International Journal of Internet Manufacturing and Services (25 papers in press)

Regular Issues

• Market-based Environmental Regulation, Green Technology Innovation and Green and Low-carbon Economy  
  by Li Jia, Xu Yibo 
  Abstract: The establishment of a green and low-carbon economic development system is of great significance in promoting the overall green transformation of economic development. Based on the Super-SBM model, this paper constructs a green and low-carbon economic development index, uses the dynamic panel data of 30 provinces and cities in China from 2006 to 2018 to study the relationship between market-based environmental regulation, green technology innovation and green low-carbon and economy. The results indicated that market-based environmental regulation has a significant inhibitory effect on green technological innovation, and the combined effect of market-based environmental regulation and green technological innovation can effectively weaken the
  Keywords: green and low-carbon economy; market-based environmental regulation; green economy demonstration area; carbon neutrality; empirical study.
  DOI: 10.1504/IJIMS.2024.10051039
   
  
• A novel decision-making scheme for hospital emergency services based on plant growth simulation algorithm  
  by Long Chen, Qinming Liu, Chunming Ye, Jiaxiang Li 
  Abstract: A novel decision model of hospital services decision-making based on cumulative prospect theory and plant growth simulation algorithm (PGSA) is proposed. First, this paper considers the bias of psychological behaviour characteristics of hospital emergency services decision makers, and the selection of patient emergency solutions is designed as a nonlinear programming model. Then, the integrated value of hospital scenarios under emergencies is calculated based on the loss and gain values of patient injury severity and emergency resource utilisation, and the cumulative prospect values of each emergency scenario are calculated based on interval probability and cumulative prospect theory. PGSA is used to weigh the accumulated prospect value vector of each decision maker. Finally, the data description shows that the decision process can make the hospital emergency services scenarios optimal under emergencies, it makes emergency decision making more in line with the actual situation and thus improves scientific and effective decision making.
  Keywords: hospital services; emergency resources; cumulative prospect theory; emergency services decision making; plant growth simulation algorithm; PGSA.
  DOI: 10.1504/IJIMS.2024.10051482
   
  
• Optimal strategies for an uncertain forward and reverse multi-period logistics network using heuristic algorithm: A case study of Shanghai perishable products  
  by Jianquan Guo, Lian Chen, Bingzi Tang, Mitsuo Gen 
  Abstract: In view of the high economic volatility, serious environmental pollution and updated demands of customers for distribution efficiency caused by uncertain demand or recovery, improper handling and untimely delivery of perishable products under various circumstances. An uncertain perishable products forward and reverse logistics network and its optimal strategies are proposed with the consideration of multi-period and multi-objective. The first objective is to minimise the overall cost of the system, the second objective is minimum environmental impact and the third one is about just-in-time delivery. Model’s validity was proved by a hybrid GA-PSO algorithm, aided by an exact solver (CPLEX) with a case of a perishable products enterprise in Shanghai. Finally, some useful references and managerial insights are proposed to create a forward and reverse logistics network for perishable products enterprises.
  Keywords: perishable products; uncertain forward and reverse logistics network; multi-period and multi-objective; hybrid particle swarm optimisation and genetic algorithm.
  DOI: 10.1504/IJIMS.2024.10051752
   
  
• High-fidelity modelling of multiple transporters block transportation based on digital twin at shipyards  
  by Tian Luo, Zhenyu Xu, Xin Song, Miaomiao Sun 
  Abstract: In order to solve the problems of poor scheduling and inefficiency of transporters in block transportation at shipyard, this paper proposed a framework of multiple transporters for block transportation scheduling systems with a digital twin-based (DT) five-dimensional model. The proposed framework can be used to identify low-efficiency of multidisciplinary, multi-physics, multiscale, and dynamic changes over time at shipyards to optimise the efficiency of multiple transporter scheduling and block transportation. DT provides the fidelity required to realistically predict transporter scheduling schemes with rescheduling actions under various interference conditions. The high-fidelity model is investigated using three levels of geometry, dynamics and data. An optimal simulation model experiment with multiple transporters is adopted to improve transportation efficiency. The results show that the proposed DT framework and high-fidelity model are effective, and both the utilisation of transporters and the transportation efficiency of blocks are improved.
  Keywords: high-fidelity modelling; simulation; block transportation; multiple transporters transportation; digital twin; DT.
  DOI: 10.1504/IJIMS.2024.10052164
   
  
• Risk Analysis of Private Equity Financing of Manufacturing Industry Based on DANP model  
  by Haotian Ma, Alia Erbolat, Rende Li 
  Abstract: The risk of private equity financing is a multi-level, multi-factor complex system, and its risk assessment has the characteristics of ambiguity and uncertainty. In order to be able to conduct a comprehensive and effective evaluation of private equity financing under carbon neutrality, a DNP method of hybrid multi-objective decision MCDM is applied to evaluate the risk of private equity financing. The results show that: The DANP method uses the ANP method to perform the double-weighted operation to obtain the mixed weight of the four-level index based on the weight of the three-level index of the DEMATEL method. It considers the interaction between levels and makes the risk analysis of private equity financing more reliable. Besides, empirical analysis of the impact of various influencing factors on private equity financing. The research results not only reduce the investment risk of private equity funds in the financing process but also make better assessments for financing companies. It also improves the DANP method and performs double-weighted mixed operations between levels to avoid the unreliability of the system, thereby enriching the risk research of private equity financing based on quantitative methods.
  Keywords: private equity financing; risk assessing indicator system; risk evaluation; DANP model; blended weight; manufacturing industry.
  DOI: 10.1504/IJIMS.2024.10052437
   
  
• Optimization of site selection for urban material distribution during the COVID-19 pandemic  
  by Houjun Lu, Tian Zhu 
  Abstract: Improving the resilience of regional logistics and supply chains in controlled cities as the COVID-19 epidemic normalises can have a positive impact on epidemic prevention and management. This paper proposes a resilient logistics mode that establishes large warehouses outside sealed and controlled urban areas to support the distribution of materials required for the public. The selection of the material distribution sites is essential to save cost and time during the distribution process. The distribution of materials in urban communities is disrupted by uncertain factors, such as regional road closures and controls, impassable roads and diminished transport capacity. Therefore, for epidemic containment and control, a site selection model for material distribution in urban areas is required. The site selection has two goals, achieving the highest satisfaction in the demand points, and obtaining the lowest total construction costs in the distribution points. In addition, a risk coefficient for road closure and control is introduced to construct a multi-attribute satisfaction decay function. The division of the epidemic prevention and control stages is described with the quantitative variables of the epidemic control measures.
  Keywords: epidemic lockdown; resilient logistics; site selection; simulated annealing algorithm.
  DOI: 10.1504/IJIMS.2024.10054388
   
  
• NSGA-II-based study of carbon emissions in service-based manufacturing supply chains  
  by Ruipeng Wang, Yuhong Tai, Li Ma 
  Abstract: This paper introduces the concept of a service-based manufacturing supply chain, and explores the different performance of logistics cost and carbon emission with different number of supply chain participants by the NSGA-II algorithm. This study expands the scope and application perspective of genetic algorithms in supply chain management and provides theoretical explanations and model support for low-carbon transformation and low-cost operations in service-based manufacturing supply chains. Three main results are presented: 1) Carbon emissions and logistics costs are negatively correlated with the number of iterations, and as the number of iterations increases, these two values first decrease, then slow down and finally stabilise. 2) The iteration curves of carbon emissions and logistics costs show a high degree of similarity. 3) Firms in service manufacturing supply chains need to focus on both carbon emissions and logistics costs.
  Keywords: carbon emission; service-based manufacturing supply chain; logistics costs; transportation; NSGA-? algorithm; manufacturer; carbon footprint; control; service integrator; optimization.
  DOI: 10.1504/IJIMS.2024.10055281
   
  
• Carbon emission efficiency and foreign direct investment: the role of green financial development  
  by Xin Guo, Jiang Wang 
  Abstract: Prevailing research suggests foreign direct investment (FDI) significantly impacts carbon emission efficiency (CEE) of the host country. However, the contribution of FDI may depend on the level of green financial development in the host country. This study examines the role of green financial development in the relationship between FDI and CEE. Based on panel data from 30 provinces in China from 2005 to 2019, we construct the system generalised method of moments (GMM) model and a dynamic threshold effect model to investigate how green financial development influences the impact of FDI on the CEE of the host country. The findings suggested that green financial development plays a moderating role in the impact of FDI on CEE. But only when it reaches a certain level can FDI significantly promote CEE. The empirical analysis suggests that policymakers should build a multilevel green financial system to allocate financial resources and maximise the spillover effects of FDI.
  Keywords: foreign direct investment; FDI; carbon emission efficiency; CEE; green financial development; generalised method of moments; GMM.
  DOI: 10.1504/IJIMS.2024.10055664
   
  
• Application of improved Shapley value method in profit distribution of On-site logistics alliance  
  by Yuli Hu, Chengji Liang, Shi Yuan Zheng 
  Abstract: To ensure the healthy development of the cooperation alliance between manufacturing enterprises and logistics providers, the on-site logistics participants need to work together to generate incremental profits for the alliance. The reasonable distribution of incremental profits among alliance members will make the alliance structure more solid. Constructing a cooperative game model provides action decision guidance under Nash equilibrium for the three participants involved. In order to make the distribution of incremental profits more equitable, a distribution scheme based on the contribution degree of all participants to the alliance is proposed, and further, the AHP-Shapley value method with the introduction of effectiveness impact factors is established. The results of trial calculations and analysis make the distribution of incremental profits more scientific. Research shows that under the guidance of incremental profits allocation, the participants in the cooperative alliance will achieve their ideal expected returns in a stable structure.
  Keywords: incremental profit distribution; improved Shapley value; on-site logistics; logistics cost; cooperative game.
  DOI: 10.1504/IJIMS.2024.10060879
   
  
• Evaluation and optimization of surface roughness in the lapping process of DIN/EN 1.2379 steel using bi-composite lapping plates  
  by Mahdi Hosseinpour, Hossein Amirabadi, Mohammad Khoran 
  Abstract: The low material removal rate (MRR) of the lapping process is an obstacle that increases the cost of this process. To increase the MRR and reduce the cost of machining, bi-composite lapping plates have been suggested, recently. In this study, the effects of different types of bi-composite lapping plates, abrasive particle size, and lapping pressure have been investigated on surface roughness and flatness error of 1.2379 steel. Steel, copper, and tin bi-composite lapping plates have been used for this research. The experiments show, as the size of grain increases, the amount of surface roughness and out of flatness will increase. The sensitivity analysis shows that lapping pressure, lapping plate type, and grit size have the maximum effect on both outputs, respectively. Minimum surface roughness and out of flatness as two objects of optimisation are obtained at pressure 10 kPa, 6 m diamond abrasive grit size by the tin bi-composite lapping plate, simultaneously.
  Keywords: lapping process; optimisation; bi-composite; single side lapping machine; DIN/EN 1.2379 steel.
  DOI: 10.1504/IJIMS.2024.10061141
   
  
• Deep Learning and IoT for Detecting and Classifying Leaf Diseases in Agriculture 4.0: A Systematic Review  
  by Swarna Prabha Jena, Sujata Chakravarty, Dr. Bijay Paikaray 
  Abstract: Advancement of digital technology in agriculture leads to agriculture 4.0. The technological revolution has provided the detection and classification of leaf diseases at an early stage. Hence, advancements are essential to reduce costs and increase quality and productivity. Deep Learning techniques empower the system's development, allowing better decisions to be made early, saving time and money. Nowadays, incorporating IoT in agriculture makes the system intelligent enough to support farmers independently. To know the direction, numerous methods have been systematically reviewed and kept in place to make the researcher understand. Learning the current state-of-the-art techniques for hardware requirements, dataset used, and performance matrices is crucial. Therefore, the core objective of the research article is to do a systematic literature review that analyses the research gaps, current trends, challenges and answers the research questions which are helpful for investigation. Finally, this systematic review can be the starting point for upcoming researchers.
  Keywords: Systematic Review; Plant diseases; Deep Learning; Embedded Platform; Internet of Things.
  DOI: 10.1504/IJIMS.2024.10061504
   
  
• The Impact of Self-Service Technology on User Satisfaction: A Study of Mobile Banking Apps  
  by Chunhua Jin, Qiqi Wang, Na Zhang 
  Abstract: With the booming development of mobile internet and financial technology, the digital transformation of banks has been pushed forward to depth, and self-service technology (SST) has been popularised on a large scale. Researchers and practitioners are increasingly concerned about whether and how the service quality of self-service technologies improves user satisfaction. The causal chain of service quality-perceived value-user satisfaction, which is derived from the China user satisfaction index model, was used to investigate the influence mechanism and boundary of SST service quality on user satisfaction. By analysing 378 mobile banking app users through structural equation modelling, the results suggest that there was a partially mediating effect of perceived value on the relationship between SST service quality and user satisfaction and that bank image has a significant moderating role in the effect of SST service quality on perceived value. Our findings provide new perspectives and evidence for the study of the mechanism of the action of SST service quality in banks.
  Keywords: self-service technology; SST; service quality; perceived value; user satisfaction; bank image.
  DOI: 10.1504/IJIMS.2025.10062401
   
  
• E-Technology 2.0: The Role of Social Media in Shaping Modern Education  
  by Sushanta Kumar Mohanty, Chandrakant Mallick, Bijay Paikaray 
  Abstract: The use of social media in e-learning has become increasingly popular in recent years due to its potential to enhance the learning experience for students of higher education institutions. Social media has significant impacts on e-learning, with both positive and negative consequences. This paper discusses the benefits of incorporating social media into e-learning, including its ability to transform traditional teaching methods, as well as the challenges associated with integrating social media into the educational environment. Additionally, the paper explores the role of social media in e-learning for university students and the impacts it has on transforming the learning experience. However, it also highlights the negative effects of social media, such as the spread of misinformation, which can negatively impact the learning experience of today’s youth. The paper concludes with strategies for optimising social media in e-learning, addressing challenges, and leveraging opportunities.
  Keywords: e-learning; social media; Massive Open Online Courses; MOOC; e-technology.
  DOI: 10.1504/IJIMS.2025.10062402
   
  
• Hierarchical and Balanced Scheduling Method of Data intensive Workflow in Industrial internet of things  
  by Yun Yang  
  Abstract: To improve the throughput of the data-intensive workflow scheduling process and shorten the task completion time, a hierarchical and balanced scheduling method for data-intensive workflow in the industrial internet of things (IIoT) was proposed. Firstly, according to the structure of the workflow system, workflow tasks are classified and processed in a top-down manner. Secondly, calculate the completion time and load balancing degree of the workflow, and construct a workflow analysis balanced scheduling objective function under the constraints of time and load balancing degree. Finally, the frog position is updated, and the frog jumping algorithm is used to solve the objective function to obtain the optimal solution, thereby generating the optimal scheduling plan. The experimental results show that the task completion time of the proposed method does not exceed 30 s, and the maximum load balancing rate reaches 37%.
  Keywords: industrial internet of things; IIoT; data-intensive; workflow; graded balanced scheduling.
  DOI: 10.1504/IJIMS.2025.10062505
   
  
• A Comprehensive Systematic Review of Progressive Applications of LoRa and LoRaWAN Networks in the Internet of Things  
  by Jitendra Pramanik, Singam Jayanthu, Abhaya Kumar Samal, Dr. Bijay Paikaray 
  Abstract: The internet of things (IoT) is a fast-expanding trend that combines cutting-edge communication technologies to transform commonplace objects into intelligent, networked gadgets. This movement is rapidly expanding. This paves the way for developing intelligent processes with applications across industries like monitoring and optimisation. LoRa has been promoted as a potential choice for the backbone of the internet of things due to its long range, low power consumption and low bit rate. Using LoRa over a single wireless hop, messages are transmitted from end devices to a central network server via gateways connected to the internet, which act as transparent bridges. This article provides in-depth information about LoRa networks and the technology's specifications. It also discusses the magnitude of projected growth trends and the challenges inherent in deploying LoRa-based networks. In addition, the paper is coupled with perceptive remarks and thought-provoking suggestions for future research endeavours. The study was conducted to encourage more research towards expanding the LoRa network and make it suitable for more widespread deployment.
  Keywords: LoRa; long range wide area network; LoRaWAN; internet of things; IoT.
  DOI: 10.1504/IJIMS.2024.10063638
   
  
• Impact of Human Resource Crisis Management on University Faculty Performance in E-Technology during COVID-19  
  by Angurbala Mishra, Subhasmita Biswal, Chandrakant Mallick, Bijay Paikaray 
  Abstract: The pandemic brings everything into society; it hinders all concerns of society and the education system. But society has to overcome any situation to work-from-home using the platform of online classes, this forces the university to improvise quickly and adopt online teaching methods. This study was to evaluate the effects of HR practices that allow faculty members to work-from-home on their performance under the challenging COVID-19 conditions. Faculty from several universities in Bhubaneswar, Odisha, India, participated in the study. This study addressed a gap in the limited body of research regarding the effects of different HR representatives when working remotely during a lockdown to control the COVID-19 pandemic. This article demonstrates the strong correlation between HR practices using the principle of reinforcement. The findings of this study offer guidance for enhancing HR practices so that university faculties can perform at a high level when working remotely.
  Keywords: human resource management; HRM; work-from-home; performance appraisal; employee participation; employee performance; e-technology.
  DOI: 10.1504/IJIMS.2025.10063648
   
  
• A Deep Learning Model with Effective Tokenisation and Feature Extraction for Detection of Rumours in Online Social Networks  
  by Chandrakant Mallick, Sarojananda Mishra, Sumanjit Das, Bijay Paikaray 
  Abstract: The proliferation of rumours on social media platforms, as well as their potential societal impact, presents a serious challenge that necessitates robust models for the precise detection and filtering of rumour posts in order to limit their harmful implications. This paper explores the persistent problem of rumour spread on social media platforms and its significant societal effects. In order to achieve highly accurate detection and filtration of rumour-related posts within online social networks, it introduces a novel deep learning model that makes use of long-short-term memory architecture along with reliable tokenisation and feature extraction techniques. The model's extraordinary effectiveness in identifying rumours is systematically assessed using well-known Twitter datasets and contrasted with other state-of-the-art models, demonstrating its efficacy in the detection of rumour posts.
  Keywords: rumour detection; online social networks; deep learning; long-short-term memory; LSTM; tokenisation; feature extraction.
  DOI: 10.1504/IJIMS.2024.10063650
   
  
• Open-Source Solutions for Real-Time Data Retrieval in Industrial Automation and IoT Environments  
  by Seema B. Hegde, Narendra Reddy T, Prasad P, K.V. Manjunath, Mervin A. Herbert, Srikanth Rao 
  Abstract: Digitalisation of the manufacturing industries due to the implementation of the industrial internet of things (IIOT) is a key enabler for improved productivity and reliability at a reduced labour cost. The industrial IOT connects all the industrial machines such as PLCs, CNCs, and robots through a robust network. The generated data by these end devices plays a vital role in industrial automation, however acquiring the data from machines specifically legacy machines using various communication protocols is the biggest challenge and costly process, especially for MSMEs. Hence this paper discusses the usage of the open-source framework for real-time data acquisition from industrial machines and its implication in Industry 4.0. The paper implements and validates the possibility of the usage of an open-source framework for data acquisition instead of vendor-specific licensed software using several test cases. The paper also validates and proves Wireshark can be a universal open-source solution for data acquisition using any standard communication protocols from various vendor-specific machines. Hence this work provides a novel solution for the digitalisation of the MSME manufacturing industries efficiently at the reduced maintenance cost and improve their productivity.
  Keywords: Industry 4.0; Communication protocols; Data retrieval; IIOT; Automation; open-source tools.
  DOI: 10.1504/IJIMS.2025.10063809
   
  
• Challenges in adopting artificial intelligence for food manufacturing and supply chain post-pandemic in Palestine  
  by Kawther Mousa, Zhang Zhenglian, Waleed Salama 
  Abstract: The COVID-19 outbreak has seriously caused food manufacturing and supply chain (FMSC) disruptions, thereby food insecurity. The paper purposes to demonstrate the challenges of adopting machine learning and artificial intelligence (MLAI) and for mitigating the effects of COVID-19 in Palestine FMSC. This study uses an integrated MICMAC-FISM to identify 19 main challenges derived from an inclusive review of publications and professionals opinions. Subsequently, the discovered challenges are prioritised using analytical network process (ANP). Results show that the most critical challenges of MLAI adoption in the FMSC are inadequate privacy and security of data and absence of governments policies. Besides, MLAI in the FMSC is an influential tool for predicting the future accurately to minimalise fears and uncertainty caused by pandemic. The paper is an initial attempt to assess the likelihood of MLAI in the FMSC post-COVID-19 using an integrated method of MICMAC, FISM, and ANP in Palestine.
  Keywords: Risk management; artificial intelligence; food manufacturing; food supply chain; COVID-19; MICMAC; Fuzzy-ISM.
  DOI: 10.1504/IJIMS.2025.10063898
   
  
• Securing Healthcare in the Cloud: A Machine Learning Perspective  
  by Suneeta Satpathy, Subhasish Mohapatra, Pratik Kumar Swain, Bijay Paikaray 
  Abstract: The healthcare sector has transformed because of cloud computing, which provides scalable and affordable methods for handling and storing enormous volumes of patient data. However, ensuring the security and privacy of sensitive healthcare information remains a significant challenge. This paper explores the application of machine learning techniques to enhance the security of cloud healthcare services. The study discusses the potential benefits of machine learning in detecting and preventing security breaches, and ensuring data privacy, and addresses the unique challenges faced by the healthcare industry in cloud computing. The present research adopts different machine learning algorithms that can be leveraged to strengthen the security of cloud healthcare services and present real-world examples of their implementation. Finally, the paper discusses the limitations and future directions of the application of machine learning in securing cloud healthcare services.
  Keywords: cloud healthcare system; machine learning; web services; security.
  DOI: 10.1504/IJIMS.2025.10063954
   
  
• Entrepreneurs adoption of Social Media Winning Platform(s) in Emerging Markets  
  by Rami Farhat, Qing Yang 
  Abstract: Little is known regarding using digital platforms (DPs) by individual entrepreneurs in emerging markets (EMs) and the strategies used for integrating these business platforms into their marketing campaigns. Introducing AI Technologies in e-marketing, many marketers misuse the new digital technologies, and there are still very few studies on how entrepreneurs use or decide on social media platforms. Applying the UTAUT theory, our study includes a new method to measure the effectiveness of online marketing in emerging countries by launching the same ad on different platforms. This study aims to discuss this issue using a qualitative approach focusing on semi-structured interviews with entrepreneurs in Lebanon. The participants on Facebook were users between 18 and 64 interested in e-commerce. This study describes an investigation to gain an understanding of the best practices of digital platforms based on benchmarks and optimising online marketing campaigns to make SMEs, entrepreneurs and digital marketers more aware of the powers of media and through utilising ads manager benchmarks based on campaigns launched on different platforms to rank the most effective social media platform.
  Keywords: digital platforms; social media; entrepreneurs; marketing campaigns; emerging markets.
  DOI: 10.1504/IJIMS.2025.10064254
   
  
• Effects of STARA Awareness on the Job Performance of Healthcare Providers: the Mediating Role of Qualitative Job Insecurity  
  by Na Zhang, Xiaoyun Liu, Chunhua Jin 
  Abstract: Smart technology, artificial intelligence, robotics, and algorithms (STARA) are transforming the practice of healthcare. The phenomenon that many healthcare providers are worried about being replaced by these technologies in the future has attracted the attention of scholars. However, few studies have explored the impact of healthcare providers' STARA awareness on their work outcomes in the medical and healthcare services industry. This study attempts to address this knowledge gap by exploring the effects of STARA awareness on the job performance of healthcare providers and to explore the mediating role of qualitative job insecurity based on the stressor-strain-outcome model. A total of 290 healthcare providers from China were investigated through an online survey. SPSS 23.0 and Mplus 8.0 were used to analyse the collected data. These results showed that healthcare providers' STARA awareness positively affects task performance and contextual performance through qualitative job insecurity. These findings highlight the influence of advanced technologies on healthcare providers and provide guidance for organisations to help them achieve higher job performance.
  Keywords: STARA awareness; qualitative job insecurity; task performance; contextual performance; mediating effect.
  DOI: 10.1504/IJIMS.2025.10064491
   
  
• How can Social Media Platforms Usage Support Investors' Green Investment Intention? An empirical study  
  by Waleed Salama, Zhang Jian, Angela Wangechi Mwaniki, Kawther Mousa 
  Abstract: This study essentially targets to examine the relationship between, perceived green consumption commitment (PGCC) and perceived investment provider's reputation (PIPR) in green investment intention (GII) with the moderating effect of social media platforms usage (SMPU) among the individual investors in Egypt. The study depended on a sample of 450 individuals' investors who have investment experience; we used (PLS-SEM) techniques using Smart PLS as new computer application to analyse the data and test hypotheses based. The outcomes indicated that attitude (ATT), subjective norm (SN), PGCC, PIPR and SMPU have significant correlation with GII. As the moderating effect, SMPU moderated the association between ATT, SN, PGCC and PIPR with GII. The study also provides some implications for investment providers, service providers, and policymakers.
  Keywords: theory of planned behaviour; TPB; social media platforms; perceived green consumption commitment; PGCC; perceived investment provider’s reputation; PIPR; investor behaviour; green investment.
  DOI: 10.1504/IJIMS.2025.10064497
   
  
• Enhancing Breast Cancer Risk Prediction through Comprehensive Ensemble Machine Learning Analysis: A Study on Clinical, Genetic, and Demographic Factors  
  by Chandrakant Mallick, Chitta Ranjan Behera, Subrat Kumar Parida, Bijay Kumar Paikaray 
  Abstract: Breast cancer is a major worldwide health problem, and early risk assessment plays a crucial role in improving patient outcomes. In this study, we employ supervised machine learning techniques to comprehensively analyse and predict breast cancer risk. Leveraging a diverse dataset comprising clinical, genetic, and demographic factors, explore the predictive power of machine learning algorithms. Our comprehensive analysis delves into feature selection, model evaluation, and performance optimisation. The proposed ensemble model has been validated on Wisconsin Breast Cancer Diagnostic medical dataset. The importance of this research in the context of improved patient care, screening programs, and risk assessment tools. It contributes to the ongoing effort to enhance breast cancer risk prediction through advanced data-driven methods, paving the way for more effective preventive strategies and early interventions. It show that effective data pre-processing performed to the raw data and feature selection the model resulted in an enhanced accuracy of 98.24%.
  Keywords: breast cancer; risk assessment; machine learning; predictive modelling; feature selection; early detection; personalised healthcare.
  DOI: 10.1504/IJIMS.2025.10064804
   
  
• Experimental Analysis of Mechanical Characteristics of fabricated Biodegradable Magnesium Hydroxyapatite MMCs for Biomedical Purposes  
  by Neeraj Kumar, R.A.J. Kumar Duhan, Bhaskar Chandra Kandpal, Varun Singhal 
  Abstract: In regards to materials, orthopaedic implants can be composed of magnesium alloys. They are absolutely biocompatible and have characteristics that comparable to the individuals of authentic bone. Their degradable ability indicates that they do not have to be removed out after the wound has healed completely. Magnesium, however, corrodes too quickly in the body's environment, so matrix composites can be a solution for controlling its corrosion rate and improving its mechanical qualities. In the present study, a stir casting technique was used for developing Mg HAP MMCs. As a reinforcing material, HAP powder has been utilised. After casting, composites were cut into required form samples for mechanical testing. In the final stage, tensile and compression tests were used. As a result of adding HAP powder, UTS, total tensile strain and compressive strength are significantly reduced. The elongation reduced, yield strength and young modulus shows distinct behaviours but compressive yield strength increased.
  Keywords: biodegradable magnesium hydroxyapatite metal matrix composites; stir casting technique; tensile test; compression test; Young’s modulus; percentage elongation.
  DOI: 10.1504/IJIMS.2025.10064809