International Journal of Applied Decision Sciences (17 papers in press)

Regular Issues

• Optimising in-vehicle gesture interaction: a study on user preferences and eye-tracking metrics in autonomous driving  
  by Xinlian Li, Jinyang Xu 
  Abstract: The complexity of in-vehicle interaction systems is rapidly increasing, leading to higher cognitive loads for drivers and impairing their ability to respond effectively in emergencies. This study addresses this challenge by optimising the design process of in-vehicle gesture interaction systems through the systematic extraction of essential interaction features and user preferences. We propose an integrated gesture interaction system that combines gesture-based controls with heads-up display (HUD) technology, aimed at reducing drivers' cognitive burden. The system's effectiveness is assessed through simulation experiments, incorporating eye-tracking metrics, driving performance indicators, and subjective evaluations using the system usability scale (SUS).The analysis shows that the HUD-integrated gesture interaction significantly enhances the frequency of forward gazes during secondary tasks, thereby reducing safety incidents associated with gaze drift and improving overall driving safety. This research contributes significantly to the theoretical and practical value of the automotive industry, supporting its sustainable development through optimised human-vehicle interaction systems.
  Keywords: in-vehicle gesture interaction system; heads-up display; HUD; user preferences; eye-tracking metrics; design optimisation.
  DOI: 10.1504/IJADS.2026.10076474
   
  
• Viable stochastic stock Portfolio 5.0 dynamic selection using weighted optimality grade multi-objective optimisation  
  by Arash Nemati, Mahan Hosseinnia, Rozhan Sadeghi-Eshkevari 
  Abstract: Although Industry 4.0 (I4.0) and Industry 5.0 (I5.0) have revolutionised the business atmosphere in emerging markets, viability, intelligence, and human-centricity have been neglected in the stock portfolio selection problems. This paper contributes to viable stochastic stock Portfolio 5.0 selection by proposing a hierarchical structure for assessing the adaptation of each stock to emerging criteria, developing a multi-objective, multi-period integer linear mathematical model, and suggesting a weighted version of the Optimality Grade (OG) method, Weighted OG (WOG). The proposed mathematical model optimises total expected returns, total risk, liquidity, sensitivity, viable P5.0 adaptation, diversification, and total remaining accessible budget simultaneously. The applicability of the proposed model is demonstrated in an illustrative numerical example, involving twenty stocks categorised into five classes during three periods using both OG and WOG methods coded by CPLEX. The results validate the proposed model’s performance and show the superiority of WOG, particularly in terms of interactive multi-objective optimisation.
  Keywords: Industry 4.0; I4.0; Industry 5.0; I5.0; Portfolio 5.0; P5.0; viability; stochastic stock portfolio; weighted optimality grade.
  DOI: 10.1504/IJADS.2026.10077371
   
  
• A novel transmuted-claim family of distributions for lifetime events  
  by Albert Ayi Ashiagbor, Sonali Das 
  Abstract: The development of new lifetime distributions is a vital, driven by the need for accurate models that capture complex data. This paper introduces the transmuted-claim family, a novel class of continuous distributions that extends the existing claim-class and transmuted families and has the ability to accommodate asymmetric and bi-modal data, a common challenge in life-time data analysis. By providing a more flexible and accurate modelling approach, the transmuted-claim family enables decision-makers to better understand and predict complex lifetime phenomena, such as equipment failure rates or patient survival times, make better informed decisions about resource allocation, maintenance scheduling, and risk management, reducing costs, and improving outcomes. The transmuted-claim family’s valuable properties, including its quantile function, moments, and moment generating function, provide actionable insights into the distribution’s behaviour. The superior fit of the transmuted-claim family to complex data is demonstrated through simulations and applications to real-world lifetime data.
  Keywords: transmuted-claim family; Burr III distribution; quantile function; Monte Carlo simulation; estimation methods; lifetime data.
  DOI: 10.1504/IJADS.2026.10077700
   
  
• Financial risk level identification method for manufacturing industry based on deep time series model  
  by Ling Pu, Ying Fang, Jun Liang, Shaojuan Ouyang 
  Abstract: This study enhances the accuracy, efficiency, and multi-subindustry adaptability of financial risk grading in manufacturing by constructing deep time series models. Based on deep time series concepts, this study designed a binary classification model, a multivariate classification model, and a financial risk score prediction model for identifying financial risks. Experimental results indicated that the binary classification model achieved an F1 score of 90.5% when supported by data from 1,000 enterprises. The multivariate classification model demonstrated an accuracy rate of 93.1% for identifying level 3 moderate risks. The risk scoring identification model achieved an R2 x 100 value of 91.0% in electronic manufacturing industry scoring predictions. Research confirms that deep time series models can overcome the limitations of traditional methods effectively, enhancing the precision with which financial risk is identified in manufacturing. These models can adapt to diverse manufacturing sub-industry scenarios and provide technical support for corporate financial risk management.
  Keywords: deep time series; manufacturing financial risk; risk level identification; sparse attention mechanism; multivariate classification identification.
  DOI: 10.1504/IJADS.2026.10077879
   
  
• Quality of financial reports, external dependency, and company innovation investment efficiency  
  by Yingjun Wu, Zhen Lei 
  Abstract: This study investigates how financial reporting quality influences the efficiency of corporate R&D investment, using a sample of Chinese A-share listed firms from 2018 to 2023. The empirical findings indicate that higher financial reporting quality is linked to lower innovation efficiency. Moreover, the negative effect of accounting transparency on innovation efficiency is stronger in firms that rely on external financing than in those that depend on internal funds. The connection between disclosure quality and over-investment in innovation is also more pronounced in state-owned enterprises than in private firms. Finally, the impact of accounting information quality on innovation efficiency is more evident in eastern coastal regions than in inland areas.
  Keywords: quality of financial reports; external dependency; innovation investment efficiency.
  DOI: 10.1504/IJADS.2026.10077944
   
  
• A new method of calculation of objective criteria weights for group decision-making  
  by Dariusz Kacprzak 
  Abstract: The paper presents a new algorithm for determining objective weights of criteria for group decision-making, with input data expressed as ordered fuzzy numbers. This method converts individual decision matrices into criteria matrices with the result that objective weights of criteria are determined for the input ordered fuzzy numbers instead of their averaged values (as in situations when aggregation of individual decision matrices is used). To this end, our method uses fuzzy entropy and fuzzy TOPSIS. A sensitivity analysis of the obtained objective weights of criteria as well as comparisons of the new algorithm with selected other methods are also presented.
  Keywords: multi-criteria group decision-making; MCGDM; objective weights of criteria; ordered fuzzy numbers; OFNs; fuzzy entropy; fuzzy TOPSIS.
  DOI: 10.1504/IJADS.2027.10078630
   
  
• An impartial Bayesian hypothesis test for audit sampling  
  by Koen Derks, Jacques De Swart, Eric-Jan Wagenmakers, Ruud Wetzels 
  Abstract: Auditors who perform audit sampling are often interested in obtaining evidence for or against the hypothesis that the misstatement in a population of items is lower than a critical limit, the so-called performance materiality. Here, we propose to perform this hypothesis test using a Bayesian approach that involves the use of an impartial prior distribution, assigning equal prior probabilities to the competing interval hypotheses. Firstly, we argue that the impartial prior distribution is sensible for auditors because it is easy to justify, interpret, and explain. Secondly, we show that Bayes factors computed using this prior distribution have desirable statistical properties. Finally, we compare these Bayes factors with traditional p-values in an audit sampling context and elaborate on the merits of the impartial Bayesian hypothesis test.
  Keywords: audit sampling; Bayes factor; evidence; impartial; prior distribution.
  DOI: 10.1504/IJADS.2027.10078699
   
  
• Research on the industry-education integration model and influencing factors in application-oriented universities — a case study of the cross-border aviation logistics major  
  by Yongchuang Zhang, Gai Hang 
  Abstract: This study investigates the models and influencing factors of industry-education integration (IEI) in application-oriented universities, using the cross-border aviation logistics major as a case. Based on systems and stakeholder theories, we propose a three-phase model of IEI evolution comprising the initial integration, continuous optimisation, and strategic win-win phases. We further identify three categories of influencing factors: internal, external environmental, and multi-party coupling. Empirical analysis reveals that the dominant factors shift across phases: external factors initiate integration; internal and coupling factors drive optimisation; and coupling factors ultimately determine strategic depth. The study concludes with phase-specific strategies for universities to advance IEI, emphasising the need for dynamic alignment between institutional initiatives and contextual factors to cultivate high-quality, industry-ready talent in specialised fields. Its primary contribution lies in shifting the discourse from static lists of influencing factors to a dynamic, stage-contingent understanding of IEI drivers, thereby offering a diagnostic framework for institutional leaders to assess and strategise their integration pathways.
  Keywords: application oriented universities; industry education integration; influencing factors; cross border aviation logistics.
  DOI: 10.1504/IJADS.2027.10078707
   
  
• A job-shop scheduling optimisation model to reduce work-in-process inventory and machine setup time: a case study in a plastic packaging manufacturing  
  by Hong-Phuc Nguyen, Doan Thi Yen Nhi, Dang Ky Tan 
  Abstract: This study addresses the production scheduling problem in job-shop environments, where demand fluctuations and resource constraints frequently lead to excessive work-in-process (WIP) inventory and delivery delays. Current methods often overlook critical factors, such as technical waiting times, WIP holding costs, and sequence-dependent setup times, resulting in inefficient resource utilisation. To solve this problem, a job-shop scheduling optimisation model is developed and solved by a genetic algorithm. The model minimises WIP inventory and machine setup times while ensuring on-time delivery, incorporating realistic production constraints. The proposed model is validated in the case of a plastic packaging manufacturing system. Experimental results demonstrate that the proposed model outperforms current methods. The WIP holding time, makespan, and setup time are reduced by 35.8%, 11%, and 4.9% respectively, and late deliveries are eliminated. These findings highlight the potential of the model and solution approach to enhance production efficiency in the job-shop environment.
  Keywords: job-shop scheduling; WIP holding time; sequence-dependent setup times; genetic algorithm.
  DOI: 10.1504/IJADS.2027.10079410
   
  
• Optimising hydrogen production from supercritical water gasification of biomass and polymer waste: a machine learning-driven approach with differential evolutionary optimisation  
  by Mingyu Cao, Zhe Zhang 
  Abstract: Hydrogen production from biomass (polymer waste) supercritical water gasification is a sustainable pathway to response the contamination of Earth’s environment and the scarcity of non-renewable energy. Given the time-consuming and costly natures of physical method and success application of the machine learning (ML) technique in prediction of hydrogen yield form gasification process, this study develops a novel ML-based approach. This work proposed a novel ensemble-based Bagging method and compared its predictive capabilities with individual histogram-based gradient boosting, light gradient boosting machine, stochastic gradient descent, lasso, and linear support vector regressor. To achieve improved stability, and accuracy each of the models are optimised using the particle swarm optimisation metaheuristic optimiser. The predictive performance of this ensemble-based Bagging is validated against each base optimised individual model, which indicated that the Bagging ensemble model with the highest R² value of 0.999 and lowest RMSE of 0.366 during test set has considerable predictive potential. In addition, the study integrates a SHAPbased sensitivity analysis for Bagging model, which identify the catalyst concentration, carbon content and biomass concentration as the most dominant parameters in determining H₂ yield production.
  Keywords: biomass; polymer waste; hydrogen; supercritical water gasification; machine learning; ML.
  DOI: 10.1504/IJADS.2027.10079936
   
  
• Product sales strategy selection for firms in the context of smart connectivity  
  by Yan Chu, Junfeng Dong, Zhiwen Yuan, Qiman Zhang 
  Abstract: Rapid advancements in IoT and cloud computing are transforming traditional products into smart connected bundles. Despite enhancing consumer value, high R&D costs require firms to strategically evaluate product conversion and sales models.This study investigates sales strategy selection and pricing decisions under heterogeneous consumer preferences. By developing decision models for three distinct strategies, we identify optimal pricing and service levels for smart bundles. Findings reveal that R&D cost coefficients, traditional product prices, and maximum perceived value are critical determinants of strategy selection. Notably, specific price and value thresholds can render certain strategies infeasible, regardless of R&D efficiency. These results provide a strategic framework for firms navigating the transition from traditional to smart connected offerings.
  Keywords: consumer heterogeneity; smart connected products; sales strategy; consumer purchase behaviour.
  DOI: 10.1504/IJADS.2027.10080394
   
  
• Intelligent screening and funding decision optimisation of college students’ innovation and entrepreneurship projects based on counterfactual reasoning of structural causal model  
  by Wenqi Fan, Ling Ji, Zhenlin Luo 
  Abstract: This study addresses inefficiencies in the management of college students’ innovation and entrepreneurship projects, particularly resource mismatch and weak adaptability to dynamic demand. It proposes an intelligent decision-making system that integrates structural causal models with counterfactual reasoning. By constructing a multimodal causal graph, the system captures complex relationships among project features, policies, and environments, while a dynamic demand drift detection mechanism enables real-time adaptation. A counterfactual reasoning engine is developed to simulate intervention effects and improve decision foresight, supported by a multi-agent collaborative framework for optimal resource allocation. Empirical results demonstrate that the system significantly enhances project identification accuracy, funding efficiency, and decision interpretability compared to traditional methods. It also shows strong robustness and adaptability across diverse scenarios. The research provides an effective data-driven tool for innovation and entrepreneurship management and offers methodological insights for intelligent decision systems in related domains.
  Keywords: structural causal model; counterfactual reasoning; innovation and entrepreneurship projects; intelligent screening; funding decision optimisation.
  DOI: 10.1504/IJADS.2027.10080705
   
  
• Supplier selection in the defence industry with sustainable criteria using Pythagorean fuzzy MCDM methods  
  by Merve Asilogullari Ayan, Beste Desticioglu Tasdemir 
  Abstract: Supplier selection in the defence industry is vital due to the importance the sector attaches to sensitive technologies' security, reliability, and protection. Selecting the right suppliers can ensure that defence industry supply chains are resilient to disruptions that can seriously impact military readiness, such as geopolitical tensions. Sustainability is essential in supplier selection in the defence industry as it ensures long-term operational efficiency, environmental responsibility, and social impact. Therefore, in this study, the researchers examined the problem of sustainable supplier selection in the defence industry in Turkey. Besides, they compare suppliers using the integrated PFAHP-PFTOPSIS method. As far as is known, this study is the first to use the PFAHP-PFTOPSIS method in supplier selection in the defence industry. The study determined the weights of 13 criteria to be used in comparison with the PFAHP method. The researchers compared seven defence industry companies operating in Turkey with the PFTOPSIS method. In the last part of the study, the researchers also developed a method to compare with other methods and performed sensitivity analysis.
  Keywords: defence industry; sustainability; sustainable supplier selection; PFAHP; PFTOPSIS.
  DOI: 10.1504/IJADS.2026.10074100
   
  
• Queueing models and inventory systems integration to address customer orbiting behaviour in service environments  
  by Karzan Mahdi Ghafour 
  Abstract: The demarcation explains a service system organised for customer orders without their presence during service delivery, allowing optimisation of customer time through orbiting. Requested services are stored in inventory space and retrieved later at customer convenience. If unavailable, customers may sample more orbits until ready, hence the term 'apparently-orbiting-while-the-service-is-taken'. The article discusses the queueing inventory multiple orbits (QIMO) system, elucidating operational features via the matrix geometric method. It also presents an economic evaluation based on probabilistic analysis to judge cost-benefit and efficiency. The intricate analysis of models and economic investigations extends knowledge about service systems with orbital behaviour, describing principles and ramifications. It contributes to managing service systems by explaining operations and evaluating economic viability, stressing 'single orbit' and 'multiple orbits' policies. Findings help practitioners and scholars optimise service delivery processes and enhance customer experiences in dynamically changing service environments.
  Keywords: queueing theory; inventory system; scheduling; orbit time.
  DOI: 10.1504/IJADS.2026.10074098
   
  
• An entropy-based resource allocation method for minimising project uncertainty under time, budget, and quality constraints  
  by Chiu-Chi Wei, Meng-Ling Chang 
  Abstract: Managing uncertainty and resource allocation is a critical challenge in modern project management. Projects often face constrained resources, complex interdependencies, and unpredictable variables that can result in delays, cost overruns, and compromised quality. Traditional resource allocation methods typically emphasise scheduling and levelling but do not adequately address systemic uncertainty, also known as project entropy. This study introduces a novel mathematical model that quantifies project entropy as a function of both resource allocation and activity uncertainty. The model enables the allocation of multiple resource types in a way that minimises entropy and enhances project predictability, efficiency, and quality. A case study was conducted to validate the model, demonstrating its effectiveness in allocating resources toward high-risk activities and reducing uncertainty without exceeding time or budget constraints. This approach not only advances theoretical understanding of project entropy but also provides practical tools for project managers to systematically address uncertainty and optimise resource use throughout a project's lifecycle.
  Keywords: project entropy; resource allocation; project uncertainty; mathematical model.
  DOI: 10.1504/IJADS.2026.10074303
   
  
• Evaluating management efficiency in the banking sector: an integrated MCDM framework  
  by Priya Das, Subir Kumar Sen 
  Abstract: This study evaluates management efficiency in Indian public and private sector banks from 2018-2019 to 2023-2024 using an integrated multi-criteria decision-making (MCDM) approach. It employs a comprehensive approach, utilising the CRITIC method for objective weighting of eight efficiency attributes and the MARCOS method to rank 33 banks. To ensure the robustness of the model, a sensitivity analysis is conducted using entropy, MEREC, and equal weighting. The Mann-Whitney U test showed no significant efficiency differences between public and private banks, while the Friedman test highlighted significant changes in overall efficiency over time. The HDFC Bank consistently emerged as the top performer, while Bank of Maharashtra demonstrated significant improvement. The high Spearman rank correlations (ρ > 0.90) across various methods confirm a strong level of agreement and reliability. The study proposes an Integrated Efficiency Adaptation Model that merges concepts of X-efficiency, institutional theory, financial intermediation, dynamic capabilities, and decision theory.
  Keywords: banks; management efficiency; CRITIC; MARCOS; entropy; MEREC.
  DOI: 10.1504/IJADS.2026.10075209
   
  
• The impact mechanism of scenario-driven and AI-enabled approaches on the competency of cross-border air logistics talent  
  by Yongchuang Zhang, Gai Hang 
  Abstract: China-ASEAN economic cooperation and AI advancement are reshaping cross-border air logistics, driving demand for interdisciplinary talent. This study examines how 'scenario-driven' and 'AI-enabled' approaches enhance talent competency. Based on situated learning and technology enablement theories, a dual-path mediation model was tested using mixed methods: grounded theory analysis identified key scenarios and AI technologies, followed by a survey of 268 students analysed via structural equation modelling. Results show that both approaches directly improve competency and indirectly enhance it through practical ability and innovative thinking, with AI-Enabled approaches having a stronger total effect. The study supports a progressive, integrated teaching model and offers theoretical and practical insights for constructing a deeply integrated 'scenario-AI-capability' talent cultivation model in digital-era logistics education.
  Keywords: scenario-driven; AI-enabled; talent competency; cross-border air logistics; structural equation modelling; SEM.
  DOI: 10.1504/IJADS.2027.10078092