The female labor force participation holds significant implications for various aspects of society, the economy, and individual lives. Understanding its significance involves recognizing the multifaceted impact of women’s participation in the workforce. In this context, the current study investigates the factors influencing the female labor force participation rate in Saudi Arabia while using a set of independent variables such as GDP growth, employment-to-population ratio, inflation, urban population growth, tertiary school enrollment, labor force with advanced education, fertility rate, and age dependency ratio, covering a period from 2000 to 2022. The results reveal that the employment-to-population ratio, inflation rate, urbanization, and age dependency ratio have positive and statistically significant impacts on the female labor force participation rate. This research offers valuable insights for formulating policies to foster female empowerment and overcome the obstacles that hinder their economic participation.

The Indonesian government is currently carrying out massive infrastructure development, with a budget exceeding 10. Risk mapping based on good risk management is crucial for stakeholders in organizing construction projects. Projects financed by government, whether solicited or unsolicited schemes, should also include risk mapping to add value and foster partnerships. Therefore, this study aimed to develop a risk management model for solicited and unsolicited projects, focusing on the collaborative management system among stakeholders in government-financed projects. Risk review was conducted from various stakeholders’ perspectives, examining the impacts and potential losses to manage uncertainty and reduce losses for relevant parties. Furthermore, qualitative analysis was conducted using Focus Group Discussion (FGD) and in-depth interviews. The results showed that partnering-based risk management with risk sharing in solicited and unsolicited projects had similarities with Integrated Project Delivery (IPD). This approach provided benefits and value by developing various innovations in the project life cycle.

Tangerang City is characterized by its dense residential, commercial, and industrial activities and strategic proximity to Jakarta. This study aims to evaluate the strategic planning and implementation of innovative city initiatives in Tangerang, Indonesia, focusing on integrating blockchain, Internet of Things (IoT) big data technologies and innovation in urban development. This study has employed explanatory survey data from a structured questionnaire distributed to a diverse Tangerang community sample, including users and non-users of the “Smart City Tangerang Live” application. The survey was conducted for 2-months March to April 2022, included 71 and the sample included individuals across 13 districts, utilizing cluster sampling to ensure representativeness. The findings reveal a positive community response towards the smart city initiatives, with significant Engagement and interaction with the “Tangerang Live” application. However, technology access and usage disparities among different community segments were noted. The study highlights the critical role of intelligent technologies in transforming urban infrastructure and services, improving the quality of life, and fostering sustainable urban development in Tangerang. The implications of this study are multifaceted. For urban planners and policymakers, the results underscore the importance of strategic planning in innovative city development, emphasizing the need for inclusive and accessible technological solutions. The study also suggests potential areas for improvement in community engagement and public awareness campaigns to promote the adoption and efficient use of smart technologies.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.

This paper provides a unique empirical analysis of the effects of political factors on the adoption of PPP contracts in Brazil. As such, it innovates along two different lines: first, political factors behind the adoption of PPPs have been largely ignored in the vast body of empirical literature, and second, there is scant work done on the motives of any kind behind the adoption of PPPs in Brazil. Various economic and financial reasons have been evoked to justify the use of PPPs in general. These include the goal of promoting socio-economic development in a tight public budgetary framework or of improving the quality of public services through the use of economically efficient and cost-effective mechanisms. Any possible underlying political motives, however, have been overlooked in the PPP research. And yet, there is abundant literature suggesting a link between the adoption of PPPs and the ideology of the governing body or the political cycles associated with elections. This study examines the impact of ideological commitment and opportunistic political behavior on the process of PPP contracting in Brazil, including the stages of public consultation, the publication of tender, and the signature of the contract, using federative-level data for the period between 2005 and 2022. Consistent with the outstanding literature, the two hypotheses are tested: first, conservative parties tend to celebrate more PPP contracts than left-leaning parties, and second, the electoral calendar has a significant effect in the process, allowing for opportunistic behaviors. Empirical results suggest that there is little evidence for the relevance of ideological leanings in the process of adopting PPPs in Brazil. Additionally, regardless of ideology, parties significantly choose to enter PPPs at specific points in the electoral cycle, suggesting decisions are influenced by political considerations and electoral strategy rather than by purely financial or ideological considerations. This may pose severe constraints on the efficiency and cost-effectiveness of the contracts, negatively impacting public governance and leading to protracted costs for taxpayers.