International Journal of Technology, Policy and Management (10 papers in press)

Regular Issues

• Financial Fraud Identification Technology Based on FL Model and MLP Neural Network  
  by Zhenyu Chu, Xuewu Lai 
  Abstract: The phenomenon of data silos in financial institutions leads to poor application performance of federated machine learning models. Therefore, a financial fraud recognition system based on a federated learning model and a multi-layer perceptron neural network was studied and designed. Firstly, a federated learning model based on the federated average algorithm was used to achieve joint training of various financial institution nodes, and a composite minority class oversampling algorithm was used for data balancing preprocessing. The validation in the credit card transaction dataset confirmed that the accuracy of the financial fraud recognition system in sample classification recognition in the training set was 99.60%, and the recognition effect in the test set was 99.21%. Compared with other training models, the proposed system had an average increase of 2.15% in area under the curve and an average increase of 2.18% in geometric mean. These results confirm that the proposed financial fraud identification system has superior financial fraud identification performance while meeting the conditions of user data privacy and security.
  Keywords: Federated machine learning; Multi-layer perceptron; Financial fraud identification; Joint training; Data balancing preprocessing.
  DOI: 10.1504/IJTPM.2026.10070744
   
  
• Rethinking ICT and Digital technology Policy Designing to Harness Artificial Intelligence for Accelerating Gender Equity and Youth Empowerment in Sub-Saharan Africa.  
  by Blessing Masamha, Palesa Sekhejane, Uzma Alam 
  Abstract: Mainstreaming gender in ICT policies in Africa remains challenging since women and youths are disproportionately affected by AI technologies. The mapping of policy gaps and subsequent rethinking of ICT and AI policy development pathways to advance gender equity and youth empowerment in Africa was done. A case study design was adopted with a qualitative archival and reflective analysis of policy blueprints, plans, and strategy documents (62) from eleven African countries. High-level regional multi-stakeholder workshops were held in the ECOWAS, SADC, and COMESA regions. The policy gaps exist due to limited bottom-up consultative processes, policy incoherence, minimal multi-sectoral co-designing and co-implementation frameworks, and limited verifiable achievement indicators. Thematic analysis used NVIVO 14 software, and few countries referred to women and youths in their policy blueprints, with Liberia (14%), Sierra Leone (10%), and South Africa (5%) emerging high. Only Rwanda had an AI policy with no focus on gender and youth empowerment.
  Keywords: Policy; ICTs; Artificial intelligence (AI); gender equity; youth empowerment; sub-Saharan Africa.
  DOI: 10.1504/IJTPM.2026.10071137
   
  
• Research on Innovative Grid Data Management based on Traceability Model and Community Blockchain Technology  
  by Silong Wu, Junting Deng, Jiaojiao Zhang 
  Abstract: Smart grid data centres hold a lot of high-value data, but since each data centre has a limited variety of resources, data exchange is crucial to carrying out an efficient data mining process. However, centralised systems that lack shared object authentication are often used in conventional data-sharing models, which makes it challenging to build trust and protect data privacy. This makes overcoming the issue of data islands challenging. This article suggests a blockchain-based data-sharing incentive model for edge smart grid scenarios in order to address the aforementioned issues. Initially, the approach achieves the security and traceability of the smart grid data-sharing process by using blockchain and proxy re-encryption technologies. Second, game theory is used in the design of the data-sharing incentive algorithm to optimise data owners' readiness to share their data. The scheme in this paper has significant advantages over the others in terms of functionality and computational overhead.
  Keywords: blockchain; smart grid data; smart contract; conditional proxy re-encryption.
  DOI: 10.1504/IJTPM.2026.10071326
   
  
• Evaluating Technological Impacts on Stock Market Behaviour: a Machine Learning and NLP Approach to Socio-Economic Analysis  
  by Richa Handa, Pavani Sirigiri, Bisahu Ram Sahu, Bijay Kumar Paikaray, Madhusmita Mohanty, Lata Algamkar 
  Abstract: Indian stock market is influenced by Politics, finance, and various other internal and foreign issues. It is very challenging for academicians to predict the behavior of the stock market accurately. Nowadays, people communicate their ideas on social media on various topics depending on what they wish to write. Social media plays a very important role in knowing about the current trends of stock data as people nowadays share their views on social media, whether positive or negative. In this study, we analyze sentiments of people on the stock market using Twitter data and classify it using machine learning techniques to develop an analytical model such as Bernoulli Naive Bayes, SVM (Support Vector Machine), and Logistic Regression and perform a comparative study to find out which model is outperforming for sentiment analysis of Indian stock market.
  Keywords: Stock Data; Semantic Analysis; Twitter; social media; Bernoulli Naive Bayes; SVM; Socio-Economic.
  DOI: 10.1504/IJTPM.2026.10071605
   
  
• The Knowledge City and Regional Application of Labour Insurance Supply Management from Big Data Algorithms  
  by Yanhua Wang, Jie Duan, Wei Gu, Jing Duan, Haitao Liu 
  Abstract: This paper investigated the effectiveness of labour insurance supply management in knowledge cities and regional applications through the use of big data (BD) algorithms. It compared the Big Data Algorithm-Based Management (BDAM) model with the traditional rule-based management (RBM) model across key metrics, including efficiency, safety, innovation, sustainability, costs, and resource utilisation. Results indicated that the BDAM model significantly outperformed the RBM model, with higher resource allocation efficiency (16% to 39% vs. 17.54% to 67.71%), superior safety levels (53.49% to 62.10% vs. 29.32% to 36.57%), and better innovation and sustainability scores. Although the BDAM model incurred higher initial costs, it demonstrated cost savings over time, with costs decreasing from 18.95 to 16.25, and maintained higher resource utilisation efficiency (0.737 to 0.810 vs. 0.573 to 0.635). The study emphasised the BDAM models flexibility, scalability, and potential for integration with other smart city components.
  Keywords: big data algorithms; labor insurance supply management; knowledge city; regional applications; resource utilization efficiency.
  DOI: 10.1504/IJTPM.2027.10071606
   
  
• The Impact of Globalisation on Technology Transfer: Assessing the Role of Multinational Corporations and International Trade in Driving Technological Innovation in Emerging Economies  
  by Siyang Mei, Yuxi Zhang, Renfeng Deng, Xin Liu, Feng Li 
  Abstract: The study's goal is to find out how changes in geopolitics and geoeconomics impact the process of creating a global energy system. There is a lot of talk in the paper about how the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the Regional Comprehensive Economic Partnership (RCEP) deals have changed. There are more and more people going around the world. A global SWOT analysis and the New Globalization Scenario Matrix (NGSM) help us figure out and guess what the main global trends will be in the future. The current events in the world could improve the system's performance, leading to faster energy changes. There is now a new way to look at the IPE of energy around the world after this event. This is one way that realism and liberalism work well together to create a new type of global liberalism.
  Keywords: The Correlative SWOT Analysis; Geoeconomics ; Modern Geopolitics; Descriptive Statistics; Basic Inspection; Regulation Effect Test; Heterogeneity Test.
  DOI: 10.1504/IJTPM.2026.10072210
   
  
• A Multi-Level Approach for Asymmetric Technological Cooperation  
  by Seyedamirreza Enjavi, Shaban Elahi, Ali Shayan, Esmaeil Mousavi 
  Abstract: Asymmetric technological cooperation (ATC) is crucial for the knowledge-based economy, enabling significant science and technology transfer. This study highlights ATCs importance in public policy and examines various ATC processes and programs, alongside policymakers motivations to intervene. Using meta-synthesis, the research consolidates relevant literature to define a typology of ATC activities and the policies that promote them. The study adopts a multi-level framework niche, regime, and landscape levels to analyse policy measures that facilitate ATC by driving socio-technical system changes. It argues that effective ATC depends on active policymaker involvement to support and manage these systemic transitions. Ultimately, the paper emphasises that policymakers play a vital role in enabling ATC, which is essential for fostering technological progress and sustaining knowledge-based economic growth.
  Keywords: asymmetric technological cooperation; collaboration; facilitation policy; incumbent; startup.
  DOI: 10.1504/IJTPM.2027.10072399
   
  
• The Application and Optimisation of Extensive Data Analysis in the Evaluation of the Effect of Marketing Strategies on Agricultural Machinery Enterprises  
  by Wei Shen 
  Abstract: Effective decision-making is crucial for business growth, particularly when financial factors are involved. This study employs AI models to analyze the relationship between selected agroeconomic indicators and digital marketing data. It is essential to explain how these metrics influence decision-making. Data was collected from the websites of five leading agricultural companies, where index values were recorded and compiled. Psychological stress and depression assessments were used to explore potential correlations with digital marketing usage. Artificial Neural Network (ANN) models were applied to establish these connections. Key metrics include advertising traffic sources, business-related expenses (both incurred and avoided), and overall digital engagement. Increasingly, large agricultural firms are being advised to invest in AI and digital marketing tools. These technologies help them better understand employment trends and fluctuations in prices of equipment, medications, and agricultural inputs. As a result, companies can make more informed decisions and develop more effective business strategies.
  Keywords: Agroeconomic indexes; big data; AI; ANN; digital marketing; digital transformation; predictive analytics; agriculture; decision support systems (DSS).
  DOI: 10.1504/IJTPM.2027.10073781
   
  
• The Cyber Security Spending Paradox: Unravelling the Enigma of Escalating Threats in the Face of Increased Investment in the USA (2010-2023)  
  by Esra Merve Caliskan  
  Abstract: This study examines the paradoxical relationship between increasing United States of America (USA) cybersecurity investment and rising cyberattacks from 20102023. Despite over $20 billion in cybersecurity spending, attack frequency and complexity continue escalating. Through mixed-methods analysis utilising comprehensive statistical techniques, including time-series decomposition, correlation analysis, and regression modeling with gross domestic product (GDP), internet usage, and technological advancement controls, this research reveals that traditional reactive spending strategies, asymmetric offence-defence dynamics, and inadequate measurement frameworks better explain this paradox than simple investment ineffectiveness. Using official data from Cybersecurity and Infrastructure Security Agency (CISA), FBI IC3 (Federal Bureau of Investigation Internet Crime Complaint Center), and National Institute of Standards and Technology (NIST), the analysis demonstrates that spending-security relationships are more complex than previously understood. Traditional security models prove insufficient against current threats. The study advocates for fundamental policy shifts toward adaptive, intelligence-driven approaches prioritising proactive defence, cross-sector coordination, and dynamic resource allocation, urging radical cybersecurity strategy reassessment.
  Keywords: cyber security; policy making; security budget; national security; USA.
  DOI: 10.1504/IJTPM.2027.10074063
   
  
• The Role of Government Policy in Accelerating Technology-Driven Economic Growth  
  by Jing Chang 
  Abstract: The economy of cities could grow if the government made it easy for businesses to go digital. This will help cities grow better by giving politicians more information. A big reason why towns economies do well in the long run is that businesses are becoming more digital. The purpose of the study is to find out if making the government digital really does make businesses more likely to go digital, which helps urban economies grow. This is done with information from A-share listed companies from 2012 to 2022 and information made by the urban big data administration office. Most studies show that when the government goes digital, it speeds up the digital change of businesses by a lot. The variety study shows that the policies of a country can be different depending on its location, the type of government it has, and the speed at which its economy grows.
  Keywords: digital government; digital transformation; urban business environment; information search costs; urban economic sustainable development.
  DOI: 10.1504/IJTPM.2027.10074276