This study investigates the effects of green growth (GG) and green technological innovation (GTI) on environmental degradation, measured by per-capita CO₂ emissions (LED), across the BRICS economies (Brazil, Russia, India, China, and South Africa) over the period 1990–2023. Motivated by the urgent need to balance fast-paced economic development with sustainable ecology in high-growth emerging economies, presents three key innovations: (i) the analysis of GG and GTI is conducted concurrently with the EKC dynamics in the context of the integrated PMG-ARDL approach; (ii) a newer dataset (1990–2023) compared to previous studies focusing on BRICS countries; and (iii) cross-sectional dependence tests and model robustness verification through Panel DOLS and FMOLS. Unit root testing shows mixed order of integration between I(0) and I(1), which fulfills PMG-ARDL requirements, while Kao residual co-integration test confirms the existence of long-term equilibrium among variables. The long-run estimation results show that GG and GTI have a significant impact on lowering CO₂ emissions by 0.38% and 0.65% per unit increase, respectively, which supports the Porter Hypothesis and decoupling paradigm. FDI also has a significant impact on lowering emissions (0.32%), which aligns with the Pollution Halo Hypothesis. Contrarily, fossil fuel use and population expansion contribute to worsening environmental quality. More importantly, the significant positive value of GDP and the negative value of GDP² validate the EKC hypothesis in BRICS.The error-correction term (−0.680) indicates that 68% of short-run deviations are corrected per period. Granger-causality tests reveal unidirectional causality from GG and GTI to LED, and bidirectional causality among GDP, FDI, fossil fuels, population growth, and LED. These findings advocate for coordinated policy frameworks that prioritise green technology investment, renewable energy transition, and carbon pricing across BRICS member states to attain Sustainable Development.
Social media platforms have become central arenas for identity formation and community interaction, yet their role as instruments of disciplinary power within religious communities remains underexplored, particularly in non-Western contexts. This study examines how digital social interactions within a youth Muslim religious community function as a panoptic mechanism that shapes members' self-presentation practices and collective identity on Instagram. Grounded in Michel Foucault's theoretical framework of panopticism, discursive power, and subjectivation, the study employs a qualitative approach within a critical constructivist paradigm. Digital ethnography was used as the primary research strategy, combining digital observation of the community's official and personal member accounts with semi-structured interviews conducted with six active members of the MM community (pseudonym), a youth-based Islamic organization in South Tangerang, Indonesia. Data were analyzed using thematic analysis following Braun and Clarke's framework. Findings reveal three interconnected dynamics. First, religious knowledge held by community advisors and leaders operates as a dominant discursive authority that disciplines members' digital behavior both overtly and covertly. Second, members internalize communal norms of piety, resulting in self-censorship, cautious content selection, and the reproduction of symbolic religious narratives on their Instagram accounts. Third, members actively negotiate spaces of personal freedom through the use of alternative accounts, selective audience features such as "close friends," and strategic content curation. These findings demonstrate that Instagram functions simultaneously as a site of disciplinary surveillance and a contested space for identity negotiation. This study contributes empirically and theoretically by demonstrating that digital panopticism in religious communities is not a replication of classical surveillance models, but a discursively constructed process in which religious knowledge itself becomes the primary instrument of normalization.
Firm value remains a central indicator of corporate performance. However, empirical evidence on how internal financial decision-making processes contribute to firm value remains limited, particularly in manufacturing firms operating in emerging economies and in studies based on primary managerial data. This study addresses this gap by conceptualizing financial decision-making quality as a multidimensional managerial capability and examining its effect on perceived firm value. A quantitative research design was employed using survey data collected from managers and financial executives of manufacturing firms. Financial decision-making quality was measured through investment analysis, financing structure decisions, and financial risk evaluation, while perceived firm value was assessed using indicators related to competitiveness, growth prospects, and sustainability. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that financial decision-making quality has a positive and statistically significant effect on perceived firm value. Firms with higher-quality financial decision-making processes tend to report stronger perceived firm value. However, the findings should be interpreted with caution due to the cross-sectional design and the use of perception-based measures, which may be subject to respondent bias. This study contributes to the literature by providing direct empirical evidence on the role of managerial financial decision-making quality as an internal determinant of perceived firm value in emerging economy contexts. The findings highlight the importance of strengthening managerial financial capabilities as a strategic lever for enhancing firm performance.
This study investigates the dual role of cocoa as both a cultural resource and an economic asset within the Dayak Kenyah community in Lung Anai, East Kalimantan, Indonesia. Existing research on cocoa in Indigenous contexts in Kalimantan remains limited, with most studies focusing on broader agrarian issues, particularly oil palm and coal extraction. Adopting an ethnographic approach based on field observations and in-depth interviews, this study demonstrates that cocoa extends beyond its function as a market commodity. It is embedded in cultural practices that shape collective identity, social relations, and local ecological knowledge. Cocoa cultivation and consumption are closely linked to ritual activities and community gatherings, reinforcing social cohesion and the community’s relationship with nature. The study further examines the socio-economic implications of Indonesia’s new capital development, Ibu Kota Nusantara (IKN), which introduces both opportunities and challenges for the Dayak Kenyah community. These include pressures on land rights, shifts in livelihood strategies, and processes of cultural and identity transformation. The findings contribute to interdisciplinary discussions on Indigenous economies, cultural sustainability, and rural transformation, highlighting the importance of integrating local knowledge systems into national development agendas.
This study presents an educational alternative aimed at improving the academic performance of students who benefit from Colombia’s free tuition policy at two public higher education institutions: Institución Universitaria Antonio José Camacho and Universidad del Tolima. The research identifies persistent learning deficiencies in mathematics, communication, and language, which often lead to low academic achievement and increased dropout rates among first-year students. To address this issue, a pedagogical model was implemented that integrates technological and virtual tools—particularly the Genially platform—based on the TPACK (Technological Pedagogical Content Knowledge) framework. The study employed a non-experimental mixed-method design supported by the case study methodology. Quantitative and qualitative data were collected to analyze how interactive digital resources and gamified activities influence student motivation, participation, and learning outcomes. Preliminary results from the first phase show that students demonstrated greater engagement, improved comprehension of abstract concepts, and increased persistence in completing academic tasks. The use of virtual tools promoted a dynamic learning environment characterized by autonomy, collaboration, and critical reflection. The findings suggest that the integration of technological innovation with pedagogical practice contributes to reducing dropout rates and strengthening educational equity for economically vulnerable students. This proposal is grounded in hermeneutic and socio-critical paradigms that emphasize reflection, dialogue, and transformation of the teaching–learning process through praxis. The study concludes that the sustained implementation of this pedagogical model can enhance academic quality, promote inclusive learning environments, and serve as a replicable strategy for other higher education institutions in Latin America.
This study presents an analytical investigation of the dynamic response of a circular reinforced concrete (RC) bridge pier subjected to lateral vehicle impact loading. The pier is idealized as an equivalent single-degree-of-freedom (SDOF) system, in which the distributed mass is represented by a lumped mass at the pier head based on first-mode participation. Three commonly used simplified impact load models—rectangular, half-sine, and triangular pulses—are considered, each defined by the same peak force and duration. Closed-form solutions are derived using the Duhamel integral to evaluate the displacement response under transient loading, and the analytical results are validated against numerical integration performed using MATLAB ODE45. For the representative case with a load-duration ratio of td/T=0.822t_d/T = 0.822td/T=0.822, the peak displacements are 9.48 mm, 8.34 mm, and 7.36 mm for the rectangular, half-sine, and triangular pulses, respectively, corresponding to dynamic magnification factors of 2.010, 1.768, and 1.560. A parametric study over 0.2≤td/T≤2.00.2 \leq t_d/T \leq 2.00.2≤td/T≤2.0 shows that the rectangular pulse consistently produces the highest response, with the rectangular-to-triangular amplification ratio reaching approximately 1.55 in the intermediate dynamic regime. The results are interpreted in terms of the impulse and frequency-domain characteristics of the load functions. The findings highlight the significant influence of load shape on the predicted structural response and demonstrate that the selection of a simplified pulse model can alter displacement estimates by up to 29%, providing important implications for impact-resistant bridge pier design.
Consumer boycott behavior in digital environments continues to exhibit an attitude–behavior gap, whereby extensive access to ethical information does not consistently translate into ethical purchasing decisions. The central problem addressed in this study is the limited understanding of how consumers cognitively interpret ethical information before converting it into behavior. The novelty of this research lies in the development and empirical validation of Ethical Information Interpretation (EII) as a multidimensional cognitive mechanism that explains this interpretive process. The aim of the study is to examine the mediating role of EII in linking digital ethical stimuli to clean buying behavior. A quantitative research design was adopted using survey data collected from 610 active users of consumer boycott applications in Indonesia. Measurement instruments were developed to operationalize EII across four dimensions: information processing depth, ethical evaluation, personal value connection, and source credibility assessment. Structural Equation Modeling (SEM) was applied to test both measurement and structural models, including indirect effects of opinion leaders, boycott application features, and electronic word of mouth. The results indicate that EII significantly mediates the effects of all examined digital stimuli on clean buying behavior. Among these factors, boycott application features demonstrate the strongest indirect effect. The model explains 87.3% of the variance in clean buying behavior, indicating strong explanatory power within the sample. The findings suggest that ethical consumption in digital environments depends not only on the availability of information but also on consumers’ cognitive capacity to interpret and internalize ethical content. This study advances ethical consumption theory by positioning EII as a key explanatory mechanism in digitally mediated ethical decision-making.
Smallholder farmers in Southwest Nigeria face increasing production risks due to climate change, as their farming systems are largely rain-fed and most farms are smaller than three hectares. Although previous studies have documented farmers’ perceptions and coping responses, limited attention has been given to the combined use of sustainable agricultural practices (SAPs) as scalable resilience strategies. This paper addresses this gap by drawing on original survey data from 500 smallholder households across the six states of Southwest Nigeria, supplemented by a structured literature review of regional evidence. The determinants of adoption and the resilience outcomes of high-impact SAP combinations were examined using logistic and multiple regression models. The results show that integrated SAP bundles, particularly cover cropping, crop rotation, agroforestry, and organic soil amendments, generate greater resilience gains than single practices. Farms adopting four or more SAPs remained productive during periods of moderate climate stress, with yield stability exceeding 80%. Institutional access to extension services emerged as the strongest institutional predictor of increased SAP integration. Based on these findings, the study proposes a four-pillar framework focused on context-specific practice selection, capacity building, institutional support, and knowledge systems. The results provide policymakers, development programmes, and extension agencies with practical policy implications for enhancing climate resilience in Southwest Nigeria and comparable agro-ecological systems.
Sengon (Paraserianthes falcataria) bark, an abundant lignocellulosic biomass waste in Indonesia, was utilized as a renewable precursor for the development of cellulose-based biomembranes. Cellulose fibers were isolated and chemically modified using urea and epichlorohydrin (ECH) to fabricate cross-linked three-dimensional biomembranes for essential oil storage and controlled-release applications. The isolated cellulose appeared as fine pale-white fibers with a crystallinity index of 34.6%, indicating a predominantly amorphous structure. Fourier-transform infrared (FTIR) spectroscopy confirmed characteristic cellulose peaks at 3350 and 1000 cm⁻¹, while a weak band at 1650 cm⁻¹ suggested the presence of residual lignin. Increasing ECH concentration resulted in smoother and denser membrane morphologies, as observed by scanning electron microscopy (SEM), indicating enhanced cross-linking density. FTIR spectra of the modified biomembranes further revealed the emergence of N–H and C–N stretching bands, confirming successful chemical modification. The unmodified membrane (CFM0) exhibited the highest patchouli essential oil loading capacity (24.7 mg/cm²), whereas commercial filter paper showed the lowest capacity (10.1 mg/cm²). Increasing ECH concentration reduced oil loading capacity but significantly improved controlled-release behavior. Overall, cellulose biomembranes derived from sengon bark demonstrate strong potential as a sustainable and tunable platform for essential oil encapsulation and controlled-release systems.
Predicting event outcomes, particularly in sports, has attracted increasing research attention due to the growing availability of historical and performance-related data. Football match outcome prediction has traditionally relied on expert judgment, statistical analysis of past results, and qualitative assessments of team strengths and weaknesses; however, such approaches may be limited by subjectivity, incomplete feature representation, and restricted predictive consistency. This study develops and compares predictive models for football match outcomes using ensemble learning and deep learning algorithms applied to tabular sports data. A publicly available football match dataset obtained from Kaggle was used, and five algorithms were implemented: Deep Neural Network (DNN), TabTransformer, Neural Oblivious Decision Ensembles (NODE), XGBoost, and LightGBM. Model performance was evaluated using standard classification metrics, including precision, recall, F1-score, and accuracy. The results show that the deep learning models achieved moderate predictive performance, with accuracies ranging from 78% for NODE to 87% for the best-performing deep learning model. In contrast, XGBoost demonstrated strong performance across all metrics, achieving 0.96 precision, 0.96 recall, 0.95 F1-score, and 96% accuracy. LightGBM achieved the highest overall performance, with 0.98 precision, 0.98 recall, 0.98 F1-score, and 99% accuracy. These findings indicate that LightGBM is the most effective model for this tabular classification task, followed closely by XGBoost. Although the deep learning models, particularly TabTransformer, show potential, they did not outperform the boosting algorithms in this evaluation. The study recommends the use of ensemble-based algorithms for football match outcome prediction, especially when working with structured tabular datasets. Future research may extend this work by applying advanced hyperparameter optimization techniques, such as grid search, random search, or Bayesian optimization, to further improve the performance of LightGBM and XGBoost.
Credit card fraud detection remains challenging because real transaction data are extremely imbalanced, where fraudulent cases represent only a tiny fraction of total observations. Models trained on such skewed data can achieve very high overall accuracy while still failing to detect fraud reliably, limiting practical usefulness. This study investigates whether resampling-assisted deep learning can improve minority-class (fraud) detection without generating an impractical false-alarm burden. Using the publicly available Kaggle Credit Card Fraud Detection dataset (284,807 transactions with 492 fraud cases), we evaluate three deep learning architectures—Multilayer Perceptron (MLP), Deep Belief Network (DBN), and Convolutional Neural Network (CNN)—under three training settings: no resampling, Random Under-Sampling (RUS), and Synthetic Minority Over-Sampling Technique (SMOTE). The novelty of this work lies in a controlled comparison of SMOTE versus RUS across multiple deep architectures under a consistent preprocessing pipeline, where resampling is applied only to the training set to prevent information leakage and preserve realistic testing. Model performance is assessed using accuracy together with precision, recall, and F1-score to reflect rare-event detection priorities.The results show that RUS increases fraud recall (0.90–0.92) across models but yields very low precision (0.03) and low F1-scores (0.05–0.07), indicating excessive false positives that reduce deployment feasibility. In contrast, SMOTE produces a balanced improvement in fraud detection while maintaining very high accuracy. DBN + SMOTE achieves the best overall balance with 99.89% accuracy, 0.63 precision, 0.86 recall, and the highest F1-score of 0.73, while MLP + SMOTE achieves the highest accuracy (99.90%) with 0.59 precision, 0.86 recall, and F1-score of 0.70; CNN + SMOTE also performs competitively (99.85% accuracy, 0.54 precision, 0.81 recall, F1-score 0.65). These findings demonstrate that SMOTE-assisted deep learning provides a more deployable precision–recall trade-off than RUS for imbalanced fraud detection, improving fraud recognition while controlling false alarms.
Metabolite profiling is an important approach for understanding plant physiological responses to abiotic stresses, including salinity, which significantly affects membrane stability, carbon metabolism, and ion homeostasis. This study aims to characterize the metabolite profile of cayenne pepper leaves (Capsicum frutescens L.) under salinity stress using Gas Chromatography–Mass Spectrometry (GC-MS) to identify volatile and semi-volatile compounds. The results showed that cayenne pepper leaves contained various metabolite groups, including alcohols, carboxylic acids, esters, sugars, aromatic heterocyclic compounds, and stress-related bioactive components. The dominant compounds detected included D-mannose, 3-furaldehyde, 5-hydroxymethylfurfural (HMF), palmitic acid, and stearic acid, which may contribute to osmoregulation, oxidative protection, and membrane lipid stability. The diversity of these components indicates metabolic adjustment to salinity stress through the strengthening of osmotic regulation, antioxidant defense, and membrane structure maintenance. Overall, the findings suggest that cayenne pepper leaves exhibit an adaptive metabolite profile that may be relevant to salinity tolerance. These results provide a useful basis for further research in stress metabolomics and for the development of chili varieties with improved tolerance to extreme environmental conditions.
Hotelling’s T² test is a foundational multivariate statistical method widely used for hypothesis testing involving mean vectors. However, its classical formulation relies on strong assumptions, including multivariate normality, low dimensionality relative to sample size, and the absence of outliers. In recent decades, a growing body of literature has proposed robust extensions of Hotelling’s T² test to address violations of these assumptions, particularly in high-dimensional and contaminated data settings. Despite rapid methodological development, simulation-based evidence on the performance of these robust extensions has not yet been systematically synthesized. Guided by the PRISMA framework, this study conducts a systematic literature review of simulation studies published between 1974 and 2025 that examine robust variants of Hotelling’s T² test and related multivariate tests. Searches were conducted in Scopus and Web of Science, resulting in 35 eligible studies for qualitative synthesis. Using thematic analysis, three major themes were identified: robustness in high-dimensional and small-sample regimes; robustness to distributional deviations and outlier contamination; and calibration and computational robustness through resampling and adaptive procedures. The review highlights the consistent performance advantages of robust methods over the classical Hotelling’s T² test under assumption violations, identifies methodological gaps in simulation design, and provides recommendations for future research.
Concrete bridge crack detection plays a critical role in intelligent infrastructure inspection and structural safety assessment. However, existing automated approaches still face significant challenges, including the high miss rate of fine cracks, strong interference from complex backgrounds, and insufficient boundary delineation accuracy. To address these limitations, this study proposes a novel two-stage collaborative detection–segmentation framework that integrates an enhanced YOLOv11 detector with a dual-branch DualUNet architecture. The framework follows a “coarse localization–guided fine segmentation” strategy. In the detection stage, YOLOv11 is improved by incorporating a P2 high-resolution feature layer and a C2PSA attention module, enabling accurate localization of fine cracks while effectively suppressing background noise and generating reliable region-of-interest (RoI) priors. In the segmentation stage, a dual-branch DualUNet with a shared ResNet34 encoder is developed. The global branch captures contextual semantic information from the full image, whereas the RoI branch refines local crack features by integrating detection priors with a Multi-Scale Squeeze-and-Excitation (MSSE) module. This design enhances the representation of fine structural details and mitigates the limitations of single-stage segmentation methods. To address the severe class imbalance caused by sparse crack pixels, a hybrid loss function combining weighted binary cross-entropy and Dice loss is adopted. Additionally, a boundary supervision mechanism is introduced to improve contour accuracy. Experimental results on the Crack500 dataset demonstrate that the proposed framework achieves superior performance, with recall and precision reaching 0.79 and 0.75, respectively. Compared with baseline models, the proposed method improves Boundary IoU by 26%, indicating significantly enhanced edge delineation. Visual results further confirm that the method effectively suppresses background interference while preserving crack continuity, making it suitable for practical bridge inspection applications.
This study examines Shaykh Nawawi al-Bantani’s thought on the role of tawhid (Islamic monotheism) as the foundation of moral education. It aims to analyze how the values of tawhid shape moral principles within the framework of Islamic educational thought. The study employs a qualitative approach using content analysis of Shaykh Nawawi’s major works, including Tafsir Munir, Nashoihul ‘Ibad, Maraqil ‘Ubudiyah, Bahjatul Wasa’il, Nurudzdzolam, Qami’uttughyan, and Fathul Majid. The analysis focuses on identifying key concepts related to tawhid and examining their implications for moral education. The findings reveal that tawhid serves as the central foundation for moral formation. The internalization of tawhid encourages the development of noble character, directs the purpose of learning toward seeking the pleasure of Allah, and fosters sincerity in worship, thereby strengthening spiritual devotion. In addition, tawhid cultivates acceptance of divine decree and promotes recognition of the unity of creation, humanity, life guidance, and life purpose. Collectively, these values construct a moral identity rooted in the principles of Islamic monotheism. This study highlights the conceptual relationship between tawhid and moral education in Shaykh Nawawi al-Bantani’s thought and contributes to the development of Islamic moral education by emphasizing tawhid as its fundamental ethical and spiritual foundation.
Temporomandibular joint (TMJ) internal derangement is a common cause of pain and functional limitation that can negatively affect quality of life. Arthrocentesis combined with intra-articular hyaluronic acid (HA) injection is widely used as a minimally invasive treatment; however, the additional benefit of oral glucosamine–chondroitin–methylsulfonylmethane (GCM) supplementation remains uncertain. This randomized clinical trial included 30 patients with TMJ internal derangement who were randomly allocated to two equal groups. The intervention group received arthrocentesis followed by intra-articular HA injection combined with oral GCM supplementation for three months, whereas the control group received arthrocentesis with HA injection alone. Pain intensity was evaluated using the Visual Analog Scale (VAS), and maximum mouth opening (MMO) was measured at baseline and at 1 week, 1 month, 3 months, and 6 months after treatment. Both groups demonstrated statistically significant reductions in pain and improvements in MMO over time (p < 0.001). Although absolute pain and MMO values differed between groups at certain follow-up intervals, comparisons of changes from baseline revealed no statistically significant differences between the two treatments (p > 0.05). Within the limitations of this study, arthrocentesis combined with intra-articular HA injection was effective in improving clinical outcomes in patients with TMJ internal derangement, whereas adjunctive oral GCM supplementation did not provide additional measurable benefit during the 6-month follow-up period.
This study investigates the relationship between environmental, social, and governance (ESG) disclosure, corporate governance mechanisms, and firm value in listed companies. As capital markets increasingly integrate non financial information into valuation processes, ESG disclosure and governance quality have become critical signals for investors. Using panel data from publicly listed firms and employing panel regression techniques, this research examines whether ESG disclosure and corporate governance enhance firm value, measured by Tobin’s Q. The findings provide empirical evidence that ESG disclosure has a positive and significant effect on firm value. Furthermore, corporate governance mechanisms not only directly enhance firm value but also strengthen the valuation effect of ESG disclosure. These results support signaling theory and agency theory, suggesting that transparent ESG reporting and effective governance reduce information asymmetry and agency conflicts, thereby increasing market valuation. The study contributes to the corporate finance and sustainability literature by integrating ESG disclosure and governance perspectives in explaining firm value, with important implications for managers, investors, and policymakers.
Oxidative stress contributes to pancreatic β-cell dysfunction and mitochondrial dysregulation in diabetes. Although controlled ozone exposure may activate endogenous antioxidant responses, its relationship with the pancreatic SIRT1–Ucp2 axis remains unclear. This exploratory study investigated whether ozone therapy induces redox adaptation associated with SIRT1 restoration and Ucp2 suppression in streptozotocin (STZ)-induced experimental diabetes. Male Sprague–Dawley rats (n = 28) were randomly assigned to four groups: healthy control (CT), STZ-induced diabetes (45 mg/kg) (STZ), STZ plus oxygen (STZ + O₂), and STZ plus ozone (150 μg/kg/day, intraperitoneally for 7 days) (STZ + O₃). Fasting glucose, insulin, and HOMA-IR were measured. Pancreatic SIRT1 levels were quantified by ELISA, Ucp2 expression was assessed by RT-qPCR, SOD and GPx activities were measured using activity assays, and MDA levels were determined by the TBARS assay. Ozone therapy significantly increased pancreatic SIRT1 levels and suppressed Ucp2 upregulation compared with untreated diabetic rats. These molecular changes were accompanied by increased SOD and GPx activities and reduced MDA levels. However, fasting glucose remained elevated, and HOMA-IR did not significantly improve compared with untreated diabetic rats. These findings suggest that short-term ozone therapy induces pancreatic redox adaptation and modulates the SIRT1–Ucp2 axis in STZ-induced experimental diabetes. Further studies are needed to determine whether longer treatment periods and models with preserved β-cell function can translate these molecular effects into systemic metabolic improvement.
K.H. Ali Yafie and K.H. Sahal Mahfudh may be regarded as representatives of the NU traditionalist faction, particularly in their approaches to interpreting the Qur’an. The Majlis Tarjih of Persatuan Islam (PERSIS) may be seen as representing the puritan tendency, while Dawam Rahardjo, associated with Muhammadiyah intellectual circles, developed a modernist interpretation of the Qur’an in relation to social contexts. Traditionalist and puritan groups are often categorized as forms of orthodoxy, whereas modernist approaches are commonly associated with heterodoxy. This study examines whether such categorizations are consistent with their interpretations of social verses in the Qur’an and whether their interpretive approaches demonstrate methodological dynamism. The study aims to analyze the dynamics of contemporary Indonesian Islamic figures in understanding religious texts related to social themes. It employs content analysis of selected works grounded in Qur’anic doctrines, using an Ulumul Qur’an approach that integrates social-scientific perspectives into the contemporary paradigm of Qur’anic studies. The research objects were selected on the basis that the figures under study served as advisors or influential intellectuals within Indonesian Islamic mass organizations in their respective periods. The findings show that K.H. Ali Yafie and K.H. Sahal Mahfudh emphasized the importance of a comprehensive and integrated approach to thematic interpretation of Qur’anic verses. Dawam Rahardjo’s Encyclopedia of the Qur’an represents a combination of comparative interpretation (al-tafsīr al-muqāran) and thematic vocabulary-based interpretation. In the social sphere, the puritan approach appears to be guided by the principle that matters related to worldly and social affairs are permissible unless explicitly prohibited by the Qur’an or authentic Hadith. Persatuan Islam (PERSIS), through its official institution known as the Hisbah Council, conducts collective ijtihad (ijtihad jamā‘ī) when addressing issues not directly covered by the explicit texts of the Qur’an and Hadith.
The increasing environmental pressure associated with the rapid growth of micro, small, and medium enterprises (MSMEs) has intensified the need for business models that integrate economic performance with environmental responsibility. Despite the growing interest in sustainable entrepreneurship, empirical studies examining how sustainable environmental practices and green innovation jointly influence MSME sustainability through financial mechanisms remain limited, particularly in tourism-driven regional economies. This study aims to examine the influence of sustainable environmental practices and green innovation on sustainable MSME performance, with financial performance acting as a mediating variable. The research was conducted among environmentally oriented MSMEs in West Sumatera, Indonesia, specifically in Agam Regency, Bukittinggi City, Pasaman Regency, and Padang Panjang City. Using a quantitative approach, data were collected from 301 MSMEs and analyzed using Structural Equation Modeling with Partial Least Squares (PLS-SEM). The findings reveal that sustainable environmental practices and green innovation significantly improve financial performance, which subsequently enhances sustainable MSME performance. Green innovation also demonstrates a direct positive influence on sustainable business performance, highlighting the importance of innovation in strengthening long-term competitiveness and environmental responsibility. However, the direct influence of environmental practices on sustainable business performance appears relatively weaker, indicating that financial capacity and innovation capability play critical roles in translating sustainability practices into tangible business outcomes. The novelty of this study lies in integrating sustainability practices, innovation capability, and financial performance within a unified analytical framework applied to MSMEs operating in tourism-oriented regional economies. The findings contribute to the literature on sustainable entrepreneurship and provide practical implications for policymakers, financial institutions, and MSME development agencies to promote green innovation, sustainability training, and green financing initiatives in supporting environmentally responsible MSME development.
