HighTech and Innovation Journal
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Understanding Continuance Intention of Merchants as End User in Online Food Delivery After COVID-19
Full text linkThis study explores the continuance intention of merchants in using Online Food Delivery (OFD) services post-pandemic, employing an extended Expectation-Confirmation Model (ECM). While existing research on OFD predominantly examines food consumers, this study focuses on the merchant side and investigates how confirmation, perceived usefulness, satisfaction, perceived risk, and perceived critical mass influence their continuance intention. Using Structural Equation Modeling (SEM) on data collected from 378 Indonesian merchants, the findings highlight several critical factors. Perceived critical mass fosters platform adoption by creating a sense of widespread utility, while perceived risk indirectly affects continuance intention through its impact on satisfaction, emphasizing the need to address merchants' concerns. This research enhances the understanding of merchant behavior in the OFD ecosystem by incorporating context-specific factors that influence their decisions. The findings offer practical recommendations for OFD providers to improve system reliability, mitigate perceived risks, and foster a robust user community to ensure sustained engagement. Future studies could build on these insights by investigating similar dynamics in different regions or by including other stakeholders, such as delivery drivers, to provide a broader understanding of the OFD ecosystem. Doi: 10.28991/HIJ-2025-06-01-012 Full Text: PD
Optimization of the Ground Motion Intensity Measure for Long-Span Suspension Bridges Considering the Impulse Effect
Full text linkTo study the intrinsic relationship between the structural response of long-span suspension bridges and the intensity measures (IMs) and to select the optimal IM to reduce the discreteness in the prediction of structural responses, this paper uses the Incremental Dynamic Analysis (IDA) method to amplitude adjust near-fault pulse-like ground motions and analyzes the response using the curvature at the base of the tower as the structural response index. Then uses the four evaluation indices: efficiency, sufficiency, practicality, and proficiency to evaluate the intrinsic relationship between the structural response and the IMs. The study results indicate that, according to the four evaluation indices, the velocity-related IMs all performed well, while those displacement-related IMs performed the worst. Among them, the effective peak velocity (EPV) performed the best, being optimal in all evaluation indices except for the sufficiency relative to magnitude, which was lower than the maximum incremental velocity (MIV). Therefore, the EPV can be considered the best ground motion IM for predicting the dynamic response of long-span suspension bridges under the action of near-fault pulse-like ground motion. This result can provide a basis for the selection of IMs and structural response prediction for near-fault long-span suspension bridges, considering the impulse effect. Doi: 10.28991/HIJ-2025-06-01-07 Full Text: PD
Smart Data Placement Strategy in Heterogeneous Hadoop
Full text linkBig Data platforms are becoming increasingly essential these days, given the volume of data generated every moment by millions of people around the world. The Hadoop framework is a solution that allows storing and processing these large amounts of data in parallel on a cluster of machines. The default data placement strategy adopted by the Hadoop Distributed File System (HDFS), initially designed for a homogeneous cluster where all machines are considered identical, relies on distributing data to nodes based only on their disk space availability. Implementing this strategy in a heterogeneous environment, where nodes have varying computing or disk storage capacities, may result in performance degradation. In this paper, we propose a smart data placement strategy (SDPS) in heterogeneous Hadoop clusters that aims to place high-access data on high-performance nodes. It takes cluster heterogeneity into account when distributing data by first dividing nodes into groups based on their performance levels using a clustering algorithm and then allocating data blocks to appropriate nodes based on their hotness. SDPS also allows dynamically specifying the replication factor of data blocks to reduce storage space waste while maintaining data availability. Experimental results show that SDPS is more efficient in a heterogeneous environment compared with the default data placement policy of HDFS, and it improves MapReduce data processing, data locality, and storage efficiency. Doi: 10.28991/HIJ-2025-06-01-03 Full Text: PD
The Impact of Self-Efficacy on Telemedicine Adoption in Emerging Country
Full text linkTelemedicine has emerged as a vital innovation in healthcare, improving access to medical services by reducing the need for physical interactions. Previous studies revealed that self-efficacy influences individuals' perceptions and behaviors toward adopting new technologies, especially telemedicine. However, these studies do not emphasize understanding how three sources of self-efficacy, namely, enactive mastery (EM), vicarious experience (VE), and verbal persuasion (VP), affect telemedicine adoption (TA) through perceptions of telemedicine technology. Based on the sample of 240 respondents, structural equation modeling (SEM) analysis was utilized to examine the proposed hypotheses. The results revealed that enactive mastery and vicarious experience positively influence perceived ease of use (PE), with vicarious experience also significantly impacting perceived usefulness (PU). Perceived ease of use significantly impacted perceived usefulness, strongly influencing telemedicine adoption. These findings confirm the impact of self-efficacy, especially enactive mastery, and vicarious experience components in sharping perceived ease of use and perceived usefulness. These are essential for driving telemedicine adoption and facilitating its adoption in an emerging country. The findings highlight the importance of these self-efficacy sources in telemedicine adoption strategies and suggest that enhancing individuals' direct experiences and observational learning can foster telemedicine usage in emerging markets. Doi: 10.28991/HIJ-2025-06-01-09 Full Text: PD
Machine Learning Algorithms in Predicting Prices in Volatile Cryptocurrency Markets
Full text linkThis study aims to develop a predictive model for cryptocurrency prices in highly volatile markets. The methodology includes an exploratory data analysis, followed by designing and implementing machine learning (ML) algorithms, focusing on the Long Short-Term Memory (LSTM) neural network. The model's performance was optimized through hyperparameter tuning, and its stability was validated using an analysis of variance (ANOVA). We conducted a benchmark comparison with other ML approaches. Our LSTM model achieved an R² of 99.41% on the first day of prediction and maintained an accuracy above 97% up to the seventh day, demonstrating its robustness even for extended forecasts. During training, the LSTM model reached an RMSE of 5,038.46 and a MAPE of 6.83%. In comparison, alternative models such as Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Random Forests exhibited significantly higher error rates; for instance, XGBoost recorded an RMSE of $17,849.66 and a MAPE of 27.74%. Overall, these findings highlight the superior performance of the LSTM model in addressing the challenges of cryptocurrency price forecasting. Doi: 10.28991/HIJ-2025-06-01-017 Full Text: PD
Global Brand Equity Patterns in High-Tech Industries: A 24-Year Analysis
Full text linkThe objective of the study is to investigate major brand equity trends among High-Tech brands. Using Interbrand's Top 100 global brands list from 2001 to 2024 as its population, the methodology focused on 48 extracted High-Tech brands. The analysis employed descriptive statistics, including cumulative brand equity and growth rates, alongside country- and region-specific analyses and cluster formation. This approach allowed for an examination of the 2008 global financial crisis and the recent pandemic's impact. Brands from nine countries were categorized into Hardware, Software, and Internet Services, with the analysis grounded in Interbrand's reported brand equity values and annual growth rates. Analysis of High-Tech brands reveals common strategic lessons alongside unique nuances. Resilience during global financial crises and pandemic COVID’19, the country-of-origin effect (US dominance), maintaining financial thresholds, diversified global earnings, and adapting to evolving market standards are universally important. However, High-Tech particularly emphasizes innovation as core, the dominance of internet services and software, and the rapid rise and fall in niche areas in internet-based services. Hardware faces distinct challenges. The emergence of industry "Giants" and strong niche players underscores diverse success paths. Digital transformation is foundational, and effective brand portfolio management is crucial. This research provides novel strategic lessons for brand managers, emphasizing the crucial role of rapid, continuous innovation and strategic digital transformation for maintaining brand equity
Using PID-BP Digital Virtual Reality for Non-Heritage Protection: Recognition and Assessment
Full text linkThe objective of this paper is to address the issues of low accuracy and slow real-time performance in the existing algorithm for digitally protecting and evaluating non-heritage culture. To achieve this, we propose an improved method for the identification and assessment of non-heritage digital protection by optimizing the BP neural network using the PID search algorithm. This method aims to enhance the precision and real-time capabilities of the algorithm. We extract a set of feature vectors from the digital protection process of non-heritage culture and construct a recognition and evaluation system. The PID search algorithm is employed to optimize the BP neural network, which helps in establishing a mapping relationship between the feature vectors and the assessment values of non-heritage digital protection. We apply this method to the digital protection of non-heritage culture in Dali Xizhou as a case study. The results show that our method significantly improves the accuracy and real-time performance of the assessment compared to traditional BP and other optimized BP network models. This study provides a novel and effective approach to the digital protection of non-heritage culture
Impact of Green Building Strategies on Environmental, Economic, and Social Outcomes in Construction
Full text linkThis research investigates the impacts of applying Green Building (GB) principles within Jordanian construction firms, focusing on their effects on environmental, economic, and social dimensions. A descriptive analytical method was employed, suitable for social and humanitarian research contexts. The study targeted a sample of 15 large construction companies listed on the Amman Stock Exchange, utilizing a random sample of 150 individuals, including heads of departments, engineers, designers, architects, supply chain managers, and directors. Data collection was conducted through questionnaires, with 150 distributed and 110 valid responses received, resulting in an 80% response rate. Data analysis was performed using SPSS software version 22 to calculate means, frequencies, and standard deviations. The findings revealed significant impacts of applying GB principles, with a correlation coefficient of 0.754 for environmental quality, indicating that GB practices account for 56.8% of the variance in environmental outcomes. For residents' health, the correlation was 0.643, explaining 41.3% of the variance, while resource preservation showed a strong correlation of 0.749, indicating substantial contributions. Economically, the principles demonstrated a correlation of 0.705, accounting for 57.3% of the variance in economic performance. These findings underscore the necessity of integrating GB practices into construction projects to enhance sustainability, with recommendations including early integration of green design in project development, establishment of comprehensive green education programs, provision of incentives for existing building owners, and securing funding for renewable energy initiatives. Implementing these strategies is crucial for maximizing the effectiveness of GB practices and advancing sustainable development in Jordan
Research on RAG-Based Cognitive Large Language Model Training Method for Power Standard Knowledge
Full text linkElectrical standards encompass complex technical requirements across multiple disciplines, making their management and application a significant challenge that urgently requires efficient solutions. This paper proposes a knowledge graph retrieval-enhanced training method for large language models (LLMs). By leveraging a pre-trained language model (PLM), highly similar subgraphs are retrieved from the electrical standards knowledge graph. These subgraphs are then parsed into triples using entity linking and semantic reasoning. The triples are converted into natural language text by the LLM, which combines them with the input question to perform reasoning and generate accurate answers. The proposed method addresses the complexity of question answering for electrical standards and offers a novel approach for managing and applying these standards in the field of electrical engineering. Experimental results demonstrate that this approach significantly enhances the model's understanding of electrical standards, enabling it to generate more accurate answers
Analysis of Success Factors for Green IT Implementation in Higher Education
Full text linkThis study aims to identify success factors in the implementation of Green Information Technology (Green IT) in the higher education sector in Indonesia, which is increasingly challenged to implement sustainable practices. Using the Interpretive Structural Modeling (ISM) method, this study analyzes and maps the hierarchical relationships between key factors to provide a more structured understanding of the dynamics of Green IT implementation. The results show that external and social pressures are the main drivers in shaping sustainability policies in higher education. These policies then influence various important aspects such as management commitment, environmental awareness, infrastructure development, and budget allocation. In addition, this study highlights how the interaction between these factors creates an ecosystem that supports the sustainability of Green IT in the academic environment. The novelty of this study lies in the finding that external factors play a more dominant role than internal motivations in driving the implementation of Green IT in Indonesia, unlike the pattern common in developed countries. The practical implications of this study provide insights for policymakers in designing responsive sustainability strategies, strengthening institutional commitment, and increasing environmental awareness in higher education. However, this study also found several major obstacles in the implementation of Green IT, including lack of financial resources, resistance to change, lack of technical skills, and suboptimal coordination between stakeholders. Therefore, recommended improvement strategies include strengthening incentive-based policies, increasing environmental literacy through educational programs, optimizing investment in green infrastructure, and integrating Green IT into academic curricula. This study provides practical insights for policymakers to design more effective sustainability strategies, strengthen institutional commitment, and increase environmental awareness across higher education institutions