Sustainability and Empowerment in the Context of Digital Libraries

Table of Contents

1. Frontmatter

2. Cultural Data Analysis

   1. Frontmatter

   2. A BERT-Based Method of Named Entity Recognition for Ukiyo-e Titles

      Bohao Wu, Akira Maeda

      Abstract

      Named entity recognition (NER) is a particularly challenging task, especially for historical documents that lack extensive annotated datasets [6, 14]. The titles of ukiyo-e, a genre of Japanese artworks, contain a significant number of entities and are composed of short texts rich in historical information. The complexity and brevity of these titles pose considerable challenges for analysis. This paper presents the construction of an ukiyo-e NER dataset and introduces a BERT-based NER methodology that achieves notable success in entity recognition within ukiyo-e titles. The study underscores the effectiveness of BERT and its derivative models on the ukiyo-e NER dataset, proposing a viable NER solution for historical documents. The proposed approach demonstrates the capability to perform NER tasks on ukiyo-e titles using pre-trained models on a relatively small annotated dataset, achieving an accuracy exceeding 80%. Furthermore, this paper examines the distinct characteristics of BERT and its derivative models, optimizing their application to the ukiyo-e NER dataset.

   3. Digitization in Indonesian Libraries and Archives: Mapping Cultural Heritage Across the Archipelago

      Widiatmoko Adi Putranto, Regina Dwi Shalsa Mayzana, Emi Ishita

      Abstract

      Despite the country’s cultural wealth, Indonesia is still struggling to identify and map its cultural heritage collections across the dispersed islands as well as to digitize and make them accessible online. This study aims to identify and examine the provision and digitization of cultural collections by state libraries and archives in 38 provinces across the archipelago. Out of the 32 institutions, 65.6% of responses obtained showed that state libraries and archives in Indonesia hold a diverse range of cultural heritage collections. These are being digitized and provide valuable resources that may not be found elsewhere. However, multiple layers of constraints ranging from policy and copyright issues, traditional beliefs and distrust of the local communities, and the lack and unequal development of digital infrastructure and skilled staff challenge many digitization projects and further possibilities in making them more open and available online. Overcoming this complex situation requires a collaborative approach from different stakeholders to ensure that these cultural resources are more widely accessible and beneficial to the public.

   4. Retrospectively Mining War Impacts upon Collective Moral Motivation from Historical Newspaper Archives

      Zihan Ma, Feng Yu, Liang Zhao

      Abstract

      Shall people be more moral during the war? By mining large-scale historical newspaper archives, we provide promising evidence through big data analysis to answer this question. Taking the American Civil War as the scenario, we exploited the historical newspaper database Chronicling America as a corpus with widely-used moral lexicons, and retrace the diurnal dynamics of collective moral motivation of American society from 1849 to 1876. Sufficient statistics revealed that compared with Pre-war and Post-war, the collective moral motivation of the society increased significantly during wartime. Regression analysis suggested the situational sensitivity of moral motivation, i.e., during the first half of wartime the moral motivation boosted while then decreased in the second half when the war entered a stalemate. Moreover, the two component dimensions, agency and communion, also increased during wartime.

   5. Does Oral Knowledge Belongs to Library: Library Professionals’ Perspective

      Nilakshi Sharma

      Abstract

      Certain knowledge is transfer from one generation to the next through spoken word, a process known as oral tradition or oral knowledge transfer. Oral knowledge refers to the sharing and preservation of information, stories, and experiences through verbal communication. Oral knowledge are major sources of information which includes cultural and traditional beliefs, historical events, ancestral histories, developmental journey, myths, legends, and skills, among other things. It portrays a geographical and political landscape of a community or a social group. At the same time, since it passes from generation to generation orally and there is no documented form of these information. And at this point, the question arises whether these information’s are in a vulnerable phase of missing out? Proper documentation, archiving is the necessity of the hour to preserve these knowledges for sustainable use by budding generations. The study focuses on perspective of library professionals towards oral traditions and their roles and responsibilities in preserving cultural heritages where the initiatives taken by various institutions to preserve the oral knowledge is studied. To collect data of the study, a structured questionnaire was constructed and distributed among the library professionals of North East India through social media platforms. The results states that library professionals’ holds the responsibilities of collecting, documenting, preserving, and disseminating oral traditions, where technologies like Artificial Intelligence (AI) can be involved. There was unanimous agreement among all respondents that the preservation of oral traditions is crucial and significant. Out of total 68 respondents, 83.82% (57) respondents stated that there are no initiatives taken by their institutions regarding preservation of oral knowledge till the date of response. 5.88% (4) stated that their institutions are planning to act in this regard. And 10.29% (7) institutions have already taken some initiatives of preserving oral traditions. However, awareness of oral traditions among important stockholders like library professionals is not satisfactory. In this regard, culturally rich site like north east India is still in a position where education, awareness about oral knowledge is in theoretical form; without proper practical implementation of theories of creating awareness among people, preservation of our oral heritages for sustainable use will remain a myth.

   6. Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents

      Emanuela Boros, Maud Ehrmann

      Abstract

      This paper investigates the presence of OCR-sensitive neurons within the Transformer architecture and their influence on named entity recognition (NER) performance on historical documents. By analysing neuron activation patterns in response to clean and noisy text inputs, we identify and then neutralise OCR-sensitive neurons to improve model performance. Based on two open access large language models (Llama2 and Mistral), experiments demonstrate the existence of OCR-sensitive regions and show improvements in NER performance on historical newspapers and classical commentaries, highlighting the potential of targeted neuron modulation to improve models’ performance on noisy text.

3. Design and Evaluation

   1. Frontmatter

   2. An Experimental Framework for Designing Document Structure for Users’ Decision Making: An Empirical Study of Recipes

      Rina Kagawa, Masaki Matsubara, Rei Miyata, Takuya Matsuzaki, Yukino Baba, Yoko Yamakata

      Abstract

      Textual documents need to be of good quality to ensure effective asynchronous communication in remote areas. However, defining a preferred document structure (content and arrangement) for improving lay readers’ decision-making is challenging. First, the types of useful content for various readers cannot be determined simply by gathering expert knowledge. Second, methodologies to evaluate the document’s usefulness from the user’s perspective have not been established. This study proposed the experimental framework to identify useful contents of documents by aggregating lay readers’ insights. This study used 220 online recipes as research subjects and recruited 1,340 amateur cooks as lay readers. The proposed framework identified six useful contents of recipes. Multi-level modeling then showed that among the six identified contents, suitable ingredients or notes arranged with a subheading at the end of each cooking step significantly increased recipes’ usefulness. Our framework contributes to the communication design via documents.

   3. Digital Library Models: A Systematic Review

      Misganu Fekadu, Daniel Alemneh

      Abstract

      Digital library as an organization involves information system (DLMS), Content (digital resources), users (targeted populations), and services (Access methods such as browsing, searching, and downloading). To understand these components more, a model or theory that explain each component is needed to predict the successful implementation of a Digital library. Thus, this study aims to examine digital library models in the literature and to develop an overall model of Digital library as a consolidation of examined existing digital library models. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 was followed. 16 Original articles published in English from 2018 to 2024 which are retrieved from Emerald, Google Scholar, IEEE, Springer, and Wiley online library were selected for final review. Different searching strategies such as advanced/filter searching provided by databases, phrase search and Boolean search are incorporated to refine the search result. From synthesis of 16 studies, the Delone and McLean IS success model is most frequently adopted DL model. Overall, 12 significant constructs from overlap and a combination of other constructs are extracted for the development of the overall model of DL. Facilitating condition is the base influencing factor of overall model of DL implementation and use. As a whole, development of digital library models is more favorable when concepts are mapped together to reduce an ambiguity among concepts. Thus, this study consolidates different findings of DL models by considering relationships among constructs and mapping them with each other to develop a new overall DL model.

   4. Practice and Reflection on the Co-construction and Sharing of Information Resources Within a Consortium Context: A Case Study of Chinese National Agricultural Big Data and Information Service Alliance

      Zhang Jie, Li Tian, Sun Yuan, Kou Yuantao

      Abstract

      The Chinese National Agriculture Big Data and Information Service Alliance is a national professional library and information institution consortium in the field of agriculture, which was established in 2016. This paper makes clear the necessity for the consortium to conduct information resources co-construction and sharing, and introduces in detail its development history and matched information infrastructure. Then, using the SWOT method, the internal strengths and weaknesses as well as external opportunities and threats for the consortium to carry out information resources co-construction and sharing are analyzed.The consortium should adhere to the diversified and reversal development strategies to promote information resources co-construction and sharing in the next step. The paper could give some suggestions for information resources co-construction and sharing development of Chinese National Agriculture Big Data and Information Service Alliance and other similar professional library and information institution consortia.

   5. Semantic Models of Flows

      Robert B. Allen

      Abstract

      As part of our ongoing work on rich structures for representing complex knowledge, we explored qualitative descriptions of systems composed of interacting flows. Here, we consider the issues in developing low-level descriptions of flows, such as by specifying constraints for transitions, and coordinating entities at different levels of granularity. Based on this example, we propose a unified flow model framework.

   6. Preliminary Analysis of Research Data Policies in Japanese Universities

      Kana Fukushima, Yukiko Watanabe, Emi Ishita, Sakura Yasuda, Tokinori Suzuki

      Abstract

      Japanese universities are strongly encouraged to formulate policies on research data and many have published research data polices in the last five years. When designing their research data services, universities must bear their institution’s research policy and the standard policy in Japan in mind. This study aimed to clarify the overall trends and characteristics of the research data policies developed by Japanese universities. This study presents a preliminary analysis of 70 research data policies that Japanese universities have formulated and published from March 2020 to June 2024. Policy titles, elements of structure, policy subjects, responsibility for data management, and terms used for researchers’ responsibilities and rights were examined. It transpired that while the elements described were generally common, there were differences in the policy titles and terminology used. Most of the policies incorporated the management and sharing of data, but did not address the early stages of the research process, such as data management planning.

   7. Development of a Common Data Set for Smart City in Thailand: Research Concept Paper

      Paporn Ruangwicha, Kulthida Tuamsuk

      Abstract

      Thailand has numerous data sources related to smart city development; however, these data are often fragmented, focusing on specific systems, aspects, and technologies. This fragmentation poses challenges for inter-organizational use and collaborative development efforts. This research aims to develop a common data set for smart city development in Thailand, comprehensive characteristics of the seven key areas in smart city development: smart environment, smart mobility, smart energy, smart economy, smart people, smart living, and smart governance. The study employs a research and development methodology divided into three phases: (1) analyzing foundational data for smart city development from international and national organizations through a preliminary source, (2) investigating information and data management practices from key Thai organizations, and (3) synthesizing these findings to develop a common data set, designing a data dictionary for smart city in Thailand, and evaluating it with expert input. The results indicate significant advancements in city management and sustainability through the use of a standardized dataset. The data dictionary details the data domains, elements, and their properties, providing a valuable resource for expanding knowledge on common data sets for smart city. This tool will benefit Thailand’s administrators in managing smart city data and developing comprehensive databases. Additionally, it offers a foundation for scholars and researchers to further explore and develop fundamental data for smart city initiatives in other cities.

4. Generative AI and Digital Libraries

   1. Frontmatter

   2. Expectancy-Value Beliefs as Predictors of Student Intentions in AI Learning and Application

      Stella Xin Yin, Dion Hoe-Lian Goh

      Abstract

      Despite the growing emphasis on artificial intelligence (AI) education, there is relatively little research on the motivational factors that influence students’ intention regarding AI knowledge acquisition and the utilization of AI applications. Understanding these factors not only enhances our knowledge of AI education but also helps educators and researchers to develop appropriate interventions to promote AI learning that align with students’ needs and expectations. Guided by expectancy-value theory and theory of planned behavior, we investigated the role of expectancy-value beliefs in fostering university students’ intentions to learn and use AI. 141 university students participated in this study. Our findings revealed that intrinsic and utility value beliefs played a mediating role in promoting students’ behavioral intentions in AI learning. We also found that while effort cost negatively affected these intentions, opportunity cost positively influenced intentions to acquire AI knowledge and use AI applications. Additionally, we identified gender differences in students’ expectancy-value beliefs, which can inform educators in designing gender-specific interventions to enhance female students’ motivation in AI learning.

   3. Can Pre-trained Language Models Generate Titles for Research Papers?

      Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay

      Abstract

      The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics. We find that fine-tuned PEGASUS-large outperforms the other models, including fine-tuned LLaMA-3-8B and GPT-3.5-turbo, across most metrics. We also demonstrate that ChatGPT can generate creative titles for papers. Our observations suggest that AI-generated paper titles are generally accurate and appropriate.

   4. Generative Agents Navigating Digital Libraries

      Saber Zerhoudi, Michael Granitzer

      Abstract

      In the rapidly evolving field of digital libraries, the development of large language models (LLMs) has opened up new possibilities for simulating user behavior. This innovation addresses the longstanding challenge in digital library research: the scarcity of publicly available datasets on user search patterns due to privacy concerns. In this context, we introduce Agent4DL, a user search behavior simulator specifically designed for digital library environments. Agent4DL generates realistic user profiles and dynamic search sessions that closely mimic actual search strategies, including querying, clicking, and stopping behaviors tailored to specific user profiles. Our simulator’s accuracy in replicating real user interactions has been validated through comparisons with real user data. Notably, Agent4DL demonstrates competitive performance compared to existing user search simulators such as SimIIR 2.0, particularly in its ability to generate more diverse and context-aware user behaviors. To further support the digital library research community, we also present Agent4DLData (https://github.com/padas-lab-de/icadl24-agent4dl), a concise yet comprehensive collection of simulated user search sessions generated by Agent4DL.

   5. Developing an AI-Enhanced Conversation Application on DSpace: Technical Procedure and Details

      Le Yang, Zhongda Zhang

      Abstract

      Open repository projects serve as valuable resources for scholarly communication and knowledge dissemination. However, interacting with these repositories can be challenging due to the vast amount of data, the restriction of browsing features, and the limitation of relational database queries. This paper presents a technical procedure and coding details that leverage Retrieval-augmented Generation (RAG), Large Language Models (LLMs), embeddings, and LangChain to develop an AI-enhanced conversation application tailored for communication with open repository systems, specifically focusing on DSpace. The chatbot uses RAG to enhance response generation by integrating relevant information retrieved from repository structural data and text documents. LLMs form the chatbot’s core generation capabilities, ensuring coherent and contextually appropriate interactions. Embeddings are used to semantically enrich queries and responses, thereby enhancing understanding and relevance. LangChain coordinates among these components, managing the information flow and interaction between users and repositories.

   6. Exploring GenAI’s Role in Digital Cultural Memory at Museums and Art Galleries in Indonesia: AR and VR Perspectives

      Syifa Adiba, Febriyanto, Nur Sanny Rahmawati

      Abstract

      As generative artificial intelligence (GenAI) becomes significantly widespread, its influence on digital libraries cannot be disregarded. This research aims to explore the role of generative AI in digital libraries, focusing particularly on its application in digital cultural memory initiatives at museums and art galleries in Indonesia. The study will investigate how the integration of augmented reality (AR) and virtual reality (VR) technologies can boost the preservation, accessibility, and engagement of cultural artifacts and exhibitions. One notable digital cultural memory initiative is ImersifA, a 360-degree permanent installation of video mapping at the National Museum of Indonesia. By utilizing AR mode, ImersifA brings cultural artifacts to life, delivering contextual information and interactive elements that enrich the understanding and experience of visitors. In addition, virtual tours of permanent exhibitions in the National Gallery of Indonesia allow users to explore the artworks and cultural heritage distantly, conquering geographical barriers and increasing access to a wider public. Qualitative methods with direct observation will be used in this study. The findings show that AR and VR have a significant effect in contributing to the advancement of cultural preservation, accessibility, and audience engagement in the digital age.

   7. Evaluation of the Quality of AI-Generated Scientific Text Under Different Types of Cognitive Complexity Tasks

      Hui Peng, Shujun Liu, Lei Li

      Abstract

      As Artificial Intelligence Generated Content (AIGC) continues to deepen its application in the field of scientific research, this study aims to explore the current quality of AIGC in completing research tasks, providing insights for improving AIGC in the scientific research domain. This study first reviews and summarizes existing information quality evaluation frameworks and AIGC-related research to propose quality evaluation criteria for AIGC in the research context. Then, by setting research tasks with different cognitive complexities, user experiments were conducted on the ChatGPT and ERNIE Bot platforms to select appropriate AIGC quality evaluation criteria for these tasks. The quality of AIGC generated by ChatGPT and ERNIE Bot was evaluated based on the selected criteria, revealing the strengths and weaknesses of current AIGC in meeting users’ research information needs. The results show that users generally value relevance, professionalism, and readability when evaluating AIGC for research tasks. However, attention to specific criteria such as accuracy, diversity, coherence, and creativity varies depending on the cognitive complexity of the research tasks. Additionally, AIGC performs well in understanding, evaluating, and creating tasks but has significant shortcomings in remembering and analyzing tasks, particularly in terms of accuracy and professionalism.

   8. Generating Surprising and Diverse Ideas Using ChatGPT

      Marie Tachibana, Toshiyuki Shimizu, Yoichi Tomiura

      Abstract

      Generative AIs have become widespread and are being used for various purposes. However, a phenomenon called hallucination, in which generative AI outputs plausible lies, is being viewed as a problem. While hallucination is a problem when it is important to be factual, in some cases, such as idea generation, it is not a problem. In fact, in some cases, it may be better to have hallucination occur to come up with surprising and diverse ideas. In this study, we compared several prompts with the aim of getting ChatGPT to output information that differs from the training data for GPT when generating ideas for a fictional historical novel. The results showed that it was more effective and led to generation of more surprising ideas to directly instruct ChatGPT to include content that differs from the facts within the prompt rather than using adversarial prompts that cause hallucinations.

5. Information Retrieval

   1. Frontmatter

   2. Using Annotator Labels Instead of Golden Labels for Fine Emotion Detection

      Alvin Liang Hao Lu, Mizuho Iwaihara

      Abstract

      Textual fine-emotion detection is a challenging task that has yet to achieve powerful performance in both Language model (LM) and Large Language models (LLM). In this paper, we analyze a fine-emotion dataset and current approaches to provide insight of existing issues. We propose the idea of treating fine-emotion detection as having multiple appropriate answers, and to consider annotator-level labels instead of the golden label. We then evaluated treating neutral label separately and using LLM as aid for mistake filtering and augmentation. We show that using annotator labels instead of golden label allows BERT model to predict different interpretations without being penalized despite the weaker performance. Large potential has yet to be explored on annotator-level label fine-emotion detection and we provide several ideas through the approaches evaluated and the analysis of these approaches. We hope to encourage a change in how fine-emotion detection is detected, allowing multiple accurate answers instead of one.

   3. Clarifying Questions Generation for Conversational Search Based on “People Also Ask” Feature

      Navdeep Singh Bedi, Ivan Sekulić, Fabio Crestani

      Abstract

      Conversational search is an emerging paradigm of information retrieval, enabling users to engage in dynamic, interactive dialogues that more closely mimic natural human communication and have the power to address complex, multi-turn queries. To this end, under the mixed-initiative paradigm, conversational search system can ask clarifying questions to the user, which helps close the gap between user’s query and their underlying information need and thus improve the quality of the entire search experience. However, finding appropriate clarifying questions is not straightforward. To this end, in this work, we approach the problem of finding relevant clarifying questions by exploiting the “People Also Ask” (PAA) feature of a popular search engine. We perform a qualitative assessment to verify the quality of the extracted questions and their potential applicability to clarification in search. Next, we convert the PAA questions into clarifying questions using various transformer-based models, such as T5, BART, GPT2, and use established natural language generation metrics to evaluate the performance of different LLMs for paraphrasing the questions. Finally, we discuss the results and the relation between PAA questions and clarifying questions to draw useful conclusions and directions of future work.

   4. Digital Nudge Alerts: Fact-Checking Generative AI Responses

      Chei Sian Lee, Kok Khiang Lim, Heechan Lee, Dion Hoe-Lian Goh

      Abstract

      Generative artificial intelligence (GenAI) chatbots have reshaped human-AI interaction behavior and transformed industries and educational sectors. Despite its advantages, GenAI presents several limitations and concerns. This study addresses fact-checking responses from GenAI to minimize the negative impacts of artificial hallucination. Artificial hallucination is a response generated by GenAI that contains false or misleading information. Addressing this problem necessitates appropriate interventions to remind users to ensure the accuracy of the GenAI-generated information, thus cultivating responsible and accountable usage. To address this concern, this study employed a behavioral economics approach using a digital nudge-based intervention to subtly remind users to fact-check the AI-generated output. To ensure cost efficiency, the proposed digital nudge intervention is delivered through a browser extension that automatically triggers popover alerts within the GenAI environment.

   5. Evaluating Large Language Models for Healthcare: Insights from MCQ Evaluation

      Shuangshuang Lin, Hamzah Bin Osop, Miao Zhang, Xinxian Huang

      Abstract

      This study investigates the performance of general and medical-specific Large Language Models (LLMs) in obstetrics and gynecology, focusing on their ability to accurately handle medical multiple-choice questions (MCQs). We evaluated models like Llama2, Mistral, PMC_LLaMA, and BioMistral, to assess and enhance their reliability and accuracy. Despite the expectations, general-purpose models occasionally outperformed specialized medical models. Our methods, including Structural Influence Testing and Contextual Enhancement Testing, demonstrated significant potential in improving model accuracy and reducing misinformation. Specifically, Structural Influence Testing increased Mistral’s accuracy from 40% to 46% and Llama2’s from 28% to 43% with five shots. Contextual Enhancement Testing yielded a 4% accuracy gain for Mistral and 6% for Llama2 using search terms. This research highlights the importance of optimizing LLMs to empower healthcare professionals with precise and reliable medical information, ultimately improving patient outcomes and supporting informed clinical decisions.

   6. Capabilities and Challenges of LLMs in Metadata Extraction from Scholarly Papers

      Yu Watanabe, Koichiro Ito, Shigeki Matsubara

      Abstract

      Research data are cited in scholarly papers, and the construction and use of datasets are mentioned. The descriptions of research data in papers may be used as information for its metadata. In this paper, we focus on large language models (LLM), which have achieved high performance in various natural language processing tasks, and we investigate the ability of LLMs to extract metadata from papers. In the experiment, we analyzed LLMs’ metadata extraction capabilities quantitatively and qualitatively. The results demonstrate that while LLMs can extract metadata from papers extensively, the extraction accuracy is not necessarily high. We confirm that there are challenges in identifying the names of research data and linking information related to the research data.

6. Information Seeking and Use

   1. Frontmatter

   2. The Power of Warning: Unpacking the Impact of Fact-Checking Flag on News Sharing and Verification

      Jiayu Han, Alton Yeow Kuan Chua

      Abstract

      Grounded in information gap theory (IGT), the objective of this paper is to develop and empirically validate a conceptual model comprising perceived believability, information curiosity, fact-checking flag, intention to share and intention to verify. The proposed model is tested through a between-participants experiment using a simulated scenario where users browse Facebook news posts. A total of 177 participants were randomly assigned to one of two experimental conditions: one where a fact-check flag was present in a Facebook news post, and the other where the fact-check flag was absent. Four hypotheses were tested using one-way ANOVA and PROCESS MODEL 4. Results show that the presence of fact-checking flag has both a direct negative impact on the intention to share and an indirect effect through perceived believability. However, it influences the intention to verify only indirectly by increasing information curiosity. This study enriches the literature by not only shedding light on the underlying process by which fact-checking flag affects users’ behavioral intentions but also extending the boundary conditions of IGT.

   3. A Framework to Facilitate Older People in Leveraging Online Financial Services

      Dain Thomas, Gobinda Chowdhury, Ian Ruthven

      Abstract

      Older people are encountering digital exclusion due to the evolving technological realm. The use of digital financial services among older people aged 65 and over is low in comparison to other age groups. A wide range of challenges are associated with older people’s low usage of online financial services. Hence, interventions have to be developed to reduce the exclusion. In order to create interventions, factors which contribute to their challenges have to be identified. This paper elucidates a framework which was developed from qualitative data that could be leveraged to develop potential solutions by focusing on the main factors which could prevent them from fully utilizing digital financial services. Not only would this framework be beneficial for older people but also for intermediaries who assist older people in accessing digital financial information as this tool could aid them in choosing the appropriate solution required to help the individual to use online financial services.

   4. Freedom of Information and Information Policy in Southeast Asia: The Cases of Thailand and Indonesia

      Pimphot Seelakate, Rayhan Musa Novian

      Abstract

      This study analyses the issue of enforcement of the Freedom of Information (FoI) laws in Thailand and Indonesia and aims to identify the information policy in compliance to particularly FoI laws in Thailand and Indonesia regarding records and information management in public sectors. This study is limited to the context of the FoI, records and information services in Thailand and Indonesia. The findings indicate that FoI laws, designed to promote good governance, do not completely ensure transparency, accountability, or the realization of governance goals, primarily caused by to the inconsistent execution of information disclosure, deriving from the restricted capability of government entities to efficiently utilize, re-use, and disseminate information and public records. Secondly, the FoI laws do not sufficiently impact on effective records and information management systems, as government officials sometimes lack awareness, expertise and competencies in these domains leading to problems in providing information services and in fulfilling the FoI compliance requirements. Ultimately, the FoI laws, as an information policy, have not substantially improved understanding of the necessity for effective public records, archives, and information management systems in the public sector, due to the limited awareness among the public and government officials of the FoI laws and the associated human rights to access public documents and information maintained by public institutions. Moreover, both citizens and government officials demonstrate a constrained awareness of the relationship between the right to access information and effective records and information management, resulting in insufficient acknowledgement of the necessity for robust records and information systems in the public sector.

   5. A Research Study Investigating the Impact of COVID-19 on the Information Needs of International Students in the UK

      Ibrahim Abu Asba, Gobinda Chowdhury, Ian Ruthven

      Abstract

      Globally, COVID-19 has affected university students. Throughout the pandemic, international students have been brutally hit. Loneliness and difficulties are the main factors affecting the mental health of overseas students. Transitioning to online learning has presented challenges regarding living conditions and internet access reliability. With the overall aim of investigating the impact of COVID-19 on international students, this study used a questionnaire survey with 545 participants. NVivo and SPSS software were used to analyse data. The outcome demonstrates that several issues were encountered by international university students in the UK during the COVID-19. They mostly used social media and their respective institutes as information sources.

7. Backmatter