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Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

As the current Coronavirus 2019 pandemic eases, international tourism, which was greatly affected by the outbreak, is gradually recovering. The attraction of countries to overseas tourists is related to their overall performance in the pandemic. This research integrates the data of vaccination of different countries, border control policy and holidays to explore their differential impacts on the overseas tourists’ intention during the pandemic. This is crucial for destinations to built their tourism resilience. It will also help countries and industry organizations to promote their own destinations to foreign tourism enterprises.

This study proposes an analysis based on panel data for ten countries over 1,388 days. The coefficient of variation is used to measure monthly differences of Chinese tourists’ intention to visit overseas country destinations.

Results show that, for tourist intention of going abroad: border control of the destination country has a significant negative impact; daily new cases in the destination country have a significant negative impact; domestic daily new cases have a significant positive impact; holidays have significant negative impact; daily vaccination of the destination countries has significant positive impact; and domestic daily vaccination have negative significant impact.

First, there is a large uncertainty in studying consumers’ willingness to travel abroad in this particular period because of unnecessary travel abroad caused by the control of the epidemic. Second, there are limitations in studying only Chinese tourists, and future research should be geared toward a broader range of research pairs.

First, from the government perspective, a humane response can earn the respect and trust of tourists. Second, for tourism industry, to encourage the public take vaccine would be beneficial for both the tourism destination and foreign tourism companies. The same effect can be achieved by helping tourists who are troubled by border control.

First, this research provides suggestions for the government and the tourism industry to deal with such a crisis in the future. Second, this study found that vaccination has a direct impact on tourism. This provides a basis for improving people’s willingness to vaccinate. Thirdly, this study proves suggestion for the destinations to build tourism resilience.

This study analyzes the unique control measures and vaccination in different countries during the pandemic, then provides suggestions for the tourism industry to prepare for the upcoming postpandemic tourism recovery. This study is valuable for improving the economic resilience of tourism destinations. Additionally, it helps to analyze the advantages and disadvantages of different restrain policies around the world.

Security in large-scale agile is a crucial aspect that should be carefully addressed to ensure the protection of sensitive data, systems and user privacy. This study aims to identify and characterize the security practices that can be applied in managing large-scale agile projects.

A qualitative study is carried out through 18 interviews with 6 software development companies based in Portugal. Professionals who play the roles of Product Owner, Scrum Master and Scrum Member were interviewed. A thematic analysis was applied to identify deductive and inductive security practices.

The findings identified a total of 15 security practices, of which 8 are deductive themes and 7 are inductive. Most common security practices in large-scale agile include penetration testing, sensitive data management, automated testing, threat modeling and the implementation of a DevSecOps approach.

The results of this study extend the knowledge about large-scale security practices and offer relevant practical contributions for organizations that are migrating to large-scale agile environments. By incorporating security practices at every stage of the agile development lifecycle and fostering a security-conscious culture, organizations can effectively address security challenges in large-scale agile environments.

The majority of Malaysian firms are small and medium-sized enterprises (SMEs), and the hospitality industry is no exception. However, a study on environmental management systems (EMS) reveals that relatively few studies have observed EMS implementation in small and medium-sized enterprises hotels (SMEHs). Hence, this study aims to determine the link between institutional influences and mediating effects of top managers' perceived benefits toward EMS implementation by Malaysian SMEHs.

The results of a questionnaire survey of 252 SMEHs managerial-level professionals using Smart-PLS software provide some explanation for the marginal action taken by these SMEHs to improve their environmental performance.

Based on the findings, five variables might stimulate the implementation of an EMS: regulatory influences, customer influences, competitor influences, employee influences and manager attributes influences.

This study significantly advances understanding of EMS and SMEHs, offering a replicable research framework applicable across industries and countries. Scholars can leverage the framework for further exploration, while managers should recognize institutional influences on EMS implementation.

Given that this study is one of the few that investigates the institutional influences and perceived benefits of EMS implementation, it may provide valuable information for future research on the hospitality industry and the SMEHs among researchers and practitioners.

This study scrutinizes the attitudes of postgraduate students specializing in education toward research at one University of Education in Myanmar.

Having collected data from 121 Master in Education (MEd) students in three education departments, the study validated Papanastasiou's (2014) Revised-Attitude toward Research (R-ATR) scale by performing confirmatory factor analysis (CFA). A one-way MANOVA was used to establish the significance of mean differences between groups and ANOVA was used for the same purpose among groups.

The R-ATR scale is fit with the sample (χ²/df = 1.60), and the students' attitudes toward research are favorable with mean value for research usefulness (5.98), positive research predispositions (5.55) and research anxiety (3.61), despite feeling stressed and anxious. There was a significant difference in the students' attitudes toward research regarding their departments, while no significant difference was found based on their research experience, gender and age.

There should be an effective mechanism in implementing the research course and in online delivery mode, collaboration between the faculties in the process of revision and review of the research courses, effective strategies of improving research instructors' quality and also internationalization to sustain students' positive attitudes toward research and to reduce their stress and anxiety.

This research is empirically novel, and the initiative attempts to first use the R-ATR scale in teacher education in Myanmar.

The purpose of this research is to provide a theoretical and practical theory and application that provides understanding and means to manage complex infrastructures.

In this research, complexity, nonlinear, noncontinuous effects and aleatoric and data unknowns are bypassed by directly addressing systems' responses. Graph theory, statistics and digital signal processing (DSP) tools are applied within a theoretical framework of the theory of faults (ToF). Motivational complex infrastructure systems (CISs) are difficult to model. Data are often missing or erroneous, changes are not well documented and processes are not well understood. On top of it, under complexity, stalwart analytical tools have limited predictive power. The aleatoric risk, such as rain and risk cascading from interconnected infrastructures, is unpredictable. Mitigation, response and recovery efforts are adversely affected.

The theory and application are presented and demonstrated by a step-by-step development of an application to a municipal drainage system. A database of faults is analyzed to produce system statistics, spatio-temporal morphology, behavior and traits. The gained understanding is compared to the physical system's design and to its modus operandi. Implications for design and maintenance are inferred; DSP tools to manage the system in real time are developed.

Sociological systems are interest driven. Some events are intentionally created and directed to the benefit and detriment of the opposing parties in a project. Those events may be explained and possibly predicted by understanding power plays, not power functions. For those events, sociological game theories provide better explanatory value than mathematical gain theories.

The theory provides a thematic network for modeling and resolving aleatoric uncertainty in engineering and sociological systems. The framework may be elaborated to fields such as energy, healthcare and critical infrastructure.

ToF provides a framework for the modeling and prediction of faults generated by inherent aleatoric uncertainties in social and technological systems. Therefore, the framework and theory lay the basis for automated monitoring and control of aleatoric uncertainties such as mechanical failures and human errors and the development of mitigation systems.

The contribution of this research is in the provision of an explicatory theory and a management paradigm for complex systems. This theory is applicable to a wide variety of fields from facilities and construction project management to maintenance and from academic studies to commercial use.

The aim of this study was to determine data literacy (DL) training needs of researchers at South African public universities. The outcome of this study would assist librarians and researchers in developing a DL training programme which addressed identified needs.

A survey research method was used to gather data from researchers at these universities by convenience. Online questionnaires were distributed to public universities through library directors for further distribution to researchers.

The results indicate low levels of DL training at the respondent South African public universities with most researchers indicating that they had not received any formal training on DL. A few researchers indicated that they would welcome DL training.

This study was exploratory in nature and data was received from eight universities, which is not representative of all the 26 public universities in South Africa. Nonetheless, the low DL confirmed by the majority in the realised sample is indicative of the need to further investigate the subject.

Librarians and research support personnel should collaborate on the development of DL training courses, workshops and materials used by researchers at institutions of higher learning to enhance DLs on campus.

This study may be novel in South Africa in investigating the DL training needs of researchers at several universities and contributes to the growing body of literature on research data management

The purpose of this paper is to explore the integration of business intelligence (BI) systems in the digital transformation context and its impact on management control (MC) and organizational performance by delving into the implementation of a BI pilot project in a multinational Engineering and Construction (E&C) group.

This research adopts a qualitative insider action research approach. The initial stage of a BI system (BIS) is implemented by leveraging the critical success factors (CSFs) framework, based on the organizational, process and technological dimensions.

The results show that the BI solution leverages the capabilities of the reporting process with impacts on MC and overall organizational performance. The study uncovers the intricate dynamics of integrating BI and reports gains that span across streamlined and standardized processes, optimized use of resources, improved data flexibility and nurturing a data-driven culture.

This research adds to the current literature on MC and accounting information systems by providing an in-depth analysis of the early stages involved in implementing a BIS. The approach uses a well-established CSFs framework, which enhances the comprehension of how these factors impact the success of the implementation process. Focusing on the E&C sector, where the adoption of digital transformation is still gaining traction, this study provides practical knowledge and extends the theoretical understanding of BI technology applications with empirical evidence from the accounting field.

This exploratory research aims to unravel how Chinese international students form their decisions regarding the school-to-work transition (sometimes also referred to as the university-to-work transition) when studying abroad.

The authors use the concepts of school-to-work transition and cultural approaches to investigate how Chinese graduate students make career decisions to navigate the school-to-work transition in the context of international mobility. The authors' empirical study is based on 30 semi-structured interviews conducted with Chinese graduate students studying in the Netherlands.

The authors discovered four patterns with two major subgroups among Chinese students studying in the universities in the Netherlands based on their initial intentions: return-return, open-return, open-stay and stay-stay. These patterns made sense when navigating the school-to-work transition: participants experienced varied international experiences, cultural perceptions and acknowledgment influences when enhancing employability and shaping their career decisions. Participants in the return-return and stay-stay groups indicated strong resilience and consistency in achieving their goals and strongly focused on long-term objectives. However, participants' open-return decision demonstrated a thoughtful alignment of personal goals and knowledge of the possible beneficial influence they may have in their home country. Open-stay participants utilized the overseas study opportunity to finalize their decisions and increase their employability in the local labor market, thereby creating a transition from university to work in the host country.

The authors highlight the way Chinese graduate students manage their international experience and provide novel insights into the role played by the cultural characteristics of their home country and host country.

This paper aims to understand the impact of big data on the UAE audit profession. Mainly exploring whether the emergence of big data threatens the reliability of audit standards and whether audit standards need to be improved. Also, exploring the impact of big data on the collection of audit evidence.

Semistructured interviews were used to collect data, mainly targeting the audit-related workers of the Big Four and Non-Big Four audit firms in the UAE. Thematic analysis is adopted to analyze the original data, and the main factors affecting the audit standard and audit evidence collection.

This study found that the reliability of audit standards and the way audit evidence is collected can be affected by big data. It concludes that audit standards need to be improved and strengthened to include detailed essential elements associated with big data to ensure audit reliability, legitimacy and regularity. The results also identify the impact of big data on audit evidence in terms of adequacy, appropriateness, authenticity, consistency and reliability, as well as the impact on the validity and completeness of evidence collection. The research highlights the importance of big data skills and knowledge education, the contribution and challenges of big data to auditing, and the use of big data in future auditing.

This research provides specific empirical evidence from both Big Four and Non-Big Four audit firms in the UAE, which is lacking in the literature on the use of big data technology by auditors to assist audit works in UAE. It may serve as a reference for other researchers or those interested in relevant research.