International Journal of Applied Decision Sciences (38 papers in press)

Regular Issues

• Toward a CNN-based Approach for Usability Re-quirements Generation  
  by Dorra Zaibi, Riadh Ksantini, Meriem Riahi, Faouzi Moussa 
  Abstract: The explosion of data science in many sectors of technology combined with the enormous rise of deep learning techniques in the last decade has resulted in new automation applications. The most prevalent deep learning architecture is the convolutional neural network (CNN) which has been widely applied for face recognition and various applications, has recently emerged as an effective and potentially useful tool for feature extraction. Unfortunately, human factors engineering (HFE), also known as usability engineering, which is concerned with interactive systems, often has limited attention and awareness of deep learning applications and is therefore unable to provide the important breakthroughs that are required to ensure the success of these emerging interactive systems. This article addresses the issue by exploring the generation of consistent usability requirements based on deep CNN models to enable accurate classification in context-aware environments. Our methodology has been tested and performance analysis is carried out through a case study.
  Keywords: human-computer interaction; HCI; usability; context-aware environment; deep learning; convolutional neural network; CNN.
  DOI: 10.1504/IJADS.2024.10055796
   
  
• A comparative investigation to determine whether or not to maintain strategic inventory for a two-period green supply chain model using the game theory approach  
  by Animesh Mondal, Ranjan Jana, Dipak Jana 
  Abstract: In this research paper, we have developed a green supply chain (GSC), a novel model with a retailer and a manufacturer where the manufacturer produces green products and sells them to the customers by offering quantity discounts via the retailer. Based on four-game structures, viz., manufacturer Stackelberg (MS), retailer Stackelberg (RS), Nash and cost-sharing (CS), we have explored the comparative analysis to determine whether to maintain strategic inventory or not to maintain strategic inventory for a given two-period GSC. The analysis outcome may be beneficial to decide which power strategy supply chain players will adopt and maintain strategic inventory for better profit. A total of eight analytical models have been derived and explored. The novel demand function depends on the retail price, green level and quantity discount. At last, numerical examples and sensitivity analysis are provided to understand the better model comparison. The optimal results are computed in terms of closed-form solutions and displayed graphically. This study indicates that the preference is highly responsive to the game structure.
  Keywords: green supply chain; GSC; game theory; quantity discount; strategic inventory; cost sharing.
  DOI: 10.1504/IJADS.2024.10056089
   
  
• Risk Management and Project Performance: An Examination of Construction Industry during COVID-19 Pandemic and Future Prospects  
  by Xuhui Han 
  Abstract: The scope of risk management concerning to firm’s performance has widely experimented over the past decades by researchers. Nevertheless, how risk management practices (RMP) may optimise project performance such as construction projects during disastrous times like COVID-19 got far less consideration from developing nations. This study investigates the relationships of risk management practices within three streams, i.e., operational risk (OR), financial risk (FR), and political risk on project performance within the Chinese construction industry through an empirical approach. Using structural equation modelling along with confirmatory factor analysis, the study affirmed a positive connection between risk management and project performance based on 328 sample size (n = 328) obtained from project managers. In addition, the study found a positive nexus between each dimension of risk management and project performance, i.e., OR, FR and PR. This study endows with considerable applications for project managers along with strategies for future challenges. Besides, future research possibilities are recommended to scholars of the world with consideration of the current limitations.
  Keywords: risk management; operational risk; financial risk; political risk; project performance; COVID-19; construction industry.
  DOI: 10.1504/IJADS.2024.10056090
   
  
• Currency Crisis Early Warning Signal Mechanisms Based On Dynamic Machine Learning  
  by Omur Saltik, Wasim Ul Rehman, Bahadir Ildokuz, Suleyman Degirmen, Ahmet Sengonul 
  Abstract: The foremost objective of the study is to reveal that if credit default swap (CDS) is an early warning signals for currency crises by considering stock and flow variables such as external debt stock and reserves (foreign currency and gold) under free exchange rate regimes. As an original aspect of the study, which differs from other studies, is the machine learning methods used and the inclusion of both one lag and lag values of the cds variable, which is an inclusive crisis indicator, into the model. The CDS variable was not detected as a strong signal by the logistic regression model. However, the best-performing XGBoost and GB algorithms show the differenced, and one-lagged values of the CDS variable produce significant signals in forecasting currency crises. Consistent with theoretical underpinnings of study on currency crises, this implies that central banks proactively reacted by increasing monetary policy interest rates and the non-current value CDS but its lagged value performed strong early warning signal that is a follower or supplementary indicator of the credibility of monetary authorities and policies. These results demonstrate that the high and rising interest rate signifies that domestic currencies are being supported against speculative attacks.
  Keywords: currency crisis; currency pressure index; credit default swap; CDS; panel logistic regression; machine learning classification.
  DOI: 10.1504/IJADS.2024.10056204
   
  
• Exploring intention to use cryptocurrencies payment across age groups and gender: a multi-stage machine learning application on the extended UTAUT model  
  by Dong L. Tong, P.C. Lai, Ewilly Jie Ying Liew 
  Abstract: Given the increasingly widespread use of smartphones for cashless payment, the potential for using cryptocurrencies as an alternative e-payment asset becomes attractive. Promoting blockchain-based cryptocurrencies for e-payment faces challenges in payment conversion latency, where hard-earned crypto coins are not a recognised asset for actual payment. This study investigates users’ intention to use cryptocurrencies in daily e-payment transactions. A multi-stage artificial intelligence-based analysis pipeline was employed to identify key factors of crypto-based e-payment usage and model the interaction between these factors. Experiments were conducted based on different target criteria, and factors corresponding to these target criteria were evaluated. Results show that platform reliability, improved usefulness, user-friendliness, and self-efficacy were directly associated with users’ intention to use crypto coins for e-payment services. Age and gender differences were also evaluated across factors affecting users’ intentions to use the new technology. Implications for management and cryptocurrency coin providers were discussed.
  Keywords: blockchain; cryptocurrency coin; e-payment; artificial intelligence; genetic algorithm; artificial neural network; ANN; decision tree.
  DOI: 10.1504/IJADS.2024.10056311
   
  
• A comparative study of interval forecasting using GARCH models under symmetric and asymmetric distributional assumptions  
  by Zhe Zhang, CHOO W.E.I. CHONG, Jayanthi Arasan 
  Abstract: Interval forecasting provides decision-makers with a range of possible future values, along with associated probabilities, which allows for a more informed decision-making process. Although GARCH models under different distributional assumptions are commonly compared for their volatility forecasting performance, their performance in interval forecasting is rarely discussed. This study aims to fill this gap by comparing the interval forecasting accuracy of GARCH models under symmetric and asymmetric distributions. SGARCH, EGARCH, and GJR-GARCH models under normal, student-t, GED distributions, and their skewed extensions are applied for one-day-ahead rolling interval forecasting on five major European and American stock indices: S&P 500, FTSE 100, CAC 40, DAX 30 and AEX. The average Winkler score (AWS) is used to measure the accuracy of interval forecasting. The conclusions of this study can be summarised as follows: In pairwise comparisons, the GARCH models under asymmetric distributional assumptions have better interval forecasting accuracy than the GARCH models under symmetric distributional assumptions. In comparisons among GARCH-type models, GJR-GARCH has better interval forecasting accuracy than SGARCH and EGARCH, while SGARCH and EGARCH exhibit similar interval forecasting performance.
  Keywords: comparative study; interval forecasting; GARCH models; symmetric distributions; asymmetric distributions; distributional assumptions; conditional variance; average Winkler score; AWS.
  DOI: 10.1504/IJADS.2024.10056334
   
  
• Research on influencing factors of value co-creation in product service system development based on Z-DEMATEL-ISM model  
  by Yang Xu, Xiuli Geng 
  Abstract: Product service system (PSS) is an integrated set of products and services with value co-creation as the core. Therefore, a profound understanding of factors and interrelationships that influence value co-creation in PSS development is essential for manufactures. This paper proposes a systematic framework to analyse the influencing factors of value co-creation in PSS development. First, 22 factors are identified through the extensive literature and identification of a panel of experts. Second, decision-making trial and evaluation laboratory (DEMATEL) is adopted to identify the interactions between readiness factors, while Z number is introduced to capture the ambiguity and uncertain information in the decision-making process. Finally, a six-level hierarchical structure is determined based on interpretive structural model (ISM) to demonstrate the complex interrelationships among factors. The proposed framework is illustrated by taking the case of PSS value co-creation in Haier Group. The results indicate intelligent level being the most important factor of value co-creation in PSS development , followed by innovation potential and customer attraction. This finding could help manufacturers who aim for value increment to take appropriate steps to realise value co-creation of PSS.
  Keywords: product service system; PSS; value co-creation; influencing factors; Z number; DEMATEL.
  DOI: 10.1504/IJADS.2024.10056372
   
  
• Applying Data Science to gauge Virtual Assistants’ impact on students' well-being during the pandemic  
  by Swapnil Morande 
  Abstract: The research offers insights into the impact of the pandemic on students and ruminates on mitigating the negative consequences by using a virtual assistant (VA). In an experimental setting, the study treats 'stress' as a critical factor that relates to students' well-being. The research is exploratory, where mixed-mode data is captured from students using a questionnaire and integrated virtual assistant simultaneously. The research findings establish the role of a virtual assistant to support preventive, predictive, and personalised health. It illuminates heart rate variability as one of the key indicators of perceived stress. As this research is based on the extensive literature on a method called 'photoplethysmography', it can further be scaled for supporting large groups of students. The outcome of the study can be further extended to industries where stress might be detrimental to well-being. The study contributes to the innovative application of data science to reflect on students' well-being.
  Keywords: virtual assistant; COVID-19; machine learning; DeepNet modelling; well-being; data science.
  DOI: 10.1504/IJADS.2024.10056392
   
  
• Cognitive Technology in Human Capital Management: A Decision Analysis Model in Banking Sector during COVID-19 Scenario  
  by Senthil Kumar, Joji Abey, Seranmadevi R, Shefali Srivastava, Ashish Dwivedi 
  Abstract: Cognitive technologies are products of artificial intelligence (AI) domain which execute tasks that only humans used to perform. The impact of cognitive technologies on the management of human capital (HC) has a massive effect in the banking sector. This paper objectives to study the transformation of cognitive technology to human capital management (HCM) in the banking sector during the COVID-19 pandemic. The study draws data from 201 bank employees working in private, public, and foreign banks using a multi-stage sampling method in India. A number of hypotheses were framed and tested using multivariate and regression analyses. The results from the study indicate a significant change in the performances of bank employees statistically during the transformation of cognitive technologies. Cognitive technologies such as payment, product customisation, self-services, workload alleviation, automated back-office function, and a personalised experience significantly contribute to the HCM.
  Keywords: artificial intelligence; AI; banking; cognitive technologies; COVID-19; human capital management; HCM; machine learning; ML.
  DOI: 10.1504/IJADS.2024.10057610
   
  
• Pricing strategies in a risk-averse dual-channel supply chain with manufacturer services  
  by Zhenhua Yang, Lin Huang, Yu Xia, Jian Guo Liu 
  Abstract: This paper studies a dual-channel supply chain consisting of one risk-averse manufacturer and one risk-averse retailer with stochastic demand. Herein, the manufacturer provides value-added services to enhance channel demand. First, the optimal pricing and service decisions of the channel members are investigated under different settings, i.e., the cooperative game, Bertrand game, and manufacturer Stackelberg (MS) game models. Second, the effects of channel members’ risk aversion on optimal channel prices and expected utilities are analysed under the assumption that the manufacturer service is a decision variable and an exogenous variable, respectively. Third, sensitivity analysis and numerical simulation are performed to verify our propositions consistently and seek more managerial implications. The findings suggest that the manufacturer’s value-added services in his direct channel will improve the direct price while decreasing the retail price. Consumers’ channel loyalty degree has a great influence on the optimal price decisions and the performance of the channel members. The direct price increase while the retail price decrease in the manufacturer’s value-added services. The retailer’s risk aversion has a greater influence on her own price decisions than that of the manufacturer.
  Keywords: dual-channel supply chain; risk aversion; value-added services; Bertrand game; Stackelberg game.
  DOI: 10.1504/IJADS.2024.10057611
   
  
• A Novel IoT-based Arrhythmia Detection System with ECG Signals Using a Hybrid Convolutional Neural Network and Neural Architecture Search Network  
  by Umit Senturk, Ceren Gülen, Kemal Polat 
  Abstract: Electrocardiogram (ECG) signals are the most common tool to evaluate the heart’s function in cardiovascular diagnosis. Irregular heartbeats (Arrhythmia) found in the ECG play an essential role in diagnosing cardiovascular diseases (CVD). In this paper, we proposed an arrhythmia classification method with the neural architecture search network (NASNet) model, an optimised version of the convolutional neural networks (2D-CNN) model, which performs very well in visual information analysis classification. We aimed to use the arrhythmia classification problem in IoT and mobile devices with the NASNet model with low parameter numbers and processing capacity without performance loss. 2D input data have been obtained by converting the classified heart rate signals in the datasets into image files. The 2D data obtained have been classified by machine learning, CNN, and NASNet models. As a result of classification, 2D CNN accuracy was 97.51%, and the NASNet model was 96.89% accuracy. As a result of arrhythmia classification, the accuracy rates of the NASNet and the 2D CNN models were close. In conclusion, the proposed IoT-based arrhythmia detection system with ECG signals using a hybrid CNN and NASNet is a promising tool for the early detection of arrhythmias. Furthermore, it could help to reduce the mortality associated with these potentially fatal conditions.
  Keywords: arrhythmia detection; neural architecture search network; NASNet; IoT; network optimisation; time series; real-time measurement.
  DOI: 10.1504/IJADS.2024.10057677
   
  
• Channel competition, manufacturer incentive and supply chain coordination  
  by Zhi Pei, John Wang, Barbara Ross Wooldridge, Jie Sun 
  Abstract: COVID-19 created a surge in e-commerce usage, leading to fierce channel competition between the manufacturer’s online sales and the offline retailer. Hence, the imperative need for effective and innovative optimisation strategies to mitigate channel competition. Manufacturer-coupons are widely practiced in market, yet research on the importance they play in coordinating channel competition to achieve optimisation in channel distributions is scarce. This research addresses this gap by examining the effectiveness of manufacturer-coupons on the coordination of the manufacturer’s online sales and offline retailer’s sales. The findings indicate that issuing a manufacturer-coupon to the customers who buy from the offline retailer reduces the competition in the different channel distributions, but cost sharing of the retailer coupon is a better strategy. We thus examine if profit sharing is an effective strategy to facilitate the use of manufacturer-coupon in the market. After comparing different scenarios, we conclude that advanced profit-sharing can be effective in making manufacturer-coupon prevalent in the market and thus alleviate channel competition effectively.
  Keywords: e-commerce; channel competition; manufacturer-coupon; coordination strategies; game theory.
  DOI: 10.1504/IJADS.2024.10058177
   
  
• A DEA-based decision framework for performance evaluation and ranking of workers in a real case from food industries  
  by Sadegh Niroomand 
  Abstract: In this study, a performance evaluation problem of operational employees in a real case study is considered. The case study is a firm from food industries where different tomato pastes are produced there. In addition, further than performance evaluation, in this study the operational employees are ranked based on their performances. As the main contribution of this study, a real-world problem from food industries is focused and solved by the optimisation-based approaches. For this aim, the most important criteria from the literature such as salary, work conditions, responsiveness, motivation, and productivity are selected. A decision framework based on data envelopment analysis (DEA) is used. For this aim, the Russel DEA model is used as the first stage to evaluate the performance of each operational worker (DMU) by determining efficient and inefficient DMUs. As the second stage, an Anderson-Peterson approach based on a modification of the Russel model is proposed to rank the DMUs based of their performances. Finally, based on the obtained results, some comparisons with the other DEA models of the literature is performed and the managerial insights are presented.
  Keywords: food industry; performance evaluation; performance ranking; data envelopment analysis; DEA; Russel model.
  DOI: 10.1504/IJADS.2024.10058382
   
  
• A new model for efficiency estimation and evaluation:DEA-RA-Inverted DEA model  
  by B.-J. Zhang, Shi-Lan Su, Qizhou Gong 
  Abstract: Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.
  Keywords: data envelopment analysis; DEA; inverted data envelopment analysis; inverted DEA; regression analysis; efficiency evaluation; sample decision unit; preference cone.
  DOI: 10.1504/IJADS.2024.10058443
   
  
• A Fuzzy-probabilistic Bi-objective Mathematical Model for Integrated Order Allocation, Production Planning, and Inventory Management  
  by Solikhin Solikhin, Sutrisno Sutrisno, Abdul Aziz, Purnawan Adi Wicaksono 
  Abstract: An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.
  Keywords: decision-making support; fuzzy parameter; order allocation; probabilistic parameter; production planning; uncertain programming.
  DOI: 10.1504/IJADS.2024.10058528
   
  
• Behavioural Finance Research and Knowledge Mapping: A Comprehensive Bibliometric Analysis from 2010 to 2022  
  by Anshita Bihari, Manoranjan Dash, Padma Charan Mishra, Sukumar Dash 
  Abstract: The study aims to explore the patterns and connections in the behavioural biases and investment decisions of existing literature on the Web of Science database using Science mapping and performance analysis tools. This study selected 512 research papers from the Web of Science database published between 2010 and 2022 after deep screening all influential authors with their citations exposed along with top journals. The pattern of the papers highlighted and the connection between literatures gives direction for future research. Publication on behavioural biases and investment decisions increased since 2016. The Journal of Behavioural Finance is leading in published documents, the Journal of Financial Economics has the highest citation count, and the USA is the top country in publications and citations. The outcome of this study provides valuable insights into the intellectual structure of biases of investors and adds value to the existing knowledge. This review offers knowledge and theories for the behavioural finance discipline and provides a road map for the future trend of research on behavioural biases and investment decisions.
  Keywords: knowledge mapping; behavioural biases; investment decision; bibliometric analysis.
  DOI: 10.1504/IJADS.2024.10058529
   
  
• Does the optimal model always perform the best? A combined approach for interval forecasting
  by Zhe Zhang, CHOO W.E.I. CHONG, Jayanthi Arasan 
  Abstract: Interval forecasting is widely applied by decision makers for it can provide more comprehensive information. In the literature, GARCH models under different distributional assumptions are applied and evaluated to find the optimal interval forecasting model for the experimental data. However, the optimal model selected based on sample data from a specific period may not always perform the best in future periods. Therefore, this study employs GARCH models based on different distributional assumptions for interval forecasting of the daily return data of the Nasdaq Composite Index. The results show that the forecasting performance of some models exhibits significant differences across different periods. To address this issue, this study proposes a Monte Carlo-based non-parametric interval forecasting combination method. The results demonstrate that this method can effectively avoid the risk of forecasting inaccuracies caused by relying on a single model.
  Keywords: interval forecasting; optimal model; combined approach; GARCH model; distribution assumptions; Monte Carlo.
  DOI: 10.1504/IJADS.2025.10058959
   
  
• Sentiment Analysis on Stocks: A Hybrid Feature Extraction Technique on Fourteen Classifiers
  by MEERA GEORGE, R. Murugesan 
  Abstract: Accurately predicting stock prices is challenging and has garnered massive attention from researchers and investors alike. Though the literature has shown sentiment analysis as a promising approach for efficient stock price prediction, it has found a considerable gap in studies using multiple feature extraction techniques with hybrid models for the efficient sentiment classification. Under these circumstances, this study aims to perform sentiment analysis using five feature extraction techniques including a hybrid and 14 classifiers for the accurate classification of stock tweets. The study extracted 21121 tweets spanning March 2022 to December 2022 using Twitter application programming interface. The empirical result shows the superiority of the hybrid feature extraction technique over the other methods. The support vector machine classifier with a hybrid feature extraction technique is found to be the best-performing sentiment analysis model for Twitter stock data. The study has potential applications in building optimal investment strategies and decision-making.
  Keywords: stock price; sentiment analysis; classifiers; feature extraction; hybrid.
  DOI: 10.1504/IJADS.2025.10059408
   
  
• Environmental Disclosure and Firm Performance: Current State and Future Avenues
  by Rubina Michela Galeotti, Daniela Cicchini, Fabiana Roberto 
  Abstract: This study aims to review the literature on environmental disclosure and firm performance in order to show current trends, challenges, and adventures for future research. We attempted a systematic and structured literature review of 67 studies published in 2001-2023 using the accounting journal in the Chartered Accounting Business School (CABS) ranking. The analysis revealed three main research streams: 1) firm performance, CSR and sustainability; 2) accounting and environmental disclosure; 3) board of directors and decision-making process in the field environment and firm performance. The relevance of this literature review is due to understanding the state of the art between environmental disclosure and firm performance. Through our work, we highlight the current and future issues in the firm performance and environmental disclosure. This paper supports future research and it is directed to academics, practitioners, policy makers and decision makers.
  Keywords: environmental disclosure; firm performance; accounting; sustainability.
  DOI: 10.1504/IJADS.2025.10059810
   
  
• Predictors of purchase intention in gastronomic establishments in the city of Medellin
  by Rodolfo Casadiego, Isabella Marín-Cuartas, Cristian Santiago Toro-Herrera, Alejandro Silva-Cortés, Marianella Luzardo-Briceño 
  Abstract: The pandemic caused by COVID-19 adversely impacted the gastronomy sector, affecting company-customer interactions. This research aimed to identify the variables influencing purchase intentions within Medellin’s gastronomic establishments, for this, a quantitative exploratory approach was conducted, and 418 consumers were surveyed. Data analyses were conducted via RStudio, entailed confirmatory factor analysis for construct validation and structural equation modelling to examine causal relationships. As results factors like hedonic value, user-generated content, perceived risk, corporate image, perceived quality, attitude towards the restaurant, and restaurant type were assessed for their impact on purchase intention. Notably, while the perceived quality was statistically significant, it is inversely correlated with purchase intention, implying a diminished inclination to dine upon perceiving quality decline. These insights provide a comprehensive understanding of consumer decision making during the pandemic, facilitating strategies for optimal market positioning amidst evolving demographic-cultural dynamics and business chaos. This assessment has broader implications than the gastronomic domain.
  Keywords: hedonic value; perceived quality; purchase intention; restaurant type; structural equation modelling; SEM.
  DOI: 10.1504/IJADS.2025.10060198
   
  
• A Deep Learning Approach Using Modified Xception Net for Oral Malig-nancy Detection Using Histopathological Images of Oral Mucosa  
  by Madhusmita Das, Rasmita Dash 
  Abstract: The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In second stage, loss and accuracy graphs analysis is done and in the third stage, proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%.
  Keywords: Xception net; histopathological oral image; deep learning; swish activation function; oral cancer.
  DOI: 10.1504/IJADS.2024.10060616
   
  
• Optimal Planning of Electric Vehicle Charging Facilities Considering Demand Stimulus Effects
  by Yongzhong Wu, Zhi Jie Zhu, XIANGYING CHEN, Huihui Chen 
  Abstract: The development of electric vehicle charging facilities can positively influence user demand and demand landscape, a factor often neglected in existing models that focus on current demand and organic growth. We present models for determining the quantity and location of charging facilities while considering their impact on future demand landscape. Employing a gravity model, we analyse the interaction between charging station network planning and future demand. We develop an optimisation model for charging facility planning, aiming to minimise total social costs. Our proposed solution employs the weighted Voronoi polygon graph algorithm. Through a case study, we demonstrate the significance of the proposed model by comparing the solution obtained that considers the impact of charging network construction on user demand with a solution that neglects this stimulus. The results underscore the importance of incorporating user demand stimulus in infrastructure planning, providing valuable insights for electric vehicle charging facility investors and operators.
  Keywords: electric vehicles; charging facilities; facility siting; gravity model; total social costs.
  DOI: 10.1504/IJADS.2025.10060814
   
  
• Reliability optimization of supply chain system based on seru production  
  by Zhe Zhang, Yalin Li, Lili Wang 
  Abstract: This paper focuses on the manufacturers implementing seru production, and constructs an integer programming model aiming at maximising the reliability of supply chain system based on considering the supplier’s on-time supply rate of parts, worker assignment, and production plan comprehensively. In view of the complexity of proposed problem, a hybrid search strategy is designed by combining genetic algorithm (GA) and variable neighbourhood search (VNS), i.e., a hybrid genetic algorithm with variable neighbourhood search (HGAVNS) algorithm based on the multi-level encoding method. To verify the advance of proposed HGAVNS algorithm, the comparison with GA and VNS is presented, and experimental results show that HGAVNS performs better in solving proposed problem.
  Keywords: seru production; supply chain reliability; genetic algorithm; variable neighbourhood search; hybrid algorithm.
  DOI: 10.1504/IJADS.2024.10060920
   
  
• An MINLP Model for Project Scheduling with Feeding Buffer  
  by Ashkan Ayough, Masood Rabieh, Mohammad Naeem Javadi, Behrooz Khorshidvand 
  Abstract: This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm’s performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed.
  Keywords: critical chain project scheduling; CCPS; feeding buffer; genetic algorithm.
  DOI: 10.1504/IJADS.2024.10060921
   
  
• Development trust among customer and producer Inventory Model, when demand dependent on selling price with shortage under the environment of uncertainty
  by S. V. Singh Padiyar, Kanchan Joshi, S. Pundir, Dipti Singh 
  Abstract: Mostly it is observed that when individual inventory model is given importance then demand is either constant or time dependent but at the same time selling value of inventory cannot be ignored. Generally. For many consumer products, it is observed that the selling price of an item greatly influences the rate of consumption. Due to which the demand of the product may vary with the selling price, also it is necessary to check the reliability of the inventory maintained during production. And when the demand of the product depends on the selling price and is favorable to the customer, then the possibility of shortage is also high. Therefore, it is necessary to give importance to the impact of selling price and shortage while developing the inventory model. Along with this many types of uncertainties arise during the production and to overcome these uncertainties the model is developed by making fuzzy model and graded mean representation is used to deform the total cost function of the system.
  Keywords: Imperfect production; selling price demand; time dependent deterioration; shortage; fuzzy environment.
  DOI: 10.1504/IJADS.2025.10061130
   
  
• Triple Voting: Hybrid Cardiovascular Diseases Prediction Model
  by Dahlak Daniel Solomon, Karan Aggarwal, Sonia Sonia, Kushal Kanwar, Kemal Polat 
  Abstract: Currently, cardiovascular diseases are a high-risk cause of death in both developed and developing countries. Thus, heart disease prognosis has received substantial interest in the medical field worldwide. The incidence of heart disorders is escalating at an alarming rate, and it is crucial and worrisome to anticipate their occurrence. Predicting and detecting cardiovascular disease using machine learning and data mining might be clinically useful, but difficult. There are numerous machine learning algorithms accessible, several studies have developed machine learning algorithms for early cardiac disease prediction to help physicians suggest medical treatments. The accuracy of the model will be evaluated to determine whether the performance of the model is accurate or not. Seven machine learning methods are compared in this study, with the data obtained from the UCI Laboratory’s cardiovascular patient database. In essence, this research presents a majority voting-based hybrid model which is called triple voting. The hybrid model uses voting of Naive Bayes, logistic regression (LR) and support vector machines (SVM) experimental outcomes show the proposed triple voting model’s accuracy is 89%, which is higher than the individuals and other proposed hybrid models.
  Keywords: cardiovascular disease; machine learning; majority voting; ensemble learning.
  DOI: 10.1504/IJADS.2025.10061195
   
  
• An Inventory Model with Piecewise Cost Functions and Multivariate Deterioration  
  by Shilpy Tayal 
  Abstract: The study highlights that the temperature of surroundings affect the rate of deterioration and deterioration is a major parameter to check the quality of any product. Here an inventory model for routine items with time and temperature dependent deterioration and piecewise cost functions has been developed. Combining the above mentioned factors together and considering all possible cases of temperature the optimal values of total average cost has been discussed at different points in the assumed range. With the help of numerical example it is concluded that total average cost is minimum at that temperature value which is feasible for that particular item. With numerical analysis the optimality of the system in all the three cases has also shown graphically. Further to verify the system stability, sensitivity analysis has been performed and the system is found to be quite stable.
  Keywords: Temperature and Time Dependent Deterioration; Piecewise Cost Functions; Demand; Inventory; Shortages; Partial Backlogging; Lost sale.
  DOI: 10.1504/IJADS.2025.10061559
   
  
• A Financial Auditing Approach Using Failure Effect Mode Analysis  
  by Saeed Askary, Davood Askarany, Yusuf Joseph Ugras 
  Abstract: This paper proposes adopting Failure Effect Mode Analysis (FMEA) in audit procedures in order to improve the prediction of corporate failures and improve reliance on financial reports. It is well known that corporate failures adversely affect the accounting profession’s reputation, public interest, social costs, capital markets, and the national financial and monetary economic system, causing wasted resources by companies and losses by shareholders. In this article, we incorporate various recommendations of Brydon (2019) by suggesting the use of FMEA to strengthen the auditing profession. We are focusing on fundamental financial measures of liquidity and profitability, intending to increase the reliability of going concern issues for the company being audited. This article proposes the FMEA model for a substantial audit reform. FMEA adoption can prevent corporate failures in the future through the proposed model. This research is helpful to standard setters, managers, auditors, and governmental agencies and, finally, can protect the public interest.
  Keywords: corporate failures; failure mode analysis; going concern; liquidity; profitability.
  DOI: 10.1504/IJADS.2025.10061883
   
  
• Empirical evidence of a preference for uncertainty in intertemporal risky prospects  
  by Viviana Ventre, Roberta Martino, Francesco Panico, Laura Sagliano, Luigi Valio, Luigi Trojano 
  Abstract: Recent studies suggest that uncertainty should be added to the risk to evaluate financial decisions. The present study examines the interaction between uncertainty and subjective probability in risk evaluation. Subjective probability relates to an individual’s confidence in event occurrence. To explore the impact of different temporal perspectives on the perception of probability, random probabilities are assessed over different time frames. Empirical data and modelling validate that subjective probability varies over time, aligning with the non-rationality behaviour observed in hyperbolic discounting. Notably, applying the belief function theory formalises the link between a steeper discount function and choosing ‘larger later’ options in risky intertemporal prospects. Remarkably, results unveil a ‘preference for uncertainty’, where individuals exhibit greater patience in pursuing rewards. This experimental approach improves the qualitative and quantitative understanding of the risk-uncertainty dynamic.
  Keywords: behavioural finance; delay discounting; financial decision-making; FDM; hyperbolic discounting; impatience; imprecise probability; intertemporal choice; intertemporal prospect theory risk.
  DOI: 10.1504/IJADS.2025.10062032
   
  
• An optimization model of time-of-use pricing for ride-hailing platforms  
  by Wei Zhang, Shujing Wan 
  Abstract: For the problem of online ride-hailing market pricing, time-of-use pricing plan on ride-hailing platform is studied in this paper, considering the interests of the platform, drivers and passengers. Firstly, the multi-objective optimisation model of time-of-use pricing is built, in which the different price coefficients are used in peak hours and off-peak hours. The method of determining the drivers-passenger matching quantity is proposed. Then the algorithm solving the model is designed based on non-dominated sorting genetic algorithms. Finally, the validity of the time-of-use pricing method proposed in this paper is verified by a case study, and the relevant rules of time-of-use pricing are analysed. The research shows that the method can effectively improve the interests of the platform, driver and passenger. The revenues of the platform and driver can be increased by 12.9% and 4.15%, respectively, and the passenger payment can be saved by 8.64% at most relative to single price.
  Keywords: urban traffic; time-of-use pricing; multi-objective optimisation; online ride-hailing; genetic algorithm.
  DOI: 10.1504/IJADS.2025.10062959
   
  
• Adapting CRISP-DM to model enteric fermentation emission: farm level application  
  by Philippe Belmont Guerrón, Maria Hallo, Sergio Lujan-Mora 
  Abstract: Enteric fermentation contributes substantially to greenhouse gas emissions (GGEs) in agriculture, but may be reversible in the short-term. To date, numerous attempts have been made to model the environmental impact of agriculture, but have failed to integrate multiple dimensions of production. The objective of this study is to adapt the cross industry standard process for data mining (CRISP-DM) at farm level, using the concept of life cycle assessment (LCA) and implemented a modified version of the global livestock environmental assessment model (GLEAM). Using local data collected over 20 years and secondary data, our results show that for dairy cattle, the methane emissions factor from cattle is lower among marginal farms 86 Kg CH⁴ head⁻ ¹ year⁻ ¹ compared to semi-intensive and intensive farms across time and geographical regions (107.4 and 113.5 respectively) and demonstrate that this type of application is relevant for developing countries and smallholder agriculture, where production data is often unavailable.
  Keywords: CRISP-DM; design science research; DSR; model integration; data mining; GLEAM; life cycle assessment; LCA; agriculture data; enteric fermentation.
  DOI: 10.1504/IJADS.2025.10063368
   
  
• Adaptation of plant propagation algorithm for waste collection vehicle routing problem  
  by Nur Azriati Mat, Aida Mauziah Benjamin, Syariza Abdul-Rahman, Ku Ruhana Ku-Mahamud, Mohammad Fadzli Ramli 
  Abstract: Solid waste management (SWM) is an important service the government offers to residents of a country to manage generated residual waste. Failure to manage this waste can lead to unpleasant circumstances, such as environmental contamination and outbreaks of pest-borne diseases. Therefore, an efficient and cost-effective SWM system is required to improve the services. This research highlights one of the main issues of the SWM system, which is the waste collection vehicle routing problem (WCVRP). Essentially, this research addresses the adaptation of the plant propagation algorithm (PPA), which has never been considered in prior studies to resolve waste collection problems. The quality of the PPA solution was evaluated in terms of total travel distance, the number of vehicles/drivers required, the total working hours of drivers, and total fuel consumption. The proposed algorithm was tested on a WCVRP benchmark problem. Upon comparing PPA and other best-known solutions depicted in the literature, the solutions achieved on benchmark problems were extremely competitive.
  Keywords: waste collection; vehicle routing problem; benchmark problem; solid waste management; SWM; plant propagation algorithm; PPA.
  DOI: 10.1504/IJADS.2025.10063685
   
  
• Extended fuzzy AHP for decision under the DeLone McLean model  
  by František Zapletal, Radek Nĕmec 
  Abstract: Fuzzy analytic hierarchy process (F-AHP) has been introduced in many variations requiring different conditions and assumptions. In this paper, we deal with a problem when the uncertainty is not primarily implied by a linguistic evaluation scale, but by the hesitance of decision-makers. This assumption is essential in our case study of the project success factor evaluation under the DeLone and McLean model because the evaluators have different skills, knowledge, and even competences. To get the level of hesitance of each decision-maker, we introduce the so-called hesitance degree, which defines the shape of fuzzy evaluations. To derive the fuzzy weights of criteria, we use the linear goal programming priority method introduced by Wang and Chin (2008), and the possibility and necessity measures to interpret the results. We also provide a novel diagram visualising the results. The presented F-AHP approach is used to evaluate the success factors of information system implementation.
  Keywords: hesitance; fuzzy; analytic hierarchy process; AHP; DeLone and McLean model.
  DOI: 10.1504/IJADS.2024.10052829
   
  
• Sentiment analysis and support vector machine one versus one for collectibility classification of bank's house ownership loan  
  by Carmelia Nabila Permatasari, Adji Achmad Rinaldo Fernandes 
  Abstract: Not many applications of sentiment analysis have been developed for the Indonesian language. One classification method that can be applied to extracting information for large databases is the support vector machine (SVM). This study aims to compile research variables based on the results of sentiment analysis and examine SVM performance to solve multi-class cases using the one versus one method with linear kernel functions, quadratic polynomial kernel functions, and radial basis function (RBF) kernel functions in classifying the collectibility classification of bank's house ownership loan based on Pernyataan Standar Akuntansi Keuangan (PSAK) 71, among others: 1) performing loan; 2) under-performing loan; 3) non-performing loan. The results showed that SVM one versus one with the kernel RBF is the most appropriate method in classifying collectibility levels bank mortgage debtors based on the Big Five personality because the accuracy, sensitivity, and specificity values obtained on the perfect testing data are 100%.
  Keywords: performance; debtor collectibility; bank: house ownership loan: one versus one: support vector machine; SVM.
  DOI: 10.1504/IJADS.2024.10055335
   
  
• Vulnerability to poverty due to schooling: a discussion from the spatial perspective  
  by Rafael de Freitas Souza, Julio Araujo Carneiro da Cunha, Hamilton Luiz Correa, Claudia Orsini Machado de Sousa 
  Abstract: Economic theories point out that regional poverty is more concentrated in regions far from urban centres. However, at Brazilian Midwest, wealth-generating localities are not concentrated in those centres and may create misunderstandings about the theoretical understanding of this phenomenon, and for policymakers who need predictive tools to address the fight against poverty. Thus, it was intended to predict poverty vulnerability from educational levels in a distinct phenomenon of decentralisation. A geographically weighted regression was performed using United Nations Development Programme data on Midwest municipalities. It was pointed out that education levels can predict vulnerability to poverty, confirming the theories existing in the main agricultural area in the world. As a contribution, public policies need to be thought individual and spatially, to consider actions beyond the institutional boundaries of municipalities, in an integrated and coordinated manner between neighbouring cities.
  Keywords: poverty; education; public policies; Brazilian Midwest; poverty vulnerability.
  DOI: 10.1504/IJADS.2024.10054467
   
  
• Enhance personalised recommendations by exploring online social relations  
  by Xiaoyun He, Chuleeporn Changchit 
  Abstract: Personalised digital recommendations are widely used to improve customer experience and drive sales. Although recent research suggests that online social relations influence users' both product choices and ratings, few studies have examined them in the context of personalised recommendations. In this study, we aim to explore how online social relations can be leveraged to enhance personalised recommendations. The empirical results demonstrate that incorporating the ratings from a user's social circle improves accuracy and coverage of personalised recommendations; In addition, differentiating these social ratings helps increase the recommendation diversity while limiting the loss of accuracy. The findings have important implications for the applicability of recommender systems in modern online business and social environment.
  Keywords: social relations; recommendation accuracy; the diversity; online recommendation; personalised recommendation; online relations.
  DOI: 10.1504/IJADS.2024.10054181
   
  
• Facility delocation model considering efficiency and equity: banks merger  
  by Saeed Zarghami, Maghsoud Amiri, Mohammad Taghi Taghavifard, Ahmad Makui 
  Abstract: Facility delocation emerges due to governance conditions, strategies, technological changes and the new demands in the competition market. Bank merger is no exception in such a condition. In this study, a delocation-merging model is presented for the so-called military banks of Iran. The government has taken into account the issues of efficiency and equity, considering the claims of the stakeholders and based on its own goals. Therefore, a multi-objective model is considered and equality is considered with the help of constraints. Meaning that, when subjected to constraints, equality is introduced into the model using fuzzy logic and also the efficiency score of banks is considered as the importance of facilities using data envelopment analysis method. The constraints of the model determine the number of remaining facilities and numerical examples are analysed, using the epsilon-constraint method augmented in the GAMS software.
  Keywords: bank; data envelopment analysis; delocation; epsilon-constraint; fuzzy logic; efficiency and equity; multi-objective model.
  DOI: 10.1504/IJADS.2024.10055584
   
  
• Exploring international market selection for Indonesian tuna - with a staggered elimination round in ELECTRE I  
  by Elia Oey, Hanijanto Soewandi 
  Abstract: International market selection is a classical strategic application for multi-criteria decision-making (MCDM) analysis. The study aims to assist Indonesia tuna stakeholders in determining potential export country destinations. Data of 210 countries were screened to get 30 shortlisted countries for a systematic MCDM analysis. Global weight of sub criteria was calculated with analytical hierarchy process by interviewing experts related to Indonesia tuna export. Ranking of shortlisted countries was done using several iterations with ELECTRE I, where results of each round are plotted into a 2 × 2 quadrant, and those in the worst quadrant were eliminated. After six elimination rounds, we propose Japan, Singapore, China, Malaysia, Denmark, and South Korea, as the top six export-country destinations for Indonesian tuna. Using scatter plot visualisation of net superiority vs. net inferiority index into a 2 × 2 quadrant helps enhance the ELECTRE I result in a more pragmatic way, and creates more insight for a sensitivity analysis.
  Keywords: AHP; ELECTRE; international market selection; IMS; tuna.
  DOI: 10.1504/IJADS.2024.10056140