Authors - Bhavya Balakrishnan, Srinivasa HP Abstract - The massive deployment of heterogeneous, resource- con-strained and always-on devices underlying the Internet of Things (IoT) has introduced complex cybersecurity challenges. The rapid growth of the Internet of Things (IoT) due to the large-scale deployment of heterogeneous, resource-constrained and always-on devices has resulted in complex cybersecurity challenges. The physical and digital components in the IoT systems are tightly bound which increases the attack sur-face and makes them highly prone to threats of malware infections, data theft, unauthorized access and distributed denial of service. Traditional security mechanisms and rule-based intrusion detection systems cannot manage the dynamic, large-volume and evolving IoT traffic. The solutions provided by machine learning have been widely concerned due to its capability of learning data patterns and finding abnormal and malicious activities. However, existing machine learning models have serious constraints such as lack of labelled information, extreme class imbalance, and inability to generalize to new and never-seen attacks. In recent years, Generative Adversarial Networks (GANs) have emerged as a promising paradigm to improve the cybersecurity of IoT through artificial generation of realistic synthetic data, adversarial sample enhancement, alleviating data imbalance and modelling adversarial attack-defense dynamics. GAN based models have showed great gains in intrusion detection, anomaly detection and malware analysis in the IoT networks . However, modern studies are still divided on this issue due to variations in GAN architectures, datasets, evaluation procedures, and experimental procedures. In addition, most of the researches have been more concentrated on offline benchmark databases, with less focus on checking through realistic IoT testbeds, which could be more precise in capturing the actual deployment conditions.
Authors - Ferry Setyadi Atmadja, Sabo Hermawan, Eka Dewi Utari, Suciati Putri Nurjanah, Siti Dwi Hastuti Abstract - The exorbitant costs associated with professional Content Management Systems (CMS) have precipitated a severe theory to practice gap in digital archive education. This infrastructural barrier disproportionately disadvantages institutions with constrained budgets, fundamentally threatening the inclusive education mandates of Sustainable Development Goal (SDG) 4. To bridge this ped-agogical divide, this study developed and validated a zero-license educational framework utilizing Microsoft Excel's Visual Basic for Applications (VBA) to simulate a professional electronic records environment. Employing an R&D methodology (ADDIE model) with a cohort of 40 undergraduate students, the proposed framework circumvented hardware and financial constraints by operating offline on low-specification devices. Results indicated high expert validation (4.35/5.0) and a statistically significant enhancement in students' practical archival skills, evidenced by a moderate to high Normalized Gain (N-Gain) of 0.61. Furthermore, the system demonstrated exceptional usability with a System Usability Scale (SUS) score of 76.5. These findings provide empirical evidence that strategic, low-cost technological interventions can effectively democratize digital archive learning, offering a highly scalable solution for marginalized educational ecosystems in developing regions.
Authors - Rayyan Naufal Anandito, Muhammad Fedylopa Ginting, Trias Septyoari Putranto Abstract - The rise of Automated Biometric Boarding Systems (ABBS) for public transportation, driven by the potential to enrich convenience while integrating artificial intelligence into their activities has not been without the desire among policymakers and business leaders to get a better grasp on how biometry could be integrated in mandatory adoption contexts. Abstract This study aims to investigate passenger acceptance and continuance intention of AI-based face recognition boarding system in PT Kereta Api Indonesia (KAI) Gambir Railway Station 2023. Based on an integrated framework of Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM), complemented with Trust and Perceived Privacy Risk, this study explores the pathways through which affective factors and institutional factors influence long-term behavioral intentions in a compulsory acceptance context. Data from cross-sectional, quantitative. 150 purposively sampled passengers were analyzed by PLS-SEM using SmartPLS 4.0. This is the first time that these findings challenge many of the assumptions about technology adoption and provide relevant policy recommendations for transport authorities based on a framework for AI governance aligned with Indonesia's Personal Data Protection Law (UU No. 27/2022).
Authors - Ridhi Sharma, Ashok Kumar Abstract - This manuscript discovers the role of information theoretic measures for feature selection while dealing with high dimensional data sets. The study uses entropy, mutual information and divergence measures to address the issues of classification and high computational complexity of real data set which is affect by redundant and irrelevant features, by analyzing the dependency patterns and feature relevance in complex data set. Under different data conditions, the proposed approach for feature selection, in comparison to traditional methods, handles the non-linear relationships and noisy attributes effectively in terms of relevance, classification and interpretation. In-formation theoretic methods provide more precise feature selection and pattern identification results in the data sets. Despite the challenges of computational cost and scalability, the study shows that information theoretic measures can perform better in feature selection and decision making of the data mining.
Authors - Nishi Doshi, Shrey Shah Abstract - Hospitals run more machine learning on GPUs while the carbon footprint of grid electricity rises and falls through the day. Using a computer simulation, we compare 13 scheduling rules on mixed GPU hardware, with synthetic patient-style jobs, urgency tiers, and time-ofday carbon traces. We do not study patient outcomes; every percentage we report is a simulator queue number, not a clinical finding. We ask whether running non-urgent jobs overnight is almost as good as a richer rule that mixes urgency and carbon (CUCA at weight 0.45, written CUCA 0.45). The comparison keeps carbon reduction secondary to clinical priority and deadline compliance, so each policy is judged on both average kg CO2e and missed-deadline behavior. CarbonGreedy and CarbonShift are carbon-first stress tests that demonstrate how poorly wrong vendor presets can disrupt clinical priorities, and are not meant for production. Numbers are averages over many test settings, with wide run-to-run spread and no statistical adjustment, so headline ratios are exploratory. On an eight-GPU baseline, the overnight rule closes about 78% of the carbon gap between urgency-only and CUCA 0.45 while missing fewer urgent deadlines than either. CarbonShift lets about 46% of the most urgent jobs miss their deadline; this is simulated queueing, not bedside harm. At 48 jobs per hour, the carbon footprints almost tie, yet the overnight rule still misses fewer urgent deadlines. A geography test, where regions share one daily carbon shape with only timezone shifts, trims under one percentage point of average carbon; a twelve-hour routine window saves a little carbon for CUCA 0.45 but raises overall missed deadlines. Overnight batching stays competitive on average modelled carbon; carbon-only rules belong only in stress tests.
Authors - Abhijit Dnyaneshwar Jadhav, Prashant G. Ahire, Madhuri Hiwale Abstract - In vitro fertilization (IVF) is currently one of the most powerful assisted reproductive technologies for infertility treatment. However, the embryo selection process still represents a bottleneck that greatly influences the rates of implantation and live birth. Traditional methods of embryo evaluation involve embryo morphology grading. But this approach suffers from subjectivity, variability, and heavily depends on the skill and experience of the embryologist. To go beyond the limitations of human assessment, the latest improvements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have made possible the automated embryo evaluation using pictures, time-lapse morphokinetics, and clinical data. This paper reviews comprehensively the currently available AI-enabled IVF systems while also first introducing the conventional embryo assessment and later presenting the most sophisticated multimodal deep learning frameworks. The paper also discusses some of the major outstanding issues such as the poor performance of models on new datasets, the lack of the shared and agreed upon benchmarks, and the limited explainability of the models. We have also developed a Multimodal Explainable Artificial Intelligence Frame-work for IVF (MEAIF-IVF) to fill in these gaps in which image of the embryo, time-lapse video of the embryo, and clinical patient information are all combined into one deep learning model. This system uses convolutional neural networks and vision transformers for spatial feature extraction, recurrent neural networks for temporal modeling, and attention-based fusion for multimodal integration.
Authors - Peruru Gayathri, Rohini M, Anand R Nair Abstract - Cyber threats are getting more sophisticated and conventional security solutions are not keeping up with detecting cyber-attack. In this research, a hybrid detection and prediction system for TTP (Tactics, Techniques and Procedures) based on deep learning and graph-based is presented. The planned study is based on an analysis of data originating from cyber security systems at large scale, which can be used to detect attack patterns and correlations of attacks. Host logs and threat intelligence data are trained using deep learning models to detect discriminative features, while graph-based models are used to model the structural relationships between users, systems, and attack patterns. Combined these techniques will result in more complex attacks and lateral movement being easier to detect. It also assumes probable attack methods to move to the next level, so that it can predict the attacks and take proactive actions to mitigate attacks in the future. The entire predictive and graph based solution enhances threat visibility and threat response speed, while boosting threat detection accuracy. The system enables the detection of the APTs and real time monitoring them by the Cyber Security analysts. The experimental results show that the highest accurate transformer is able to achieve 95% classification accuracy, and the graph neural network is demonstrated to achieve 78.26% accuracy for predicting next technique. The framework has been shown end-to-end, with the intent of showing it can be utilized as an extra layer of Intelligence on the enterprise security side, with Splunk.
Authors - Vishwa Kumaresh Abstract - A local supplier delay or demand shock in multi-echelon supply chains can make upstream orders volatile long before the full costs appear in planning dashboards. In this study, we propose an interpretable warning-to-action layer for supply-chain digital twins. This layer sits above the replenishment controller: it estimates disruption-regime risk from rolling demand, inventory, backlog, order, and lead-time telemetry, then maps that risk to bounded changes in responsiveness, safety stock, and order caps. We calibrate a gradient-boosted stump classifier that combines standard warning indicators, cross-echelon imbalance measures, and nonlinear stress descriptors. A small mode table converts the resulting probability into five auditable replenishment modes. This method is tested on twelve disruption scenarios grouped into six mechanism classes, using ten baselines and an untouched lockbox of 576 observations. The proposed policy reduces aggregate system expenditure by 15.2% and cross-echelon volatility (bullwhip) by 44.5%, relative to a linear guard that uses the same broad action family. The largest gains occur in lead-time disruptions and backlog cascades. Compound shocks demonstrate marginal performance gains, as existing linear guards effectively capture these dynamics within standard monitoring frameworks.
Authors - Wannakorn Phornprasert, Waraporn Phothirin, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study uses Learning Analytics to assess university students’ eye health risks based on social media usage data, focusing on descriptive and diagnostic analyses. Data collected from 44 undergraduates via a self-reported questionnaire with 82 key questions covered general details, social media habits, device and screen environments, symptoms of Computer Vision Syndrome, and Felder–Silverman learning styles. The descriptive analysis revealed Instagram as the most popular platform, frequent nighttime use after 20:00, and many students spend over six hours daily on social media. While most respondents were categorized as low risk, symptoms such as watery eyes, eye pain, light sensitivity, and neck pain were commonly reported. The diagnostic analysis linked risky sitting postures, looking below eye level, prolonged daily usage, and nighttime social media activity to increased eye health risks. These findings support initiatives for digital well-being and learning support in higher education.
Authors - Mariel Leo T. Violeta Abstract - The increasing incidence of academic credential fraud, inefficient verification procedures, and reliance on centralized record management systems present significant challenges for higher education institutions. This study proposes and evaluates a blockchain-based academic credential issuance and verification platform using Hyperledger Fabric to improve the security, authenticity, and efficiency of academic credential management. The platform enables university registrars to issue digital academic credentials, allows students to securely access and share academic records, and provides employers and external entities with a reliable credential verification mechanism. To ensure data integrity while maintaining scalability and privacy, the framework integrates blockchain-based cryptographic hashing with off-chain cloud storage. A quantitative descriptive research design was employed using the Technology Acceptance Model (TAM) as the theoretical framework. Data were collected from 40 registrar personnel at the Polytechnic University of the Philippines through a structured survey instrument measuring Perceived Usefulness and Perceived Ease of Use. Findings revealed that respondents strongly agreed that the platform improves security, credential verification, operational efficiency, accessibility, and flexibility. The results demonstrate that Hyperledger Fabric can provide a secure, tamper-resistant, and efficient infrastructure for managing academic credentials in higher education institutions. The study contributes to the growing adoption of blockchain technology in education by presenting a practical and institution-oriented framework for secure and verifiable digital credential management.
Authors - Bhagyalakshmi S Pai, Jeevanand E S, Radhika P.C, Krupa B Nair, Sreeja Radhakrishnan, Dhanalakshmi Menon Abstract - The present study attempts to empirically investigate how the customers’ awareness relates to the adoption of green banking initiatives of commercial banks in Kerala, India. The study employs data gathered from 540 customers of five banks (SBI, Canara, PNB, ICICI Bank, HDFC Bank, and Axis Bank) by using a structured questionnaire, and builds and validates the structural model for green banking adoption. Customer awareness is considered as a higher order construct which consists of Environmental Awareness and General Awareness. The analysis used descriptive statistics, reliability analysis, Confirmatory Factor Analysis (CFA), two-stage analysis of Structural Equation Modeling (SEM), and Z test and One-Way ANOVA test to determine awareness levels and differences in demographic data. The results show that there exists a high Awareness–Adoption Gap, that is, a superficial awareness of green banking, which is not yet accompanied by a conceptual understanding of it. The study also reveals that adoption of e-banking is mainly for convenience and that practice in key life-stages and occupations have a strong bearing on adoption behaviour.
Authors - Wannakorn Phornprasert, Ratchanin Intham, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study explored learning well-being and indicators of academic burnout associated with pseudo depression risk among university students at the University of Phayao. Data collection involved a general information questionnaire, an academic burnout assessment scale, and the DASS-21. Descriptive and diagnostic statistics were applied. Results indicated a moderate level of overall academic burnout, with academic fatigue scoring higher than academic withdrawal. Emotional risk assessment found that 50.0% of students showed mild to severe pseudo depression symptoms. Additionally, scores for academic fatigue, academic withdrawal, and overall burnout were positively linked to depression, anxiety, and stress. These results suggest that descriptive and diagnostic approaches can serve as initial tools for screening and promoting students' learning well-being in Thai higher education.
Authors - Pratya Nuankaew, Panisara Paksasuk, Thanapon Thiradathanapattaradecha, Thapanapong Sararat, Wongpanya S. Nuankaew Abstract - This study analyzes student behavioral data to understand factors influencing secondhand fashion purchases in the digital age. A survey was conducted with 40 University of Phayao students who are experienced in buying secondhand fashion items. Data analysis included descriptive statistics and diagnostic approaches to profile students, their purchasing habits, perceptions, and key factors. Results indicated that all participants had prior secondhand shopping experience, using both physical stores and online platforms as key channels. Product quality received the highest average score of 4.20, followed by a positive attitude toward second-hand fashion at 4.05, frugality at 4.00, and brand reputation and environmental responsibility at 3.85, with sustainable fashion close behind at 3.83. These findings suggest that students’ choices are influenced more by quality, value, personal attitudes, and sustainability awareness than by social media influencers alone. The research provides valuable insights for promoting sustainable fashion, designing platforms, and developing future predictive analytics.
Authors - Pratya Nuankaew, Duangjai Pongsawan, Supan Tongphet, Thapanapong Sararat, Wongpanya S. Nuankaew Abstract - This research aimed to examine the use of student-pet interaction data to enhance understanding of university students' mental well-being. Descriptive and diagnostic data analyses were conducted. The sample comprised 40 students. Data collection was conducted using questionnaires to collect baseline information, characteristics of interaction with pets, and evaluations with the CCAS, PSS-10, and ST-5 instruments. The analysis revealed that the majority of students experienced a high level of attachment and comfort with their pets, with an average CCAS score of 3.57. The average PSS-10 score was 20.48, indicating moderate stress levels, and the mean ST-5 score was 7.43. Diagnostic analysis suggested that the duration of contact with pets, pet type, living conditions, and pet ownership status were potentially associated with students' stress levels. These findings may serve as an initial guideline for developing monitoring and support programs to promote the mental well-being of university students.
Authors - Wannakorn Phornprasert, Papimon Novichai, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This investigation aimed to analyze the VARK learning style and ergonomic data to identify the risk of office syndrome among university students. A quantitative, cross-sectional approach was employed, utilizing questionnaire data from 40 students. The analysis used descriptive statistics to summarize general characteristics, learning styles, and risk levels, and diagnostic analyses to identify factors associated with office syndrome risk. The most prevalent learning styles identified were Read/Write (30.0%) and Kinesthetic (25.0%). Ergonomic assessments revealed that 42.5% of students were at high risk, while 35.0% were at moderate risk. Factors correlated with risk included excessive phone usage (exceeding 4 hours per day), inappropriate chair height, unsuitable armrests, incorrect screen positioning, and improper keyboard posture. These findings indicate that combining learning preferences with ergonomic data can serve as an initial screening tool for risk assessment and facilitate the development of learning environments tailored to students in the digital era.
Authors - Avni Tyagi, Suman Madan Abstract - Machine Learning Operations (MLOps) has become a paradigm necessary to simplify machine learning systems development, implementation, and operation. Although MLOps focuses on automation, scalability, and fast deployment based on CI/CD practices, issues of security are usually under-explored, making ML pipelines very vulnerable.To examine the main security risks of contemporary ML pipelines, the paper explores the intersections between adversarial machine learning and MLOps and DevSecOps. It determines key attack vectors, such as data poisoning, model tampering, and infrastructure-level exploits, which may impair data integrity, model reliability, and system trustworthiness, through a review of recent literature (2020-2026).It also analyzes mitigation measures like adversarial robustness testing, cryptographic model signing, and continuous monitoring models and looks at new frameworks like SecMLOps and MLSecOps that help to put security in the ML lifecycle.It points out trade-offs between improved security, system performance, and complexity, and the importance of balanced architectures. Results show that adversarial testing and verifying the model with secure artifacts can decrease model failure rates by 3060 percent, and that continuous monitoring can improve the latency of anomaly detection by almost 40 percent.The paper ends with description of future research directions such as standardized benchmarks, enhanced robustness testing, and hardware-aided security of robust AI systems.
Authors - Sachin Ramling Jadhav, Rajveer Nandkar, Srushti Rajput, Rajvardhan Desai, Gunjan Ramteke,Samruddhi Rajput Abstract - Dealing with city problems like cracked roads, trash piles, leaks in pipes, or dark lamp posts keeps urban teams busy. When fixes depend on old paper methods, pieces of info get lost, trust dips, responses drag. A new setup steps in - CCIRS - running through a basic website made with PHP tools. Instead of guessing what comes first, supervisors follow a clear score called PI, shaped by how bad things look, where many reports cluster, plus how long issues wait. Behind the scenes, staff watch live updates, study trends, trace progress using their control view online. Half a year of testing in three city areas of Pune cut response times by 59.0%. Because of this change, meeting service targets got better by nearly half. Old ways of handling issues were clearly outperformed. Math behind sorting locations was built and tested. Ranking urgency used formulas that matched real outcomes well.
Authors - Mrityunjaya Chavannavar, Melita Simoes , Nikhil Shetty , Chirivella Vishal Abstract - Over the years, there is a rapid growth of social media-based financial content. Finfluencers have been emerging as influential sources that provide investment information to young retail investors. This research is inclined towards understanding the influence of finfluencers on numerous behavioural biases that include herd mentality, overconfidence, and FOMO. This study also examines their influence on decision-making when it comes to investments and the overall risk perception in the current digitally enabled investment landscape. There is interplay between social media platforms, financial influencers, and behavioural biases and can be observed among young retail investors in India. Most traditional theories in finance assume that a majority of investors behave rationally while behavioural finance acknowledges the impact of cognitive and emotional biases influence investment decisions. This quantitative study makes use of a descriptive-analytical approach. The primary data used here was gathered with the help of structured online questionnaires distributed to 120 young retail investors. Data analysis was carried out with the help of IBM SPSS Statistics. Tests such as correlation analysis, multiple regression models, and ANOVA with post-hoc Tukey HSD were undertaken. Findings showed that general social media usage frequency had no significant relationship with the four behavioural biases examined. Perceived credibility of finfluencer content demonstrated significant negative relationships with all four biases (overconfidence: β = -0.387, p = 0.001; herding: β = -0.252, p = 0.044; confirmation: β = -0.321, p = 0.006; availability: β = -0.354, p = 0.003). This indicates that high-quality financial influencers may serve a corrective rather than amplifying function. Indiscriminate following of numerous finfluencers positively predicted confirmation bias (β = 0.191, p = 0.025). Investors with over five years of experience revealed significantly lower biases. This study can be used for better investor protection, and financial literacy initiatives and can be embedded in various regulatory frameworks.
Authors - April L. Macasieb-Gumnad, Roberto M. Arguelles Abstract - The study focuses on transformational leadership, entrepreneurship, and sustainability in higher education. Using Saint Louis University (Philippines) as a case study, the purpose was to (1) identify the role transformational leadership has in developing (or affecting) the characteristics of an entrepreneurial university, (2) identify how transformational leadership fosters sustainable innovation, and (3) assess the effect entrepreneurial university characteristics have on achieving sustainable outcomes. This quantitative research used three different instruments that were previously validated (HEInnovate Questionnaire; Sustainability Assessment Questionnaire; and Survey of Transformational Leadership) to gather data from a sample of 795 respondents at SLU and analyzed the resulting data using Spearman-rank correlation analysis and simple linear regression. This study provided practical applications to the literature on higher education management through empirical evidence of relationships between types of leadership styles, achievement of SDGs, organizational structures/models/characteristics, and sustainability of innovation in higher educations.The SLU CARES Innovation Framework was proposed to provide actionable insights for academic and administrative leaders seeking to align Catholic educational missions with contemporary demands for innovation and sustainability.
Authors - Adin Nasywa Alifah, Puspita Kencana Sari Abstract - Metaverse gaming platforms like Minecraft and Roblox have evolved into important social spaces for Gen Z globally, including in Indonesia. These platforms are also emerging as high-risk environments for cybersecurity threats with implications for user security behavior and privacy protection. This study applies Protection Motivation Theory (PMT) to examine how self-efficacy and attitudes toward sharing personal information online predict phishing susceptibility among Indonesian Gen Z users. Using PLS-SEM on data from 200 users aged 18–28, results show that self-efficacy reduces phishing susceptibility both directly and indirectly through information-sharing attitudes, indicating partial mediation. These findings provide behavioral intelligence to support cybersecurity strategy, risk governance, and user privacy in emerging metaverse gaming ecosystems, particularly among Gen Z users.
Authors - Darrel A. Cardana, Ethel Zean M. Anosa, Angeline B. Elegio, Jes Maries Mendez, Ivy Corazon Mangaya-ay Abstract - Agri-Aqua Technology Business Incubators (ATBIs) play an important role in promoting innovation, entrepreneurship, technology commercialization, and institutional collaboration within higher education institutions. This study assessed the operational performance and institutional development of the BISU Agri-Aqua Technology Business Incubator (ATBI). Specifically, the study evaluated the accomplishments of the incubator in terms of personnel capacitation, partnership and linkage development, awareness and promotional activities, incubation services, technology incubation initiatives, intellectual property generation, and policy institutionalization. The study also examined the capacitybuilding activities, partnership initiatives, intellectual property outputs, and the problems and strategic solutions encountered during implementation. The study employed a descriptive-evaluative research design utilizing documentary analysis of the official accomplishment report and supporting institutional documents of the BISU ATBI. Frequency counts, percentage analysis, and thematic analysis were utilized in analyzing the collected data. The findings revealed that the BISU ATBI successfully implemented several operational and institutional initiatives. The incubator conducted seventeen (17) trainings and workshops, forged twelve (12) MOUs with incubatees and six (6) institutional partnerships, conducted eight (8) awareness seminars, developed ten (10) business plans, filed ten (10) trademarks and five (5) copyrights, and enrolled seventeen (17) incubatees in the incubation program. However, only two (2) technologies were successfully co-incubated despite the target of ten technologies, indicating challenges in technology commercialization and adoption. The study also identified regulatory hurdles, technology readiness concerns, partnership issues, and low technology adoption as major implementation challenges. Overall, the findings indicate that the BISU ATBI established a strong operational and institutional foundation for technology business incubation, although continuous enhancement of commercialization and technology adoption initiatives remains necessary.
Authors - Nguyen Quoc Cuong, Nguyen Ha, Mai Thi Bich Ngọc Abstract - The proliferation of short-form video platforms has reshaped consumer decision-making, yet how electronic word of mouth (eWOM) attributes influence Generation Z fashion shopping intention in emerging markets remains underexplored. Grounded in the Information Adoption Model (IAM) and Attitude–Intention framework, this study examines the impact of TikTok-based eWOM on fashion shopping intention among Vietnamese Generation Z consumers. Using PLS-SEM analysis of 263 valid survey responses, results reveal that eWOM Information Quality and Credibility significantly predict Attitude toward eWOM, while Information Usefulness is the strongest predictor of eWOM Adoption; Information Quantity exerts a positive but weaker effect. Both Attitude and Adoption significantly influence Fashion Shopping Intention, with Attitude as the dominant predictor, and mediation analysis confirms their roles as key intervening mechanisms. These findings extend the IAM to short-form video and social commerce contexts, demonstrating that Generation Z engages in evaluative, quality-oriented content processing rather than responding passively to volume. Practically, results offer actionable guidance for fashion marketers to prioritize authentic, credible, and informative eWOM strategies on TikTok.
Authors - Rakhmonova Nargiza Rashidovna, Rajapov Shukhrat Zaripbaevich Abstract - The growing volume of international trade is increasing pressure on road border customs posts, making their operational efficiency a key factor in facilitating foreign trade. Chronic congestion, long vehicle queues, and procedural delays at land border crossings hinder logistics efficiency and increase trade costs. Digitalization is increasingly viewed as a strategic solution for modernizing customs administration while ensuring effective control and economic security. This study examines ways to further improve the efficiency of road border customs posts through digitalization, using the case of Uzbekistan. The analysis is based on data from 322 road border customs posts and employs economic and statistical methods, including regression analysis and structural equation modeling (SEM). The model assesses the impact of human resources, infrastructure capacity, and digital inspection technologies—specifically, the number of employees, traffic lanes, inspection and verification complexes (ISC and Z-portal), passenger flows, and reported violations—on daily vehicle traffic volumes. The results consistently show that human resources are the most significant factor in customs post efficiency. An increase in the number of employees has a strong and statistically significant positive effect on daily vehicle flow across all parameters of the model. In contrast, the expansion of physical infrastructure, measured by the number of traffic lanes, shows a negative or weakly significant relationship, indicating that infrastructure alone does not guarantee increased throughput. Digital control systems show a positive but statistically insignificant effect, suggesting incomplete integration into operational processes. The results indicate that to achieve significant efficiency gains, digitalization must be combined with effective human resource management and organizational optimization. Policy measures should prioritize capacity building, intelligent traffic management, and deeper integration of digital systems to reduce congestion, speed up logistics, and improve conditions for foreign trade.
