We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = −0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.

Government performance means the results of government work. Its use is to evaluate government accountability, decision-making, efficiency, effectiveness, transparency, and achievement of goals. Purpose: This paper aims to explore the understanding of performance measurement tools commonly used in government, the reasons for using them, and the implementation of performance measurement in Indonesia. Method: This study uses a meta-synthesis method, an integrative review approach from 2000–2021, in the Scopus database using the keywords measurement system, performance measurement, performance measurement government, measurement system government. Results and Discussion: The final sample consisted of 23 studies, and the results showed that the most commonly used performance measurement was the balanced scorecard. This is because the balanced scorecard is able to explain the vision, mission, strategy, results, and operational actions, so that it can achieve local government goals. Research implications: Insight into government performance measurement can be used to determine the strengths and weaknesses of various performance measurement tools so that the government can implement performance measurement tools that are more appropriate for its government. Originality/Value: This study offers an adaptation of existing methods to measure government performance more effectively. In addition, this study focuses on the context of developing countries, which can provide new contributions to the literature.

Countering cyber extremism is a crucial challenge in the digital age. Social media algorithms, if designed and used properly, have the potential to be a powerful tool in this fight, development of technological solutions that can make social networks a safer and healthier space for all users. this study mainly aims to provide a comprehensive view of the role played by the algorithms of social networking sites in countering electronic extremism, and clarifying the expected ease of use by programmers in limiting the dissemination of extremist data. Additionally, to analyzing the intended benefit in controlling and organizing digital content for users from all societal groups. Through the systematic review tool, a variety of previous literature related to the applications of algorithms in the field of online radicalization reduction was evaluated. Algorithms use machine learning and analysis of text and images to detect content that may be harmful, hateful, or call for violence. Posts, comments, photos and videos are analyzed to detect any signs of extremism. Algorithms also contribute to enhancing content that promotes positive values, tolerance and understanding between individuals, which reduces the impact of extremist content. Algorithms are also constantly updated to be able to discover new methods used by extremists to spread their ideas and avoid detection. The results indicate that it is possible to make the most of these algorithms and use them to enhance electronic security and reduce digital threats.

The construction industry is a significant contributor towards global environmental degradation and resource depletion, with developing economies facing unique challenges in adopting sustainable construction practices. This systematic review aims to investigate the gap in sustainable construction implementation among global counterparts. The study utilizes the P5 (People, Planet, Prosperity, Process, Products) Standard as a framework for evaluating sustainable construction project management based on environmental, social, and economic targets. A Systematic Literature Review from a pool of 994 Sustainable Construction Project Management (SCPM) papers is conducted utilizing the PRISMA methodology. Through rigorous Identification, Screening, and Eligibility Verification, an analysis is synthesized from 44 relevant literature discussing SCPM Implementations worldwide. The results highlight significant challenges in three main categories: environmental, social, and economic impacts. Social impacts are found as the most extensively researched, while environmental and economic impacts are less studied. Further analysis reveals that social impacts are a major concern in sustainable construction, with numerous studies addressing labor practices and societal well-being. However, there is a notable gap in research on human rights within the construction industry. Environmental impacts, such as resource utilization, energy consumption, and pollution, are less frequently addressed, indicating a need for more focused studies in these areas. Economic impacts, including local economic impact and business agility, are further substantially underrepresented in the literature, suggesting that economic viability is a critical yet underexplored aspect of sustainable construction. The findings underscore the need for further research in these areas to address the implementation challenges of sustainable project management effectively. This research contributes towards the overall research of global sustainable construction through the utilization of the P5 Standards as a new lens of determining sustainability performance for construction projects worldwide.

Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

The world economy needs a growth-lifting strategy, and infrastructure financing seems to hold the key. Based on the New Structural Economics (Lin, 2010; 2012) we discuss the heterogeneity of capital focusing on the long-term versus short-term orientation (STO). Traditional neoliberalism assumes that capital is homogenous, complete capital account liberalization is “beneficial”. However, previous studies have found evidence of long-term orientation (LTO) in the culture of many Asian economies (Hofstede, 1991). In this exploratory paper, we suggest that the LTO can be considered a special endowment which, under certain circumstances, can be developed into a comparative advantage (CA) in patient capital. If these countries can turn their latent CA into a revealed CA in patient capital, and develop the ability to “package” profitable and non-profitable projects in meaningful ways, they would have a “revealed” competitive advantage in infrastructure financing. The ability to “package” public infrastructure and private services is one of the key institutional factors for success in overseas cooperation.