 articles with — theory, context and methodology (TCM) antecedents, decision and outcome (ADO) framework for 74 Scopus-indexed studies using the SPAR-4 (scientific procedures and rationales for systematic literature review) protocol. SMEs face strategic resource challenges that limit their growth, competitiveness, operational efficiency, and financial capability. Emerged as a transformative enabler, SCF helps SMEs overcome operational and financial bottlenecks while reinforcing supply chain partnerships and enhancing overall resilience. Constrained by awareness gaps, perceptual hurdles, and institutional barriers, SMEs leave key growth opportunities untapped. This review bridges existing gaps by offering an integrated and holistic review of the SCF-SME dimension. It establishes how SCF alleviates financial and operational efficiency and barriers. It reveals challenges such as resistance, structural gaps, and technological hurdles. Overcoming these challenges requires strategic policy alignment, advanced digital solutions, and trust-driven collaboration to strengthen SMEs resilience and promote sustainable growth. The study provides SMEs, policymakers, and industry leaders with actionable insights to leverage SCF for resilient and sustainable growth.
  Keywords: supply chain finance; SCF; small and medium enterprises; SMEs; bibliometric analysis; systematic literature review; theory; context; methodology framework; TCM framework; antecedents; decisions and outcomes framework; ADO framework; conceptual framework.
  DOI: 10.1504/IJRM.2026.10079374