International Journal of Information Technology and Management (9 papers in press)

Regular Issues

• A Comprehensive Evaluation Method of Chinese Online Teaching Effect Based on Cluster Analysis  
  by Yuanyuan Zhang 
  Abstract: In order to improve the accuracy of evaluation results, a comprehensive evaluation method for the effectiveness of Chinese online teaching based on cluster analysis is proposed. Firstly, the AHP is used to select evaluation indicators, and the weighted average method is applied to quantify the evaluation indicators. Secondly, the K-means algorithm is used to comprehensively evaluate the teaching effectiveness, calculate the average value of all sample points within each cluster to locate the cluster centres, and iteratively update the centre position. Finally, repeat the calculation of the distance from each point to the nearest cluster centre, select new cluster centres, ensure that the distance between the initial cluster centres is as far as possible, and use the optimised K-means algorithm to achieve teaching effectiveness evaluation. The experimental results show that the proposed method has a low mean square error, indicating that its evaluation results are relatively accurate.
  Keywords: Cluster analysis; Online teaching; Effect evaluation; K-means algorithm.
  DOI: 10.1504/IJITM.2025.10070102
   
  
• Accurate Recommendation Method for Enterprise Product Network Marketing Information under the Background of Big Data  
  by Chang Liu 
  Abstract: Aiming to achieve personalised and precise recommendation of marketing information, a method for accurate recommendation of enterprise product network marketing information under the background of big data is proposed. Firstly, collect user information data and pre-process the data to construct a user profile that comprehensively describes user interests and preferences based on the data processing results. Secondly, a collaborative filtering algorithm based on users and items is adopted for predicting user preferences. Finally, the three-dimensional features of marketing information are obtained through the serial parallel convolutional gate valve recurrent neural network in deep learning, and combined with user profiles and preference prediction results, the matching between users and marketing information is achieved, thereby realising personalised recommendation of marketing information. The experimental results show that the proposed method has high recommendation accuracy, high user satisfaction, and high data processing efficiency, indicating its good application effect.
  Keywords: Big data; Online marketing; Information recommendation; User profile; Collaborative filtering.
  DOI: 10.1504/IJITM.2025.10070109
   
  
• A balanced scheduling method for multi-threaded tasks based on two-level parallelism between clusters and big data clustering  
  by Xian Yang, Jue Huang, Yun Zhao, Hui Tong, Shibing Chen, Yuxin Lu, Wei Cao 
  Abstract: To improve the efficiency of task scheduling and enhance the negative load-balancing effect of tasks, this paper proposes a multi-threaded task-balancing scheduling method based on two-level parallelism between clusters and big data clustering. Firstly, use fuzzy C-means clustering to group task data into multiple clusters based on feature similarity. Then, build a multi-threaded task model that allows tasks to be executed in parallel on multiple threads, achieving two-level parallel processing between and within clusters. Finally, by determining task priority and hierarchical sorting, a task scheduling manager is designed to achieve balanced task scheduling. The experiment shows that the maximum standard deviation of the load in this method is 0.05, and the maximum over-time task ratio is 0.015, indicating that this method has strong load-balancing ability and can achieve real-time processing of tasks.
  Keywords: multi-threaded model; task scheduling; balanced scheduling; fuzzy C-means clustering; task scheduling manager.
  DOI: 10.1504/IJITM.2025.10070570
   
  
• Supply Chain Information Sharing and Collaborative Innovation based on Social Network Analysis  
  by Yiming Shen, Jingyi Qiu, Jie Mei, Xin Sun, Luyao Qu 
  Abstract: In order to improve the completeness of supply chain information sharing and shorten the sharing time, a supply chain information sharing and collaborative innovation method based on social network analysis is proposed. Firstly, through dynamic programming and cost function optimisation, the optimal cluster of supply chain information is divided. Secondly, generate anonymous sequences of supply chain information through social network analysis and dynamic grouping strategies. Once again, establish a node reputation evaluation system that combines direct and indirect reputation to achieve secure sharing of supply chain information. Ultimately, leveraging social network analysis, the supply chain's information sharing mechanism is refined, fostering synergistic collaboration between resources and competencies. Additionally, network governance structures are crafted to enhance collaborative innovation within the supply chain. Empirical outcomes reveal that the proposed approach in this study achieves a 0.97 completeness rate for supply chain information sharing, concurrently reducing the duration required for information dissemination.
  Keywords: Social network analysis; Supply chain; Information sharing; Collaborative innovation.
  DOI: 10.1504/IJITM.2025.10072063
   
  
• Eliminating Duplicate Values of Enterprise Financial Big Data based on Dynamic Grid Generation Technology  
  by Xiaoyang Li 
  Abstract: To improve the spatial reduction rate of the processed dataset and adjust the Rand coefficient, this paper designs a method for removing duplicate values in enterprise financial big data based on dynamic grid generation technology. Firstly, denoising of enterprise financial big data is implemented through fast orthogonal wavelet transform. Secondly, based on dynamic grid generation technology, the fusion correlation features of enterprise financial data are constructed, and the correlation degree between data is calculated. Finally, use similarity clustering algorithms to cluster data with high correlation. For highly similar data in the same cluster, retain one record and exclude other identical data entries. The experimental results show that after applying this method, the spatial reduction rate of the dataset ranges from 9.61% to 15.55%, and the highest adjusted Rand coefficient of the dataset can reach 0.997, indicating that this method effectively achieves the design expectations.
  Keywords: Enterprise financial data; Data duplicate value; Elimination process; Dynamic grid generation technology; Fusion of associated features; Similar clustering algorithm.
  DOI: 10.1504/IJITM.2025.10072066
   
  
• Can Blockchain Help Organizations Streamline Their Operations The Case of Luxury Brands  
  by Sarah Bouraga 
  Abstract: Luxury brands have been facing various pain points over the years, such as counterfeiting or the global issue of sustainability. They also have to create a deep connection with their consumers, making customer relationships essential. Blockchain can help address these issues and opportunities. Using a multiple-case study where we apply an exploratory and inductive approach, we address the following research questions: (i) How can blockchain streamline luxury brand business processes? (ii) How can luxury brands use blockchain to bring value to customers? (iii) How can DApps developers/designers make existing blockchain-based solutions more effective and efficient? This study allows us to draw various conclusions that can have implications in theory and practice In particular, the contribution lies in its identification of strategic and technological concerns around the implementation of a blockchain-based solution, the importance of sustainability, the recognition of characteristics of the blockchain-based application, and the acknowledgment of remaining
  Keywords: Blockchain; Non Fungible Token (NFT); Sustainability; Luxury Brand; Fashion; Counterfeiting 
  
  
• Can Blockchain Help Organisations Streamline Their Operations? The Case of Luxury Brands
  by Sarah Bouraga 
  Abstract: Luxury brands have been facing various pain points over the years, such as counterfeiting or the global issue of sustainability. They also have to create a deep connection with their consumers, making customer relationships essential. Blockchain can help address these issues and opportunities. Using a multiple-case study where we apply an exploratory and inductive approach, we address the following research questions: (i) How can blockchain streamline luxury brand business processes? (ii) How can luxury brands use blockchain to bring value to customers? (iii) How can DApps developers/designers make existing blockchain-based solutions more effective and efficient? This study allows us to draw various conclusions that can have implications in theory and practice In particular, the contribution lies in its identification of strategic and technological concerns around the implementation of a blockchain-based solution, the importance of sustainability, the recognition of characteristics of the blockchain-based application, and the acknowledgment of remaining challenges.
  Keywords: Blockchain; Non Fungible Token (NFT); Sustainability; Luxury Brand; Fashion; Counterfeiting .
  
  
• A Novel Leader Election Mechanism for Distributed Systems using Dynamic Priority Scores  
  by Remyakrishnan P 
  Abstract: Efficient coordination in distributed systems relies on selecting a suitable leader node, a process often challenged by asynchronous operations and node failures. Traditional leader election algorithms struggle with scalability and fault tolerance, necessitating more adaptive approaches to address these challenges. This paper introduces Priority Enhanced Quorum Consensus (PEQC), a novel leader election mechanism that integrates dynamically computed priority scores with quorum-based decision-making. By utilising priority-based selection, PEQC ensures the election of the most suitable node while addressing node downtime and enhancing system responsiveness. A comparative analysis with existing algorithms demonstrates PEQC's superior performance in reducing election overhead, improving fault recovery, and ensuring fair leadership distribution.
  Keywords: Leader Election; Distributed systems; Threshold; Distributed Algorithms; Quorum; Priority.
  DOI: 10.1504/IJITM.2025.10075446
   
  
• VRPM-HCM: Enhancing Cloud Power Efficiency Through Hybrid Machine Learning and Model Predictive Control  
  by Sai-Feng Zeng 
  Abstract: The proliferation of cloud computing has intensified energy consumption in data centres, demanding innovative solutions to balance power efficiency and performance. Traditional methods struggle to address dynamic interactions between virtual machines, workloads, and hardware in virtualised environments. This paper proposes VRPM-HCM, a novel framework that synergises model predictive control with machine learning to optimise power consumption while ensuring service-level agreement compliance. The framework introduces a dual-layer control mechanism for real-time CPU frequency adjustments and VM migrations. Experiments demonstrate that VRPM-HCM achieves 23.9% average power savings, reduces SLA violations by 1.2%2.7%, and maintains 91%94% resource utilisation, outperforming state-of-the-art baselines. These results validate its effectiveness in harmonising energy efficiency, performance guarantees, and hardware longevity in dynamic cloud environments.
  Keywords: virtual cluster; quality of service; power control; control theory.