In light of the metaverse’s vast expansion, it’s a crucial intellectual platform that’s transforming the video game industry and spurring creative innovation and technological advancement. Considering the distinctive niche that Taiwan occupies within the realm of the video game industry, this study uses a total of 11 video game companies in Taiwan as samples. The study spans a period of 16 years, from 2007 to 2022, and utilizes the random effect regression model for analysis. The study results illustrate that intellectual capital efficiency exerts varying contributions to the creation of value across different corporate value indicators within the video game industry. Among the factors, HCE, SCE, and CEE demonstrate the highest explanatory power for ROE, reaching up to 82.23%. Following this, they account for 73.57% of the variance in market share, but only a meager 13.67% for Tobin’s Q. This study is the empirical evidence that different methods of measuring intellectual capital and various definitions of value creation in an industry may lead to divergent results and managerial implications in intellectual capital research. Hence, it is worthwhile for subsequent studies to continue clarifying and delving deeper into these aspects.

The study has formulated the objective of synthesizing the extent to which technological barriers intervene in the transparency and effectiveness of public management (PM). Methodologically, the study was of a fundamental or basic nature, with a systematic review design, the databases of Scopus (369), SciELO (2), Web of Science (184) were explored, after the review process a set of 22 articles was available. The registration was made in an Excel table where the main data of the articles were included. 32% of the articles selected for the analysis of the evidence are from the period 2020, 27% were from 2022 and 18% from the year 2023; as far as origin is concerned, 14% of the articles come from Peru and 9% from Australia, Brazil, South Korea, Spain and Indonesia. In summary, the study points out that government institutions are making progress in digitizing and improving the citizen experience through electronic services, but they face challenges in areas such as resource management, the low adoption of advanced technologies such as blockchain and artificial intelligence, as well as the lack of transparency in PM. Despite this, it is highlighted that e-government improves citizen satisfaction, and the need to invest in digital innovation, training and overcoming technological barriers to achieve an effective transformation in state administration and promote a more inclusive and advanced society is emphasized.

This paper aims to analyze the narratives that have emerged in the process of bureaucratic reform in Indonesia. The analysis is conducted using the Narrative Policy Framework at the mesa level. Using data from articles published in 6 credible national media about “bureaucratic reform” from 2010 to 2023. The collected data was classified according to the Narrative Policy Framework (NPF) elements in the article: Issue setting, the cause of the issue, plot, character (villain, victim, hero), and recommendations for solutions offered. There were 31 articles analyzed. The result showed that the main plot in the process of bureaucratic reform in Indonesia is based on the corrupt bureaucracy and the slow public service provided. The victims in the plot are the people who will access the services. The villains of the narrative are civil servants who do not improve the required competencies. The heroes of the narrative are several government institutions (Ministry of State Apparatus Utilization and Bureaucratic Reform, Commission of Corruption Eradication, and The Audit Board of The Republic of Indonesia) that are considered to expose the problem.

Social media has become one of the primary sources of communication, information, entertainment, and learning for users. Children gain several benefits as social media helps them acquire formal and informal learning opportunities. This research also examined the effect of social media on formal and informal learning among school-level children in Ajman, United Arab Emirates (UAE), moderated by social integrative and personal integrative needs. Data was gathered by using structured questionnaires, which were distributed among a sample of 364 children. Results revealed that social media significantly affects Informal and formal learning among children, indicating its usefulness in child education and development. The results also indicated a significant moderation of social integrative needs on social media’s direct effect on informal learning, indicating the relevant needs as an important motivating factor. However, the moderation of personal integrative needs on social media’s direct effect on formal learning remained insignificant. Overall, this research highlighted the role of social media in providing learning opportunities for children in the UAE. It is concluded that children actively seek gratifications from social media, shaping their learning within structured educational contexts in their daily lives. Through the lens of UGT, certain needs play a critical role in strengthening the gratification process, affecting how children derive learning advantages from their interactions on social media platforms. Finally, implications and limitations are discussed accordingly.

In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.