JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

AI REVOLUTION IN LITERARY TRANSLATION: A BIBLIOMETRIC ANALYSIS OF TRENDS, CHALLENGES, AND POTENTIALS

Muhammad Firdhaus Zulkifli — 2025-08-18

AI has significantly affected literary translation through tools such as Neural Machine Translation and Large Language Models, providing greater speed and accessibility. However, their grasp of literary subtleties, like metaphor, tone, and culture, remains subject to debate. This study uses a bibliometric analysis of 254 peer-reviewed articles from 2005 to 2025 in the Scopus database, employing OpenRefine, VOSviewer, and Scopus Analyser. Findings indicate a surge in publications after 2020, reaching a peak in 2024. Most research comes from technical disciplines; Computer Science (26.6%), Engineering (21.7%), and Mathematics (10.3%), with limited input from the Arts and Humanities (1.8%). China leads in publications (41.3%), followed by India and the U.S. Keyword analysis reveals a focus on AI methods, often neglecting translation-specific challenges. The study calls for interdisciplinary collaboration and culturally sensitive AI to preserve literary depth.

A STUDY ON THE DIGITAL COMPETENCE CHARACTERISTICS OF VOLUNTEER CHINESE LANGUAGE TEACHERS IN SECONDARY SCHOOLS IN THAILAND BASED ON O*NET JOB ANALYSIS METHOD

Zhulin — 2025-08-11

This study uses the O*NET job analysis questionnaire to investigate the work skills and work styles required for volunteer Chinese language teachers in secondary schools in Thailand. The analysis results of 265 subjects show that: (1) The important work skills of volunteer Chinese language teachers in secondary schools in Thailand include digital technology operation ability, digital teaching problem-solving ability, digital teaching strategy thinking ability, digital teaching guidance and evaluation ability, and digital teaching integration and application ability, and (2) The important work styles of volunteer Chinese language teachers in secondary schools in Thailand are basic professional characteristics and teacher social cognitive style.

CLICK, MEASURE, SUCCEED: A BIBLIOMETRIC JOURNEY AND EMERGING RESEARCH GAPS IN INFORMATION SYSTEMS (IS) EFFECTIVENESS

Nor Asiah Taib — 2025-08-11

Information Systems (IS) effectiveness has emerged as a critical research area in the face of rapid digital transformation and the increasing reliance on technology across various sectors. Despite the field’s maturity, theoretical development and research focus remain fragmented, necessitating a comprehensive synthesis of its evolution. This study presents a bibliometric analysis titled “Click, Measure, Succeed: A Bibliometric Journey of Theoretical Contributions and Research Gaps in Information Systems Effectiveness” to uncover historical trends, influential works, key contributors, and potential research gaps. The problem underpinning this research lies in the limited understanding of how theoretical contributions—particularly the DeLone and McLean Information Systems Success Model (D&M ISSM), the Technology Acceptance Model (TAM), and the Task-Technology Fit (TTF) model have shaped and evolved within the IS effectiveness domain over time. To address this, bibliographic data were extracted from the Scopus database using defined keywords, yielding a refined dataset of 1,121 publications spanning from 1975 to 2025. The data were cleaned and standardized using OpenRefine, analyzed quantitatively via Scopus Analyzer, and visualized through VOSviewer version 1.6.20, enabling the mapping of co-authorship, keyword co-occurrence, and citation networks. Key findings reveal a notable publication surge after 2020, with the United States, Indonesia, and Malaysia leading in output. Seminal works by DeLone and McLean dominate citation metrics, underscoring the enduring influence of the D&M ISSM. In contrast, research grounded in TAM and TTF increasingly highlights user acceptance and alignment between tasks and technology as critical factors in the success of IS. Keyword clustering reveals four thematic concentrations: system quality, service performance, user satisfaction, and digital transformation. Despite growing interest, collaboration among countries remains limited, and theoretical diversification is still narrow. This study contributes to academic discourse by mapping the intellectual structure of IS effectiveness research and identifying gaps that warrant further exploration, particularly in emerging economies and across disciplinary boundaries. By consolidating fragmented knowledge, this research aids scholars in navigating past contributions while paving strategic directions for future inquiry into IS effectiveness.

THE EVOLUTION OF RESEARCH ON PHISHING: A BIBLIOMETRIC ANALYSIS (2005–2025)

Norlizawati Ghazali — 2025-08-11

Phishing has emerged as one of the most pervasive cyber threats in the digital age, evolving in both technical sophistication and psychological manipulation. While considerable research has been conducted on phishing detection and user susceptibility, a comprehensive bibliometric analysis of this field remains limited. This study addresses that gap by examining the evolution of phishing-related research from 2005 to 2025 through a systematic bibliometric approach. Using data extracted from the Scopus database (n = 1196) and analyzed through Scopus Analyzer, OpenRefine, and VOSviewer, the study explores annual publication trends, identifies the most cited articles, highlights prolific authors and contributing countries, and maps keyword co-occurrence, co-authorship, and co-citation networks. There has been a significant surge in publication output post-2016, with peak contributions between 2020 and 2024, likely influenced by global digitalization and the COVID-19 pandemic. The most cited works emphasize machine learning, persuasion tactics, and phishing susceptibility. India, the United Kingdom and the United States emerged as leading contributors, with the United States exhibiting the highest total link strength in collaborative networks. Among the 2000+ keywords identified, “phishing,” “machine learning,” “cybersecurity,” “phishing detection,” and “social engineering” were most prominent. Keyword co-occurrence mapping illustrates a dual focus on technical detection and human-centered analysis. The co-authorship analysis shows moderate international collaboration, concentrated among select academic hubs, while co-citation analysis reveals key intellectual influencers shaping the field. The findings present an in-depth overview of the intellectual structure and worldwide development of phishing research, highlighting the interdisciplinary nature of the field and informing future research directions for cybersecurity scholars and practitioners.

TRENDS IN C++ PROGRAMMING EDUCATION: A BIBLIOMETRIC ANALYSIS

Ahmad Zaki Mohmad Amin — 2025-08-24

This bibliometric analysis investigates global research trends in C++ programming education by examining scholarly outputs from the Scopus database. The study addresses a growing concern over the lack of comprehensive insights into the evolution of C++ education research, particularly in terms of thematic focus, publication patterns, and geographical distribution. To bridge this gap, a systematic search strategy was employed using three keyword variations related to C++, retrieving a total of 2,044 documents. The analysis utilized Scopus Analyzer for data filtering and statistical insights, OpenRefine for data cleaning and normalization, and VOSviewer for visualization of co-authorship networks, keyword co-occurrence, as well as citation mapping. The findings reveal that educational themes are closely connected with core topics such as programming pedagogy, curriculum design, and digital learning tools. The United States (US), the United Kingdom (UK), and China emerged as the leading contributors, with the US dominating in both document count and citation impact. Keyword mapping shows a consistent shift from basic language instruction to applied learning contexts like game-based education and computational thinking. Despite a strong research presence in developed nations, collaboration across countries remains limited, indicating potential for broader global engagement. In conclusion, this study highlights key publication trends and research gaps, offering valuable direction for educators, curriculum developers, and policy-makers aiming to strengthen and innovate C++ programming education worldwide.

A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS IN ARTIFICIAL INTELLIGENCE (AI) AND FINANCE (2015-2025)

Nabilah Wafa’ Mohd Najib — 2025-08-18

This study explores the research trends in the field of Artificial Intelligence (AI) and Finance through a detailed bibliometric analysis of relevant literature published between 2015 and 2025. In recent years, AI-driven innovations such as blockchain technology, digital platforms, and intelligent financial systems have transformed the financial sector, prompting a surge in academic interest. Despite this growing body of research, a systematic understanding of its structure, development, and key contributors remains limited. To address this gap, the study applies a bibliometric methodology, using the Scopus database to extract scholarly publications with the keywords "AI," "digital," "technology," and "finance" in the title. The dataset was refined using OpenRefine to ensure consistency and data cleanliness and analyzed using Scopus Analyzer and VOSviewer software to uncover key patterns in publication volume, subject areas, influential authors and countries, popular keywords, and collaborative networks. The final dataset comprises 1,388 English-language journal articles. The findings reveal a significant growth in scholarly output beginning in 2020, with strong growth observed in 2023 and 2024, reflecting increasing global interest in the digital transformation of financial services. China emerged as the dominant contributor, followed by the United States, the United Kingdom, and Malaysia. Keyword co-occurrence mapping highlights prominent themes such as digital finance, fintech, digital inclusive finance, and blockchain. Highly cited works focus on the role of digital finance in sustainability and the impact of AI on financial systems and policy. The analysis also shows increasing collaboration among authors and institutions across regions. In summary, this bibliometric study maps the development of research in AI and finance, identifies emerging themes and guiding future research directions in this interdisciplinary field.

DIGITIZING YANGJIABU WOODCUT NEW YEAR PRINTS: AUGMENTED REALITY APPLICATIONS IN INTANGIBLE CULTURAL HERITAGE PRESERVATION

Run Li — 2025-08-11

This article is a case study on the digitization of Yangjiabu woodcut New Year prints. The article first compares the basic information of Yangjiabu woodcut New Year prints, including their characteristics and classification. Consequently, it analyzes the digitization case study of Yangjiabu woodcut New Year prints. Finally, it examines the Augmented Reality (AR) applications of other intangible Cultural Heritages (CH) and proposes suggestions and outlooks for the AR applications of Yangjiabu woodcut New Year prints. According to past literature, it can be observed that Yangjiabu Woodcut New Year prints underwent innovations and changes in themes and content during different periods, necessitating their creation to reflect the characteristics and themes of the times. The development strategy for Yangjiabu woodcut New Year prints in AR should focus on enhancing the expression of cultural connotations and the user's interactive experience. It aims to integrate the virtual and real worlds through lightweight mobile devices.

A BIBLIOMETRIC ANALYSIS OF ARTIFICIAL INTELLIGENCE IN PERFORMANCE EVALUATION: TRENDS AND GAPS IN CONSTRUCTION RESEARCH

Nurshikin Mohamad Shukery — 2025-08-11

The integration of Artificial Intelligence (AI) into performance evaluation across various domains has emerged as a transformative research area, promising improved accuracy, objectivity, and efficiency in decision-making processes. However, despite growing interest, the literature remains fragmented, and a comprehensive understanding of global research trends is lacking. This study aims to bridge that gap by conducting a bibliometric analysis to map and evaluate the development, focus areas, and collaborative networks of research related to performance evaluation using AI. The study employs a robust methodology by retrieving bibliographic data from Scopus, refining and cleaning it using OpenRefine and Mendeley for consistency and accuracy, and analyzing it through VOSviewer to visualize keyword co-occurrences, country collaborations, and citation networks. A total of 801 relevant documents were identified and analyzed, spanning a diverse range of industries and applications. The findings reveal a sharp increase in publication trends since 2019, with India, China, and the United States emerging as the top contributing countries. “Machine learning,” “performance evaluation,” and “artificial intelligence” were the most frequently occurring keywords, indicating their centrality in the discourse. Highly cited papers focused on domains such as structural engineering, cybersecurity, and healthcare, reflecting the interdisciplinary application of AI in performance contexts. The visualization maps show strong international collaborations and evolving research clusters. This study concludes that AI-based performance evaluation is a growing field characterized by diverse applications and expanding global interest. However, there remains a need for more harmonized and cross-disciplinary research efforts. These insights provide researchers, practitioners, and policymakers with a clearer understanding of the research landscape and future directions in the application of AI for performance measurement.

RESEARCH TREND ON CHATBOT IN CONSTRUCTION: A BIBLIOMETRIC ANALYSIS

Fuziah Ismail — 2025-08-11

The rapid advancement of Artificial Intelligence (AI) technologies has catalyzed transformative changes across various sectors, including the construction industry. Among these innovations, Chatbot has gained significant attention for its potential to enhance communication, automation, and decision-making processes. Despite its growing relevance, scholarly exploration of Chatbots within the construction domain remains fragmented and under-characterized. Thus, this study aims to address this gap through a comprehensive bibliometric analysis, offering a data-driven overview of current research trends, influential contributions, and thematic structures related to Chatbots in the construction industry. Utilizing a systematic search strategy, three key terms, “chatbot,” “ChatGPT,” and “construction,” were applied to extract data from the Scopus database. The initial search yielded 537 relevant publications. Data cleansing and refinement were performed using OpenRefine to ensure analytical precision and consistency, followed by detailed quantitative and network-based analyses conducted via Scopus Analyzer and VOSviewer software. The results reveal a noticeable increase in publication activity from 2023 onwards, with significant contributions from countries such as the United States, China, and Malaysia. Thematic clustering highlights core research areas, including AI-assisted project scheduling, prompt engineering, and educational applications of Chatbots in engineering curricula. High-impact authors and journals were also identified, delineating the intellectual structure of the field. Citation and keyword co-occurrence analyses further emphasize the interdisciplinary nature of the topic, linking AI, natural language processing, and construction management. In conclusion, this study offers a foundational mapping of the emerging research landscape at the intersection of Chatbot and the construction industry. It also provides valuable insights for academics, practitioners, and policymakers seeking to harness AI-driven tools for innovation in construction processes and education.