Artificial intelligence in the public sector: a study of the evolution of the academic literature

“Projectification” is an emerging subdomain of project management research which argues that proliferation of projects is one of the most important current trends in the public sector. As an emerging sub-field within projectification research, “public sector projectification” has been given increasing attention in the past few years. This article presents a structured literature review (SLR) on “public sector projectification”, with the aim of systematising the existing empirical knowledge guided by the research question: “What are the empirical implications of public sector projectification at the personal, organisational and societal levels in journal articles?” The SLR search detects 53 articles published between 2009 and 2021. Articles were detected by a literature search in three selected scholarly research databases and by reviewing cited references in the articles detected. By analysing researched empirical implications from the projectification literature at the three levels of personal, organisational, and societal, the SLR demonstrates that public sector projectification is a multilevel phenomenon with contradictory implications and interesting dynamics between the levels, which should gain increased attention in both research and practice to release the potential for organising projects in the public sector context.

The main aim of the paper was to reveal the main opportunities that the environment offers to the digitalization of public organizations, based on the systematic review of the specialized literature published in the Web of Science databases. From a methodological point of view, the research was carried out according to the PRISMA 2020 flow chart for the systematic reviews. In the initial phase, 347 articles/studies published in Web of Science on the topic of digitalization in public institutions were identified from which 43 articles/studies were excluded because they were not relevant for the purpose of the study. The systematic review of the literature that there is a significant amplification of concerns for the digitalization of public organizations, a fact demonstrated by the upward trend of publications and citations in the field, especially in recent years. The most important opportunities from the perspective of the digitalization of public institutions are economic (economic development and economic growth; Reducing administrative burden and designing a user-oriented approach; the delivery of appropriate services by the public sector and the restructuring of public institutions) and technological (big data, cloud storage, artificial intelligence, robots, chatbots, drones, virtual platforms and others).

Objectives: A systematic review of the literature was undertaken (1) to investigate research trends on how artificial intelligence is being used for assessment and diagnosis in the field of communication disorders and (2) to suggest consideration and a directions for the effective use of artificial intelligence in clinical settings. Methods: A total of 328 articles published in foreign journals between January 2016 and August 2021 were searched using 6 databases and a manual search, and 18 articles were finally selected according to PICO strategy (Population, Intervention, Comparison, Outcome) inclusion and exclusion criteria. Four authors determined the report selection and data extraction. They also independently analyzed the quality of the selected papers using QUADAS-II (Quality Assessment of Diagnostic Accuracy Studies-II). Results: Firstly, the selected studies had a generally low risk of bias. Secondly, the major subjects of studies were children with communication disorders. Thirdly, most of the studies included in the analysis were experimental studies to verify the effectiveness of using artificial intelligence. Lastly, the extracted features for assessment and diagnosis were biased against acoustic features at the levels of phoneme and word in speaking tasks. The performance of artificial intelligence in the selected studies differed according to the research purpose and evaluation metrics. Conclusion: This study suggests that in order for artificial intelligence to be used in the assessment and diagnosis system, it is essential to acquire clinically reliable and high-quality big data on the characteristics of speech and language of people with communication disorders.

It has been claimed that employee engagement can harness public service motivation in ways that better staff functioning and positive organizational outcomes, and can help address the increasingly complex challenges associated with public service in an era of austerity. Despite this, there has not yet been a systematic review of the literature that would enable researchers to understand more about the antecedent factors and the outcomes of engagement in the public sector. To address this issue, we undertook a systematic narrative synthesis of the empirical research on engagement that yielded 5771 published studies; of which just 59 were conducted in public sector settings and met our inclusion criteria. The evidence was far from conclusive, suggesting a need for much more rigorous research focused on the specific challenges of public sector settings. Despite this, studies generally found that motivational features of jobs (such as autonomy), groups (such as social support), management (such as leader consideration), and organizations (such as voice mechanisms) as well as psychological resources were key antecedents of engagement within the public sector; and that engagement was associated with positive employee health/morale and enhanced performance behaviors. We make recommendations for further research on this important topic, particularly with regards to understanding the connection between public service motivation and engagement and examining engagement across different public sector/service contexts.

Aim/Purpose: This study carried out a systematic review of the literature on artificial intelligence (AI) in English language teaching (ELT). The objective was to delineate the current research progress in the field and to further understand the challenges. Background: The study analyzed articles published between 2015 and 2021. Methodology: The qualitative research method was employed. Five steps were taken to steer the review. 200 articles were scrutinized; 64 were retained. Contribution: Prior research on AI in ELT has not investigated how the literature is progressing or what areas of AI are being covered. Without a holistic picture, some important research findings could be missed. Understanding how studies on AI in ELT are designed and implemented will contribute to a greater understanding of the existing state of research. Findings: Findings show that there is a promising future for AI in ELT. AI in ELT yielded positive results in terms of optimizing the English language skills, translation, assessment, recognition, attitude, satisfaction, etc. It was also found out that more and more articles on the topic are being published; the mixed research method is the most commonly used, higher education level is the most sampled, students as participants are the most sampled, and most studies developed novel AI-based systems. Various AI approaches have been identified in the reviewed studies, including machine learning, neural network, support vector machine, genetic algorithms, deep learning, decision tree, expert system, natural language processing, data mining, cloud computing, and edge computing. However, AI in ELT is still in its infancy, where little research has been conducted and gaps in the literature are still present, especially in terms of inherent issues related to body language, gestures, expressions, emotions, translation, lack of elaborate description of teaching material used for learning driven by AI, uncertainties and vagueness with regards to what can be considered under the realm of AI, and most authors being outside of the ELT discipline. Recommendations for Practitioners: This literature review is likely to provide practitioners with an overview of the current adopted technology, research method, instruments and/or tools, educational level, language skill, and the effects reported by the AI-based studies for designing effective systems for the use of AI in their ELT classrooms. Recommendation for Researchers: Researchers need to conduct research on AI in ELT along with a detailed in-depth description of the methodology, research design, and the proposed systems used to achieve AI in ELT. Furthermore, it is recommended that researchers explore the efficiency of AI-based systems used in previous research and ensure their relevance and functionality. They are also required to provide in-depth analysis of the challenges inherent to systems that have been highlighted in the literature, which will maximize the potentials of these AI-based technologies. Impact on Society: The findings of this paper can provide visualization of research findings that could particularly benefit researchers, educators, and AI specialists who are involved in the study of the applications of AI in ELT. Future Research: Future AI research needs to seriously include more detailed descriptions of the method in further research.

This research aims to analyze the implementation of accounting reform in public administration in Portugal through three studies. The first discusses the adoption of the International Public Sector Accounting Standards (IPSAS) in the public sector in developed and developing countries, identifying differences in constraints and stimuli to implementation. Through a systematic review of the literature, it was identified that there are differences between constraints and stimuli in the implementation of accounting standards between developed and developing countries. In developed countries, the lack of training stands out as the main constraint and modernization is a stimulus for the implementation of the reform. In developing countries, the greatest constraint is the limitation of financial resources, with external and internal pressure as a stimulus. The second study analyzes the factors that influence the implementation of accounting reform in central public administration in Portugal and how this implementation affects transparency in organizations. The results, obtained through questionnaires and analyzed using the structural equation model and the application of the comparative qualitative analysis of diffuse sets, show that the most important positive factor is the experience and involvement of the teams, with the involvement of professional organizations being the factor with the greatest negative impact. It was also concluded that the implementation of the accounting reform results in greater transparency in the public sector, effectively achieving one of the main objectives of the reforms driven by IPSAS. Finally, the third study analyzes the impact of the implementation of the management accounting reform on the central public administration in Portugal and how it affects control, transparency, and management reporting within these organizations. The results, analyzed using a structural equation model, show that the reform has a positive effect on increasing the quality of management reporting and the transparency of public administrations, as well as contributing positively to increased control in these organizations.

The steadily growing epidemic of diabetes mellitus poses a threat for global tuberculosis (TB) control. Previous studies have identified an important association between diabetes mellitus and TB. However, these studies have limitations: very few were carried out in low-income countries, with none in Africa, raising uncertainty about the strength of the diabetes mellitus-TB association in these settings, and many critical questions remain unanswered. An expert meeting was held in November 2009 to discuss where there was sufficient evidence to make firm recommendations about joint management of both diseases, to address research gaps and to develop a research agenda. Ten key research questions were identified, of which 4 were selected as high priority: (i) whether, when and how to screen for TB in patients with diabetes mellitus and vice versa; (ii) the impact of diabetes mellitus and non-diabetes mellitus hyperglycaemia on TB treatment outcomes and deaths, and the development of strategies to improve outcomes; (iii) implementation and evaluation of the tuberculosis 'DOTS' model for diabetes mellitus management; and (iv) the development and evaluation of better point-of-care diagnostic and monitoring tests, including measurements of blood glucose and glycated haemoglobin A(1c) (HbA(1c)) for patients with diabetes mellitus. Implementation of this research agenda will benefit the control of both diseases.

Purpose This study aims to investigate ethical consideration into the data quality management and biases for fairness in artificial intelligence (AI) algorithms. It looks at how biases in AI systems could make it difficult to maintain data quality control in medical travel and tourism. Design/methodology/approach The systematic review of the literature from January 2019 to March 2025 (72 out of 925 articles) considers a variety of keyword combinations used for data quality control in AI systems. The process of systematic literature review aids in identifying studies on data quality management for medical tourism and travel. Findings The results of the study show that data quality visibility should be increased, data rectification should be done consistently and data momentum should be managed. It indicates that audience behavior in medical travel and tourism is significantly impacted by AI-powered digital branding and marketing. Research limitations/implications The research is limited to the single source of methodology, and it is limited in terms of articles collected from Jan 2019 to March 2025. It can be extended to the last decade for more insights and issues in AI use for medical travel and tourism. Practical implications Practical applications include efficient patient processing for international medical travel and tourism, as well as automated AI machine learning. Future research investigations should consistently segment data evaluation to overcome the limited regulatory compliance of data quality management. Social implications Patients (medical tourism) are primary elements in healthy social settings that can be facilitated through AI’s efficient use and maintaining the quality of the data management. Originality/value This study aims to address the main ethical concerns about the quality of AI data, data use monitoring and ethical support for data decisions and implementations inside the AI framework. The usage of digital branding, mobile app development, digital audience behavior, digital marketing principles and artificial intelligence in marketing are some of the important concepts used to accomplish the research goals.

Artificial Intelligence holds the potential to fundamentally challenge how companies manage their human resources. Guided by the need to organize a growing yet seemingly segmented body of research on the topic, this review addresses the intersection between Artificial Intelligence and human resources management over the last five years. We consulted the scientific database Business Source Ultimate (EBSCO) and selected 87 papers from leading academic journals between 2019 and 2024 for the analysis. Our work seeks to consolidate current knowledge on how technology is shaping the workplace, employees, and human resources management systems. This not only has the potential to bring some coherence to a segmented literature on the impact of Artificial Intelligence on human resources management, but also to unveil overlooked areas of utmost importance for both scholars and practitioners.

You're the One that I Want! Public Employment and Women's Labor Market Outcomes

Peer-reviewed disaster management publications were identified using a comprehensive search of: MEDLINE (US National Library of Medicine, National Institutes of Health; Bethesda, Maryland USA); CINAHL (EBSCO Information Services; Ipswich, Massachusetts USA); EMBASE (Elsevier; Amsterdam, Netherlands); PsychInfo (American Psychological Association; Washington DC, USA); and the Cochrane Library (The Cochrane Collaboration; Oxford, United Kingdom). A total of 9,433 publications were identified. The publications were overwhelmingly descriptive (74%) while 18% of publications reported the use of a quantitative methodology and eight percent used qualitative methodologies. Only eight percent of these publications were classified as being high-level evidence. The publications were published in 918 multi-disciplinary journals. The journal Prehospital and Disaster Medicine (World Association for Disaster and Emergency Medicine; Madison, Wisconsin USA) published the greatest number of disaster-management-related publications (9%). Hurricane Katrina (2005; Gulf Coast USA) had the greatest number of disaster-specific publications, followed by the September 11, 2001 terrorist attacks (New York, Virginia, and Pennsylvania USA). Publications reporting on the application of objective evaluation tools or frameworks were growing in number. The "science" of disaster management is spread across more than 900 different multi-disciplinary journals. The existing evidence-base is overwhelmingly descriptive and lacking in objective, post-disaster evaluations. SmithEC, BurkleFMJr, AitkenP, LeggattP. Seven decades of disasters: a systematic review of the literature. Prehosp Disaster Med. 2018;33(4):418-423.

This study provides a systematic review of the literature on the operationalization of artificial intelligence (AI) within small and medium-sized enterprises (SMEs), aiming to develop an integrated conceptual framework for understanding AI adoption. The findings indicate that Technological readiness plays a pivotal role, with SMEs requiring knowledge of AI applications, methods, and capabilities to adopt AI effectively. AI adoption yields diverse outcomes, including enhanced operational efficiency, improved customer engagement, and greater innovation, but these vary based on industry, firm size, and resource capacity. The study emphasizes that AI is not a unitary concept but a multi-dimensional construct, with operationalization requiring alignment with organizational dynamic capabilities. This review offers a framework for understanding AI adoption, helping to bridge fragmented findings in the literature.

This paper investigates the application of artificial intelligence (AI) in diagnosing and managing neurodegenerative disorders that require neurosurgical intervention, such as Parkinson's disease (PD) and Alzheimer's disease (AD). A systematic review of the literature was conducted to identify studies that utilized AI techniques for diagnosis, treatment planning, or post-operative monitoring of these conditions. The results demonstrate the potential of AI to improve early detection, treatment outcomes, and patient care. However, challenges such as data quality, explainability, and ethical considerations must be addressed to ensure the responsible and effective integration of AI into clinical practice.

This study focuses on the growing impact of Artificial Intelligence (AI) and automation on the Human Resource Management (HRM) processes of organizations. It examines how these emerging technologies are redefining HRM practices, with a particular focus on recruitment, training and human capital management. A methodology was used that integrates a quantitative analysis, derived from data collected through a structured survey on a Likert scale, and a systematic review of the literature. This two-pronged approach allowed us to assess perceptions about the implementation of AI in HRM and to identify emerging trends and patterns in recent studies. The results show that AI and automation contribute to greater efficiency in recruitment and selection processes, promoting greater equality in the identification and progression of talent. There was also a positive impact on skills development and vocational training, underlining the importance of adaptive strategies for the continuous development of employees. We face ethical and privacy challenges regarding the use of AI in HR. It highlights the need for robust practices for the protection of employee data and the prevention of bias and discrimination in algorithms. The importance of human intervention in automated decisions is highlighted, ensuring the maintenance of fairness and ethics, as well as the need for professional requalification in the face of technological changes. The study concludes that despite the challenges, AI and automation offer unprecedented opportunities for innovation in HRM. Organizations that integrate these technologies with the human elements of work are better prepared for a dynamic and technologically advanced future.

In the artificial intelligence era, the investment in human resources represents the best way to ensure the public organization's success and bright future. The overall objective of the paper is to capture a comprehensive and genuine image of career management in the public sector and to evaluate the impact of using artificial intelligence (AI) at the central and local government levels. The research questions are: How is Romania improving the management of civil servants’ careers? How is Romania’s performance in applying artificial intelligence in the public sector? Is Romania prepared to face the challenges of exponential artificial intelligence development? The paper explores the landscape of career management in the European and national public sectors, emphasizing the key issues for its improvement. It also analyses the use of artificial intelligence in the European and national public sectors, highlighting the main realities and trends. The conclusion of this research highlights that in the era of artificial intelligence, the leaders in public organizations should have a holistic approach, meaning to create a positive, empowering culture, enable AI innovation, support the changes brought by emerging technologies, identify the competence set specific for AI projects and support training programs in view to endow the public employees with the AI-related competences, as well as to evaluate AI impact. The methodology comprises bibliographic syntheses, socio-innovative empirical research, correlation, and regression analyses. The contribution brought by this research consists in presenting the realities and roadmap for effective management of civil servants’ careers and the use and impact of artificial intelligence in central and local government in view to be successful in times of outstanding challenges and opportunities, as well as in analyzing the statistical connection between government artificial intelligence and digital competitiveness.