JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

AN ENHANCED DARK CHANNEL PRIOR WITH ITERATIVE TRANSMISSION UPDATE FOR SINGLE

2026-02-22T14:51:51+08:00

Hazy images suffer from visibility degradation and colour distortion due to light scattering and atmospheric attenuation, which complicates downstream vision tasks and human interpretation. The Dark Channel Prior (DCP) remains a simple yet effective physically grounded method for single image dehazing; however, it tends to underestimate transmission in bright or textureless regions (e.g., sky or specular surfaces), leading to halo artefacts and colour distortions. To address this, this paper introduces a physically interpretable corrective term from the DCP-derived transmission–radiance pair to compensate for local violations of the dark-channel assumption. The proposed method iteratively refines both the transmission map and scene radiance without requiring region segmentation. Experiments on the RESIDE and NH-HAZE datasets demonstrate that our method achieves superior PSNR, SSIM, and colour fidelity than four representative model-based dehazing algorithms, while maintaining competitive computational efficiency. Overall, the proposed iterative refinement substantially enhances the robustness and visual quality of traditional prior-based dehazing while avoiding the computational burden and data dependency of deep learning approaches.

THE DIGITAL CONSUMER: A SCOPUS AI-BASED REVIEW OF ONLINE SHOPPING BEHAVIOR

2026-03-11T10:25:47+08:00

E-commerce does not stand still, and neither do consumers. As online shopping becomes more common, decisions are shaped by more than speed or convenience. Concerns about security, uneven digital skills, and the growing use of AI now sit at the center of how people shop online, sometimes encouraging participation and sometimes holding it back. This study examines digital consumer behavior by looking across four connected areas: the virtual marketplace, consumer attitudes, data analytics, and predictive modelling. Drawing on a systematic review and thematic analysis of Scopus-indexed studies, it brings together research that is often discussed separately. The review shows that AI-driven personalization, especially through recommendations and targeted promotions, can improve engagement and customer satisfaction, and may contribute to repeat purchasing over time. However, these benefits are not universal. Trust remains fragile. Security and privacy concerns continue to limit adoption, particularly in emerging economies where regulatory protection and digital literacy are still developing. While digital transformation has improved access and usability, predictive modelling reveals how deeply machine learning has become embedded in e-commerce decision-making rather than functioning as a background tool. The findings suggest that firms cannot rely on personalization alone. Trust-building measures and transparent data practices are just as critical. Policymakers also have a role to play, particularly in strengthening transaction security and supporting consumer digital skills. Future research should pay closer attention to how demographic and cultural differences shape responses to AI-enabled shopping. Overall, this study offers a grounded view of the digital consumer landscape and highlights practical directions for improving online engagement and satisfaction.

EXPLORING THE INTERSECTION OF IMMERSIVE VR/AR TECHNOLOGIES AND CULTURAL IDENTITY IN CHINESE VARIETY SHOWS

2026-03-02T14:34:04+08:00

This paper investigates the impact of VR/AR immersive variety shows on the creation of organizational cultural identity based on the cultural identity theory. In particular, it explores how the defining characteristics of immersive media, such as embodiment, interactivity, and cultural symbolism, support the identity-forming process.To determine the implications, this study integration approach will be based on the semi-structured interviews, with the participants being the producers, performers, and viewers; participant observation, analysis of program materials, and the analysis of discussions among audiences in social media platforms. Three exemplary cases are chosen to be analyzed VR headset-based formats, mobile AR formats, and live hybrid formats. The inherent design of the comparative will facilitate cross-case development and theory development.Initial therapies indicate that audiences will have a greater emotional connection with content that is rich in cultural elements. The embodied experiences are better at improving memorability of cultural symbols and the meaning-making processes depend on both the viewing context and the composition of the audience. The paper is designed to make its way into the media identity theory and provide practical implications to the content design and audience engagement approaches in the fast-changing XR environment.

ARTIFICIAL INTELLIGENCE IN CHINA'S RETAIL INDUSTRY: A SYSTEMATIC LITERATURE REVIEW

2026-03-15T15:24:51+08:00

Artificial Intelligence (AI) has emerged as a transformative force in China's retail industry, enabling intelligent decision-making across demand prediction, dynamic pricing, supply chain optimization, and consumer behavior analysis. Despite the growing body of research, existing studies remain fragmented across diverse literature sources, lacking a structured and unbiased review framework. This fragmentation limits the ability to systematically assess AI applications for operational efficiency, resilience, and sustainability in China's retail sector, making it difficult for researchers and practitioners to identify best practices and prioritize high-impact AI solutions. To address this critical gap, this study makes two primary contributions. First, we develop a comprehensive systematic review methodology tailored to the field of AI in retailing, drawing on 450 peer-reviewed articles published between 2015 and 2025, sourced from the Web of Science Core Collection. Second, leveraging this methodology, we categorize prevalent AI techniques including machine learning, deep learning, reinforcement learning, and natural language processing. We then map these techniques to their practical applications within retail operations across the dimensions of efficiency, resilience, and sustainability. Furthermore, we identify critical research gaps and propose promising directions for future investigation. The proposed review framework and novel classification scheme provide a structured foundation for future empirical research and guide industry adoption of AI strategies in China's rapidly evolving retail landscape.

SRIVIJAYAPEDIA: A DIGITAL PLATFORM FOR CULTURAL HERITAGE PRESERVATION OF THE SRIVIJAYA CIVILIZATION

2026-02-22T15:22:57+08:00

This paper introduces Srivijayapedia, a digital platform developed to preserve and promote the cultural heritage of the Srivijaya civilization. Documenting historical environments is essential for enhancing public understanding of the past and reinforcing individual and national identities. Such knowledge fosters cultural unity, instills pride, and aids in sustainable heritage preservation. The key question posed is how a digital encyclopedia can enhance public engagement with heritage, enabling individuals to explore their identity and understand historical evolution. Although abundant historical information on Srivijaya exists, it remains scattered and underutilized in digital formats. Digital tools, such as virtual reconstructions and interactive platforms, offer accessible and immersive ways to preserve and disseminate cultural knowledge. Srivijayapedia is an interdisciplinary project rooted in Human-Computer Interaction (HCI), aiming to transform traditional historical documentation into an engaging digital experience. A thematic literature review guides the focus on three core areas: (1) the educational impact of digital heritage platforms, (2) the current state of Srivijaya's digital representation, and (3) the economic potential of digital heritage in promoting tourism. The platform embraces intercultural and community-based approaches to ensure inclusivity and sustainability. Ultimately, Srivijayapedia contributes to broader efforts aligned with the United Nations Sustainable Development Goals (SDGs), focusing on education, cultural sustainability, innovation, and economic development.

CONCEPTUAL DESIGN PRINCIPLES FOR VISUALIZATION-ENHANCED DAO GOVERNANCE IN COOPERATIVES

2026-03-15T12:17:29+08:00

Cooperatives continuously faced governance challenges related to transparency, accountability, and member participation as decision-making processes became more complex. Hence, it was proposed that blockchain-based Decentralized Autonomous Organizations (DAOs) could serve as a governance mechanism. Despite this potential, DAO governance systems remained difficult for many cooperative members to trust, adopt, and interpret. This is even more so when the governance processes involve technically complex blockchain information. Therefore, this study aims to develop a set of conceptual design principles to explain how visualization can support trustworthy DAO governance in a cooperative. This study adopted a design-oriented conceptual approach. Focusing on Cognitive Fit Theory and Trust Theory, and current research on blockchain governance and cooperative decision-making, this paper depicts how visualization functions as a cognitive mechanism that drives members’ understanding of governance processes and outcomes. The analysis identified six key principles, which included emphasized interpretability over technical completeness, cognitive load reduction, process visibility, inclusivity, and trust support in visualization-based DAO governance. These principles highlighted that transparency in blockchain was not achieved only through data availability, but via visual presentation of governance information in forms that align with users’ cognitive processing capabilities. This paper contributed to the body of knowledge involving digital governance and blockchain adoption by offering theory-informed design knowledge that extends beyond the technology acceptance model. The proposed design principles provide a foundation for future research and offer practical guidance for organizations and system developers in supporting inclusive, understandable, and trustworthy DAO-based governance in cooperatives.

GIS APPLICATIONS FOR HALAL LOGISTICS COMPLIANCE MONITORING: A CRITICAL REVIEW OF SPATIAL GOVERNANCE FRAMEWORKS

2026-02-22T14:57:49+08:00

The rapid expansion of global halal markets has intensified the need for robust logistics systems to safeguard halal integrity across increasingly complex, spatially distributed supply chains. Within this context, Geographical Information Systems (GIS) have emerged as a promising digital enabler for compliance monitoring, traceability, and governance by embedding logistics operations within explicit spatial and temporal frameworks. However, despite growing scholarly interest, the application of GIS in halal logistics remains fragmented, technologically focused, and insufficiently integrated with broader halal governance and regulatory mechanisms. Existing halal logistics practices continue to rely heavily on periodic audits, document-based reporting, and siloed information systems, limiting real-time visibility and proactive risk management. The literature further reveals a lack of a coherent synthesis of how GIS capabilities, such as spatial traceability, geofencing, risk mapping, and decision-support, can be systematically aligned with halal compliance requirements across transportation, warehousing, and distribution stages. This fragmentation highlights a clear research gap at the intersection of geospatial technologies, halal standards, and supply chain governance. Accordingly, this review aims to critically synthesize and evaluate existing studies on GIS applications in halal logistics compliance and monitoring, with particular emphasis on transparency, traceability, and trust as core governance dimensions. By integrating insights from logistics management, geospatial science, and halal studies, the review consolidates dispersed conceptual discussions into a structured understanding of GIS as a spatial governance enabler rather than a standalone technical tool. The review contributes to the literature by clarifying conceptual linkages, identifying gaps in governance and implementation, and outlining future research directions for spatially enabled halal logistics systems. It concludes that GIS holds significant potential to support evidence-based, continuous, and location-aware compliance monitoring when embedded within coherent institutional and regulatory frameworks. This review is timely and important as halal supply chains undergo digital transformation, offering scholars and policymakers a consolidated foundation for advancing resilient, transparent, and trusted halal logistics governance.

ENHANCED CAESAR CIPHER ALGORITHM USING DYNAMIC KEY GENERATION WITH TIMESTAMP TECHNIQUE FOR DATA SECURITY

2026-03-11T10:40:45+08:00

Data security remains a critical concern in the digital era due to the rising frequency of unauthorized access. While the Caesar Cipher is a foundational cryptographic technique, its static nature makes it highly vulnerable to frequency analysis and brute-force attacks. This study proposes an Enhanced Caesar Cipher algorithm that integrates Dynamic Key Generation utilizing a timestamp technique to improve data confidentiality. By generating keys dynamically based on the exact time of encryption, the algorithm ensures that the same plaintext results in different ciphertexts at different intervals, effectively mitigating basic substitution pattern recognition.Experimental results demonstrate that the proposed method maintains a lightweight computational profile, achieving an average encryption time of 0.3 seconds for a 1 MB file with a linear scaling pattern . This efficiency makes the algorithm particularly suitable for low-power environments, IoT applications, and educational purposes where modern complex encryption may be resource-prohibitive. While not intended to replace high-security standards like AES, this enhanced approach provides a significant security improvement over the traditional Caesar Cipher by introducing temporal variability into the key space.

DIGITAL COMMUNICATION COMPETENCE AND EMOTIONAL RESILIENCE: A SYSTEMATIC REVIEW OF SOCIAL MEDIA LITERACY INTERVENTIONS FOR PREADOLESCENTS

2026-03-02T14:40:17+08:00

The rapid integration of social media into preadolescents’ daily lives has redefined patterns of communication, learning, and emotional development. While digital platforms offer opportunities for connection and creativity, they also present risks to mental health, including cyberbullying, anxiety, and social comparison. These challenges underscore the urgent need to understand how digital communication competence and emotional resilience can serve as protective mechanisms that enhance psychological well-being. This study systematically reviews existing scholarship on social media literacy interventions targeting preadolescents, applying the PRISMA 2020 protocol to ensure transparency and replicability. Two major databases Scopus and Web of Science were systematically searched, yielding a total of 31 primary studies published between 2015 and 2025 that met inclusion criteria. Data were extracted, coded, and thematically synthesized. The analysis produced three significant thematic clusters: (1) Digital Communication Competence and Literacy Development (2) Emotional and Psychological Resilience among Adolescents, and (3) Social Media Interventions and Behavioral Outcomes. The review reveals that integrated interventions combining media literacy, emotional intelligence, and participatory communication yield the impacts on adolescents’ digital well-being. However, regional gaps persist, particularly in culturally grounded studies within Southeast Asia. The paper concludes that future research should advance a context-sensitive communication framework that unites digital competence and emotional resilience as twin pillars for promoting safe, ethical, and psychologically adaptive social media engagement among preadolescents.

EXAMINING THE MODERATING EFFECT OF EMPLOYEE READINESS ON TECHNOLOGY-DRIVEN PERFORMANCE IN THE MANUFACTURING SECTOR

2026-03-15T15:42:46+08:00

Research points that most technological development not innovative and this effect negatively on employee performance. New technological innovation will help employees to work effectively and increase their output dramatically. The aim of this study is to explore more information towards the technology innovation that influence the employee performance. This study examines the relationship between advanced robotics, artificial intelligence (AI) and electronic monitoring system with employee performance in manufacturing industry can be moderated by employee readiness. In addition, this research is intended to examine the relationship between several variables in a quantitative technique. In contrast, 384 respondents are the sample size. Therefore, this study seeks to gather at least 384 respondents from targeted respondents. The unit of research is individual. This research uses convenience sampling since it is readily accessible for the sample to be collected. To conduct this research, researcher use the primary data collection method to gather the data. The questionnaire method is recorded in this research instruments and measurement to collect quantitative data. Moreover, the data analysis is discussed in five studies such as frequency analysis, descriptive analysis, reliability analysis, Pearson correlation analysis and multiple regression analysis. There are six hypotheses tested in this study. In conclusion, there were only four accepted hypotheses and two rejected hypotheses.

INTEGRATION OF ARTIFICIAL INTELLIGENCE (AI) IN SHAPING USER INTERFACE (UI) AND USER EXPERIENCE (UX) IN MOBILE PACKAGING DESIGN: iDPAC APPS

2026-03-02T14:24:32+08:00

Intelligence Digital Packaging (iDPAC) is a digital integration of artificial intelligence (AI) in mobile applications' user interface and user experience (UX) design modules. This approach addresses the lack of educational resources and samples in packaging design, leading to unrealistic designs. The study focuses on AI in UI and UX mobile app prototypes and packaging design processes to optimize its advantages in the creative design industry. The qualitative methodology involves observation and interviews with educators, learners, and industry experts to gather insights on potential improvements in AI integration in UI and UX mobile apps. The study uses Design Developmental Research (DDR) and Design Thinking (DT) frameworks to discover user needs, design prototypes, and test them with real users. The DDR-DT framework has effectively facilitated the development of mobile apps that meet user satisfaction in packaging design.

ARTIFICIAL INTELLIGENCE – DRIVEN PERSONALIZATION: SYSTEMATIC REVIEW OF FEATURE INFLUENCE ON JAWI LITERACY MODEL FOR DYSLEXIA

2026-03-15T13:01:21+08:00

The effectiveness of personalized educational technology is important for students with complex orthographic processing disorders. Considering the unique challenges posed by the Jawi script among dyslexic students, the objective of this research is to identify the artificial intelligence (AI) features that influence the development of personalized model for Jawi literacy among dyslexic students. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a Systematic Literature Review (SLR) was conducted using advanced search strategies across Scopus and Web of Science (WoS) databases. A total of 23 published papers were included. From these, 21 categories of grouped codes which were synthesized into five analytical themes reflecting the development of personalized models: Data Processing and Input Features, Core AI Architecture and Models, Adaptation and Personalization, Interface and Presentation, and Feedback, Fluency and Assessment. This research identifies five themes of AI features demonstrates for intervention development, concluding that successful personalization relies on the synergistic integration of multimodal feature extraction and cognitive load mitigation techniques. The findings demonstrate significant practical implications for educators, developers and policymakers aiming to advances the inclusive educational technology through a shift from standardized digital tools toward neurodiverse centric design. Thus, it moves beyond to create a more responsive literacy environment for dyslexic students.

DEFECT ENHANCEMENT DETECTION METHOD IN COMPLEX BACKGROUNDS BASED ON MIXED ATTENTION

2026-02-22T15:16:29+08:00

This paper proposes a defect enhancement detection method based on Mixed attention, aiming to address the difficulty of feature extraction caused by complex background interference in metal surface defect detection. The core of this method is the design of a lightweight Mixed Attention Module (MAM). This module integrates channel attention and spatial attention mechanisms in parallel, working collaboratively at multiple levels: channel attention adaptively recalibrates channel feature responses by modeling the interdependencies between feature channels, emphasizing feature maps related to defects; spatial attention focuses on key spatial locations in the feature maps, generating spatial weight masks to highlight defect regions and suppress texture and noise interference from irrelevant backgrounds. Simultaneously, the module employs an efficient structural design, achieving effective capture and fusion of multi-scale contextual information without introducing significant computational overhead, thereby enhancing the discriminative representation of defect features in complex backgrounds. Experimental results demonstrate that this method achieves significant improvements in detection accuracy (mAP) on the NEU-DET and GDUT-DET public metal surface defect datasets.

THRESHOLD IMPROVEMENT USING THE OTSU METHOD WITH HISTOGRAM ON PADDY IMAGES

2026-03-11T10:55:26+08:00

Accurate image segmentation methods play a very important role in improving paddy grain classification performance. One of the segmentation stages is determining the threshold, usually using classic Otsu. This method is widely used in the segmentation process, and the instability of the threshold in classic Otsu in uneven lighting and complex histogram distribution. This study proposes an improvement to the Otsu-based segmentation method by combining histogram normalization and trigonometric variance modulation, namely Otsu with normalization, Otsu Sine, and Otsu Tangent. The proposed methods were evaluated using Random Forest as a classifier and texture features with GLCM. Experimental results show that the Otsu Sine method achieves the best performance among the other methods, with an accuracy of 0.92, precision of 0.93, recall of 0.93, and F1 score of 0.93. Five-fold cross-validation yields superior results for Otsu-Sine, with the highest average accuracy (94.83%) among all methods. Further pairwise tests showed that the improvement in performance compared to classic Otsu was statistically significant (p = 0.04). Threshold stability analysis showed that Otsu-Sine maintained low variance while adapting effectively to changes in intensity distribution, whereas Otsu-Tangent showed high instability.