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INDONESIA
ARRUS Journal of Social Sciences and Humanities
Published by Yayasan Ahmar Cendekia Indonesia
ISSN : 27767930     EISSN : 28073010     DOI : https://doi.org/10.35877/soshumv1i1
Core Subject : Religion, Economy, Humanities, Social,
Religion Humanities Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Social Sciences
Social Sciences: Anthropology, Asian Studies, Communication, Demography, Development, Gender Studies, Government & Public Policy, Human Ecology, International Relations, Media Studies, Peace and Conflict, Political Science, Science, Technology & Society, Sociology. Humanities: Cultural Studies, Education, History, Human Geography, Linguistics, Philosophy, Religion.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 322 Documents
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The Impact of Organizational Commitment, Flexibility, and Autonomy on Performance: JD-R-GROW Framework Syaeful Bahri; Ahmad Zainuri; Yoga Adiyanto
ARRUS Journal of Social Sciences and Humanities Vol. 6 No. 3 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/soshum4968

Abstract

Purpose: This study investigates the influence of Organizational Commitment (X1), Workplace Flexibility (X2), and Work Autonomy (X3) on Employee Performance (Y), with Job Engagement (Z) acting as a moderating factor, within the context of the JD-R-GROW Framework at Dinas Kesehatan Provinsi Banten. Methodology/Approach: A quantitative research design was employed, with 141 respondents from Dinas Kesehatan Provinsi Banten. Structural equation modeling (SEM) using SmartPLS 3.0 was used to test the direct, moderating, and indirect effects of the variables. Results: All three independent variables (X1, X2, and X3) had significant positive effects on employee performance (Y). Job engagement (Z) significantly moderated and mediated the relationship between these organizational factors and employee performance. Conclusions: This study confirms that organizational commitment, workplace flexibility, and work autonomy positively influence employee performance, with job engagement playing a critical role in amplifying these effects. Limitations: The cross-sectional design and focus on a single healthcare organization limit generalizability. Future studies should employ longitudinal designs and include multiple organizations to enhance their external validity. Contributions: This study provides insights into how healthcare organizations can improve employee performance by fostering organizational commitment, offering workplace flexibility, and increasing work autonomy, while also focusing on enhancing job engagement.
Earth System Variables as Drivers of Environmental Commodity Price Dynamics: A Systematic Review of Physics-Informed and Data-Driven Modelling Approaches (2010–2026) Muhammad Nusrang; Ansari Saleh Ahmar; Abdul Rahman; Agung Tri Utomo; Muh. Qodri Alfairus
ARRUS Journal of Social Sciences and Humanities Vol. 6 No. 3 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/soshum4973

Abstract

Environmental commodity markets (carbon allowances, electricity, natural gas, and renewable energy instruments) are, at their root, earth system markets: their price-generating processes are as much a product of atmospheric circulation, hydrological regimes, and climate teleconnections as of supply-demand fundamentals or regulatory signals. Despite this physical reality, the machine learning forecasting literature has treated these markets primarily as benchmarking arenas, decoupling predictive architectures from the physical processes that drive price-relevant forcing. This systematic review follows PRISMA 2020 guidelines on a corpus of 413 peer-reviewed studies drawn from 652 Scopus records (2010–2026) and examines how earth system variables, including temperature anomalies, precipitation regimes, wind resource indices, atmospheric pollution metrics, and climate teleconnections such as ENSO and NAO, have been integrated into ML-based environmental commodity price models, and evaluates the evidence for whether physics-informed feature engineering confers measurable accuracy advantages over purely data-driven approaches. Bibliometric analysis reveals rapid field expansion (71.6% of publications in 2021–2026), geographic concentration in Chinese ETS research (?68% of high-impact output), and methodological dominance of hybrid decomposition-deep learning architectures. Models incorporating earth system variables consistently outperform endogenous-only ML architectures by 12–35% on MAPE, yet fewer than 30% of corpus studies include any physical predictor and climate teleconnection indices appear in under 4% of studies despite their relevance at energy market planning horizons. Six research priorities are identified, centred on numerical weather prediction ensemble integration, cross-climate-regime validation, and probabilistic forecasting grounded in physical uncertainty quantification.

Page 33 of 33 | Total Record : 322

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