Abstract: This paper examines the connection between Environmental, Social, and Governance (ESG) factors and the risk of geopolitics, as defined by the Geopolitical Risk (GPR) index. The concept of geopolitical risk is conventionally defined as the direct result of political incidents, war, and international tensions. The current study argues that the concept should be understood in a more structural and sustainable manner, relating to the underlying forces driving geopolitical risk. The main research question is whether and how the three pillars of the ESG factors contribute to the explanation and understanding of cross-country and over-time variations in geopolitical risk. In an effort to avoid the information losses associated with the aggregate nature of the ESG index, the three factors are considered separately and the three pillars are analyzed individually. The empirical context is a balanced cross-country panel data set including 42 countries over the 2000-2023 time period. The data for the three factors is obtained from the World Bank dataset in an effort to standardize and compare the data in a cross-country and cross-time manner. The GPR index is used to measure the level of geopolitical risk and is defined by Dario Caldara and Matteo Iacoviello. The GPR index captures the level of geopolitical tensions based on the analysis of media signals. The combination of the three sources allows for the direct connection and correlation between the three factors and the internationally recognized GPR index. The paper uses an integrated methodological approach that combines the results from three different approaches. The first method uses panel data analysis in an effort to identify the average marginal effects while controlling for unobserved heterogeneity. The second method uses the technique of clustering in an effort to identify structural patterns and divide the countries into groups based on their unique characteristics and risk profiles. The third method uses machine learning regressions and nonparametric analysis in an effort to capture the complex relationships and interactions in the data. The three-step method is used for each pillar in an effort to ensure consistency and comparability. The results suggest that the three factors contribute to the GPR index in a unique manner. The environment and energy structure contribute to the GPR index as a risk multiplier, the social factor is related to the exposure to instability, and the governance factor is a central stabilizing factor. The paper makes a unique contribution to the literature by defining the concept of the three factors and their relationship to the GPR index in a unique and sustainable manner.

Abstract: Building on the ancient and spiritual call to care for self and others, recent research (2020–2025) demonstrates that self‑compassion improves resilience, self-care, emotional regulation, and recovery from workplace stress across diverse sectors including healthcare, education, and public safety. Defined through as self‑kindness, common humanity, and mindfulness, self‑compassion fosters meaning and supports inner hope by helping individuals hold their suffering self-lovingly with understanding rather than self‑criticism. Emerging findings reveal that compassion‑based interventions improve psychological wellbeing, reduce burnout, and enhance physiological markers of recovery such as heart rate variability (HRV). This paper synthesises current evidence and highlights implications for chaplains, carers, and leaders in developing compassionate work environments that support sustainable wellbeing and people’s search for meaning. Some practical suggestions for chaplains and organisational leaders particularly in the areas of using self-kindness and naming to gently acknowledge and releasing stress and not ruminating when mistakes or stress arise. This may require workplace education programs. While more research is needed, recent research affirms that self-compassion leads to notable improvements in self-reflection, psychological empowerment and reduces burnout and workplace stress. Future research directions are also offered.

Abstract: In Brazil, municipal open-air markets (varejões) have become key spaces where rural-urban transitions unfold, not as a linear process of urban encroachment, but as a contested terrain of heterogeneous livelihood strategies, institutional regulation, and everyday agency. Based on 25 in-depth interviews with small-scale farmers and vendors in Piracicaba (SP), this study examines how spatial governance, exercised by the Municipal Secretariat of Agriculture and Food Supply (SEMA), interacts with the realities of peri-urban producers who rely on direct sales to urban consumers. While SEMA’s interventions (e.g., fixed pricing, MEI formalization, standardized infrastructure) aim to “modernize” market operations, they often overlook the diversity of production logics, particularly among organic farmers, family-based producers, and mixed vendors. Farmers report that rigid price controls ignore rising input costs, technical assistance remains generic or absent, and bureaucratic requirements fail to recognize informal yet functional modes of cooperation and value creation. Crucially, “feirantes” (smallholder farmers and resellers who sell agricultural products at open-air markets) are not passive recipients of policy; they actively reinterpret, adapt, and reshape governance through diversification (e.g., organic production, minimally processed goods), informal cooperatives, direct sales networks, and the redefinition of agricultural work as both economic necessity and cultural identity. The paper argues that spatial governance in Piracicaba is neither a success nor a failure, but a continuously negotiated process, contested and co-produced through the agency of those it seeks to regulate. This case reveals a distinctly Brazilian modality of rural-urban transition, where agricultural livelihoods persist not through top-down planning, but through the resilience, creativity, and collective negotiation of small producers navigating the gaps between policy design and lived reality.

Abstract: This study as part of a postdoctoral research takes a first look on evolving University Business Incubators’(UBIs) emerging business models based on business model transformation, adaptation and innovation. The study utilizes philosophical (essentialism and empiricism), psychological (cognitive schemas with analog and conceptual combination) and entrepreneurship perspectives in classifying emerging UBI models using case studies. The classified UBI business models via essentialism(typology) include: Core Business and Entrepreneurship BMs, Core R &D commercialization, regionally initiated BM, Industry focused BM and Opportunity Based UBI BMs. Cognitive Generation based on analogue reasoning and conceptual recombination of schemas are further applied to see new UBI BMs that can also emerge due to endogenous (cognitive reasons) evolving from cognitive strategic decisions. In addition to this, a conceptual combination of modeling UBIs as a ‘corporate business incubator located in a University with the host University as the parent company or Corporate Enterprise with attributes of corporate innovation, strategic renewal and venturing, while the UBI is involved in both Corporate(University) venturing and innovation is proposed. This study will take a look at the entities of each of these UBI business model (and also across different industries) and later map them with typical CBIs (Corporate Business Incubators) for value creation in further studies. The study is intended to give an insight into the different business models UBIs can adopt due to endogenous, exogenous and life-cycle transformation overtime.

Abstract: Total Quality Management (TQM) is increasingly adopted by higher education institutions (HEIs) to enhance institutional effectiveness under growing performance and accountability pressures. However, empirical evidence remains limited regarding the mechanisms through which TQM practices influence non-financial and financial performance outcomes. This study examines the mediating roles of Hard Quality Management practices and Non-Financial Performance in the relationship between Soft Quality Management practices and Financial Performance in higher education institutions. A quantitative research design was employed using survey data collected from academic and administrative staff across public and private HEIs. The hypothesised direct and indirect relationships were tested using mediation analysis implemented through Hayes’ PROCESS macro, enabling a robust examination of multiple mediation pathways. The results indicate that Soft Quality Management practices significantly enhance both Hard Quality Management practices and Non-Financial Performance. Hard Quality Management practices partially mediate the relationships between Soft Quality Management practices and both Non-Financial and Financial Performance, while Non-Financial Performance also serves as a significant mediator linking Soft Quality Management practices to Financial Performance. The persistence of significant direct effects suggests partial mediation, indicating that Soft Quality Management practices operate through both formalised systems and complementary behavioural and cultural mechanisms. Overall, the findings position TQM as a governance-oriented framework that strengthens institutional performance through interconnected quality pathways in higher education institutions.

Abstract: The concept of sustainability has evolved far beyond its initial environmental foundations, expanding into a multidimensional framework that integrates multinational policies, multicultural values, and multigenerational knowledge, but there is a paucity of bottom up or grass roots research. This paper is a narrative of oral history intersects supported by rigorous documentation including military records, census data, genealogical records, and scholarship extending over four centuries . By synthesizing individual lived experiences with systemic goals, such as those outlined by the United Nations Sustainable Development Goals (SDGs) [1], a more nuanced understanding of resilience and adaptation emerges. The analysis of recent scholarship indicates that sustainability is a dynamic, narrative - driven process that requires an in- depth understanding of the spatial and temporal consequences of global shifts, ranging from climate catastrophes to the translocal flows of capital and people [2] . This paper uses oral history to show the adaptation of a multigeneration, multicultural “ordinary” family in North America to the social, political, economic, and technological challenges faced over 400 years with a focus on sustainability.

Abstract: Background: U.S. hospitals have increasingly affiliated with multi-hospital chains, raising questions about whether consolidation yields operational efficiencies or primarily reflects integration costs and market power. Evidence on the dynamic financial response to chain entry—especially in recent years—remains limited.Objective: To estimate dynamic association in hospital financial margins around sustained chain joining using a staggered-adoption-robust event-study design.Methods: We analyzed a RAND-processed HCRIS hospital panel (2014–2023). Dynamic effects were estimated using the Sun & Abraham interaction-weighted event-study estimator with hospital and year fixed effects and hospital-clustered standard errors. We implemented two baseline rules (“has2014” and “entry”) and examined total, operating, and cash-flow margins. To reduce ratio outliers, margins were cleaned using year-specific denominator screening and hard caps for extreme values (|margin|>500%), then winsorized within year (p1/p99) for main analyses. Depending on outcome and baseline rule, models used approximately 13,000–24,000 hospital-year observations, covering ~2,600–2,900 hospitals and roughly 600 sustained chain joins. Robustness checks included a balanced-panel restriction, a treated-only stacked specification, placebo assignment among never-treated hospitals, and ownership-stratified estimates.Results: Lead coefficients were generally small, but cash-flow margins exhibited a statistically detectable negative lead at t = −4 in both baseline rules (p≈0.03), while other leads were typically indistinguishable from zero. Post-entry effects were modest and imprecise across outcomes. Total margins showed near-zero contemporaneous changes at t=0 and small negative estimates in years 1–3 that attenuated by year 4. Operating and cash-flow margins displayed small post-entry declines around t=2 (≈1 percentage point in magnitude; p≈0.06–0.09). Robustness checks (balanced panel, stacked design, placebo) broadly supported a null or weak-transient pattern. Ownership stratification suggested modest longer-run improvements for nonprofit hospitals in later post years (e.g., t=4: +3.7 percentage points; p=0.045), while for-profit estimates were mixed and imprecise.Conclusions: Over 2014–2023, sustained chain joining was not associated with consistent, sustained improvements in hospital financial margins on average. Observed changes were small, often imprecise, and in some outcomes suggest modest short-run declines consistent with integration costs. Continued monitoring with longer post-entry windows and additional outcomes is warranted.

Abstract: To ensure economic stability, accurately forecasting the effects of domestic and external factors has become increasingly critical. This study aims to develop a novel model to predict Mongolia’s macroeconomic dynamics by integrating theoretical economic relationships with deep learning methods. Quarterly macroeconomic data from 2015 to 2024 are employed, focusing on key indicators such as inflation, unemployment, GDP growth, and the policy interest rate. The interdependence among these variables is dynamically estimated using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. For comparison, traditional ARIMA and VAR models are also applied to assess the predictive performance of deep learning approaches. The results reveal that deep learning models achieve higher accuracy in short- and medium-term forecasts (MAPE ranging from 3.7% to 5.2%) and exhibit greater sensitivity to business cycle fluctuations and policy shifts. Moreover, by incorporating a theory-guided deep learning framework, the model’s interpretability is enhanced, enabling a more realistic representation of the dynamic trade-off between inflation and unemployment. The primary contribution of this research is the development of a theoretically consistent deep learning state-space forecasting model that bridges economic theory and artificial intelligence. The proposed framework provides practical insights for macroeconomic policy analysis, fiscal planning, and monetary decision-making in Mongolia.

Abstract: This study examines the short-run relationship between artificial intelligence (AI), re-newable energy, and economic growth across the G7 countries, China, and South Korea over the 2010–2025 period. Motivated by the ongoing debate on whether AI-driven digital transformation can coexist with environmental sustainability, the analysis integrates technological and energy-economics frameworks. Using panel data and the Fixed Effects (FE) estimator with Driscoll–Kraay robust standard errors, four models (A1–A2–B1–B2) are estimated to explore how AI investment affects economic growth and energy demand. The results reveal that AI investment alone does not significantly enhance short-run economic growth; however, its interaction with renewable energy capacity yields positive and significant effects, confirming the moderating role of sustainable energy infrastructure. Conversely, AI development initially increases energy demand due to the expansion of data-driven infrastructure, but a non-linear (inverted U-shaped) relationship suggests that efficiency improvements emerge beyond a certain adoption threshold. Financial development and energy prices also play significant roles in shaping energy consumption dynamics. Overall, the findings indicate that AI-driven growth and energy efficiency are complementary in the presence of strong renewable capacity and innovation systems. The study provides empirical evidence for integrating AI policies with green energy strategies to foster sustainable digital transformation.

Abstract: This study is intended to examine how Human-AI collaboration-based identity threat appraisals in the form of loss of autonomy and loss of skill, triggers professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with 507 employees. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4, which enabled the assessment of both measurement and structural models Perceived loss of skill and loss of autonomy are positively associated with professional identity threat, which mediates their relationships with cyberloafing. AI-inclusive identity strengthens these associations for loss of autonomy suggesting employees high in AI-inclusive identity exhibit stronger professional identity threat and higher cyberloafing under autonomy loss. This study used self-reported data from a single cultural context, which may limit generalizability. The counterintuitive effect of AI-inclusive identity highlights the need for future research to examine when it serves as a protective versus a risk-enhancing factor. When integrating AI, organizations should mitigate autonomy and skill-erosion appraisals through participatory design, role redesign, and communication that emphasizes unique human contributions. Supporting healthy AI–human identity integration may reduce counterproductive behaviors such as cyberloafing. By positioning identity threat appraisals as Human-AI collaboration–driven antecedents of professional identity threat and cyberloafing, this study extends social identity theory to human–AI contexts. It further demonstrates that over-identification with AI may heighten professional identity threats by diminishing the value of uniquely human contributions.

Abstract: This study uses an endogenous growth framework to investigate the impact of foreign direct investment (FDI) stocks on sustainable economic growth in Türkiye between 1970 and 2024. Unlike most existing literature, this analysis uses the perpetual inventory method to construct separate stocks of domestic and foreign capital, allowing the growth effects of different types of capital to be disentangled. The model also incorporates a human capital index developed by Hall and Jones (1999) and a proxy for institutional quality based on the liberal democracy index. The study uses the autoregressive dis-tributed lag (ARDL) approach to examine the short- and long-run dynamics between real GDP per worker, FDI stocks, domestic capital, human capital, and institutional quality. Furthermore, the robustness of the findings is checked using FMOLS, DOLS and CCR cointegration estimators, which suggests that the results are robust to the choice of es-timator. The results indicate that, despite some transitory positive effects in the short run, FDI stocks exert a negative and statistically significant effect on output per worker in the long run. Human capital is also found to be negatively associated with growth, whereas improvements in institutional quality are linked to higher real GDP per worker. Do-mestic capital stock does not display a robust long-run contribution to growth.

Abstract: Uncertainty related to climate, energy, and trade impacts the magnitude of economic activity, owing to their evolving attributes. This study introduces a novel CEEMDAN-based multivariate Nyström quantile-on-quantile (C-MN-QQKRLS) methodology to evaluate the relationship of the uncertainty triad (CPU-EUI-TPU) with economic activity in the United States. This next-generation approach not only extends the bivariate setup, but isolates asymmetries over time-frequency spectra, concurrently controlling for other pertinent shocks. Additionally, it solves the computational discrepancies of the traditional kernel designs. The short-term outcomes establish that economic agents exercise instant tactical adjustments, validated by the positive association of EUI and TPU with economic activity (EAC). In contrast, the trilogy of uncertainty and EAC have negative association in their median-to-high quantiles. These outcomes underscore that prolonged uncertainty precludes economic activity. The long-run negative impacts of CPU-EAC, EUI-EAC, and TPU-EAC demands exigent attention. It requires the solution of climate-related issues, balanced energy mix, and appeased trade frictions between the US and its trading partners. The study concludes that unlike temporary policy uncertainty shocks, long-term or sustained uncertainty structurally inhibit economic activity in the USA.

Abstract: Two main perspectives exist regarding the interaction between fiscal deficits and expansionary monetary policy. The first perspective argues that fiscal deficits raise interest rates, thereby increasing interest payments and complicating monetary stabilization efforts. The second posits that expansionary monetary policy enhances nominal GDP growth, which in turn reduces the government debt-to-GDP ratio and strengthens the fiscal position. Using panel data from the IMF World Economic Outlook covering advanced economies between 1980 and 2025, this study empirically evaluates which perspective is more consistent with observed data, while accounting for the dynamics of tax revenues, government expenditures, interest rates, and nominal GDP growth. Empirical evidence indicates that moderate monetary expansion—raising nominal GDP—tends to stabilize budget deficits, as government revenues generally outpace expenditures and interest rates do not increase proportionally with nominal growth. These results are further illustrated through case studies of Greece, Italy, Portugal, Spain, Japan, the United Kingdom, and the United States.