Journal of Information Science Research and Practice

April - June

2026-06-30

Soft Power Knowledge Mapping of Nakhon Nayok Province Grounded in the Royal Duties of Her Royal Highness Princess Maha Chakri Sirindhorn: A Documentary Research Approach

Amarin Sonsuwan — 2026-10-02

Purpose: This research aimed to analyze soft-power knowledge in Nakhon Nayok province and to develop a Soft Power Knowledge Map for the province, grounded in the Royal Duties of Her Royal Highness Princess Maha Chakri Sirindhorn. The map was designed to systematically represent the structural relationships among the key knowledge components identified in the study.

Methodology: This study employed a documentary research approach. Data were drawn from 26 secondary sources, including books, textbooks, journal articles, research reports, theses, and government agency websites. The sources were selected based on criteria of relevance, credibility, currency, and data saturation. The collected data were then assessed through content analysis, categorization, and faceted knowledge structuring.

Findings: Content analysis of the selected sources identified five categories of soft-power knowledge, which were synthesized into three core domains: Royal Duties, Cultural Capital, and Soft Power for the Creative Economy. These domains were further structured into a five-layer conceptual knowledge map, with storytelling functioning as the mechanism connecting and linking all the layers together.

Applications of this study: The knowledge map can support soft-power policy planning, value-based tourism promotion, and sustainable knowledge management for government agencies, educational institutions, and civil-society organizations in Nakhon Nayok province. It may also serve as a reference model for other regions to consider. Furthermore, the study offers a methodological framework that may be applicable in other areas of information science.

Development of Information System for Enhancing Risk Management Efficiency: A Case Study of the Faculty of Associated Medical Sciences, Khon Kaen University

Darunee Chookunhom — 2026-10-03

In Process

Libraries and Artificial Intelligence: A Scoping Review

Surachart Putthima — 2026-10-03

In Process

Comparative Accuracy of Generative AI in Bibliographic Cataloging Analysis According to MARC21 Standards and AACR2 Rules

Phawida Maden — 2026-10-02

Purpose: This research aims to conduct a comparative study of the accuracy of Generative AI in analyzing bibliographic cataloging based on MARC21 standards and AACR2 rules.

Methods: This study employed an evaluation research design utilizing document analysis to describe the bibliographic characteristics of a sample of 30 books. The analysis focused on key bibliographic elements, including titles, authors, publication information, and International Standard Book Numbers (ISBNs). The data were analyzed using descriptive statistics, such as frequency and percentage.

Findings: ChatGPT demonstrated the highest overall performance, achieving an average accuracy score of 86.18%, followed by Claude (80.08%) and Gemini (73.17%). At the individual data-field level, ChatGPT achieved 100% accuracy across nine MARC21 fields: Tags 020, 050, 110, 245, 250, 260, 300, 650, and 700. Claude also showed high accuracy in basic and core MARC21 bibliographic fields, including Tags 020, 110, 245, 260, 300, 650, and 700; however, minor discrepancies were observed in certain fields requiring more complex contextual interpretation. In contrast, Gemini exhibited limitations in processing in certain subfields, with lower accuracy observed in fields such as Tags 020 and 050.

Applications of this study: Although Generative Artificial Intelligence (Generative AI) serves as a convenient tool to facilitate information resource cataloging, librarians or information professionals should carefully verify the accuracy of AI-generated bibliographic data before practical application to maintain data quality, cataloging standards, and academic credibility.