International Journal of Management and Decision Making (5 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
   
  
• Smart logistics with a maturity assessment perspective  
  by Elifcan Göçmen Polat, Onur Derse 
  Abstract: The logistics sector is influenced by digitalisation and technological advancement in improving sustainability and efficiency. Logistics companies, which are highly capable of incorporating innovative technologies into their business flows, have made profound impacts, ultimately leading to a transformation in logistics services. In this context, this study presents the current and target maturity levels to make an international logistics company smarter and provides recommendations as an improvement road map for the logistics sector. Within the scope of this research, nine dimensions and 36 maturity items of the smart logistics (SL) concept are discussed to assess the maturity levels using the weighted maturity score calculation model (WMSC), in which evaluation of all maturity items using the capability maturity model (CMM) maturity levels and calculation of the criteria weights using fuzzy DEMATEL (F-DEMATEL) is integrated. Computational results show that warehouse management systems and material flow control systems have lower maturity levels. Thus, product picking by voice, integrating the warehouse management system, ensures real-time updates and data integration, reducing stockouts and overstock.
  Keywords: maturity model; smart logistics; SL; Industry 4.0; fuzzy DEMATEL.
  DOI: 10.1504/IJMDM.2027.10078548