This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

In the Indian context, financial planning for salaried individuals has gained increased importance due to economic fluctuations, rising living costs, and the need for robust retirement planning. Despite its importance, there is limited research on the specific factors that influence financial decision-making among salaried employees in India. Understanding these determinants is essential for developing effective strategies to enhance financial well-being among employees. This study explores the key factors influencing financial decision-making among employees, including financial goals, emergency savings, retirement planning, budgeting, financial confidence and literacy, financial stress, use of tax-saving instruments, income level, risk tolerance, and debt levels. A sample of 549 employees from diverse sectors in Uttar Pradesh participated in this research, highlighting the critical aspects of personal financial management that impact financial well-being. The study used a questionnaire-based survey to gather data on factors affecting financial decision-making. Descriptive statistics, correlation, and regression analyses were employed to identify significant predictors. The results reveal that financial literacy, access to resources, attitudes toward retirement planning, and cultural norms significantly influence financial decisions. Additionally, income level, job stability, and social support are crucial in shaping employees’ financial planning. The study recommends enhancing employees’ financial decision-making by offering financial education programs, budgeting tools, retirement planning assistance, debt management programs, tax planning workshops, financial counselling services, and employer match programs for retirement savings. These initiatives aim to boost financial literacy and confidence, enabling employees to make informed financial decisions and improve their financial well-being.

With the rapid increase in electric bicycle (e-bikes) use, the rate of associated traffic accidents has also escalated. Prior studies have extensively examined e-bike riders’ injury risks, yet there is a limited understanding of how their behavior contributes to these accidents. This study aims to explore the relationship between e-bike riders’ risk-taking behaviors and the incidence of traffic accidents, and to propose targeted safety measures based on these insights. Utilizing a mixed-methods approach, this research integrates quantitative data from traffic accident reports and qualitative observations from naturalistic studies. The study employs a binary logistic regression model to analyze risk factors and uses observational data to substantiate the model findings. The analysis reveals that assertive driving behaviors among e-bike riders, such as running red lights and speeding, significantly contribute to the high rate of accidents. Moreover, the lack of protective gear and inadequate safety training are identified as critical factors increasing the risk of severe injuries. The study concludes that comprehensive policy interventions, including stricter enforcement of traffic laws and mandatory safety training for e-bike riders, are essential to mitigate the risks associated with e-bike use. The findings advocate for an integrated approach to urban traffic management that enhances the safety of all road users, particularly vulnerable e-bike riders.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.

Poverty is a major challenge caused by various situations as well as cultural, social, economic, and political interactions. Therefore, poverty alleviation programs and strategies require an integrated approach carried out in consistent and organized stages. It required the participation of all parties, both regional heads, Regional People’s Representative Assembly (RPRA) members, entrepreneurs, and other elements of society. This study aimed to investigate the effect of local spending efficiency on public welfare in Indonesia, using a quantitative and explanatory method. The analysis method used in this study is the panel data regression model. The research population in all provinces in Indonesia was 34 provinces, and a purposive sampling method was used, where a total of 26 provinces were selected. The research period is 2017–2021. The efficiency of local spending (education, health, and infrastructure) is estimated using the Stochastic Frontier Analysis (SFA) cost function approach. The results showed that the higher the efficiency of education spending, the more it will increase public welfare in Indonesia. Meanwhile, the health spending efficiency and the infrastructure spending efficiency do not affect public welfare. The implications of this study for the development of science are that the efficient allocation of education spending will be able to improve the quality of education which is a long-term solution to overcome poverty in Indonesia and for policymakers to be able to optimize education spending to achieve the expected educational goals.

In the domain of public management, the concept of agency refers to the capacity of individuals or groups to effectively utilise power and resources to achieve certain goals. The formation of agency is significantly influenced by the external institutional environment and how actors perceive social structures. Thus, the agency to win a game can be generated as players familiarise with the game’s operations and understand the story line. But beyond this, there are also players who make mods on a non-profit basis, modifying the game’s program to meet the needs of others. mods, as a form of patching, are different from other fan-created mediated texts. Therefore, studying the agency in gaming community management, where both players and developers interact, offers valuable insights for understanding how to promote public participation, innovation, and effective governance in the context of public management. This approach bridges the gap between the digital world and real-world public management practices.