Search Results (76)

While the operational benefits of Artificial Intelligence (AI) are well-documented, the mechanisms through which firms leverage AI for strategic exploration and radical innovation remain under-theorized. This study addresses the “black box” of AI value creation by integrating the Technology–Organization–Environment (TOE) framework with the Dynamic Capabilities View (DCV). We propose that AI adoption is not a direct antecedent to performance but a multi-stage process wherein technological, organizational, and environmental factors enable the development of sensing capability, which in turn fosters a novel capability we term “AI-Enabled Exploration.” Analyzing survey data from 245 senior executives in Saudi Arabia, a high-growth economy undergoing state-led digital transformation, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the model. The results confirm a serial mediation chain: organizational readiness and technology compatibility drive sensing capability, which subsequently powers AI-enabled exploration to enhance innovation performance. Contrary to expectations, government support was not a significant predictor of sensing capability, suggesting that in resource-rich environments, external incentives are necessary but insufficient for capability building. Furthermore, competitive pressure was found to positively moderate the relationship between organizational readiness and exploration, acting as a critical catalyst that converts latent resources into active experimentation. These findings offer a theoretical roadmap for firms attempting to transition from AI-driven efficiency to AI-driven ambidexterity. Full article

Sustainable entrepreneurship depends on a firm’s ability to turn opportunities into durable systems of value creation, value proposition, and value capture. Prior studies link business model innovation (BMI) to firm performance, but the evidence is largely correlational and treats BMI as a single aggregate construct, leaving it unclear which component most directly converts business model change into sustainable innovation outcomes. Using firm-level data on 2798 Korean firms from the 2022 Entrepreneurship Survey, this study adopts a progressive empirical design that moves from ordinary least squares (OLS) to Double/Debiased Machine Learning (DML), and from aggregate BMI to a multi-treatment specification of its three components. The findings indicate that aggregate BMI shows a positive baseline association with innovation performance. When the three components are modeled jointly, value proposition emerges as the most consistently and strongly associated component of sales-based innovation performance, whereas value creation and value capture display weaker and more conditional patterns. The value proposition association is stronger in B2C firms. This study advances sustainable entrepreneurship research by identifying customer-facing value articulation as the BMI component most consistently associated with sustained innovation performance under observable-confounder adjustment. Full article

This study examines how Environmental, Social, and Governance (ESG) performance influences firm financial performance within the ASEAN context by integrating internal mechanisms and external institutional dynamics. Using a quantitative approach, this study analyzes 270 financial and non-financial companies from five ASEAN countries over the period 2019–2023. Structural Equation Modeling using Partial Least Squares (PLS-SEM) with WarpPLS is employed to test direct, mediating, and moderating relationships, complemented by multi-group analysis to assess cross-country and sectoral differences. The results show that ESG performance has a positive and significant effect on firm financial performance, both directly and indirectly through innovation capacity. At the same time, stakeholder trust and resource efficiency do not exhibit significant mediating effects. Furthermore, institutional quality, policy effectiveness, and cultural sustainability orientation strengthen the ESG–performance relationship, whereas market competition intensity does not play a significant moderating role. The findings also reveal substantial heterogeneity across countries and sectors, indicating that ESG effectiveness is highly context-dependent. Overall, this study highlights that ESG creates value not only through internal capabilities but also through supportive institutional and cultural environments, emphasizing the importance of contextual factors in shaping sustainability outcomes in emerging markets. Full article

Accurate state of charge (SOC) estimation is essential for reliable battery management systems operating over a wide temperature range. This study proposes a joint estimation framework that combines a temperature-matched dual open-circuit-voltage (OCV)-SOC model, online forgetting-factor recursive least squares (FFRLS), and an adaptive improved multi-innovation unscented Kalman filter (AIMIUKF). The dual OCV-SOC model separately calibrates charging and discharging branches at 0 °C, 25 °C, and 45 °C, reducing the voltage bias caused by thermal dependence and charge–discharge hysteresis. On this corrected voltage baseline, FFRLS identifies the time-varying parameters of the second-order RC equivalent circuit model. The updated parameters are then used by AIMIUKF, where a finite multi-innovation window improves convergence under initial SOC deviation, and covariance matching adjusts process and measurement noise online. Validation on the CALCE 18650 dataset under the Dynamic Stress Test (DST) profile shows sub-1% SOC errors at all tested temperatures. Full article

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. Full article

Growing consumer awareness of environmental and health issues has increased demand for sustainable products, yet a persistent gap remains between positive attitudes and actual purchasing behavior. This study addresses inconsistent findings in prior literature regarding the effects of psychological drivers on willingness to pay a premium for green products. Drawing on the Theory of Planned Behavior and value-based perspectives, this study examines how environmental concern, health consciousness, and consumer innovativeness influence purchase intention and willingness to pay a premium (WTP) for green cosmetics. Data were collected from 872 respondents in Indonesia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with multi-group analysis (MGA) to capture demographic heterogeneity. The results show that all three drivers significantly influence purchase intention, which in turn affects WTP and acts as a partial mediator. Demographic differences further moderate several relationships, highlighting heterogeneity in green consumer behavior. This study contributes by integrating psychological drivers, behavioral mechanisms, and demographic heterogeneity into a unified framework to explain willingness to pay for green cosmetics. The findings offer practical insights for developing targeted strategies to promote sustainable consumption in emerging markets. Full article

Despite strong policy support for prefabricated interior decoration systems (PIDSs) in China, residential consumer uptake remains limited. Existing research has focused primarily on adoption drivers or industry-side promotion; in contrast, in this study, Innovation Resistance Theory (IRT) is employed to investigate the functional and psychological barriers to consumer acceptance in the Chinese residential market. Utilizing data from 476 Chinese consumers, partial least squares structural equation modeling (PLS-SEM) is applied to test a hierarchical mediation framework. The results demonstrate that functional obstacles, specifically risk and usage barriers, do not exhibit a direct association with resistance intention; rather, a significant indirect effect via perceived value and image is observed. Notably, the tradition barrier emerged as the most dominant predictor of resistance, reflecting a deep-seated cultural path dependency on traditional masonry methods and a perceived loss of construction rituals that disrupts system adoption. Furthermore, multi-group analysis (MGA) reveals a paradox of experience: while uninitiated users are resistant based on abstract stereotypes, those with traditional renovation experience are driven by status quo bias, and early adopters of PIDSs are resistant due to negative disconfirmation regarding usage friction and functional inflexibility. These findings suggest that, to achieve system equilibrium, the industry must transition from an industry-centric narrative to one focused on premium quality and user-centric design. Practical implications include the need to de-stigmatize prefabrication as precision manufacturing and to align policy and market interventions more closely with the concerns of individual end-consumers in order to improve residential market acceptance. Full article

This study investigates the determinants of FinTech adoption and its role in supporting financial inclusion among micro, small, and medium enterprises (MSMEs) in South Sumatra, Indonesia. The analysis applies an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework that incorporates digital financial literacy, artificial intelligence literacy, green self-identity, and perceived green finance. Data from 632 MSMEs, comprising 377 rural and 255 urban enterprises, were analyzed using partial least squares structural equation modeling (PLS-SEM), multi-group analysis (MGA), and importance performance map analysis (IPMA). The results indicate that facilitating conditions represent the most influential determinant of FinTech adoption among rural MSMEs, while effort expectancy emerges as the dominant factor in urban enterprises. FinTech adoption also significantly strengthens both FinTech continuance intention and financial inclusion across the two groups, highlighting the role of digital financial technologies in promoting inclusive economic development. In addition, the IPMA shows that rural MSMEs place strong emphasis on facilitating conditions as the key driver of FinTech adoption, whereas urban MSMEs prioritize effort expectancy. By extending the UTAUT framework with sustainability-related constructs, this study provides new evidence on how digital financial innovation can support inclusive growth and contribute to Sustainable Development Goal 8. Full article

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article

This paper mainly considers the fractional parameter identification algorithms of the bilinear-in-parameter autoregressive (AR-BIP) system. The data filtering technique is introduced to improve the parameter estimation accuracy of the AR-BIP system, which involves using a filter to filter the data of the identification model. The filtering-based hierarchical fractional least mean square algorithm (F-HFLMS) and the filtering-based multi-innovation hierarchical fractional least mean square algorithm (F-MHFLMS) are proposed for effective and accurate parameter estimation of the AR-BIP system. Using the multi-innovation theory and expanding the scalar innovation into the innovation vector, the F-MHFLMS could take full advantage of the input and output data information of the system. The performance of the F-MHFLMS algorithm is compared with the F-HFLMS strategy for the AR-BIP system using the values of the mean square error (MSE) and the average predicted output error. The effectiveness and accuracy of F-HFLMS and F-MHFLMS algorithms are demonstrated under the numerical experimentation based on different noise variances, fractional orders and innovation lengths. Compared with the F-HFLMS algorithm, the F-MHFLMS algorithm can acquire more accurate and robust parameter estimation. Full article

In the context of rapid urbanization and the pursuit of the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), cities face multifaceted challenges such as high population density, limited green space, ecosystem degradation, and an insufficient supply of ecological products, all of which undermine urban sustainability. As crucial ecological units, urban park green spaces (UPGS) play a vital role in alleviating environmental pressures and providing cultural ecosystem services (CES) that are essential for human well-being and social sustainability. However, systematic insight into how residents perceive and value CES, along with the underlying drivers, remains underdeveloped, impeding the advancement of refined park management practices. Based on 12,083 social media texts, this study employed BERTopic topic modeling to identify five core dimensions of CES perception: recreational services (RS), aesthetic experiences (AE), health-promoting activities (HA), social interactions (SI), and educational services (ES). Additionally, four underlying drivers with corresponding measurable indicators were also identified: residents’ socioeconomic backgrounds (RSB), external built environment of parks (EBE), internal landscape composition (ILC), and quality of services management (QSM). Subsequently, using 313 valid questionnaires and geographic park data, a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework was constructed to analyze the influence mechanisms of EBE, ILC, and QSM on CES perception differences, with residents’ satisfaction with CES serving as the measure of their perceived CES levels. Hierarchical regression analysis was further employed to examine the moderating effects of RSB on these driving pathways. The findings reveal the following: (1) Significant synergies and heterogeneities existed among CES dimensions, with notable synergistic effects observed between AE and SI, as well as between HA and RS. (2) EBE, ILC, and QSM significantly influenced CES perception differences (p < 0.05). EBE affected these differences through pathways such as EBE → ILC → QSM → CES and EBE → QSM → CES. Notably, QSM was identified as the most critical mediating factor affecting CES perception differences. (3) Age exerted a significant positive moderating effect on the QSM → CES pathway, while monthly income showed a marginally significant negative moderating trend on the ILC → QSM pathway. This study elucidates the multi-level driving mechanisms underlying differences in residents’ perceptions of CES in UPGS. A key innovation lies in the integration of large-scale social media text data with questionnaire surveys, combined with the application of the BERTopic model and PLS-SEM to analyze these perceptual differences. The findings offer both theoretical foundations and practical insights for landscape optimization and service enhancement in park planning and management, contributing to the development of more equitable, resilient, and sustainable urban environments. Full article

Tourism Micro, Small, and Medium Enterprises (MSMEs) in underdeveloped regions play a crucial role in driving local economic development and sustaining the tourism ecosystem. Yet they face limitations in innovation capacity and organizational performance. This study aims to develop and test a green innovation model to improve MSME organizational performance and strengthen the tourism ecosystem in East Sumba Regency, Indonesia. This study employed a quantitative approach, collecting data through questionnaires from tourism MSMEs, which were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that green innovation, represented by product value, technology, networking, marketing, and market demand, is positively and significantly associated with organizational performance, which, in turn, is positively associated with perceived ecosystem performance, as reflected in productivity and resilience. These findings support the view that the relationship between green innovation and perceived tourism ecosystem performance operates indirectly and is dependent on strengthening the operational and financial performance of MSMEs. The novelty of this study lies in integrating the empirical PLS-SEM model with an implementation approach, including the development of training modules and the digitalization of learning, in the context of 3T regions (Frontier, Outermost, and Underdeveloped). The limitations of this study include the use of data from a single time period; further research is recommended to use multi-period data to capture the dynamics of change better. Full article

Accurate state-of-charge (SoC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional methods such as ampere-hour (AH) integration and the extended Kalman filter (EKF) often suffer from error accumulation, sensitivity to initial conditions, and inadequate handling of strong nonlinearities and time-varying noise. To overcome these limitations, this paper proposes a novel LDL-Decomposition-Based Multi-Innovation Adaptive Unscented Kalman Filter (LDL-MIAUKF) algorithm that integrates three key innovations: (1) multi-innovation theory to exploit historical measurement sequences for enhanced state correction; (2) an adaptive mechanism to dynamically adjust process and observation noise covariances in real time; and (3) LDL decomposition (instead of Cholesky) to guarantee numerical stability and positive definiteness of the covariance matrix during sigma point generation. A second-order RC equivalent circuit model is established for the lithium battery, and its parameters are identified online using the forgetting factor recursive least squares (FFRLS) method under Hybrid Pulse Power Characterization (HPPC) test conditions. The proposed LDL-MIAUKF algorithm is then applied to estimate SoC using real battery data. Experimental results demonstrate that the LDL-MIAUKF achieves a maximum SoC estimation error of less than 1% at 25 °C and effectively tracks the reference SoC with high robustness. Furthermore, the terminal voltage prediction error of the identified model remains within ±0.1 V, confirming model accuracy. These results validate that the proposed LDL-MIAUKF algorithm significantly improves estimation accuracy, stability, and adaptability, making it a promising solution for advanced battery management systems. Full article

Reconfigurable intelligent surfaces (RISs) represent a paradigm shift in wireless communications, offering unprecedented control over electromagnetic wave propagation for next-generation 6G networks. This paper presents a comprehensive framework for high-precision indoor localization exploiting cooperative multi-RIS deployments. We introduce the adaptive multi-stage hybrid localization (AMSHL) algorithm, a novel approach that strategically combines fingerprinting-based and geometric time-difference-of-arrival (TDoA) methods through condition-aware adaptive fusion. The proposed framework employs a 4-RIS cooperative architecture with strategically positioned panels on room walls, enabling comprehensive spatial coverage and favorable geometric diversity. AMSHL incorporates five key innovations: (1) a hybrid fingerprint database combining received signal strength indicator (RSSI) and TDoA features for enhanced location distinctiveness; (2) a multi-stage cascaded refinement process progressing from coarse fingerprinting initialization through to iterative geometric optimization; (3) an adaptive fusion mechanism that dynamically adjusts algorithm weights based on real-time channel quality assessment including signal-to-noise ratio (SNR) and geometric dilution of precision (GDOP); (4) a robust iteratively reweighted least squares (IRLS) solver with Huber M-estimation for outlier mitigation; and (5) Bayesian regularization incorporating fingerprinting estimates as informative priors. Comprehensive Monte Carlo simulations at 3.5 GHz carrier frequency with 400 MHz bandwidth demonstrate that AMSHL achieves a median localization error of 0.661 m, root-mean-squared error (RMSE) of 1.54 m, and mean-squared error (MSE) of 2.38 m², with 87.5% probability of sub-2m accuracy, representing a 4.9× improvement over conventional hybrid fingerprinting in median error and a 7.1× reduction in MSE (from 16.83 m² to 2.38 m²). An optional sigmoid-based fusion variant (AMSHL-S) further improves sub-2m accuracy to 89.4% by eliminating discrete switching artifacts. Furthermore, we provide theoretical analysis including Cramér–Rao lower bound (CRLB) derivation with an empirical MSE comparison to quantify the gap between practical algorithm performance and theoretical bounds (MSE-to-CRLB ratio of approximately $4.0\times {10}^4$), as well as a computational complexity assessment. All reported metrics have been cross-validated for internal consistency across formulas, tables, and textual descriptions; improvement factors and error statistics are verified against primary simulation outputs to ensure reproducibility. The complete simulation framework is made publicly available to facilitate reproducible research in RIS-aided positioning systems. Full article

Grain aging during storage leads to quality deterioration and significant economic losses. Traditional analytical approaches are often labor-intensive, slow, and inadequate for modern intelligent grain storage management. This review summarizes recent advances in the intelligent discrimination of grain aging using volatile organic compound (VOC) fingerprints combined with machine learning (ML) techniques. It first outlines the biochemical mechanisms underlying grain aging and identifies VOCs as early and sensitive biomarkers for timely determination. The review then examines VOC determination methodologies, with a focus on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), for constructing volatile fingerprinting profiles, and discusses related method standardization. A central theme is the application of ML algorithms, including Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN)) for feature extraction and pattern recognition in high-dimensional datasets, enabling effective discrimination of aging stages, spoilage types, and grain varieties. Despite these advances, key challenges remain, such as limited model generalizability, the lack of large-scale multi-source databases, and insufficient validation under real storage conditions. Finally, future directions are proposed that emphasize methodological standardization, algorithmic innovation, and system-level integration to support intelligent, non-destructive, real-time grain quality monitoring. This emerging framework provides a promising powerful pathway for enhancing global food security. Full article