Innovation and Technology Studies (ITS) is an international peer-reviewed, open access journal.
PRICING AND LIQUIDITY OF CROSS-BORDER TRADE FINANCE ASSET TRANSFERS BASED ON BILATERAL PRICING
Cross-border trade finance, as a critical component of global supply chain financing, has been constrained by persistent pricing opacity, liquidity fragmentation, and structural information asymmetries that impede efficient price discovery for market participants. This paper presents a comprehensive bilateral pricing framework for trade finance asset transfers that explicitly incorporates bidirectional counterparty credit exposure (including CVA and DVA adjustments), term liquidity premiums (TLP), and cross-border settlement risk into a unified valuation architecture. Through empirical validation using trade finance securitization programs and cross-border payment systems, we demonstrate that bilateral pricing models substantially reduce pricing heterogeneity and enhance market liquidity relative to traditional unilateral approaches. Leveraging transaction-level data from Deutsche Bank's Trafin securitization series (USD 3.5B), Standard Chartered's tokenized asset initiatives, and the IMF's multilateral payment platform assessments, this paper provides quantitative evidence that a dynamic bilateral pricing framework achieves capital allocation efficiency improvements ranging from 23% to 165 basis points (bps) in cost optimization relative to conventional methodologies. We verify complete compatibility with Basel III counterparty credit risk (CCR) capital requirements and provide a systematic pricing methodology applicable to both traditional trade finance instruments (letters of credit, accounts receivable) and emerging tokenized asset platforms. The framework explicitly models the value contribution of exporter creditworthiness (DVA) that traditional unilateral pricing methodologies systematically ignore, while providing transparent decomposition of all pricing components to facilitate market efficiency and reduce information asymmetries in cross-border commercial transactions.
Copyright (c) 2026 Yang Wu https://creativecommons.org/licenses/by/4.0
THE FUSION OF MARKET AND FAMILIAL LOGICS: A THEORETICAL CONSTRUCTION OF INVENTOR CEO PARTICIPATION AND INNOVATION STRATEGIES IN CHINESE FAMILY BUSINESSES
This paper aims to construct a contextualized theoretical framework integrating institutional logic and upper echelons theory to explore how the hands-on involvement of inventor CEOs in Chinese family firms influences the balance between exploitative and exploratory innovation within the unique context of China's transitional economy. Focusing on the dynamic fusion process between market logic and familial logic in China, this study posits that such fusion forms distinctive mechanisms of "institutionalized compromise" and "strategic synergy" through practices such as intergenerational succession, governance structure evolution, and open innovation. This paper systematically develops a series of propositions elucidating (1) how hybrid governance models like "consortium + professional manager," driven by market logic, reshape the decision-making environment of inventor CEOs by introducing external capital and professional expertise, thereby moderating the impact of their involvement on innovation ambidexterity; (2) how intergenerational succession, constrained by the "socioemotional wealth" and driven by the "succession intention" inherent in familial logic, alters the participation paradigm and innovation orientation of founder-inventor CEOs through value integration and knowledge structure renewal; and (3) how the tension between the "openness" demanded by market logic and the "control" preferred by familial logic in open innovation contexts is moderated by formal governance mechanisms and intellectual property regimes. This research not only deepens the understanding of the contextualized drivers of CEO behavior but also provides a systematic theoretical explanation and managerial implications for Chinese family firms to achieve innovation leaps within institutional fusion.
Copyright (c) 2025 GuangHui Hou https://creativecommons.org/licenses/by/4.0
HETEROGENEOUS GRAPH TRANSFORMERS FOR END-TO-END SUPPLY CHAIN RISK ASSESSMENT: INTEGRATING SUPPLIER NETWORKS, CLIMATE DATA, AND MARKET DYNAMICS
Modern supply chains face unprecedented challenges from diverse and interconnected risk sources, including supplier network disruptions, climate change impacts, and volatile market dynamics. Traditional risk assessment methods struggle to capture the complex, heterogeneous relationships inherent in global supply networks. This paper proposes a novel framework leveraging Heterogeneous Graph Transformers for comprehensive, end-to-end supply chain risk assessment. Our approach integrates multi-source heterogeneous information from supplier networks, climate data, and market dynamics into a unified graph representation that captures system structure, behavior, and strategic elements. The framework employs a specialized graph transformer architecture with multi-head attention mechanisms and edge feature integration to model different node types including suppliers, facilities, and products, alongside diverse relationship types such as procurement dependencies, logistics connections, and climate exposure linkages. Through experiments on multi-tier supply chain networks, we demonstrate superior performance in risk prediction accuracy compared to conventional graph neural network approaches, with particular effectiveness in identifying cascading risk propagation patterns across hierarchical supply structures. The framework provides interpretable risk assessments across multiple organizational levels, enabling proactive supply chain risk management strategies that account for node-level vulnerabilities, network-level topologies, and link-level dependencies.
Copyright (c) 2025 Kenji Nakamura; Laura Schneider https://creativecommons.org/licenses/by/4.0
LLM AND SOCIAL MEDIA FAKE NEWS DETECTION
The proliferation of fake news on social media has made automatic detection methods necessary. Traditional approaches often rely on dynamic or unavailable social context, while this study explores text-based classification methods. We empirically evaluated the performance of several classic machine learning models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, XGBoost) and the advanced large language model DeBERTa on a real-world dataset for fake news detection. For the classic models, we used the TF-IDF vectorizer to extract features. The results show that tree-based ensemble methods, especially Random Forest and XGBoost, performed exceptionally well, with accuracy and F1 scores exceeding 99%. In contrast, the DeBERTa model's performance was only slightly better than random guessing (accuracy of 50.32%), which is attributed to catastrophic overfitting due to its large number of parameters (184 million) when trained on a relatively small dataset. This highlights a key challenge in applying powerful LLMs to specialized tasks: their performance is highly dependent on a large amount of high-quality training data. The research findings suggest that in tasks with limited data, robust classic models may be more effective than complex models that require a large amount of data.
Copyright (c) 2025 ChenYe Zhao https://creativecommons.org/licenses/by/4.0
A REINFORCEMENT LEARNING-BASED ARCHITECTURE FOR HIERARCHICAL CONTROL OF API WORKFLOWS IN ENERGY-CONSTRAINED AD SYSTEMS
Modern advertising systems face increasing complexity in API workflow management due to interconnected service dependencies, dynamic resource requirements, and stringent energy efficiency constraints. Traditional workflow orchestration approaches struggle to optimize complex API execution sequences while maintaining energy consumption within operational limits. The heterogeneous nature of advertising workflows, including real-time bidding pipelines, content personalization processes, and analytics aggregation tasks, requires sophisticated control mechanisms that can adapt to varying performance requirements and energy availability. This study proposes a Reinforcement Learning (RL)-based architecture for hierarchical control of API workflows in energy-constrained advertising systems. The framework employs a multi-tier control structure where high-level workflow coordinators manage execution strategies while low-level API controllers optimize individual service performance within energy budgets. Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms enable adaptive workflow control policies that balance execution efficiency with energy consumption across distributed advertising infrastructure. Experimental evaluation using enterprise advertising system traces demonstrates that the proposed architecture achieves 41% improvement in workflow completion rates while reducing energy consumption by 37% compared to traditional orchestration methods. The hierarchical approach successfully manages complex workflow dependencies and energy constraints, resulting in 33% better resource utilization efficiency and 29% reduction in workflow execution latency.
Copyright (c) 2025 Kelvin Wong; Mei-Ling Chan; Victor Leung https://creativecommons.org/licenses/by/4.0
CHINA’S DIGITAL VILLAGE DEVELOPMENT: REALITIES, PATHWAYS, AND MECHANISMS
Digital technology is an indispensable productive force for rural development. Building digital villages requires transforming the inherent advantages of digital technology into effective mediators for rural modernization, driving comprehensive innovation in rural production and lifestyles. However, China faces practical challenges in leveraging digital technology for rural modernization: insufficient momentum in "new infrastructure" development, a lack of key actors, inadequate government oversight, digital capability gaps within grassroots organizations, and spatial digital divides. Addressing these requires strengthening physical infrastructure, cultivating digital literacy among stakeholders, optimizing policy design, integrating autonomy, rule of law, and virtue-based governance, and bridging spatial disparities. The Chinese government has established a systematic action mechanism for digital village development: (1) Foundation-building mechanism: Consolidating physical infrastructure; (2) Co-creation mechanism: Integrating external forces to ensure stakeholder engagement; (3) Capacity-enhancement mechanism: Addressing capability gaps through digital education; (4) Collaborative mechanism: Restructuring organizations to synergize multi-stakeholder efforts.
Copyright (c) 2025 Ye Pan; YunJie Yang; Nan Yang https://creativecommons.org/licenses/by/4.0
SCIENCE AND TECHNOLOGY INNOVATION EFFICIENCY IN GUANGDONG, HONG KONG AND MACAO GREATER BAY AREA BASED ON STATIC AND DYNAMIC DEA
In the context of the integration of science, technology, and finance, this study aims to scientifically assess the efficiency of science and technology innovation in the Guangdong-Hong Kong-Macao Greater Bay Area. To this end, a system of evaluation indexes, encompassing input-output dimensions, has been constructed based on panel data from 11 cities in the Greater Bay Area from 2019 to 2024. The DEA-Malmquist index method has been utilized in the construction of this system. The regional science and technology innovation efficiency is measured with precision through static efficiency measurement and dynamic total factor productivity decomposition. The reasons for the efficiency differences are revealed from the perspective of technological progress and efficiency changes. The findings of the study demonstrate that (1) the average value of the comprehensive efficiency of science and technology innovation in the Guangdong-Hong Kong-Macao Greater Bay Area exhibits fluctuations and increases, and (2) there are discrepancies in the magnitude of the fluctuations in the efficiency of science and technology innovation and the factors influencing it across different cities. (3) Considering the issue of the Guangdong-Hong Kong-Macao Greater Bay Area's high overall innovation efficiency, coupled with significant internal disparities, there is a compelling need to optimize resource allocation in a targeted manner.
Guangdong-Hong Kong-Macao Greater Bay Area
Copyright (c) 2025 ZiKe Xiong; RuFei Ou; WeiHua Lin; YaYi Lao https://creativecommons.org/licenses/by/4.0
THE INTEGRATION PATH OF IDEOLOGICAL AND POLITICAL EDUCATION IN UNIVERSITY STUDENT MANAGEMENT—TAKING THE SCHOOL OF BUSINESS AT JIANGXI UNIVERSITY OF APPLIED SCIENCE AND TECHNOLOGY AS AN EXAMPLE
This research examines the integration pathways of ideological and political education into student management practices at the School of Business, Jiangxi University of Applied Science and Technology. Using a mixed-methods approach combining quantitative surveys and qualitative interviews, the study explores the theoretical foundations, current status, challenges, and implementation strategies for effective integration. The findings reveal that while formal organizational structures for integration exist, they often function as parallel systems rather than truly integrated mechanisms. The research proposes a multi-dimensional integration framework encompassing curricular, co-curricular, digital, administrative, and cultural domains. Implementation case studies demonstrate that successful integration depends on authentic alignment between management practices and educational values, participatory approaches that engage students as active contributors, and developmental continuity across multiple educational contexts. The study contributes to both theoretical understanding of educational integration processes and practical guidance for institutions seeking to enhance the effectiveness of ideological and political education within student management systems.
Copyright (c) 2025 Qing Xu https://creativecommons.org/licenses/by/4.0
THE DEVELOPMENT AND APPLICATION OF INFORMATION TECHNOLOGY IN TRACK CONSTRUCTION TRAINING
As a core course in the rail transit specialty, practical teaching in track construction plays a crucial role in cultivating students' professional skills and qualities. However, traditional methods of practical teaching in track construction face numerous challenges, including substantial investment required for constructing practical training facilities, low student engagement, and difficulty in quantifying the effectiveness of practical training. The rapid advancement of information technology offers innovative solutions to these issues. This study investigates the development and application of information technology in track construction practice training. By establishing a track construction practice training platform leveraging BIM technology, oblique photography technology, and human-computer interaction technology, this approach achieves cost-effectiveness, high student participation, on-site training capabilities, and ease of evaluation.
Copyright (c) 2025 Quan He; Qing Zhang https://creativecommons.org/licenses/by/4.0
PTSD CHARACTERISTICS OF RESCUERS BASED ON DECISION TREE
Post-traumatic stress disorder (PTSD) is a mental disorder caused by traumatic events, characterized by recurrence of memories and nightmares, avoidance of stimulation, difficulty in regulating emotions, and persistent hypervigilance. Due to the particularity of their work, rescuers are often more likely to suffer from PTSD. Identifying and clarifying the influencing factors of PTSD, establishing an effective prediction model for rescuers, and providing effective evaluation tools to provide targeted treatment and support measures for rescuers are of great significance in helping rescuers cope with and prevent PTSD. Based on the data set provided by the PLA Medical College, this study used a decision tree classification model to construct an effective prediction model for rescuers, identify important variables that affect rescuers' PTSD, and evaluate the prediction efficiency of the model by the receiver operating characteristic curve (ROC). Results: The accuracy was 95.56%, the sensitivity was 93.75%, the specificity was 95.69%, the false positive rate was 4.31%, the F1 score was 75%, and the AUC value was 94.72%. The features classified by the model were ranked according to their importance. The top eight features of the decision tree model were: ASD alertness, ASD avoidance, ASD re-experience, ASD separation, ASD nature, smoking status, psychological resilience, and age. Conclusions: The decision tree model has high accuracy and stability and can be used to guide clinical prevention and treatment.
Copyright (c) 2025 WenBei Zheng; Xi Yang; WenYun Xia https://creativecommons.org/licenses/by/4.0
MORAL DECISION-MAKING FOR AUTONOMOUS DRIVING BASED ON MULTI-OBJECTIVE REINFORCEMENT LEARNING
Vehicles must ensure safety and efficiency and deal with complex ethical dilemmas in autonomous driving. In order to deal with these ethical dilemmas effectively, moral decision-making models based on multi-objective reinforcement learning (MORL) provide a technical path to resolve such ethical dilemmas. Unlike traditional reinforcement learning (RL), MORL can generate more socially moral decision-making strategies in conflict scenarios by simultaneously optimizing multiple objectives. Of course, significant challenges remain in this research path. Assigning reward function weights is highly dependent on subjective judgement and cultural context; the dynamic environment is not adaptable enough, and the scarcity of ethical dilemma data limits model training. To address these issues, this paper points out that future research needs to focus on the dynamic weight adjustment mechanism, the construction of cross-cultural ethical frameworks, and large-scale real-world validation.
Copyright (c) 2025 XiangYu Chen https://creativecommons.org/licenses/by/4.0
ASSESSING THE POWER OF VIDEO CONTENT MARKETING IN SHAPING CONSUMER PREFERENCES AND BRAND LOYALTY: A COMPARATIVE STUDY OF YOUTUBE AND FACEBOOK ADS IN BANGLADESH
This study explores the effectiveness of video content marketing on YouTube and Facebook in shaping consumer preferences and brand loyalty in Bangladesh. Through in-depth interviews with 50 respondents from diverse professional backgrounds, the research analyzes consumer perceptions, engagement levels and the influence of video advertisements on purchasing intent. The findings reveal significant differences between the two platforms in terms of ad engagement and brand impact. YouTube is found to be more effective for building brand credibility, fostering long-term engagement and enhancing brand loyalty through detailed content, storytelling and influencer collaborations. Respondents emphasized that YouTube’s skippable ads, informative product reviews and educational content contribute to higher consumer trust and long-term recall. In contrast, Facebook ads are viewed as more suitable for immediate conversions, local promotions and targeted marketing but are often perceived as intrusive and repetitive, leading to lower engagement over time. Despite Facebook's success in driving short-term traffic, its impact on brand loyalty is limited due to its lack of depth in storytelling. The study concludes that businesses in Bangladesh should tailor their video marketing strategies based on platform strengths: YouTube for sustained brand-building and Facebook for short-term sales and localized outreach. A hybrid strategy combining both platforms can optimize marketing effectiveness. The findings of this study provide valuable insights for digital marketers in Bangladesh, helping them align their video content strategies with the strengths of each platform to maximize consumer engagement and brand success.
Copyright (c) 2025 Md. Sadiqul Azad; Md Zahid Hasan https://creativecommons.org/licenses/by/4.0
AUDITING EMERGING TECHNOLOGIES: CHALLENGES IN AI-DRIVEN IT ENVIRONMENTS
With the rapid advancement of artificial intelligence (AI) and machine learning technologies, auditing IT systems that integrate these technologies has become increasingly important. This paper examines the challenges faced in auditing IT systems that utilize AI and machine learning to ensure ethical, secure, and reliable operations. The primary focus of this study is the identification of critical issues in the implementation of AI in IT environments, such as algorithmic transparency, data accuracy, and the social and ethical impacts involved. Additionally, the paper discusses the importance of developing relevant audit standards, enhancing data security, and ensuring privacy protection in AI-based systems. To achieve these goals, an adaptive and sustainable auditing approach is required to address the complexity and dynamics of the ever-evolving technological landscape. This study provides valuable insights for audit practitioners and policymakers to ensure that AI-driven IT systems operate in accordance with appropriate principles, uphold integrity, and mitigate potential risks.
Copyright (c) 2025 Geby Patricia Siregar; Nadiyah Salsabil; Iskandar Muda https://creativecommons.org/licenses/by/4.0
ENHANCING THE SECURITY LEVELS IN INFORMATION SYSTEMS
This document provides a comprehensive guide to enhancing information security systems within organizations. It emphasizes strategies across physical, digital, and human domains to ensure the safety, reliability, and continuity of operations. Key topics include physical security measures, data protection practices, access control, employee awareness, and advanced surveillance technologies. These measures aim to mitigate risks from cyber threats, natural disasters, and internal vulnerabilities, fostering a robust security framework.
Copyright (c) 2025 Jouma Ali Al-Mohamad https://creativecommons.org/licenses/by/4.0
LEVERAGING TECHNOLOGY TO DRIVE INNOVATION: A MIXED-METHODS APPROACH TO ENHANCING ORGANIZATIONAL CREATIVITY AND COMPETITIVENESS
This research article investigates the dynamic interplay between innovation and technology, focusing on how advancements in technology catalyze innovative practices across various industries. The purpose of this study is to illuminate the mechanisms through which technology influences innovation processes, thereby offering insights into effective strategies for fostering creativity and competitiveness in organizations. To achieve this, a mixed-methods approach was employed, combining quantitative data analysis with qualitative case studies from leading technology firms. Surveys were distributed to over 300 professionals across different sectors, gathering data on their perceptions of technological impact on innovation. Additionally, in-depth interviews with 15 industry experts provided contextual insights into real-world applications of technology in innovation strategies. The findings reveal a significant correlation between the adoption of cutting-edge technologies and the enhancement of innovative capabilities within organizations. Specifically, firms that actively integrate technologies such as artificial intelligence, machine learning, and big data analytics reported higher levels of innovation output and effectiveness. Furthermore, the study identifies key factors that facilitate this relationship, including organizational culture, leadership support, and investment in training. The implications of these findings are profound, suggesting that organizations aiming to improve their innovation outcomes should prioritize technology adoption as a strategic imperative. By leveraging technology not only as a tool but as a catalyst for innovation, companies can navigate the complexities of the modern market landscape more effectively. This research contributes to the broader field of innovation and technology studies, providing a framework for understanding how technological advancements can be harnessed to drive innovative practices and enhance organizational performance.
Copyright (c) 2025 Okechukwu Chidoluo Vitus https://creativecommons.org/licenses/by/4.0
DISRUPTIVE TECHNOLOGIES AND ADAPTIVE STRATEGIES: RETHINKING SUPPLY CHAINS, MARKETING, AND MIGRATION FORECASTING THROUGH DATA ANALYTICS
As global disruptions intensify through pandemics, geopolitical conflicts, and climate volatility, the demand for adaptive and resilient systems has never been greater. This paper explores the transformative role of disruptive technologies, particularly AI and advanced data analytics, in enabling agile responses across three critical domains: supply chain optimization, digital marketing evolution, and migration forecasting. By analyzing cross-domain applications and real-world case studies, we propose a Strategic Adaptation Analytics Model (SAAM) that leverages predictive intelligence to balance speed, flexibility, and foresight in volatile global environments. The paper emphasizes how organizations and policymakers can use AI-driven insights to overcome uncertainty, build sustainable value networks, and anticipate human mobility trends. In doing so, it provides a roadmap for integrating adaptive strategies with technological disruption to foster competitiveness, social preparedness, and resilience.
Copyright (c) 2024 Okuma Kaium; Lizi Alasa; Kurtz Robert https://creativecommons.org/licenses/by/4.0
ARTIFICIAL INTELLIGENCE APPLICATION IN SUPPLY CHAIN MANAGEMENT AND LOGISTICS
The integration of artificial intelligence in supply chain management and logistics has fundamentally transformed traditional operational paradigms, ushering in an era of unprecedented efficiency and innovation. This comprehensive review synthesizes the latest research and practical implementations from 2019 to 2024, examining the multifaceted impact of AI technologies across the supply chain spectrum. Through systematic analysis of academic literature, industry implementations, and emerging trends, this paper presents a thorough examination of how AI is reshaping supply chain operations, decision-making processes, and strategic planning. Our findings indicate a significant shift toward autonomous and intelligent supply chain systems, with particular emphasis on real-time optimization, predictive analytics, and adaptive learning mechanisms that are revolutionizing the industry landscape.
Copyright (c) 2024 Linda Tan; Michael Chan https://creativecommons.org/licenses/by/4.0
RESEARCH ON THE DIGITAL TRANSFORMATION PATH OF REGIONAL AGRICULTURAL PRODUCT PUBLIC BRANDS DRIVEN BY LARGE LANGUAGE MODELS
This study aims to explore the application of large language models (LLMs) in constructing the digital transformation path of regional public brands for agricultural products. Through a review of literature and theoretical analysis, the study systematically reveals the specific roles of LLMs in brand cognition, market promotion, product innovation, and user experience. The results indicate that LLMs not only enhance the precision and interactivity of brand communication but also improve the market competitiveness of brands through intelligent personalized services and product innovation. The conclusion suggests that the intelligent application of LLMs will become a vital trend in the digital development of regional public brands for agricultural products, providing specific practical recommendations for policymakers and brand managers.
Copyright (c) 2024 XiaoDan Zhao https://creativecommons.org/licenses/by/4.0
INNOVATIONS AND PRACTICES OF AUDIT MODELS FOR PUBLIC INSTITUTIONS IN THE CONTEXT OF BIG DATA
This study aims to explore the application of big data technology in the auditing of public institutions and its innovative impact on audit models, in order to improve the efficiency and accuracy of audit work. By systematically reviewing relevant literature from both domestic and international sources and selecting typical audit cases from public institutions, the study adopts methods such as literature analysis, case study, and comparative analysis to construct a theoretical framework for a big data-driven audit model for public institutions. The results indicate that big data technology significantly enhances the comprehensiveness and real-time nature of audits in areas such as data collection, analysis and mining, risk identification, and early warning. It optimizes the allocation of audit resources, and its feasibility and effectiveness have been validated through real-world cases. In addition, the study finds that the implementation of the big data audit model faces challenges such as data security, technical complexity, and organizational cultural adaptation. Finally, it proposes corresponding countermeasures, including strengthening data security management, improving auditors' technical skills, and promoting organizational cultural change to ensure the smooth implementation of the big data audit model. The conclusions of the study provide theoretical support and practical guidance for the intelligent and efficient audit models of public institutions, with significant theoretical and practical value.
Copyright (c) 2024 Yan Xu; ChaoMin Gao https://creativecommons.org/licenses/by/4.0