The caregiving measure deserves particular attention because of how carefully it is constructed. Rather than relying on self-reported attitudes, the authors use entitlement to paid leave and workplace accommodations granted under Law 104/1992, the Italian regulation that supports employees who assist a close relative with a certified severe disability. This administrative marker provides an objective signal of substantial caregiving responsibility within the household, one that is verifiable and uniformly defined across the workforce. Its strong predictive power for remote work usage indicates that flexibility functions as a genuine substitute for on-site presence when employees face constraints that make daily commuting and full-time attendance difficult. In this sense, remote work operates as a form of informal accommodation, complementing the formal accommodations the law already guarantees, and allowing employees with demanding household circumstances to remain attached to the labor market and to their careers within the institution.

Commuting time emerges as perhaps the most intuitive of the drivers, yet its significance carries broader implications for urban economics and transportation policy. When the value of a day spent at home is measured against the cost of a day spent traveling, employees with long journeys face a much higher effective price of on-site work. The finding that commuting time strongly predicts revealed work-from-home propensity aligns with a growing body of evidence suggesting that remote arrangements function, in part, as a mechanism through which workers recapture time otherwise lost to travel. For a public institution whose workforce is concentrated in a major metropolitan area, this dynamic has practical consequences: hybrid policies may reduce peak-hour congestion, alter demand for office space, and shift the geography of where employees choose to live, since the penalty for living farther from headquarters diminishes when fewer days of physical presence are required.

Prior exposure to remote work stands out as a driver with a distinctly behavioral interpretation. Employees who had already worked from home before the formal hybrid regime, whether during the pandemic emergency or through earlier informal arrangements, carried that experience forward into the new system. This persistence suggests that remote work usage is partly a matter of habit and accumulated familiarity rather than a fresh decision made under the new rules. Once an employee has learned how to organize tasks, communicate with colleagues, and manage supervision from a distance, that learned capacity does not vanish when formal policies change. The implication is that temporary exposures can have lasting effects: the pandemic-era shift to remote work, even if adopted out of necessity, appears to have permanently altered the baseline expectations and capabilities of the workforce, a pattern consistent with the stabilization of work from home at roughly one day per week among college-educated employees globally after 2022.

The managerial findings are arguably the most consequential for organizational design. The study documents that team-level remote work patterns are significantly shaped by the behavior of supervisors, with evidence consistent with a directional influence flowing from managers to subordinates rather than the reverse. This asymmetry is important because it rules out the simpler explanation that managers and their teams simply resemble each other, whether because similar people are grouped together or because shared circumstances drive everyone’s behavior simultaneously. Instead, the pattern points to supervisors as active gatekeepers of flexibility: their own choices about when and how often to work from home set a visible standard that their teams appear to follow. Notably, the supervisors’ own socio-demographic characteristics, such as gender, age cohort, the presence of young children, or commuting distance, do not significantly predict the individual propensities of their employees, which suggests that what transmits from manager to team is behavior rather than background.

This directional influence connects to a long tradition of research on leadership and social influence within organizations. Leader-member exchange theory, developed over decades of scholarship, emphasizes that the quality of the relationship between a supervisor and each subordinate shapes how resources, trust, and discretion are allocated. Remote work arrangements fit naturally into this framework, since the decision to allow or encourage work from home is a form of discretion that supervisors exercise, whether formally or informally. The finding that managerial behavior shapes team patterns even under a formal hybrid regime highlights a tension inherent in flexible work policy: rules can establish entitlements and limits, but the day-to-day practice of flexibility is negotiated within relationships, and supervisors effectively determine how much of the formal allowance is realized in practice.

The onboarding dynamics add a temporal dimension that is often missing from cross-sectional studies of remote work. New hires begin their careers within the institution relying more heavily on on-site work and only gradually converge toward the norms established by incumbent teams. Established teams, meanwhile, remain largely unaffected by the arrival of newcomers, indicating that the direction of socialization runs from the existing group to the new member rather than the other way around. This pattern is consistent with classical theories of organizational socialization, which describe how newcomers make sense of their environment, absorb its norms, and adjust their behavior accordingly over time. In the context of hybrid work, the on-site presence of new employees during their early months may serve functions that are difficult to replicate remotely: building relationships, observing how experienced colleagues handle problems, and learning the informal knowledge that circulates within teams.

Research on the power of proximity to coworkers has emphasized exactly this trade-off, contrasting the training and mentorship benefits of physical presence with the productivity and flexibility benefits of working from home. The convergence pattern documented here suggests that new employees, or the organization acting on their behalf, effectively resolve this trade-off by front-loading on-site presence during the period when learning is most intensive and then relaxing it as the newcomer becomes fully integrated. For organizations designing onboarding programs under hybrid regimes, this offers a concrete principle: the early months of employment are when physical presence yields the highest returns in terms of socialization and skill acquisition, and policies that mandate or encourage on-site work during this window may pay dividends in later cohesion and performance.

The setting of the study, a large public institution operating under a formal hybrid regime, gives these findings particular relevance for the public sector. Administrative data from the Bank of Italy, covering both operational staff engaged in secretarial and administrative activities and managerial staff with responsibilities in economic research, financial analysis, and supervisory activities, provide a level of detail that survey-based studies cannot match. Because the measure of remote work propensity is behavioral, derived from actual usage records rather than stated preferences, it avoids the well-known gap between what employees say they value and what they actually do. This revealed-preference approach is especially valuable in a domain where stated attitudes may be shaped by social expectations, fear of appearing uncommitted, or uncertainty about how remote work will affect career prospects.

The distinction between operational and managerial areas of the workforce also enriches the interpretation of the results. Roughly half of the workforce as of December 2023 belonged to the operational area, performing tasks whose remote feasibility varies considerably, while the managerial area comprised employees whose work in research, analysis, and supervision may differ in its suitability for home-based performance. The exclusion of contractors and top executive roles from the analysis sharpens the focus on the core permanent staff, whose employment relationship with the institution is stable and whose usage patterns reflect genuine choices within the formal framework rather than contingent or contractual arrangements.

Placed in the broader context of the post-pandemic labor market, the study contributes to an accumulating body of evidence that hybrid work has settled into a durable equilibrium rather than a transitional phase. In the United States, the share of paid workdays performed from home rose from roughly five to seven percent in 2019 to nearly sixty percent during the spring 2020 lockdowns before stabilizing around twenty-eight percent by mid-2023. Global surveys of working arrangements report similar stabilization among college-educated employees. The persistence of these patterns across countries, sectors, and measurement approaches suggests that the forces documented here, including individual valuation of flexibility, managerial gatekeeping, and organizational socialization, are not artifacts of a particular moment but structural features of contemporary work organization.

The finding that hybrid arrangements are shaped by formal rules, individual preferences, managerial behavior, and socialization processes simultaneously carries implications for how organizations should approach the design and sustainability of flexible work policies. If policies are calibrated only to formal entitlements without accounting for the circumstantial drivers that make flexibility valuable to particular employees, they risk being either too generous, straining operational capacity, or too restrictive, pushing constrained employees toward less desirable alternatives. If they ignore the managerial layer, they may find that formal allowances are unevenly realized across teams, producing perceptions of unfairness even when the rules themselves are uniform. And if they neglect onboarding, they may find that new cohorts fail to integrate into the flexible norms of their teams, or conversely, that premature reliance on remote work deprives newcomers of the proximity benefits that support early career development.

The methodological contribution of exploiting variation in managerial assignment deserves emphasis as well. Because employees are assigned to supervisors in ways that are not fully under their control, the study can separate the influence of the supervisor from the characteristics of the employee and the team. This identification strategy, borrowed from research on CEO behavior and firm performance, allows the authors to make claims about causation rather than mere correlation in the managerial domain. The result that supervisor assignment shapes subordinate remote work patterns, while supervisor demographics do not, points to behavior as the channel of influence, a conclusion that would be difficult to reach with less granular data.

Finally, the study’s reliance on confidential administrative data held by the institution, which cannot be disclosed or distributed due to data protection restrictions, reflects a broader tension in empirical research on workplace practices. The richness of administrative records enables precise measurement and credible identification, but it also limits replication and transparency. As research on remote work matures, the field will likely benefit from combining studies of this kind, which offer internal validity and behavioral realism, with survey-based and experimental approaches that offer broader coverage and easier replication. The convergence of these methods on similar conclusions about the persistence of hybrid work, the importance of circumstantial drivers, and the gatekeeping role of managers strengthens confidence that the patterns documented here reflect durable features of modern work rather than temporary adjustments to an extraordinary period.

Subject of Research: Revealed Remote Work Preferences: Drivers, Managerial Impact, and Onboarding

Article Title: Revealed Remote Work Preferences: Drivers, Managerial Impact, and Onboarding

Article References: Mariani, M., Ristuccia, L., & Montanaro, P. (2026). Revealed Remote Work Preferences: Drivers, Managerial Impact, and Onboarding. Social Indicators Research, 185(2), Article 8. https://doi.org/10.1007/s11205-026-03956-z

Image Credits: AI Generated

DOI: 10.1007/s11205-026-03956-z

Keywords: remote work, hybrid work, revealed preferences, managerial influence, onboarding, work from home, organizational socialization, commuting time, caregiving responsibilities, public administration, Social Indicators Research, Bank of Italy

The research team, led by Yijie Wang of Hubei University of Automotive Technology together with Kai Zhang of Shenzhen University and Guangdong University of Finance & Economics and Bin Cao of Hanjiang Normal University, frames the problem as one of causal identification. It is not enough to observe that regions with digital governments also have dynamic economies; the two could be correlated for any number of reasons, from wealthier local tax bases to pre-existing administrative cultures. To cut through that ambiguity, the authors needed something that changed in some places and at some times but not others, a so-called institutional shock that could serve as a natural experiment.

That shock came in the form of the establishment of integrated government service platforms across China. These platforms consolidate what were once fragmented, department-by-department administrative procedures into unified digital gateways, allowing firms to register, license, permit, and comply through a single online channel. Because different regions launched these platforms at different times, the rollout created exactly the staggered pattern that economists prize. The team exploited this variation with a multi-period difference-in-differences model, a technique that compares outcomes in regions before and after platform adoption against regions that had not yet adopted, effectively isolating the effect of the digital transformation from the many other forces buffeting Chinese firms during the study period.

The dependent variable at the center of the analysis is high-quality economic development, a concept that has become central to Chinese policy discourse and to development economics more broadly. Rather than counting raw output, the framework emphasizes the quality, efficiency, and sustainability of growth, dimensions that conventional gross domestic product figures obscure. By measuring outcomes at the enterprise level among listed companies, the study grounds an abstract macroeconomic ideal in the concrete performance of individual firms, asking whether companies in regions with digitalized governments actually develop along higher-quality trajectories than comparable companies elsewhere.

The answer, according to the study, is a clear yes. Government digital transformation significantly promotes high-quality economic development, and the finding survives a battery of robustness checks designed to stress-test the result. The authors re-estimated their model using propensity score matching combined with difference-in-differences, a procedure that pairs treated regions with statistically similar untreated counterparts to guard against the possibility that platform adoption was concentrated in areas already poised for growth. They also excluded potential contamination from regional self-selection and from contemporaneous policy shocks, swapped in alternative fixed-effects specifications to control for unobserved characteristics of provinces, industries, and years, and substituted alternative variable constructions. Across all of these variations, the positive effect held.

What makes the study particularly valuable is that it does not stop at the headline result. Digital transformation, after all, is not a uniform intervention; it succeeds or fails depending on the institutional soil in which it is planted. The researchers therefore examined the conditions under which the effect is strongest, focusing on key dimensions of government behavior. Their analysis identifies three. First, strong fiscal support emerges as a critical foundation: digital government is not a cost-free upgrade, and regions that resource it adequately see far greater returns. Second, and perhaps counterintuitively, lower fiscal transparency amplifies the marginal impact of digital transformation on high-quality development, suggesting that in environments where existing disclosure is weak, the introduction of integrated digital platforms delivers an especially large incremental improvement in how government interacts with firms.

Third, the study finds that digital leadership capacity matters. Where officials possess stronger capabilities to lead and manage digital initiatives, the positive effects of transformation are further strengthened. This is a reminder that technology alone does not transform institutions; the human and organizational capacity to deploy it does. A sophisticated platform in the hands of an administration without digital expertise may deliver little, while the same infrastructure guided by capable leadership can reshape the entire interface between state and market. For policymakers, this implies that investments in platforms must be paired with investments in people.

The enterprise side of the story adds another layer of nuance. The researchers find that enterprise digitalization exhibits a complementary relationship with government digital transformation: firms that have themselves embraced digital tools are positioned to extract more value from a digitalized public sector, and vice versa. Yet the analysis also shows that government digital transformation exerts stronger inclusive effects, meaning its benefits extend more broadly across firms, including those that have not digitalized as aggressively. In other words, when the state modernizes, it does not merely reward the already-advanced; it lifts a wider swath of the corporate population, a finding with significant implications for how digital policy might narrow rather than widen gaps between firms.

Beyond its specific findings, the study speaks to one of the oldest debates in development economics: how to balance an effective government with an efficient market. In economies with complex institutional environments and pronounced regional disparities, the two are often portrayed as rivals, with stronger state intervention presumed to crowd out market dynamism. This research offers theoretical support for a more optimistic reading, in which a digitally capable government actively enhances market efficiency by lowering transaction costs, streamlining compliance, and improving the quality of public services that firms depend on. The Chinese setting, with its vast regional variation and layered bureaucracy, makes the result especially instructive for other developing economies facing similar governance bottlenecks.

The policy implications are correspondingly concrete. Governments seeking to realize the dividends of digital transformation should treat it as an institutional project rather than a purely technological one, securing sustained fiscal backing, cultivating digital leadership among administrators, and designing platforms with inclusiveness in mind so that the gains reach less digitalized firms as well as sophisticated ones. As integrated service platforms continue to spread across the developing world, this study provides some of the most rigorous evidence to date that the investment pays off, not in slogans about modernization, but in the measurable quality of economic development experienced by the enterprises that form the backbone of the economy.

Subject of Research: The effect of government digital transformation on high-quality economic development in China

Article Title: Government digital transformation and high-quality economic development: evidence from chinese listed enterprises

Article References: Wang, Y., Zhang, K., & Cao, B. (2026). Government digital transformation and high-quality economic development: evidence from chinese listed enterprises. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09286-2

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09286-2

Keywords: government digital transformation, high-quality economic development, Chinese listed enterprises, difference-in-differences, integrated government service platforms, fiscal transparency, digital leadership, enterprise digitalization, economic policy, public administration, China, econometrics

The research, conducted by Sihan Lu, Yan Zhou, and Tao Hong of the Harbin Institute of Technology in China, starts from a theoretical observation about the academic literature. Most scholarship on generative AI in the public sector has examined the technology through a siloed lens, focusing on a single role at a time. Studies of AI as a governance tool emphasize efficiency gains and service improvement. Studies of AI as a regulatory object emphasize risk, liability, and the need for oversight. A smaller body of work treats AI as a novel decision-making agent with its own capacity to act. Each perspective is useful, but each is incomplete, and the fragmentation obscures what happens at the micro level when these roles intersect inside a single administrative system.

To overcome that limitation, the authors reconceptualize the public management system as a complex adaptive system, a framework borrowed from the study of ecosystems, markets, and other environments in which many interacting agents produce outcomes no single actor intends. Within this framework, generative AI is modeled as an adaptive agent rather than a static instrument. That shift matters because adaptive agents learn, respond to feedback, and alter the behavior of the other agents around them. A chatbot that answers citizen queries changes how civil servants work; a drafting assistant changes how policy documents are produced; a predictive model changes which cases get attention. The technology does not sit outside the governance system. It becomes part of the system’s own dynamics.

The empirical core of the study is a single-case analysis of Singapore’s Smart Nation initiative, one of the world’s most sustained and comprehensive national programs for embedding digital and intelligent technologies in public administration. By tracking governance strategies and their outcomes across a range of situations within that program, the researchers were able to observe how the same underlying technology was assigned different roles in different contexts, and how those assignments generated tensions that no single regulatory rule could resolve.

The central theoretical contribution of the paper is what the authors call the Governance Impossible Triangle. According to this framework, generative AI’s triple identity creates persistent tensions among three goals that public administrators must pursue at the same time: maximizing efficiency, safeguarding the legitimacy of decisions, and maintaining effective risk control. The triangle is impossible in the same sense that classic policy trilemmas are impossible. Push hard on efficiency, for example by automating more decisions end to end, and legitimacy comes under strain because citizens and officials lose the ability to understand and contest how outcomes were reached. Push hard on legitimacy, for example by requiring extensive human review and explanation, and the efficiency gains that justified adoption begin to erode. Push hard on risk control, and both efficiency and responsiveness suffer. The three corners pull against one another continuously, and no configuration satisfies all of them at once.

What makes the triangle particularly difficult is that the three identities of generative AI activate the three corners in different combinations. As a tool, the technology is primarily an efficiency asset, and the governance question is how much discretion to delegate to it. As a regulatory object, it is primarily a risk to be contained, and the question is how to oversee systems whose behavior even their developers cannot fully predict. As a decision-making agent, it touches legitimacy directly, because democratic governance rests on the premise that consequential decisions are attributable to accountable human institutions. When a single platform shifts between these roles depending on the task at hand, the governance burden shifts with it, often faster than rules and oversight structures can adapt.

The authors’ response to this dilemma is the Contextual Identity Configuration Model, a framework that rejects one-size-fits-all regulation in favor of dynamic, context-sensitive assignment of AI’s role. The model is driven by four variables: problem complexity, risk severity, value conflicts, and explainability requirements. Simple, low-risk, low-conflict problems with clear standards can tolerate a high degree of algorithmic tool-intensity, meaning the AI acts with substantial autonomy as an efficiency instrument. As complexity rises, as stakes rise, as values come into conflict, or as the need for explanation grows, the model calls for recalibrating the system, reducing agentic power and increasing human oversight, so that the technology reverts toward a supportive tool role rather than an autonomous one.

To make this concrete, the study analyzes four prototypical scenarios of AI deployment in public governance. In routine optimization, where tasks are standardized and risks are low, generative AI can operate with high tool-intensity, delivering efficiency with minimal threat to legitimacy. In professional assistance, the AI supports trained experts who retain judgment and accountability, a middle configuration that captures productivity while keeping humans in the loop. In high-stakes discretion, where decisions affect rights, liberties, or significant resources, the model demands strong explainability and human authority, constraining the AI to an advisory function. In complex crises, where speed matters but uncertainty is extreme, the framework calls for layered defenses and continuous feedback so that algorithmic recommendations inform but never replace accountable human command. Across all four scenarios, the governing principle is the same: management systems must dynamically recalibrate algorithmic tool-intensity and agentic power through continuous feedback loops rather than fixing the technology’s role in advance.

The practical implications the authors draw are institutional as much as technical. They argue that governments should build multi-layered defense systems, in which technical safeguards, procedural review, and human accountability operate together rather than as substitutes for one another. They also propose joint accountability networks, arrangements in which responsibility for AI-assisted decisions is distributed across developers, agencies, and oversight bodies in ways that remain legible to the public. The goal, in their formulation, is a profound alignment between technological empowerment and core public values, so that the efficiency of generative AI does not come at the cost of the legitimacy and trust on which public institutions depend.

The study arrives at a moment when many governments are experimenting with generative AI faster than they are theorizing it. Its contribution is to name the structural problem: the same technology that makes administration faster also makes it harder to explain, and the same flexibility that makes it useful makes it hard to regulate. By modeling the public sector as a complex adaptive system and the AI within it as an adaptive agent, the researchers offer policymakers a vocabulary for the trade-offs they are already making, often implicitly. The Governance Impossible Triangle will not be dissolved, the authors suggest, but it can be managed, provided that regulators abandon uniform rules in favor of an agile, context-based governance paradigm in which the identity of the technology, and the controls attached to it, are deliberately configured to match the demands of each task.

Subject of Research: The role and governance of generative AI in public administration and government decision-making

Article Title: The “Triple identity” of generative AI in public governance: dilemmas and adaptive solutions

Article References: Lu, S., Zhou, Y., & Hong, T. (2026). The “Triple identity” of generative AI in public governance: dilemmas and adaptive solutions. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09400-4

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09400-4

Keywords: generative AI, public governance, complex adaptive systems, Smart Nation Singapore, algorithmic accountability, AI regulation, governance trilemma, decision-making agents, public administration, risk control, legitimacy, adaptive governance

Although the award is modest in dollar terms, it reflects a broader shift in how public agencies approach employee orientation. Traditional onboarding, in which new hires sit through days of in-person briefings, paperwork sessions, and policy walkthroughs, is expensive to deliver, difficult to standardize, and easy for overwhelmed newcomers to forget. Digital learning platforms promise a different model: training that new staff can complete at their own pace, revisit when needed, and that administrators can update centrally whenever policies or procedures change. The SUSTAIN eLearning platform, which is the focus of Smith’s funded work, is designed to serve exactly that purpose for county staff entering their roles.

The project will be carried out with support from the Center for Advancing Correctional Excellence, known as ACE!, a research center based at George Mason University. According to the announcement, ACE! will use the funding to provide support for SUSTAIN eLearning, including technical assistance with user accounts. That detail matters more than it might first appear. In large digital training deployments, account setup, access management, and troubleshooting are among the most common barriers that prevent new employees from actually completing their required modules. By building dedicated technical assistance into the project, the team is addressing a practical failure point that often undermines well-intentioned eLearning initiatives.

The connection between ACE! and a county onboarding program is grounded in the center’s specialty. ACE! focuses on advancing excellence in correctional settings, bringing research evidence to bear on how agencies that supervise, treat, and rehabilitate justice-involved individuals train their workforces and implement their programs. Correctional and related public-sector agencies face particularly acute onboarding pressures: staff turnover can be high, the work is demanding and safety-critical, and new employees must absorb a large volume of policy, procedure, and legal context in a short time. Structured, repeatable online training is one of the tools agencies use to make that knowledge transfer more reliable.

For Solano County, the fiscal-year designation in the project title signals that this is a continuing engagement rather than a one-off purchase. The FY27 label indicates that the county is funding the platform’s onboarding function for the 2026-2027 fiscal year, suggesting an ongoing relationship in which the eLearning system is treated as recurring infrastructure for workforce development rather than a single training event. Recurring funding models like this are common in public-sector software and training contracts, where agencies pay annually for platform access, content maintenance, and support services.

From a technical standpoint, eLearning onboarding platforms typically organize content into discrete modules that map to the topics a new hire must master: organizational policies, workplace conduct standards, safety procedures, role-specific duties, and administrative systems. Modern platforms track completion, quiz performance, and time spent on each section, giving supervisors and human resources staff a data trail showing exactly which parts of the onboarding curriculum each employee has finished. That data can flag where new staff struggle, informing revisions to the training content itself. While the announcement does not detail the specific architecture of SUSTAIN, the inclusion of account-level technical assistance implies a managed platform with individual user logins rather than a static library of documents.

The pedagogical rationale for moving onboarding online rests on well-established findings from learning research. Spaced repetition, self-paced progression, and the ability to revisit material after the initial orientation period all improve retention compared with single-session, in-person instruction. New employees also differ widely in their prior experience and learning speed, and a fixed classroom schedule forces everyone through the same material at the same rate. Self-directed modules let experienced hires move quickly through familiar content while newcomers spend more time on unfamiliar ground. For agencies, the approach also reduces the burden on senior staff, who would otherwise spend working hours delivering the same orientation presentations to each incoming cohort.

Smith’s role as a Research Associate at the Schar School places the project within a policy-oriented academic environment rather than a commercial training vendor. University-based research centers frequently contract with state and local governments to design, deliver, and evaluate training and technical assistance programs, drawing on faculty expertise in public administration, organizational behavior, and implementation science. The Schar School, located in the Washington, D.C. area, specializes in policy and government research, and projects like this one translate that expertise into practical tools for public agencies. The award also illustrates how relatively small contracts, in this case under five thousand dollars, can fund targeted, high-value services such as platform support and account assistance that keep a larger training system running.

The funding announcement itself is a grant and award notice distributed through EurekAlert!, the science communication service operated by AAAS, and issued by George Mason University. Such announcements serve an accountability function, documenting how public and institutional research funds are allocated and giving outside observers a window into the applied work that universities perform for government clients. In this case, the notice specifies the funder, the amount, the project title, the funding period, and the role of ACE! in providing platform support, a level of detail that allows the project to be tracked as part of the university’s broader portfolio of public-sector engagement.

As the funding period unfolds through June 2027, the practical test for the SUSTAIN onboarding project will be whether new Solano County staff complete their training smoothly and retain what they learn. The elements in place, a structured eLearning platform, recurring fiscal-year funding, and dedicated technical assistance from a university research center specializing in correctional excellence, are the ingredients that workforce development researchers generally associate with successful digital onboarding. If the model performs well, it offers a template other counties could follow: modest, targeted investments in online training infrastructure and support, backed by academic expertise, to make the first weeks of a public-sector job less chaotic and more effective for the people navigating them.

Subject of Research: Funded development of an online onboarding eLearning platform for new county staff

Article Title: Smith receives funding for online onboarding training for new staff SUSTAIN Training – An eLearning Platform FY27

Article References: Smith receives funding for online onboarding training for new staff SUSTAIN Training – An eLearning Platform FY27. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: eLearning, onboarding training, George Mason University, Schar School, Solano County, workforce development, correctional excellence, public administration, technical assistance, grant funding, digital training, new staff orientation

The new Fellows will join a membership of more than 1,000 current Fellows, a roster that includes former cabinet officers, members of Congress, governors, mayors and state legislators. Election to the Academy is not a ceremonial appointment. Fellows are chosen by the existing membership on the basis of distinguished and continuing achievements, and the selection process involves a rigorous review by a nominating committee that evaluates hundreds of applicants before making recommendations to the full Fellowship, which then votes on each nominee. That structure is designed to ensure that the Academy’s expertise remains grounded in demonstrated contributions to the effectiveness, efficiency, accountability and transparency of government at every level, from municipal agencies to federal departments.

The organization itself carries a notable institutional history. It was founded in 1967 by James E. Webb, then the administrator of NASA, together with other leading public administration practitioners, and it was subsequently chartered by Congress to provide expert advice to government leaders and agencies. The Academy convenes nationally recognized experts to address critical challenges facing public institutions and public service, functioning as a bridge between academic research and the practical demands of governance. For a scholar of public administration, election as a Fellow represents recognition not only of individual scholarship but of sustained influence on how governments prepare for and manage complex problems.

Sapat’s own research program sits at the intersection of public policy and disaster management, a field that has grown in national importance as hazards intensify and recovery costs climb. Her scholarship focuses on disaster management, community resilience, vulnerability and resilience assessment, housing and population displacement. At its core, her work examines how communities, governments and organizations can better prepare for, respond to and recover from disasters and other complex challenges. That agenda encompasses both the technical dimensions of resilience, such as the performance of infrastructure and building codes under hazard stress, and the social dimensions, including how displaced households find housing and how vulnerable populations navigate the recovery process.

At Florida Atlantic, Sapat has led and contributed to research funded by the National Science Foundation, the U.S. Department of Energy and the U.S. Department of Homeland Security. The projects span critical infrastructure resilience, port resilience, population displacement, energy and water resilience, building code regulations, post-disaster housing, disaster recovery and nonprofit collaboration, and she has secured more than 3.3 million dollars in federally funded grants. The breadth of that portfolio reflects the multidisciplinary character of modern hazards research, which requires engineers, social scientists and policy analysts to work together on questions that no single discipline can answer alone. Her studies have addressed issues ranging from the displacement of populations following disasters and the role of housing in recovery to the resilience of critical infrastructure and ports, and she has examined the role of diaspora communities and nonprofit organizations in disaster recovery as well as disaster readiness among vulnerable populations.

Her professional service extends well beyond her home institution. Sapat was a research fellow with the National Science Foundation’s Next Generation of Hazards Researchers program, an initiative designed to cultivate the leading hazards scholars of the coming decades. She also participated in a National Academies of Sciences report examining lessons learned from the COVID-19 pandemic and their implications for disaster planning, work that connected the experience of a global public health emergency to the established frameworks of emergency management. More recently, she was invited to serve as a subject matter expert and lead author on a chapter focused on disaster recovery for the third national assessment of natural hazards, a large-scale initiative launched in 2024 and 2025 to synthesize more than 25 years of U.S. disaster research, assess evolving risks and establish a research agenda for the next 50 years aimed at reducing the impacts of disasters.

Her engagement with policy has also been practical and regional. Sapat served on the Florida State Disaster Housing Task Force and on the Governor’s Hurricane Conference Scholarship Committee, and she has participated in local initiatives focused on post-disaster housing. In a state where hurricanes routinely test the housing stock, the insurance system and the coordination mechanisms of local government, that combination of scholarly expertise and hands-on policy involvement illustrates precisely the kind of continuing contribution to public administration that the Academy’s election process is intended to recognize. It also reflects the feedback loop at the heart of her discipline, in which research findings inform task force recommendations and field experience in turn shapes new research questions.

Sapat’s scholarship has appeared in leading journals in the field, including the Journal of Public Administration Research and Theory, Public Administration Review, the American Review of Public Administration, Risk Analysis, Natural Hazards Review, Natural Hazards and the International Journal of Mass Emergencies and Disasters. She is co-author of the 2014 book Displaced by Disasters: Recovery and Resilience in a Globalizing World and co-editor of the 2017 book Coming Home after Disaster: Multiple Dimensions of Housing Recovery. Together, those publications have helped define how researchers and practitioners understand population displacement as a global phenomenon and how housing recovery can be assessed across its multiple social, economic and physical dimensions. She also brings more than 15 years of academic leadership experience to the Fellowship, having served as director, Ph.D. program coordinator and undergraduate program coordinator of FAU’s School of Public Administration, and her teaching and mentoring have covered disaster management, homeland security, public policy, research methods and statistical analysis.

Within the university, the election was framed as an institutional milestone as well as a personal one. Michael Horswell, dean of FAU’s Dorothy F. Schmidt College of Arts and Letters, described the honor as an exceptional accomplishment and a powerful recognition of the national significance of Sapat’s scholarship and leadership, noting that her work demonstrates the essential role public administration research plays in addressing some of society’s most consequential challenges, from disasters and displacement to infrastructure resilience and public policy. He added that the distinction reflects well on Sapat and the college, elevates the national profile of FAU’s faculty and underscores the impact and growing reputation of the university’s research enterprise as an R1 institution. Sapat, for her part, described the selection as an extraordinary honor and a meaningful recognition of work shared with colleagues, students and research partners throughout her career, saying she is especially honored because the Academy brings together people who have dedicated their careers to strengthening public service and addressing complex challenges facing communities and governments.

The formal induction of the 2026 class will take place during the Academy’s National Conference, scheduled for Nov. 8 to 10 at the National Academy of Sciences building in Washington, D.C. For the field of public administration, the 2026 cohort arrives at a moment when questions of disaster recovery, infrastructure resilience and institutional trust have moved to the center of policy debate, and the Academy’s convening role gives its Fellows a direct channel through which research can inform governmental practice. For Florida Atlantic University, Sapat’s election signals that scholarship produced in South Florida, in one of the most hazard-exposed regions of the United States, is shaping the national conversation about how communities withstand and recover from catastrophe. As she takes her place among the Academy’s more than 1,000 Fellows, her stated intention is to contribute to that community and to learn from and work alongside Fellows whose scholarship, leadership and public service have had a significant impact, continuing a career devoted to making disaster recovery more equitable, more effective and better informed by evidence.

Subject of Research: Election of disaster resilience and public administration scholar Alka Sapat as a 2026 Fellow of the National Academy of Public Administration

Article Title: FAU’s Alka Sapat named Fellow of the National Academy of Public Administration

Article References: FAU’s Alka Sapat named Fellow of the National Academy of Public Administration. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Alka Sapat, National Academy of Public Administration, Florida Atlantic University, disaster recovery, community resilience, population displacement, public administration, public policy, post-disaster housing, critical infrastructure resilience, fellowship honor, hazards research

The study tackles a long-standing puzzle in public administration. Decades of scholarship have established that managerial quality matters for government performance, and that chief executives with greater public sector experience tend to preside over more effective organizations. Yet the evidence is inconsistent: some studies find strong effects of leaders’ backgrounds, others find none. The authors argue that much of this inconsistency stems from a neglect of context. Most research on the management-performance relationship has been conducted within a single country, treating the surrounding political environment as background noise rather than as a variable that can strengthen or weaken the link between a leader’s skills and measurable outcomes.

To build their theory, the researchers draw on the classic distinction, developed by the political scientist Arend Lijphart, between majoritarian and consociational models of democracy. In majoritarian systems, two main political blocs compete under a winner-takes-all logic, and the losing side serves as a watchful opposition expecting to alternate in power. This adversarial arrangement, the authors contend, creates powerful incentives for executives to perform well, because citizens can more easily identify and reward or punish the party responsible for policy outcomes. Party leaders, in turn, reward politicians whose performance enhances the party’s reputation, promoting their careers within disciplined, hierarchical organizations.

Consociational or power-sharing systems work very differently. Broad coalitions bring most significant parties into government, blurring the line between administration and opposition. Party leaders have incentives to bargain and collaborate with rivals rather than defeat them, and accountability becomes diffuse: when everyone shares credit, no one can be clearly blamed. The authors point to additional mechanisms that erode performance incentives in such systems, including frequent party switching, coalition-driven rotation of government posts, and the dilution of clear policy platforms through compromise. In these circumstances, a politician’s accumulated experience may simply not matter very much, because the electoral arena is not perceived as a zero-sum contest in which superior management translates into political advantage.

Testing this argument required a comparison of two countries that are alike in many respects but differ sharply in their patterns of political competition. Mexico and Colombia both have presidential systems with separation of powers, both underwent decentralization in the 1980s and 1990s, and both assign their intermediate-level governments, 32 Mexican states and 32 Colombian departments, major responsibility for education and health services. Yet their party systems diverged dramatically. Until 2018, Mexico operated a disciplined three-party system in which the Institutional Revolutionary Party, the conservative National Action Party, and the Party of the Democratic Revolution competed adversarially, with rare party switching and strong party loyalty at the state level.

Colombia, by contrast, carries a deep tradition of power sharing. Between 1958 and 1974, the Liberal and Conservative parties governed together under the National Front pact, alternating in the presidency and splitting cabinet posts evenly, an arrangement designed to end political violence. Even after fully competitive elections resumed, winning politicians continued offering co-governing roles to the losing side. The 1991 constitution opened the field to new parties, more than twenty of which now hold Senate seats, but coalition-building norms persisted. The authors illustrate the pattern with the department of Cundinamarca, where a defeated 2007 gubernatorial candidate supported the victor’s government and then won the 2011 election at the head of a coalition that included the very parties that had beaten him four years earlier.

Quantitative evidence reinforces this contrast. The authors calculated the effective number of parties in subnational elections between 1998 and 2015. Gubernatorial races looked similar in both countries, averaging roughly 2.4 effective candidacies. But assembly elections told a different story: Mexico averaged 2.64 effective parties, close to its gubernatorial figure and reflecting high party discipline, while Colombia averaged 5.43, more than double its gubernatorial figure. Colombian governors, in other words, must assemble sprawling, informal coalitions to govern, while Mexican governors could rely on cohesive partisan majorities.

The empirical analysis used two-way fixed-effects regression models on panel data covering the 32 Mexican states from 1995 to 2010 and the 32 Colombian departments from 2001 to 2015, spanning multiple gubernatorial administrations in each country. Performance was measured with two indicators managed primarily at the intermediate level: high school enrollment rates as an output of education policy, and infant mortality rates, deaths in the first year of life per 1,000 live births, as an outcome of health policy. The researchers hand-collected biographical data on every governor, classifying years of public sector experience at the local, state or departmental, and national levels. Mexican governors averaged 14.2 years of public experience, concentrated in national and state posts, while Colombian governors averaged 9.6 years, distributed more evenly across levels. The models controlled for partisan alignment with national and subnational legislatures, party affiliation, margin of victory, postgraduate education, revenue per capita, rural population density, and violence, proxied by homicide rates in Mexico and internally displaced populations in Colombia.

The results split cleanly along the adversarialism divide. In Mexican states, governors’ prior experience in local and state government significantly reduced infant mortality, with each additional year of local or state experience lowering infant deaths, while each additional year of federal-level experience was associated with worse outcomes. Partisan alignment with the health minister also improved health performance. In Colombian departments, by contrast, none of the experience measures showed a significant relationship with either infant mortality or high school enrollment, and a power analysis confirmed that this null result was not an artifact of sample size. Joint models pooling both countries confirmed the pattern: the effect of state-level experience on infant mortality differed significantly between the two systems, negative in Mexico but not in Colombia. Education enrollment showed weaker effects overall, suggesting that the experience-performance link is strongest for outcomes, like infant survival, that depend heavily on executive coordination of health services.

The authors are careful to note that their findings do not constitute a normative endorsement of adversarial politics over power sharing. Adversarial systems may extract more value from experienced executives, but they also expose governments to stronger opposition and a more turbulent external environment. The practical implications cut both ways. In adversarial systems, candidate recruitment should prioritize managerial and technical skills that translate directly into performance, while power-sharing systems may benefit more from executives skilled in coalition building and networking. And where opposition parties are co-opted rather than competitive, accountability may need to come from civil society and the media instead. More broadly, the study delivers a methodological message to the field: theories of public management built in one country, however elegant, cannot be assumed to travel. What works in Mexico may not work in Colombia, and the difference is not culture or capacity but the structure of political competition itself, a variable that shapes whether a leader’s hard-won experience ever gets the chance to matter.

Subject of Research: The moderating effect of political competition on the relationship between executives' experience and subnational government performance in Mexico and Colombia

Article Title: Adversarialism and power-sharing: the effect of political competition on the management-performance relationship

Article References: Bello-Gomez, R. A., Avellaneda, C. N., & Olvera, J. G. (2024). Adversarialism and power-sharing: the effect of political competition on the management-performance relationship. Global Public Policy and Governance, 4(4), 389-419. https://doi.org/10.1007/s43508-024-00104-6

Image Credits: AI Generated

DOI: 10.1007/s43508-024-00104-6

Keywords: political competition, government performance, public management, adversarialism, power-sharing, governors, Mexico, Colombia, subnational governments, infant mortality, education enrollment, comparative public administration

E-governance, as a field of study, sits at the intersection of public administration theory, information systems research, and political science. It asks not merely whether a government has a portal or an app, but how digital tools alter the relationship between the state and the citizen, how bureaucratic incentives shift when services are automated, and how accountability functions when decisions are mediated by software. The book under review positions itself across both theory and application, a pairing that the review treats as one of its notable strengths, since many works in the field tend to privilege one side of that divide at the expense of the other.

One of the intellectual anchors visible in the review’s framing is the technology acceptance model, originally developed by F. D. Davis in 1989. The model, among the most cited frameworks in information systems research, holds that two perceptions largely determine whether users adopt a new technology: perceived usefulness, the belief that the tool will improve performance, and perceived ease of use, the belief that operating it will be free of excessive effort. Applied to government services, the model raises an uncomfortable question for digital transformation programs. A citizen who finds an online tax portal confusing, or a civil servant who sees no efficiency gain in a new workflow system, will quietly abandon the technology no matter how ambitious the policy announcement behind it was.

The review’s reference list also points to the role of trust, drawing on work by Gefen and Straub on trust and the technology acceptance model in online shopping, and on McKnight, Choudhury, and Kacmar’s 2002 typology of trust measures for e-commerce. That lineage matters because the stakes differ sharply between buying a book online and interacting with a state. Trust in an e-commerce platform concerns whether a transaction will complete as promised. Trust in e-governance concerns whether personal data will be protected, whether an algorithmic decision can be contested, and whether the digital interface is a genuine channel of the state or a barrier placed in front of it. Scholars in the field have long argued that without institution-based trust, the adoption curves of government technology flatten regardless of usability.

A third theoretical pillar is Everett Rogers’s diffusion of innovations, in its fifth edition from 2003, which also appears among the works cited in the review. Rogers’s framework describes how innovations spread through social systems over time, carried first by innovators and early adopters and only later reaching the early and late majorities, with the laggards arriving last, if at all. Transposed to the public sector, the framework helps explain why digital government initiatives so often produce uneven outcomes. A national identity system may be embraced in capital cities while failing to penetrate rural regions, not because the technology is deficient but because the diffusion process depends on communication channels, social networks, and the perceived compatibility of the innovation with existing values and practices.

The review connects these classic frameworks to contemporary developments in the journal’s own pages. Among the works it cites is a 2024 study by Menke, Gomes, and Xavier on the impacts of AI-based anti-corruption audits on risk aversion in decision-making, using Brazil’s ALICE tool as a case study. ALICE applies artificial intelligence to detect anomalies in public procurement, one of the most corruption-prone areas of government activity. The study’s findings on how such audits change the behavior of officials, making them more risk-averse in their decisions, illustrate a theme that runs through the modern e-governance literature: technology does not merely make administration faster, it reshapes the incentives and even the psychology of the people inside the bureaucracy.

Another cited contribution, W. Potipiroon’s 2024 analysis of the TikTok ban votes in the United States House of Representatives, pushes the discussion beyond administration and into technopolitics, the terrain where governments regulate, restrict, and weaponize the same digital platforms that citizens use daily. The inclusion of such work in the review’s frame signals that e-governance can no longer be understood as a one-way story of states adopting tools. States are also actors in a contested digital ecosystem, weighing national security against platform economies, and their choices feed back into public trust in every government technology initiative.

The journal hosting the review, Global Public Policy and Governance, is published by Springer Nature under the exclusive license of the Institute for Global Public Policy at Fudan University, and its scope spans e-governance, public administration theory, science and technology governance, and technopolitics. That breadth is deliberate. The questions raised by Trotta’s book, and by Zhao’s assessment of it, do not respect the boundary between domestic administration and international affairs. A data localization rule in one country affects cloud providers on other continents. An identity platform designed for service delivery can become an instrument of surveillance. The field’s journals increasingly treat these as a single, connected problem set.

What makes the review timely, beyond its scholarly content, is the trajectory of the field it surveys. Since the book’s 2018 publication, governments worldwide have deployed artificial intelligence for everything from fraud detection to chatbot citizen services, while simultaneously confronting the governance failures those deployments can produce, including biased algorithms, opaque automated decisions, and data breaches. The theoretical toolkit highlighted in the review, spanning acceptance models, trust typologies, and diffusion theory, was largely built for an era of websites and portals. Whether those frameworks can carry the weight of generative AI in the public sector is now one of the field’s open questions, and reviews like this one serve as checkpoints on how well the existing literature has prepared researchers for that transition.

For readers outside academia, the practical takeaway is that the success of digital government is not primarily a technical achievement. It is a behavioral and institutional one, resting on whether citizens find digital services useful and easy, whether they trust the institutions behind the interfaces, and whether innovations diffuse through society rather than stalling among early adopters. The review of Advances in E-governance, published as Global Public Policy and Governance Volume 5, pages 82 to 86, consolidates a body of theory that has quietly shaped how governments around the world build their digital faces, and it underscores that the hardest problems in e-governance are the oldest ones in public administration: trust, legitimacy, and the enduring challenge of making institutions work for the people they serve.

Subject of Research: A scholarly review of a book on the theory and application of e-governance technological initiatives

Article Title: Trotta, A. M. (2018). Advances in E-governance: Theory and application of technological initiatives. Routledge

Article References: Zhao, B. (2025). Trotta, A. M. (2018). Advances in E-governance: Theory and application of technological initiatives. Routledge. Global Public Policy and Governance, 5(1), 82-86. https://doi.org/10.1007/s43508-025-00111-1

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00111-1

Keywords: e-governance, public administration, digital government, technology acceptance model, trust, diffusion of innovations, artificial intelligence, anti-corruption, technopolitics, book review, public policy, digital transformation

Street-level bureaucrats—the police officers, inspectors, social workers, teachers, and regulators who interact directly with citizens—have long occupied a paradoxical position in public administration scholarship. Since Michael Lipsky’s foundational work in 1980, researchers have recognized that these workers exercise substantial discretion: they cannot possibly apply every rule to every case with mechanical precision, so they inevitably make choices about how vigorously to enforce, whom to prioritize, and when to look the other way. What has remained harder to pin down is the psychology behind those choices. The new study, conducted by Yingwei Wang of East China Normal University and Hong Pan of Chongqing University, turns to expectation theory to supply that missing psychological mechanism.

Expectation theory holds that people’s motivation and behavior are shaped not only by outcomes they have experienced but by outcomes they anticipate. In economics, this logic underpins studies of inflation expectations and consumer spending; in education, it animates the expectancy-value theory of motivation developed by Allan Wigfield and Jacquelynne Eccles. Wang and Pan extended this framework to the enforcement context, proposing that a bureaucrat’s expectations about a policy’s time horizon and about the level of resistance it will provoke should influence how strictly that bureaucrat is inclined to enforce it—and that self-efficacy, the belief in one’s own capacity to execute the task successfully, should mediate that influence.

To test these propositions, the researchers designed a survey experiment with a two-by-two factorial structure. Participants were randomly assigned to scenarios that varied along two dimensions: time expectations, manipulated as either long-term or short-term policy duration, and resistance expectations, manipulated as either strong or weak anticipated pushback from those affected by the policy. This experimental design is a hallmark of behavioral public administration, a movement that has gained momentum since a landmark 2017 review by Stephan Grimmelikhuijsen and colleagues argued for combining the analytical tools of psychology with the substantive questions of public administration. By randomly assigning expectations rather than merely measuring them, the researchers could draw causal inferences about their effects—something observational studies of bureaucratic behavior rarely permit.

The results were striking in their symmetry. Short-term expectations weakened street-level bureaucrats’ strict enforcement behavior tendencies more than long-term expectations did. In other words, when officials believed a policy was a passing campaign rather than a durable commitment, their inclination to enforce it rigorously declined. This finding resonates with a broader literature on policy feedback and bureaucratic compliance: work by Peter Kettunen and colleagues, and by researchers examining how stability in policy signals builds trust, has suggested that long-term expectations foster a sense of seriousness and legitimacy that short-term initiatives lack. A policy framed as permanent invites officials to invest in it; a policy framed as temporary invites them to wait it out.

Resistance expectations worked in the opposite direction. Strong resistance expectations strengthened strict enforcement tendencies more than weak resistance expectations did. This result may appear counterintuitive at first glance—one might expect officials facing stiff opposition to back off. But the authors’ framework, and the surrounding literature on policy resistance, suggests a different interpretation: anticipated resistance can signal that a policy matters, that enforcement is consequential, and that a bureaucrat’s role in defending it is legitimate and necessary. Related scholarship by Gómez and Padró on anticipated resistance and strategic adaptation, and by Peters, Pierre, and Stoker on policy enforcement under pressure, has similarly portrayed resistant contexts as ones in which strict adherence becomes a deliberate, even defiant, choice rather than a default.

Perhaps the most theoretically significant finding concerns the mechanism linking expectations to behavior. Self-efficacy mediated the relationship between policy expectations and enforcement tendencies. That is, expectations did not simply push behavior directly; they worked by changing how capable bureaucrats felt. Long-term expectations and strong resistance expectations appear to bolster the sense that enforcement is achievable and meaningful, which in turn strengthens the tendency toward strict enforcement. This mediating role aligns with a growing body of work on self-efficacy in public service, including studies by Bevan and Hood on perceived policy success and bureaucratic performance, by López and Stoker on expectations of success and implementers’ actions, and by Walker on self-efficacy and public sector innovation. Together, these findings suggest that confidence is not a fixed trait but a dynamic state that policy communication can cultivate or erode.

The Chinese setting gives the findings particular weight. China’s governance system relies heavily on campaigns—intensive, time-limited pushes to achieve specific policy goals, from environmental inspection to pandemic control—that are implemented by regulatory street-level bureaucrats. Previous experimental work by the same authors examined how information frameworks affect compliance with policies that challenge traditional customs, and how digital regulation shapes enforcement attitudes during pandemic emergencies. Other Chinese studies have explored gender, situational visibility, and discretionary decision-making among regulatory bureaucrats. The new study adds expectation management to this toolkit, implying that how campaigns are framed—whether as enduring commitments or temporary drives—may systematically alter the rigor of frontline enforcement across China’s vast administrative apparatus.

The practical implications extend well beyond China. Governments everywhere struggle with the gap between policy design and street-level implementation, a gap that scholars from Lipsky to Tummers and Bekkers have documented across decades and continents. If short-term expectations weaken strict enforcement, then governments that cycle policies rapidly, or that announce initiatives without signaling durability, may be quietly undermining their own front lines. Conversely, if anticipated resistance strengthens enforcement, then preparing officials psychologically for pushback—rather than shielding them from information about it—may produce more consistent implementation. The mediating role of self-efficacy points to a further lever: training, supervisory support, and success feedback that build implementers’ confidence could amplify the effects of well-managed expectations. Hassan and colleagues’ work on supervisory support as a moderator of discretion, and Luthans and Youssef’s research on positive organizational behavior, both suggest concrete avenues for such interventions.

The study also carries methodological significance for the field. Survey experiments of this kind—randomizing vignette conditions and measuring behavioral tendencies—remain relatively rare in studies of enforcement, where access to frontline officials is difficult and behavior is hard to observe directly. By demonstrating that two theoretically distinct dimensions of expectation can be manipulated independently and that their effects operate through a measurable psychological mediator, Wang and Pan have provided a template that researchers in other countries and policy domains can adapt. The theoretical payoff is an expanded framework of street-level bureaucratic behavior, one in which anticipation, confidence, and discretion interact rather than operate in isolation. The practical payoff is a reminder that the fate of any policy is decided not only in ministries and legislatures, but in the expectations carried by the officials who meet the public—and that those expectations, unlike many drivers of bureaucratic behavior, can be deliberately shaped.

Subject of Research: How policy expectations and self-efficacy shape street-level bureaucrats' enforcement behavior tendencies in China

Article Title: Policy expectations, self-efficacy, and street-level bureaucrats’ enforcement behavior tendencies: a survey experiment in China

Article References: Wang, Y., & Pan, H. (2025). Policy expectations, self-efficacy, and street-level bureaucrats’ enforcement behavior tendencies: a survey experiment in China. Global Public Policy and Governance, 5(3), 274-296. https://doi.org/10.1007/s43508-025-00121-z

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00121-z

Keywords: street-level bureaucracy, policy expectations, self-efficacy, enforcement behavior, survey experiment, policy implementation, behavioral public administration, China, discretion, expectation theory, policy resistance, public administration

The research arrives at a moment when the question of state capacity has become urgent far beyond South Asia. Classic accounts of state-building, from Charles Tilly’s work on war-making to Fukuyama’s writing on governance and world order, have long treated capacity as something that accumulates slowly through revenue extraction, bureaucracy and coercive reach. Wealthy, well-staffed agencies were assumed to be the precondition for effective policy. Yet scholars such as David Leonard, Erin McDonnell and the ‘pockets of effectiveness’ literature have documented a puzzling counter-pattern: effective agencies and successful reforms sometimes flourish inside institutionally fragile states. The new study pushes this idea further by asking not whether such pockets exist, but what mechanisms allow them to emerge and to operate across multiple scales of government simultaneously.

To answer that question, the authors assembled three policy cases that span the full vertical architecture of Indian governance. At the national level they examined the National Action Plan on Climate Change, the flagship framework unveiled in 2008 that organized India’s climate response into a set of national missions. At the sub-national level they turned to law and order reforms in Bihar, an eastern Indian state that for decades was synonymous with poor governance before undergoing a striking turnaround in policing and criminal justice after 2005. At the city level they studied climate action in Rajkot, a fast-growing Gujarati municipality that developed a climate-resilient city action plan with help from international networks such as ICLEI and the CapaCITIES programme. Three cases, three scales, one central puzzle: how did low-capacity settings produce real policy outcomes?

The answer, the authors argue, lies in four interconnected strategies that recur across all three cases. The first is socio-political learning, the process by which policymakers absorb lessons from other jurisdictions, from their own past failures and from shifting social expectations, and convert those lessons into new policy designs. The concept echoes Hugh Heclo’s famous description of politics as ‘puzzling’ as well as powering, and the study revives that pairing as an analytical toolkit. Learning in weak states, the authors show, is not a luxury reserved for rich bureaucracies; it can be a substitute for them, allowing governments to borrow, adapt and improvise rather than build every institutional component from scratch.

The second strategy is what the literature calls puzzling: deliberate, iterative experimentation in which officials treat policy problems as puzzles to be solved rather than commands to be transmitted. In the Bihar case, police leadership experimented with fast-track courts, conviction-rate monitoring and organizational restructuring to attack a justice system that had effectively collapsed. In the national climate case, policymakers cycled through paradigms, from early framings that treated climate change as an environmental side-issue to later framings that embedded it within development and energy policy. The study draws on prior work by Deshpande and colleagues on India’s policy styles to show that this iterative puzzling, rather than any single master plan, produced the National Action Plan’s distinctive architecture of national missions tailored to different sectors.

The third and fourth strategies, political will and powering, address the political economy that surrounds any reform. Political will in the study refers to the sustained commitment of top leaders to back reforms even when they threaten entrenched interests, a factor that proved decisive in Bihar, where the state’s leadership after 2005 prioritized restoring law and order in a state long dominated by what commentators had called ‘jungle raj’. Powering, meanwhile, captures the mobilization of coalitions, resources and authority needed to push reforms through resistant bureaucracies and political networks. The authors show that these four strategies do not operate in isolation. Learning identifies what to do; puzzling works out how to do it; political will supplies the commitment; and powering assembles the coalition that makes implementation possible.

What makes the study technically interesting is its multi-scalar design. Most state-capacity research focuses on a single level, usually the national state, and most climate governance research either examines international negotiations or individual municipalities. By tracking the same mechanisms across national, state and city levels, Jha and Deshpande demonstrate that policy capacity is not simply a property that a government either has or lacks. It is built relationally, through linkages between scales. The national climate plan created frameworks and missions that cities could localize; international municipal networks such as ICLEI imported templates and technical expertise that a small city bureaucracy could never have generated alone; and sub-national reform in Bihar showed that a state written off as weak could reorganize its core coercive institutions within a few years when the political and bureaucratic incentives aligned.

The Bihar case is likely to draw the most attention from readers of comparative politics. Scholars such as Mukherjee and Moore have described Bihar’s incapacity as in part ‘by design’, the product of political strategies that deliberately kept state institutions weak to serve patronage networks. The new study does not dispute that history, but it documents what happened when the political calculus changed: fast-track courts cleared backlogs, conviction rates rose, and police reforms began to professionalize a force that had been deeply politicized. The lesson is uncomfortable for deterministic accounts of state-building. If weakness was politically constructed, it can also be politically dismantled, and the speed of that dismantling can surprise observers who assume capacity changes only over generations.

The climate cases carry equally consequential implications. India’s cities are on the front line of climate change, facing heat waves, flooding and infrastructure stress, yet most municipal governments have limited revenue, thin technical staff and fragmented authority. Studies by Bhardwaj and Khosla on how Indian city bureaucracies respond to climate change, and by Deshpande on municipal capacities under climate uncertainty, have described cities ‘superimposing’ climate tasks onto existing departments rather than building new institutions. The new research reframes this improvisation not merely as a deficit but as a strategy: low-capacity cities can assemble functional climate capacity by combining external network resources, national mission frameworks and internal learning. Rajkot’s climate-resilient action plan, developed under the CapaCITIES programme, illustrates how a mid-sized municipality can translate global climate discourse into a concrete local planning document without a large dedicated bureaucracy.

The authors are careful about the limits of their claims. The study is a qualitative case analysis built on published literature, policy documents and publicly accessible sources, and the authors state no competing interests. Three Indian cases cannot establish that the four strategies will travel to other weak states, and the abstract itself flags that future research should explore both the potential and the limitations of building policy capacity in weak states. There is also a normative tension the study leaves open: political will and powering depend on leaders and coalitions, which means the mechanisms that build capacity can also be captured by the same patronage politics that destroyed it. Whether learning-driven capacity endures after its political sponsors leave office remains an open empirical question.

Even so, the findings land at a propitious time. Donor agencies and international organizations have spent decades importing institutional blueprints into weak states with discouraging results, and the ‘pockets of effectiveness’ literature suggests the problem lies partly in ignoring how effective governance actually emerges. By specifying mechanisms, learning, puzzling, political will and powering, and by showing them operating across scales, the study offers a more actionable account than vague appeals to ‘political commitment’. For the growing community of researchers working on climate governance in the Global South, the message is particularly pointed: the capacity to act on climate change may not wait for the slow construction of a strong state, but can be assembled, case by case and scale by scale, by governments that learn faster than they grow.

Subject of Research: Building policy capacity across multiple scales of government in a weak or middling state, using Indian climate and governance reforms as case studies

Article Title: Governing across scales: building policy capacity in weak state

Article References: Jha, H., & Deshpande, T. (2026). Governing across scales: building policy capacity in weak state. Global Public Policy and Governance, 6(1), 66-81. https://doi.org/10.1007/s43508-026-00142-2

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00142-2

Keywords: state capacity, policy capacity, India, Bihar, climate governance, National Action Plan on Climate Change, multi-level governance, policy learning, political will, urban climate action, public administration, Global Public Policy and Governance

The distinction the authors draw between AI and earlier waves of digital government is technical as well as institutional. Previous generations of e-government technology primarily improved the efficiency, connectivity, and accessibility of administrative processes, digitising forms and speeding up workflows while leaving the underlying logic of human decision-making intact. AI, by contrast, increasingly performs the knowledge work of administration: it predicts, classifies, optimises, and generates scenario analyses that feed directly into judgements about rights, resources, and sanctions. The central analytical question, the review argues, is therefore no longer how governments digitalise administrative processes, but how administrative intelligence is governed and distributed across human and computational actors. In the authors’ framing, AI is best understood not as a discrete application but as a systems phenomenon whose consequences ripple across power structures, public values, institutions, decision-making, organisations, and frontline bureaucracy.

That systems perspective, drawing on the tradition of systems thinking associated with Barry Richmond and Donella Meadows, rejects the idea that AI’s effects can be assessed by examining any single agency or decision point in isolation. An algorithm deployed in one part of government depends on professional norms, institutional rules, data infrastructures, and frontline judgements that translate algorithmic outputs into real-world action. To capture these interdependencies, the editorial proposes a six-dimension framework covering the full administrative life cycle of AI. The framework moves from the strategic conditions under which governments acquire AI capacity, through the public values used to justify adoption, the institutions that authorise its use, the decisions officials make with it, and the organisations that implement it, to the street-level encounters where algorithms meet citizens. Each dimension, the authors insist, is a location where AI enters, circulates, and becomes institutionalised in government.

The first dimension, power structures, may be the most politically charged. The review argues that public administration scholarship has too often examined AI only after it arrives inside government, ignoring the political economy of the systems being procured. Governments frequently do not own the capabilities their AI depends on: digital infrastructure, specialist talent, computational resources, and research pipelines are concentrated in large technology firms. Drawing on scholarship on platform capitalism and technological sovereignty, the authors describe a fundamental asymmetry in technical capacity, epistemic authority, and regulatory knowledge between states and major tech companies. This dependence shapes what AI systems are available to governments, what officials can know about risk, bias, and explainability, and how much bargaining power agencies have when seeking access to proprietary source code or audit evidence. Supporting research in the special issue, using OpenAlex bibliometric data, finds that Big Tech’s presence in critical technology research is growing, particularly in AI, raising questions about who controls the knowledge on which public-sector AI increasingly rests.

Public value forms the second and normative core of the framework. The authors stress that in government, unlike the commercial sector, AI adoption cannot be justified by productivity or cost reduction alone; it must serve public purposes such as fairness, transparency, responsiveness, and trust. An algorithm that improves technical accuracy does not settle questions of proportionality or legitimacy, because public agencies must justify how decisions are reached and whose interests are served. A service that reduces processing time but increases exclusion or distrust cannot simply be counted as administrative improvement. Contributing papers in the collection develop this argument in detail: one proposes a value-chain approach for tracking where public value is created or lost across the stages of an AI-enabled service, while another maps 3,268 studies using BERTopic modelling and finds that after 2022, work on algorithmic fairness, ethics, and state-society relations has overtaken earlier emphases on efficiency and service delivery.

Institutions constitute the third dimension, translating public purposes into enforceable rules about who may use AI, under what conditions, and subject to which review. The editorial highlights a structural puzzle: most regulatory sandboxes were designed for private-sector innovation, where regulators supervise firms testing products, but in public-sector AI the state is simultaneously regulator, user, implementer, and accountable authority. One contributing paper adapts the sandbox model into an experimentalist governance framework for government AI, emphasising supervised testing, stakeholder participation, iterative learning, and institutional feedback. The authors connect this to Elinor Ostrom’s nested levels of institutional rules, showing how operational rules govern day-to-day AI use, collective-choice rules govern how those rules are revised, and constitutional-choice rules keep experimentation consistent with administrative law, procurement requirements, and rights protection. Institutions, in this account, do not merely permit or prohibit AI; they define the terms under which algorithmic systems can be tested, trusted, and treated as legitimate parts of public administration.

The framework’s fourth dimension turns to administrative decisions, where the editorial examines experimental evidence on whether public officials actually accept AI-assisted judgement, particularly in budgeting, a domain long understood as political rather than purely technical. The review contrasts two views of human-AI collaboration: augmentation, in which algorithms provide forecasts and rankings without displacing human responsibility, and de facto automation, in which officials defer to algorithmic outputs they lack the expertise or organisational backing to challenge. Extending Nathan Caplan’s two-communities theory, the authors suggest that technical experts, vendors, and data scientists may produce algorithmic knowledge that bureaucrats do not regard as usable or congruent with their problems, producing a decoupling between formal AI adoption and practical utilisation. Bureaucratic acceptance, they argue, is an overlooked condition for successful AI reform: officials must come to see algorithmic inputs as credible, legitimate, and compatible with the responsibilities attached to public office.

Organisations and street-level bureaucrats complete the framework. Implementation, the editorial stresses, is rarely smooth: public agencies inherit legacy systems, fragmented data arrangements, risk-averse cultures, and budget constraints, so AI reforms may look successful at launch yet lose momentum as maintenance costs rise and staff revert to familiar practices. Contributing research on AI-powered platform government shows that organisational inertia is not mere resistance but a mechanism through which prior investments and routines govern the pace and direction of AI use. At the frontline, drawing on Michael Lipsky’s theory of street-level bureaucracy, the authors argue that AI does not eliminate discretion but redistributes its materials, changing what officials see, how cases are categorised, and how departures from system recommendations must be justified. Ethnographic work in Finnish public organisations introduces the concept of the moral crumple zone: bureaucrats become accountability buffers who absorb blame for algorithmic failures while lacking meaningful control over the systems that shape their decisions. Experimental evidence from policing further finds that AI-supported enforcement can shift officers toward more formal, coercive styles, depending on the accountability mechanisms in place.

The editorial’s conclusion is stark in its ambition: AI represents a genuine paradigm shift rather than the latest phase of digital government, because it begins to change the nature of administration itself. If digital government changed the machinery of administration, the authors write, AI is displacing the human monopoly over administrative judgement. The review closes with a forward research agenda calling for scholarship to move from national AI policy to AI ecosystems, from AI ethics to AI legitimacy, from adoption to institutional capability, and from street-level discretion to behavioural adaptation. The decisive question for democratic governance, the authors argue, is no longer how governments digitalise administrative processes, but how they govern administrative systems in which computational actors increasingly participate alongside human ones, and whether citizens can still recognise, understand, and challenge the decisions that shape their lives.

Subject of Research: A systems framework for understanding how artificial intelligence enters, circulates, and becomes institutionalised across the public administration system.

Article Title: Artificial intelligence across the public administration system: public values, institutions, and bureaucrats in the algorithmic age

Article References: Cho, W., Fan, Z., & Zheng, Y. (2026). Artificial intelligence across the public administration system: public values, institutions, and bureaucrats in the algorithmic age. Global Public Policy and Governance, 6(2), 103-123. https://doi.org/10.1007/s43508-026-00151-1

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00151-1

Keywords: artificial intelligence, public administration, public value, bureaucracy, street-level bureaucracy, algorithmic governance, technological sovereignty, regulatory sandboxes, digital government, administrative discretion, AI accountability, public sector innovation