The researchers proposed a moderated mediation model to test their hypotheses. In this model, affective organizational commitment is positioned as a mediator between the work environment and employee retention. Additionally, the study investigates the moderating roles of two distinct leadership styles: transformational leadership and transactional leadership. This approach allows for a nuanced analysis of how different leadership behaviors can either enhance or diminish the effect of the work environment on employee commitment and, subsequently, retention.

To gather data, the study surveyed 410 public-sector employees in Uganda. The participants were adults who provided informed consent for their participation in the survey-based research. The data collection process was designed to ensure confidentiality and anonymity, adhering to ethical principles outlined in the Declaration of Helsinki and national guidelines from the Uganda National Council for Science and Technology. The study was reviewed and cleared by the Department of Management at SRM University AP, which deemed it minimal-risk research given its non-invasive nature.

The analysis of the collected data was performed using covariance-based structural equation modeling with SPSS-AMOS software. This statistical method is well-suited for testing complex relationships involving multiple variables, including mediation and moderation effects. The results indicated a positive relationship between the work environment and both affective organizational commitment and employee retention. This suggests that a supportive and well-structured work environment contributes to employees feeling emotionally attached to their organization, which in turn increases their likelihood of staying.

Furthermore, the findings confirmed that organizational commitment serves as a significant mediator in the relationship between the work environment and employee retention. This means that the work environment does not just directly influence retention but also does so indirectly by fostering a sense of commitment among employees. The study highlights the importance of understanding the psychological mechanisms through which environmental factors affect retention outcomes, providing a more comprehensive view than a direct correlation alone would offer.

The role of leadership styles in this dynamic was also examined. The study found that transformational leadership positively moderates the influence of the work environment on organizational commitment. Transformational leaders, who inspire and motivate employees through vision and personal development, appear to amplify the positive effects of a good work environment on employee commitment. This suggests that when the work environment is strong, the presence of transformational leadership further strengthens the employees’ emotional bond with the organization.

In contrast, the study observed a negative moderation effect of transactional leadership on the relationship between the work environment and organizational commitment. Transactional leadership, which focuses on exchanges between leader and follower, such as rewards for performance, was found to weaken the positive impact of the work environment on commitment. This finding implies that in the context of Uganda’s public sector, a purely transactional approach may not be as effective in fostering deep organizational commitment, even when the work environment is favorable.

The implications of these findings for public sector management in Uganda are significant. The study suggests that improving employee retention requires a multifaceted approach that addresses both the structural aspects of the work environment and the quality of leadership. Organizations should strive to create supportive work conditions while also cultivating leadership styles that inspire and engage employees. The negative moderation by transactional leadership serves as a cautionary note, indicating that relying solely on reward-based management strategies may not be sufficient to build strong organizational commitment.

It is important to note that the study is based on a cross-sectional survey design, which limits the ability to establish causal relationships. While the structural equation modeling provides strong evidence for the proposed relationships, longitudinal studies would be needed to confirm the direction of causality and the long-term stability of these effects. Additionally, the study focuses specifically on the public sector in Uganda, so the generalizability of the findings to other sectors or countries may be limited. Future research could explore these relationships in different contexts to validate the robustness of the model.

Despite these limitations, the study offers valuable insights for policymakers and managers in the public sector. By understanding how leadership styles and work environment interact to influence employee retention, organizations can develop more effective strategies for retaining talent. The emphasis on affective organizational commitment as a key mediator underscores the importance of fostering a sense of belonging and emotional connection among employees. As public sector organizations continue to face challenges in resource-constrained settings, these findings provide a evidence-based framework for improving retention practices and enhancing organizational performance.

Subject of Research: Psychology

Article Title: The effects of leadership styles and work environment on employee retention in Uganda’s public sector through organizational commitment

Article References: Ocema, D., & Kamesh, A. V. S. (2026). The effects of leadership styles and work environment on employee retention in Uganda’s public sector through organizational commitment. Discover Psychology. https://doi.org/10.1007/s44202-026-00876-4

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00876-4

Keywords: Employee Retention, Public Sector, Leadership Styles, Organizational Commitment, Uganda, Work Environment, effects, leadership, styles, work, environment, employee

The study, published in Discover Global Society, is notable for where it was conducted. Most empirical work on AI in accounting has focused on private firms in developed economies, where regulatory maturity, institutional capacity, and digital infrastructure differ substantially from those in emerging markets. Palestine and Jordan offer a compelling comparative setting: both are pursuing digital transformation initiatives in their public sectors while their governance frameworks are still evolving. Recent evidence from Jordanian municipalities has shown that digital transformation is increasingly relevant to the efficiency of public financial operations, and regional studies have documented the growing role of AI in accounting decision-making within Jordanian organizations. Yet no prior empirical study had systematically examined how distinct AI capabilities influence government accounting performance in these environments, or whether ethical issues function as the bridge linking technology to outcomes.

The research team, led by Naji Alslaibi of Bethlehem University together with colleagues at Al-Ahliyya Amman University, the University of Valencia, and Tikrit University, disaggregated AI adoption into four specific technological capabilities rather than treating it as a single undifferentiated construct. Big Data analytics allows accountants to instantly analyze massive volumes of financial information, supporting real-time decision-making, fraud pattern identification, and redesigned audit processes. Natural Language Processing addresses the unstructured side of accounting work—contracts, invoices, memos, and regulatory documents—automating document review and even translating natural-language accounting notes into correct entries. Machine Learning brings predictive power: trained models detect errors, forecast financial trends, and flag anomalous patterns that human reviewers might miss. Cloud Computing provides the flexible, secure infrastructure that lets financial data be processed, stored, and accessed from anywhere, reducing operating costs and the need for constant infrastructure upgrades.

Methodologically, the study was rigorous for a survey-based design. The researchers distributed an online questionnaire through official administrative channels in public institutions, receiving 630 responses of which 500 were valid and complete. Constructs were measured with multidimensional multi-item Likert scales and validated using Principal Component Analysis with Varimax rotation, which extracted six clean factors corresponding to the four AI technologies, ethical issues, and task performance. Items loaded at 0.60 or higher on their intended factors, Kaiser-Meyer-Olkin sampling adequacy exceeded 0.70, and Cronbach’s alpha coefficients ranged from 0.79 to 0.90, indicating acceptable to strong reliability. Variance inflation factors between 1.04 and 1.08 ruled out multicollinearity problems, and confirmatory factor analysis showed excellent fit, with an RMSEA of 0.018, CFI of 0.993, TLI of 0.992, and SRMR of 0.030.

The hypothesis testing combined regression analysis with structural equation modeling based path analysis, using 5,000 bootstrap replications to infer indirect effects. All four AI capabilities showed positive, statistically significant associations with task performance. In individual models, Cloud Computing showed the strongest coefficient at 0.492, followed by Big Data at 0.449 and Machine Learning at 0.419, with Natural Language Processing smaller but still significant at 0.312. Machine Learning demonstrated the highest individual explanatory power, accounting for 16.1 percent of the variance in task performance on its own. When all four technologies were entered together, coefficients declined due to shared variance but remained positive and significant, and the combined model explained 35.6 percent of the variance in task performance—a substantial figure for research on workplace performance in complex institutional settings.

The mediation results were the study’s most striking contribution. Ethical issues—conceptualized to include transparency, accountability, integrity, and fairness—significantly mediated the relationship between every one of the four AI technologies and task performance. Among the technologies, Machine Learning exhibited the strongest total association with task performance at 0.401, followed by Cloud Computing at 0.378, Big Data at 0.349, and Natural Language Processing at 0.296. Crucially, in each case the indirect effect through ethical issues was comparable to or greater than the direct effect, meaning that a large share of AI’s performance impact operates through ethical governance mechanisms rather than through raw technological capability alone. Ethics also exerted a strong independent direct effect on task performance, with coefficients ranging from 0.462 to 0.504, confirming that ethical awareness matters for accounting outcomes even apart from its mediating role.

Why would ethics carry so much weight? The authors point to the specific vulnerabilities each technology introduces. Big Data raises privacy and data misuse concerns that can hamper performance unless ethical safeguards are applied. Natural Language Processing can embed linguistic bias and misinterpret data, degrading the quality of financial reporting. Machine Learning, for all its predictive power, suffers from opacity—when the learning code cannot be explained, decisions may drift into unethical territory. Cloud Computing introduces data security risks that make ethical compliance a determining factor in preserving credibility and trust. In public sector accounting, where sensitive financial information, algorithmic opacity, data bias, accountability diffusion, and professional independence are all live concerns, these vulnerabilities are amplified. Emerging markets add further complications, including uneven regulatory maturity and limited technical capacity.

The findings carry direct implications for the United Nations Sustainable Development Goals. AI-enabled accounting systems can strengthen public financial governance by enhancing transparency, reducing corruption risks, and improving decision-making efficiency, which aligns with SDG 16’s emphasis on transparent, accountable, and effective institutions, while the technology infrastructure itself supports SDG 9’s promotion of innovation and resilient digital systems. But the study’s evidence shows these benefits are contingent: without ethical safeguards, AI adoption may undermine trust, fairness, and accountability, contradicting the very development objectives it is meant to serve. The authors frame this as a shift from technological determinism to governance-embedded technological performance—the idea that institutional legitimacy, transparency, and accountability condition whether technology is effective at all.

For managers and policymakers in public institutions, the practical message is that investments in AI must be accompanied by structured ethical governance frameworks. The researchers recommend institutionalizing transparency protocols, algorithmic accountability procedures, and data protection mechanisms alongside technological deployment. Capacity-building should integrate ethical training with technical skill development, and regulatory modernization should move beyond technical standards toward ethics-by-design principles embedded within digital accounting systems, including ethical audit trails and compliance monitoring structures. Balanced investment in both technological infrastructure and governance capacity, the evidence suggests, yields superior performance outcomes than technology spending alone.

The authors acknowledge the study’s limitations, which temper but do not erase its conclusions. The cross-sectional design captures relationships at a single point in time and cannot establish causation. The measures are self-reported perceptions, which may be affected by respondent subjectivity and common method bias. The sample was drawn through non-probability channels, limiting strict population generalization, and the empirical setting is restricted to public sectors in Palestine and Jordan. The hypothesis testing also relied on PCA-derived composite variables in a parsimonious path analysis, which simplifies the structural estimation. Future research, the team suggests, should employ longitudinal designs, incorporate archival or supervisor-rated performance indicators, extend comparisons across countries and sectors, and test moderators such as digital readiness, leadership support, and regulatory capacity. Even with these caveats, the core finding stands out as a warning and an opportunity in equal measure: in the race to digitize government finance, the ethical architecture surrounding the machines may matter as much as the machines themselves.

Subject of Research: The relationship between artificial intelligence technologies and accounting task performance in public sector institutions, mediated by ethical issues, in Palestine and Jordan.

Article Title: Artificial intelligence and accounting task performance in public sector institutions through the mediating role of ethical issues in Palestine and Jordan

Article References: Alslaibi, N., Qawasmeh, R., Samara, H., Hussein, A., & Hussein, W. (2026). Artificial intelligence and accounting task performance in public sector institutions through the mediating role of ethical issues in Palestine and Jordan. Discover Global Society, 4(1), Article 241. https://doi.org/10.1007/s44282-026-00592-3

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00592-3

Keywords: artificial intelligence, accounting, public sector, ethics, machine learning, big data, natural language processing, cloud computing, task performance, Palestine, Jordan, structural equation modeling

The study, conducted by Zander Weisman Mintz of the LBJ School of Public Affairs at the University of Texas at Austin and Ji Ma, now of Indiana University Bloomington and the University of Oxford, combines a large-scale systematic literature review with cutting-edge computational text analysis. Drawing on OpenAlex, an open scholarly database, the researchers built a corpus through citation snowballing that began with four foundational review articles and expanded forward through more than 14,900 candidate works. They then screened this mountain of material using Meta’s open-source Llama3 large language model, employing role prompting and chain-of-thought prompting to classify each candidate against six qualifying conditions. Validation against a manually reviewed sample of 200 works achieved 91 percent agreement. The final corpus comprised 3,268 works, published between the mid-1980s and May 2026.

To extract the latent thematic structure of this literature, the team applied BERTopic, a neural topic-modeling technique that leverages transformer-based document embeddings and clusters abstracts into coherent topics represented through class-based term frequency–inverse document frequency. The method outperformed conventional bag-of-words alternatives such as latent Dirichlet allocation and non-negative matrix factorization, particularly on the short texts typical of academic abstracts. Hyperparameters were optimized through random grid search to maximize topic coherence, and outlier reclassification plus manual relabeling refined interpretability. The result was a map of twenty distinct topics spanning the entire field, from healthcare AI and municipal waste optimization to algorithmic fairness and citizen participation in datafied democracies.

But topic modeling alone would simply produce another taxonomy. The study’s central contribution is a functional framework that organizes the literature around four practical functions of public governance: creating public value, delivering public services, responsiveness to the public, and protecting state–society relations. The authors deliberately rejected more familiar organizing axes. Sorting by application domain scatters shared governance problems like bias and accountability across every sector. Sorting by technology tracks capability but ignores the public stakes that make government deployment distinctive. Sorting by governance risk collapses the field into its pathologies. And sorting by public value alone absorbs distinctions a public manager most needs to see. The four governance functions, the authors argue, are the questions a public manager actually asks before deploying a system: does it create value, improve a service, change how the organization hears from citizens, and what does it do to the state’s relationship with the governed?

The framework’s dimensions are enriched by four theoretical lenses that recur throughout the scholarship: technological operationalization, which asks how AI fits specific public problems; function of government, which maps AI techniques onto sectoral activities; the policy-cycle framework, which locates AI within agenda-setting, formulation, implementation, and evaluation; and public governance, which foregrounds who is affected. Notably, the authors show that technological operationalization is not confined to the application dimensions but runs vertically through all four: a model’s opacity, its optimization target, and its hardcoded thresholds are design choices that propagate into accountability deficits and entrenched inequality downstream. Governance failures, they suggest, are often most cheaply addressed upstream at the point of technical design rather than through oversight bolted on after deployment.

The empirical findings reveal a field in dramatic flux. Output grew rapidly through the late 2010s, peaked at 627 works in 2023, and remained elevated through 2025. More striking is the compositional shift. The public value dimension, dominated by domain applications such as energy-efficiency forecasting and air-pollution monitoring, accounted for 50 to 77 percent of works through 2017 but declined to roughly 16 to 22 percent by 2024 and 2025. The state–society relations dimension, encompassing algorithmic fairness, ethics, and regulation, more than doubled its share, from around 12 to 23 percent in the mid-2010s to 52 to 54 percent in 2024 and 2025. The transition concentrates around the public release of ChatGPT in November 2022, which the authors use as a heuristic marker rather than a causal explanation, noting that generative AI diffusion, regulatory momentum such as the EU AI Act, and an ongoing accountability debate all converged in the same period.

The post-2022 pivot is stark in the numbers. Classical domain-application topics shrank from about 19 percent of the pre-ChatGPT corpus to under 5 percent afterward, while several topics essentially became new literatures: ethics and equity in educational AI grew from 2 works to 66, and AI ethics, accountability, and transparency in public service delivery surged from 9 to 291 works, a thirty-one-fold expansion. Semantic analysis of the twenty topics shows that the six state–society topics form a tight, mutually similar cluster, reading as a single coherent research conversation despite spanning multiple domains, while the public value topics spread widely across incompatible technical vocabularies. The framework thus partitions the field asymmetrically, and the authors argue this asymmetry is itself informative for anyone navigating it.

Equally important is where the literature is thin. The responsiveness dimension, concerning where AI can support the policy cycle and democratic feedback, carries only about 462 works across just two topics, making it the smallest dimension despite AI’s obvious relevance to agenda-setting, consultation, and evaluation. Intriguingly, public administration journals are already over-indexing here: responsiveness accounts for about a quarter of works in top-ranked public administration outlets, nearly double its corpus-wide share. The authors sketch four research directions for this space, including how large language models reshaping the information environment of agenda-setting, whether AI-mediated consultation strengthens or hollows the link between citizen voice and government decision, what happens when optimizing tools are embedded in satisficing policymaking processes, and whether AI-driven evaluation genuinely closes the feedback loop or merely produces dashboards.

The framework also doubles as a practical diagnostic for public managers weighing a specific AI system. Each dimension supplies a question and a concrete check: whether the system creates value the public can recognize, whether it improves a service without eroding recourse to a human, whether it widens or narrows the channel through which citizen preferences enter decisions, and whether affected citizens can learn of, understand, and contest its output while responsibility remains attributable. Applied retrospectively to the City of Chicago’s 2016 predictive policing heat list, a system later judged less effective than a simple most-wanted list, the diagnostic surfaces failures on three of four questions, illustrating how design-stage deficits that later evaluations documented could have been identified before deployment.

The study acknowledges its boundaries, including reliance on abstracts rather than full texts, the undercounting of grey literature and government reports, restriction to English-language scholarship, and the possibility of calibration drift in large-language-model screening. Yet within those limits, the contribution is a translation layer: a compact, function-based vocabulary that makes an interdisciplinary literature, previously scattered across engineering, information systems, and public administration, discoverable and comparable across the communities that study it in parallel. As governments worldwide grapple with deploying AI under democratic constraints, the researchers offer not a new theory of public administration but something arguably more useful right now: a shared map of what is known, what is contested, and where the next decade of scholarship and practice most urgently needs to go.

Subject of Research: A computational mapping of artificial intelligence research in the public sector using a functional governance framework and BERTopic modeling

Article Title: Towards artificial intelligence for the public sector: framing and bridging academia and practice

Article References: Mintz, Z. W., & Ma, J. (2026). Towards artificial intelligence for the public sector: framing and bridging academia and practice. Global Public Policy and Governance, 6(2), 124-156. https://doi.org/10.1007/s43508-026-00148-w

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00148-w

Keywords: artificial intelligence, public sector, public governance, algorithmic fairness, BERTopic, systematic literature review, policy cycle, public value, ChatGPT, accountability, e-government, computational social science

The study, authored by Aneerban Roy of Dr. B. C. Roy Engineering College in Durgapur, India, is notable for the sheer scale of evidence it brings together. Drawing on the Scopus database, the research combined bibliometric analysis of 9,152 records with a PRISMA-guided systematic review and a narrative synthesis. After systematic screening, 497 studies informed the narrative synthesis, while 72 conceptually relevant studies were synthesized through expert-led thematic analysis to construct an integrated conceptual framework. This triangulated methodology was designed to overcome the fragmentation that has long plagued E-HRM scholarship, where different researchers emphasize technological infrastructure, organizational processes, or employee interactions, often producing contradictory conclusions about whether digital HR actually delivers value.

The bibliometric data tell a compelling story on their own. Publications on E-HRM have grown at an annual rate of approximately 8.04 percent, with the average document accumulating around 34.14 citations over a productive lifespan of roughly 8.71 years. For years, scholarly and managerial interest remained tepid, but the COVID-19 pandemic changed that abruptly. Facing lockdowns and remote work mandates, organizations had little choice but to digitize their HR functions, and publication activity surged from 2020 onward as researchers rushed to document the challenges and opportunities of that rapid transition.

Geographically, however, the research landscape mirrors the very inequalities the study seeks to address. Scholars in high-income countries such as the United States, the United Kingdom, and China, along with India, dominate the field, while researchers in many low- and middle-income nations remain largely absent from the conversation. The author warns that this research gap could deepen global inequality, because organizations that understand how to deploy E-HRM effectively gain competitive advantages that translate into national economic development. A limited comprehension of digital HR technologies, the study suggests, can hinder organizational effectiveness and, by extension, the development prospects of entire nations.

When the author examined how E-HRM is actually used, the contrast between rich and poor countries proved stark. In developed economies, digital HR systems are deployed strategically to enhance organizational efficiency, support evidence-based decision-making, and strengthen workforce agility. In developing countries, by contrast, nearly half of organizations confine E-HRM to routine administrative tasks. The difference stems from inadequate digital infrastructure, resource constraints, institutional weaknesses, cybersecurity concerns, and shortages of digitally skilled employees. In many developing nations, rural regions lack even basic amenities such as reliable electricity and high-speed internet, concentrating fragmented digital adoption in urban areas and creating internal digital divides that compound the global one.

National policy plays a decisive role in these divergent outcomes. Economies such as South Korea and Malaysia saw their GDP increase by roughly 13 percent after implementing policies that boosted organizational technology investment, and countries belonging to the Organisation for Economic Co-operation and Development have committed to national digital strategies. Developing nations often fall short in both commitment and implementation, failing to provide the infrastructure, subsidies, and regulatory stability that encourage companies to integrate advanced digital systems. The study cites Africa, despite its abundant natural resources, as an example of a region whose underdevelopment is partly attributable to socio-cultural and political hindrances that deter investment.

The review also turns a critical eye on India, where initiatives such as Digital India and Aatmanirbhar Bharat have accelerated organizational digitalization while exposing deep sectoral disparities. Public sector enterprises, which accounted for 25 percent of India’s GDP in 2001, have historically underperformed relative to private firms due to concentrated ownership, bureaucratic rigidity, legacy systems, and multiple layers of control. When these enterprises do adopt digital HR technologies, they frequently outsource implementation to vendors rather than building internal capabilities, leaving them dependent on external providers and vulnerable to data security risks. Private organizations, driven by competitive pressure, consistently demonstrate faster adoption and greater digital maturity, a gap the author argues could impede India’s ambition of transitioning from a developing to a developed nation.

To make sense of this complexity, the study distills the literature into six interrelated themes. E-HRM has fundamentally evolved from an administrative information system into a strategic capability; successful implementation depends on technological readiness, leadership commitment, organizational culture, and change management; and the technology generates substantial value by improving efficiency, productivity, organizational learning, and innovation. Cross-national variation is considerable, sectoral context strongly shapes outcomes, and recent research increasingly focuses on emerging technologies such as artificial intelligence, HR analytics, and intelligent decision-support systems. Together, these themes demonstrate that the effectiveness of E-HRM is contingent on the interplay of technological, organizational, institutional, and contextual factors rather than on the technology alone.

The proposed conceptual framework integrates organizational drivers, implementation barriers, E-HRM practices, digital transformation outcomes, and contextual moderators into a unified model. Organizational enablers such as digital infrastructure, top management support, and workforce digital competencies establish the foundation for adoption, while barriers including implementation costs, resistance to change, and privacy concerns reduce organizational readiness. The framework also identifies a critical research gap: the absence of standardized maturity models and benchmarking standards for evaluating E-HRM implementation across countries and sectors. Without such benchmarks, the author contends, organizations and policymakers cannot systematically assess whether their digital HR investments are delivering intended results.

The study closes with practical recommendations that span multiple levels of action. Research and development collaboration between developed and developing nations could smooth technology transfer, while improvements in human capital have been shown to enhance technology adoption by 1.7 percent. Governments in developing countries could introduce investment and usage subsidies to encourage adoption among small and medium enterprises, which employ approximately 90 percent of the global workforce and often cannot absorb the significant costs of advanced E-HRM systems. Organizations themselves are urged to adopt participatory approaches that reduce employee resistance during implementation. Taken together, these measures, the author argues, could transform E-HRM from a source of growing global disparity into a genuine catalyst for sustainable, inclusive digital transformation worldwide.

Subject of Research: E-HRM as a strategic enabler of digital transformation across national and sectoral contexts

Article Title: Exploring E-HRM as a catalyst for global digital transformation in organizations

Article References: Roy, A. (2026). Exploring E-HRM as a catalyst for global digital transformation in organizations. Discover Global Society, 4(1), Article 253. https://doi.org/10.1007/s44282-026-00583-4

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00583-4

Keywords: E-HRM, digital transformation, human resource management, digital divide, Digital India, public sector, private sector, HR analytics, bibliometric analysis, systematic review, developing economies, organizational change