International Journal of Management and Decision Making (6 papers in press)

Regular Issues

• A systematic literature mapping of multicriteria decision-making methods for assessing technology criticality  
  by Pablo Santos Torres, Marcos Dos Santos, Antonio Antonio Eduardo Carrilho Da Cunha  
  Abstract: In recent years, the need for innovation has heightened the relevance of evaluating critical technologies. In this regard, Multicriteria Decision-Making (MCDM) methods are suitable for critical technologies prioritisation when conflicts arise between alternatives and criteria. This paper presents a Systematic Literature Mapping to identify trends in MCDM approaches and application fields on this context, highlighting research gaps. After filtering, 91 documents formed the bibliometric stage and 42 the content stage. The research shows the topic's growing importance, especially since 2021. Various approaches have been applied in different prioritisation, indicating the need for customised methods. However, the Analytic Hierarchy Process (AHP) emerged as a prominent keyword, with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) also relevant. The primary issues addressed were related to energy and environmental solutions, revealing a knowledge gap in fields like defence and health.
  Keywords: multicriteria methods; technology assessment; critical technology; decision support; literature mapping; research trends; multicriteria decision methods; MCDM; MCDA.
  DOI: 10.1504/IJMDM.2026.10074358
   
  
• What are the influences of budget participation on information sharing, organisational commitment, and managerial performance?  
  by Rosana Santos De Oliveira  
  Abstract: This study analysed the influence of budgetary participation on information sharing, organisational commitment, and managerial performance. Data were collected from 109 managers of companies in the Brazilian port sector, and the analysis was performed using the PLS-SEM technique. The main results show a positive and significant influence of budgetary participation on information sharing and affective commitment. In addition, information sharing positively affects the dimensions of organisational commitment. Affective commitment plays a direct influence on managerial performance. The results also suggest that the influence of budgetary participation on managerial performance is indirect, mediated by affective commitment. This study contributes to the literature by exploring the proposed interrelationships, providing insights into the dimensionality of organisational commitment in the budgetary context, and presenting new evidence applicable to other sectors.
  Keywords: budget participation; budget; information sharing; sharing; information; organisational commitment; commitment; managerial performance; performance; port.
  DOI: 10.1504/IJMDM.2026.10075211
   
  
• A comprehensive analysis of data normalisation techniques for the MUTRISS multi-criteria decision-making method  
  by Duc Trung Do, Vo Thi Nhu Uyen , Nazli Ersoy 
  Abstract: Multiple-triangles scenarios (MUTRISS) is a multi-criteria decision-making (MCDM) method grounded in an n-dimensional space, designed to rank alternatives and identify the optimal choice. This study investigates the effectiveness of alternative normalisation techniques across four distinct case studies to enhance the flexibility and expand the applicability of the MUTRISS method. The normalisation selection process involved two key stages: the application of distance measures and a comparative analysis. The findings suggest that the max-min normalisation technique is the most compatible with MUTRISS, while the sum normalisation technique negatively impacts its performance. By exploring the effectiveness of various normalisation processes for the first time, this study aims to improve the MUTRISS method, facilitating its use as a more versatile and robust decision-making tool.
  Keywords: multi-criteria decision-making; MCDM; multiple-triangles scenarios; MUTRISS method; data normalisation.
  DOI: 10.1504/IJMDM.2026.10075628
   
  
• Advancing in portfolio management using machine learning in Brazil  
  by Adriana Bruscato Bortoluzzo, Marcus Oliveira Da Silva, Pedro Raffy Vartanian, Alvaro Aves De Moura Junior 
  Abstract: The study aims to compare the performance of machine learning models against conventional linear models and explore their applicability in investment allocation strategies, including discerning factor significance and contributions to return predictions. We conduct a portfolio allocation analysis of Brazilian stocks returns, spanning from January 2007 to June 2022. We built five models using machine learning techniques: Gradient Boosted Trees, Random Forest, LASSO and ridge regularization, and a baseline linear model using size, price on equity value and momentum. Our results reveal significant economic benefits associated with the tree-based models, outperforming their linear counterparts. Notably, the long-short portfolio strategy combining the two superior models yields an annual Sharp Ratio of 0.24, demonstrating a remarkable 66% improvement over that of the Ibovespa index. Machine Learning can assist in optimizing investment portfolios by identifying the most attractive stocks and their respective weightings, leading to better returns for investors while managing risk.
  Keywords: machine learning; asset pricing; forecast return; gradient boosted trees; portfolio allocation; Brazilian stocks.
  DOI: 10.1504/IJMDM.2026.10075930
   
  
• Transforming portfolio optimisation: a hybrid machine learning and Monte Carlo approach for superior asset allocation  
  by Siddharth Gupta, Ompal Singh, Gautam Negi 
  Abstract: This study aims at combining machine learning (ML) methods for smart asset choices with modern portfolio theory (MPT) and Monte Carlo simulations. Hybrid strategy was applied utilising Python for combining supervised ML models (XGBoost, random forest) and unsupervised learning (K-means clustering) for selecting stocks based on engineered features like rolling mean, log returns, and volatility. The chosen assets were then optimised by MPT and Monte Carlo strategies to generate risk-aware portfolios. Data were drawn from 18 diversified stocks from developed and developing economies from 2013 - 2023. Random forest classifier performed above 70% accuracy in selecting leading-performing stocks. Monte Carlo simulations provided the best Sharpe ratio (~0.78), which surpassed MPTs optimal value (~0.74), establishing better risk-adjusted returns. ML-based filtering also proved that the study further validated for more stable and diversified portfolios. The hybrid approach provides improved accuracy, diversification, and offers investors a more practical tool for making balanced investment decisions in volatile markets.
  Keywords: portfolio optimisation; Python; modern portfolio theory; MPT; Monte Carlo simulation; random forest; machine learning; asset allocation; Sharpe ratio; efficient frontier; financial modelling.
  DOI: 10.1504/IJMDM.2026.10076408
   
  
• Factors influencing consumer behaviour of smart textile clothing from the outlook of emerging markets  
  by Sandro Alberto Sánchez Paredes, Gabriela Ramírez, Manuel Bryan Salvador 
  Abstract: This study examined how cultural, social, personal, and psychological factors influence consumers' decisions to purchase smart clothing. A quantitative, correlational-descriptive methodology with a non-experimental and cross-sectional design was used, utilizing consumer data from Lima, Peru. Validation was conducted through a survey adapted from a pioneering instrument in this market. Results show that these factors impact consumer behaviour towards smart textile clothing differently, with personal and cultural factors having a greater influence and social factors having less. Companies are advised to educate and inform customers about the benefits and drawbacks of these garments to enhance their reputation among existing customers, particularly athletes, and attract new customers from the general population unfamiliar with the product.
  Keywords: smart clothing; consumer behaviour; purchase decision.
  DOI: 10.1504/IJMDM.2026.10076598