International Journal of Applied Decision Sciences (30 papers in press)

Regular Issues

• 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
   
  
• 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
   
  
• 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
   
  
• 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
   
  
• The capital structure determinants in small and medium-sized enterprises in the information technology sector  
  by António José Mendes Ferreira, Paulo Jorge De Almeida Pereira, Mario José Batista Franco, Dagoberto Ivo Sousa Couto Dos Santos 
  Abstract: This study aims to analyse the relation between the determinants of capital structure and the level of debt in small and medium-sized enterprises (SMEs) in the information technology (IT) sector. The methodology adopted consists of applying a questionnaire to 100 IT SMEs in Portugal, followed by descriptive statistical analysis. The results obtained will provide managers and investors with valuable insights, highlighting the importance of factors such as firm size, asset tangibility, growth opportunities, business risk, profitability, age and tax benefits. The conclusion underlines that the relation between firm size and level of debt is complex, depending on contextual factors, and that pecking order theory influences financing decisions. The study fills a gap in the literature and contributes to developing the information technology sector in Portugal. The study refers to the main theories related to capital structure, such as the theory of Durand (1952), the approaches of Modigliani and Miller (1958, 1963), agency theory (Jensen and Meckling, 1976), trade-off theory (Myers, 1984) and pecking order theory (Myers and Majluf, 1984).
  Keywords: financial management; capital structure; small and medium-sized enterprises; SMEs; information technology; debt.
  DOI: 10.1504/IJADS.2025.10063772
   
  
• An Efficient Approach to Solve Order Batching, Batch Sequencing and Picker Routing Problems Simultaneously in Warehouse Operations  
  by Md. Saiful Islam, Md. Kutub Uddin 
  Abstract: Order picking is the most time-consuming and laborious part in warehouse operation. An efficient order batching approach may considerably enhance the effectiveness of the order picking process. In this research, a quadratic programming model is developed to solve the order batching, batch sequencing, and picker routing problems jointly. The objective is to minimise the sum of order processing and tardiness costs for a particular set of customer orders. The model is considered as an NP-hard problem. Therefore, as a solution methodology, a genetic algorithm (GA) based meta-heuristic approach is proposed to solve large-scale problems. A greedy routing technique is also adopted in the GA to estimate the optimal picking sequence for each batch. The effectiveness of the suggested meta-heuristic approach is compared with the earliest due date (EDD) order batching method. The experimental results show that the proposed GA-based approach generates promising results in an acceptable amount of computational time.
  Keywords: order picking; order batching; greedy routing policy; genetic algorithm; GA; warehouse management.
  DOI: 10.1504/IJADS.2025.10064103
   
  
• Green finance to achieve environmental sustainability: A review and bibliometric analysis  
  by Ravita Kharb, Neha Saini, Shabani Bhatia, Charu Shri, Dinesh Kumar  
  Abstract: The concept of green finance has evolved over time in response to economies’ aspirations. Green finance has captured the interest of academic scholars and policymakers owing to the growing global concern for environmental sustainability. It is a major initiative that the government and society take towards environmental sustainability. The current study aims to undertake a comprehensive bibliometric analysis and identify the facilitator of green finance across all economies. The intellectual framework and bibliography of the selected articles were examined using Biblioshiny. To ensure accuracy, several inclusion and exclusion criteria were applied. By examining 65 articles, the study also attempted to pinpoint the factors that facilitate and hinder green finance. This study is the earliest effort to understand the emergence of green finance and its driving factors. This study contributes significantly to the literature by identifying the enablers and barriers of green finance transformation towards green growth.
  Keywords: green finance; environmental sustainability; climate change; green growth; green innovation.
  DOI: 10.1504/IJADS.2025.10064181
   
  
• A genetic algorithm model for route optimisation of cold chain product transportation using vehicles  
  by Sheng Zeng, Bing Wang, Gang Hu, Xu-sheng Hu, Xian-jun Dai 
  Abstract: Traditional cold chain logistics vehicles are suitable for short distance transportation, long distance cold chain transportation faces more challenges, as the transportation distance increases, the time and temperature control in the cold chain link becomes more difficult. Because the driving route of the vehicle has been subject to the influence of technology, the driving route of the vehicle can not be optimised, and the traditional vehicle transportation is only for the tracking of the vehicle, the infrared sensor avoids obstacles to find the driving route of the vehicle, and the traditional driving route of the vehicle has limitations. At the same time, the genetic algorithm adds an adjustment strategy based on time window, which can effectively reduce the probability of conflict and deadlock, accelerate the convergence speed of the solution, and solve the scheme with the shortest total assembly time within the specified time. Based on the above design, in this paper, the vehicle path optimisation can shorten the transportation time, reduce the overall transportation cost, and improve the transportation efficiency.
  Keywords: transportation; genetic algorithm; GA; route optimisation; path; transportation cost.
  DOI: 10.1504/IJADS.2025.10064623
   
  
• An integrated inventory model of deteriorating items with volume agility and time dependent demand under an imperfect production process  
  by Surendra Vikram Singh Padiyar, Deepa Makholia, S.R. Singh, Vipin Chandra Kuraie, Ummeferva Zaidi, Vaishali Singh 
  Abstract: This research paper presents an integrated inventory model that address the challenges arising from deteriorating items in the presence of volume agility and time-dependent demand for vendor and supplier under imperfect production process. By incorporating rework processes into manufacturing, businesses not only contribute positively for green environment but also create a more sustainable and responsible approach to resource management. This model offers various critical aspects of inventory control, aiming to optimize the overall system performance and enhance decision-making in complex supply chain environments. The objective is to determine the optimal production volume to minimize the total cost over a specified planning horizon. To validate the results of the proposed model a numerical illustration has been done and the concavity of the objective function is shown using MATHEMATICA 12 software. Finally, to find some useful observations and managerial insights, a sensitivity analysis has also done for various parameters.
  Keywords: volume agility; imperfect production; vendor; supplier; deterioration; time dependent demand.
  DOI: 10.1504/IJADS.2025.10065313
   
  
• Optimal planning of charging station for electric vehicle based on hybrid RODDPSO and K-means algorithm  
  by Birong Huang, Huahao Zhou, Fangbai Liu, Yuhang Zhu, Peng Geng, Xiaoyan Zhao 
  Abstract: This research proposes a hybrid approach combining K-means with randomly occurring distributedly delayed particle swarm optimisation (RODDPSO) to strategically locate electric vehicle charging stations (EVCS). The method is structured in three phases: initial regional clustering for demand analysis, refined selection of charging pile locations within these regions, and consolidation into efficient charging stations. The approach enhances the traditional K-means by optimising initial centroids with RODDPSO, mitigating the risk of suboptimal solutions due to local minima. The Yancheng ride-hailing dataset is employed to validate the model, showcasing a significant improvement in utilisation rates and operational efficiency compared to the standard K-means algorithm. The findings underscore the hybrid method's potential to optimise EVCS placement for enhanced service coverage and economic viability.
  Keywords: randomly occurring distributedly delayed particle swarm optimisation; RODDPSO; K-means; cluster centre optimisation; optimal planning of charging station.
  DOI: 10.1504/IJADS.2025.10065430
   
  
• On the fringe of credit visibility: the value of alternative data for assessing the credit risk of subprime underbanked consumers  
  by Edwin Baidoo, Stefano Mazzotta 
  Abstract: In a modern economy, prospering without credit is difficult. Yet, Geraldes et al. (2022) report, for instance, that as many as 2.5 billion individuals in the world have little to no bank relationships. Referred to as underbanked consumers, they are unable to obtain credit due to their limited or non-existent credit history. Alternative data refers to data sources that are not traditionally used in credit scoring. Current research suggests that alternative data may contain predictive information helpful in assessing the creditworthiness of underbanked consumers. We use statistical and machine learning models to examine the value of alternative data for assessing the creditworthiness of the United States’ subprime underbanked consumers. We use a proprietary data set of automobile loans that includes both traditional and alternative data to compare the predictive value of each data type. Our main finding is that the informational content of alternative data is not subsumed by traditional data. In addition, we find that alternative data alone have value that can help lenders extend credit to subprime underbanked consumers, enabling them to fully participate in the mainstream economy.
  Keywords: alternative data; credit scoring; underbanked consumers; personal bankruptcy; auto loans.
  DOI: 10.1504/IJADS.2025.10066241
   
  
• Sensitized multivariate homogeneously weighted moving average charts for Phase II monitoring of the multivariate process mean  
  by Farshad Bahar, Mohammad Reza Maleki, Ali Salmasnia, Hossein Eghbali 
  Abstract: In some processes, the occurrence of assignable cause results in small disturbances in the process parameters. Moreover, a fundamental assumption in designing control charts is that the measurements are accurate. This article aims to enhance the multivariate homogeneously weighted moving average (MHWMA) chart with 2-of-2 and 2-of-3 sensitisation rules for the rapid detection of mean changes. Then, the proposed charts are developed based on additive covariate model to account for the impact of measurement errors. Finally, multiple measurement technique is utilised to reduce the error impact on run length of the sensitised charts. The performance of the proposed charts is assessed through extensive simulations. The results show that the MHWMA chart based on the 2-of-3 rule outperforms its counterpart which employs 2-of-2 rule. Secondly, the measurement error negatively affects the power of both the proposed charts. Thirdly, increasing the number of measurements for each product can improve the chart detectability.
  Keywords: multivariate homogeneously weighted moving average; MHWMA; measurements error; sensitisation rules; multiple measurements; additive covariate model.
  DOI: 10.1504/IJADS.2025.10066477
   
  
• Optimising online order batching operations in a manual order picking warehouse: a genetic algorithm approach  
  by Md. Saiful Islam, Md. Kutub Uddin 
  Abstract: This study is significant in the field of warehouse management and logistics. In manual order picking, online order batching requires balancing between order processing costs and customer expectations. The objective of this research is to address the challenges associated with online order batching, batch sequencing, and picker routing in a consistent manner, with the goal of minimizing both order processing and tardiness expenses of customer orders that arrive dynamically over time. This paper suggested a solution approach for the associated problems and combined a genetic algorithm (GA) with the variable time window batching method. The experimental findings indicate that the GA-based approach yields promising results within a reasonable computational timeframe. This significant development has the potential to transform traditional warehouse management procedures by making them more adaptable, flexible, and capable of addressing the dynamic demands of modern business environments.
  Keywords: online order batching; order picking; variable time window batching; genetic algorithm; greedy routing.
  DOI: 10.1504/IJADS.2025.10066980
   
  
• Modeling and computational simulation of operations and queue systems in a supermarket  
  by Paloma Dos Santos Alves Nunes, João Vitor Da Silva Alves, Yuri Laio Teixeira Veras Silva 
  Abstract: Queuing theory, combined with computational simulation, has been employed to analyse queue system configurations with the objective of identifying scenarios that offer the greatest benefits to the company. This study aims to develop a computational simulation approach to determine the optimal number of cashier operators in a supermarket in Brazil, considering queue-related parameters at different times of the day and month to achieve efficient wait time management. The results indicate that the best number of fast queue attendants (AQF) is two operators across all configurations. For normal queue attendants (AQN), at the beginning of the month, 15 attendants are needed on weekdays and 14 on weekends. Mid-month, the demand decreases, with 13 attendants required on weekdays and 14 on weekends. At the end of the month, the optimal allocation is 15 attendants for both weekdays and weekends.
  Keywords: queues modelling; computational simulation; discrete-event modelling; agent-based simulation; AnyLogic; supermarket operations.
  DOI: 10.1504/IJADS.2025.10067360
   
  
• Optimisation of urban distribution paths for electric logistics vehicles based on shared pre-positioning warehouse mode  
  by Yanfeng Wu, Xueping Liu, Kai Tian 
  Abstract: To address the problems of high cost, low efficiency and load rate of urban logistics transportation, this work studies the electric vehicle routing problem. First, a novel urban logistics distribution mode, namely, the shared pre-positioning warehouse mode (SPWM) is proposed, including pick-up stage and distribution stage. Second, considering the constraints of time window, vehicle capacity, and battery capacity, the electric vehicle route optimisation models are established for the two stages of the SPWM. Third, an improved genetic algorithm by combining elite retention strategy and swap operator is proposed to solve the model. Finally, we evaluated the proposed mode on a real-world case including five suppliers and thirty customers. Compared with the path optimisation results of the traditional distribution mode (TDM), the SPWM reduces the total cost by 14.85%, the travelled mileage by 66.15%, and improved the load rate by 124.09%, which verify effectiveness of the proposed model and algorithm.
  Keywords: urban logistics distribution; shared pre-positioning warehouse; electric vehicle routing problem; genetic algorithm.
  DOI: 10.1504/IJADS.2025.10067556
   
  
• Advanced statistical methods for real-time industrial process analysis: an analysis of the literature.  
  by Pedro Vaz, Ana Cristina Braga, Maria Do Sameiro Carvalho 
  Abstract: Real-time work and data-driven (DD) strategies are gaining popularity in Industry 4.0, which highlights the importance of revising the statistical methods applied in these environments. This study is the first systematic literature review on advanced statistical methods for real-time industrial process analysis (ASMs-RTIPA), offering valuable insights for future research by compiling existing recent studies (from 2014 to 2020) systematically. The review indicates a lack of publications on ASMs-RTIPA, yet it supports its application. Approximately 41% of the selected publications use case studies, 23% develop models, and 18% are conceptual. ‘Advanced process control’is the most common keyword in the publications studied. The majority of publications come from the US, UK, Germany, and the Netherlands. Engineering, generally, has the highest concentration of publications on the subject.
  Keywords: advanced statistical methods; ASMs; real-time industrial process analysis; advanced process control; process improvement; systematic literature review; SLR.
  DOI: 10.1504/IJADS.2025.10067733
   
  
• Cold chain logistics monitoring and logistics vehicle scheduling optimisation  
  by Sheng Zeng , Bing Wang, Xian-jun Dai 
  Abstract: Because of its development, cold chain logistics is gradually concerned by the public and the transportation and monitoring problems of cold chain logistics frequently appear. On the basis of the current situation, the purpose of this thesis is to offer an intelligent monitoring platform for cold chain logistics to monitor its vehicle driving path and vehicle scheduling. Secondly, due to the different routes of vehicles in the process of driving, the ant colony algorithm is taken to optimise the routes and improve the ant colony algorithm in such cases and it shows that the updated ant colony algorithm is superior to the path of the conventional ant colony algorithm. The findings of the experiment indicate that the optimisation rate can be increased by about 4% through the four route optimisation methods. The improved algorithm significantly shortens the path, reducing the cost of cold chain logistics transportation and promotes the advancement mode of traditional cold chain logistics transportation companies. It provides a basis for the subsequent logistics optimisation, and continuously improves the efficiency and quality of cold chain logistics. The contribution of intelligent monitoring and route optimisation of cold chain logistics is to improve transportation efficiency, reduce costs, ensure product quality and safety, and provide important support for the development and progress of modern logistics industry.
  Keywords: cold chain logistics; monitoring platform; ant colony algorithm; path; cost.
  DOI: 10.1504/IJADS.2025.10067857
   
  
• Digital entrepreneurship: the influence of social media on consumer buying behaviours  
  by António José Mendes Ferreira, Paulo Jorge De Almeida Pereira, Inês Gouveia Da Costa, Dagoberto Ivo Sousa Couto Dos Santos 
  Abstract: The aim of this study is to understand the extent to which consumers are willing to follow certain brands and companies, seeking to understand which content appeals to them the most, how they interact on new digital platforms, their frequency of purchases, and the impact that brands have on their purchasing decisions. In order to assess the effect that social media has on brand relationships, it is important to investigate the new entrepreneurial trend, namely, digital entrepreneurship, as this phenomenon is on the rise both in terms of business digitalisation and the creation of digital companies. For this purpose, this study adopted a quantitative methodology, based on a questionnaire survey, which gathered 199 responses. It was concluded that the majority of individuals feel influenced by brand and company posts. They consider this to be one of the reasons that lead them to purchase goods and services through these platforms, constituting an important contribution to business practice. Regarding the aspects that vary this impact, it was concluded that this variation is only significant in certain cases.
  Keywords: digital entrepreneurship; social media; online consumption.
  DOI: 10.1504/IJADS.2025.10067952
   
  
• A financial auditing approach using failure effect mode analysis  
  by Saeed Askary, Davood Askarany, Yusuf J. 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
   
  
• 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 the food industries where different tomato pastes are produced. In addition, after performance evaluation of the employees, they are ranked based on their performances. As the main contribution of this study, a real-world problem from food industries is focused on and solved by 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 on 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
   
  
• 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, Wei Chong Choo, 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 14 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 21,121 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
   
  
• Predictors of purchase intention in gastronomic establishments in the City of Medellin
  by Rodolfo Casadiego-Alzate, Isabella Marín-Cuartas, Cristian Santiago Toro-Herrera, Alejandro Silva-Cortés, Marianela 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