International Journal of Management and Decision Making (9 papers in press)

Regular Issues

• 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
   
  
• Hierarchical digital enabling technologies adoption and sustainable logistics practices in emerging economies  
  by Peter Yacob 
  Abstract: This study endeavours to empirically investigate the relationship between digital enabling technologies (IoT, BDA, CL, AI, and BCT), employee engagement, and the adoption of sustainable logistics practices within the logistics companies in Malaysia. Data were collected through a questionnaire administered to 316 Malaysian logistics companies. The relationships proposed in the developed conceptual framework were represented through four hypotheses, and partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses. The findings revealed that IoT, BDA, CL, AI, and BCT are significantly related to sustainable logistics practices. On mediation analysis, it confirms that employee engagement fully mediates the association of digital enabling technologies and sustainable logistics practices. This study recommends that digital technologies and human processes through employee engagement are essential for ensuring precise and rapid responses to rising consumer demands for logistic services in shaping sustainable logistics practices.
  Keywords: digital technologies; logistics; sustainability; employee engagement.
  DOI: 10.1504/IJMDM.2026.10077098
   
  
• Navigating Industry 4.0 barriers: a neutrosophic MCDM approach with impact analysis and mitigation strategies  
  by Vikrant Sharma 
  Abstract: The global manufacturing landscape is being reshaped with advanced digitalisation leading to the term Industry 4.0. Despite this, it faces serious challenges when adopted in emerging economies. This study addresses these challenges through identification and prioritisation of the most important barriers and the formulation of tailored mitigation strategies. The research uses a comprehensive methodology of survey data collection and doing the neutrosophic analytical hierarchy process (N-AHP) and neutrosophic combined compromise solution (N-CoCoSo) MCDM analysis to shed light into the critical barriers. The findings emphasise on Workforce training, strategic financial planning, change management programs, integration strategies and strong cybersecurity. This work is unique in its use of an empirical approach, and provides framework to companies and public policymakers in order for them to overcome adoption barriers by a structured roadmap for adopting technologies. The outcomes support competitive advantage and sustainable development by helping to guide wise resource allocation and decision-making.
  Keywords: Industry 4.0; challenges; neutrosophic MCDM; impact analysis; mitigation strategies.
  DOI: 10.1504/IJMDM.2026.10077128
   
  
• Integrating shelving technology selection and storage assignment under uncertainty: a two-stage mathematical programming approach  
  by Mohammadreza Farhadi Moghadam, Kaveh Khalili Damghani, Vahidreza Ghezavati, Alireza Rashidi Komijan 
  Abstract: The primary objective of this study is to select appropriate shelving technology while minimising intra warehouse transportation costs under probabilistic demand conditions. This research introduces four key innovations: integrating shelving technology selection and storage location assignment problem for the first time, considering capacity as a probabilistic variable to enhance realism, incorporating a multi-period framework, and allowing replenishment. Since predicting required capacity deterministically for shelving technology selection is impractical, capacity is modelled as a probabilistic variable. The next step involves allocating items to this technology. The problem is designed as multi-periodic, where, in each period, some orders are dispatched to customers, and some items are stored in the system. The problem ensures that sufficient space is always available for incoming items, forming a combined warehouse design and item allocation problem for shelving technology. Failure to address these issues integrative can lead to suboptimal solutions. A two-stage programming approach is employed to solve the model. Given the probabilistic nature of required capacity, the problem is solved under both continuous and discrete conditions, and a comparison between these approaches is conducted to determine the most effective solution method.
  Keywords: warehouse design; shelving technology; storage location assignment problem; probabilistic method.
  DOI: 10.1504/IJMDM.2026.10077527
   
  
• A systematic literature mapping of multicriteria decision-making methods for assessing technology criticality  
  by Pablo Santos Torres, Marcos Dos Santos, 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 Do Duc Trung, Vo Thi Nhu Uyen, Nazlı 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
   
  
• 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 MPT's 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 towards 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, utilising 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