Analysis Model Research Articles

Entrepreneurial intentions and family business generational transfers: The mediating role of re-organization among small and medium enterprises

PurposeThe study empirically explores the influence of re-organization on entrepreneurial intentions and family business generational transfers among small and medium enterprises (SMEs).Design/methodology/approachUsing multi-group analysis and partial least square structural equation models, data from 252 family-owned businesses were analyzed.FindingsThe results reveal that re-organization partially mediates the relationship between entrepreneurial intentions and family business generational transfers among SMEs.Research limitations/implicationsThe study used a cross-sectional survey approach and focused on Kampala business district. If required and funding permits, a longitudinal study in this field may be conducted.Practical implicationsFamily business owners ought to involve their family members in the management of the business from an early age, including them in the decision-making process, and use social exchange to strike a balance between their personal goals and the objectives of the business. In order to protect the business's goals, the business founder should mentor the next generation through quality family social interactions.Originality/valueIntegrating entrepreneurial intentions and re-organization is likely to improve the survival rate of family business generational transfers among SMEs in Uganda using social exchange theory.

Enhanced buoyancy and propulsion in 3D printed swimming micro-robots based on a hydrophobic nano-fibrillated cellulose aerogel and porous lead-free piezoelectric ceramics

This paper provides the first demonstration of additively manufactured swimming micro-robots which combine a hydrophobic nanofibrillated cellulose aerogel, to provide long-term buoyancy, with a low acoustic impedance porous piezoelectric ceramic for improved propulsion. The hydrophobic nanocellulose aerogel is shown to exhibit a high and stable contact angle that was maintained for extended periods of time, which facilitates long-term and stable buoyancy of the micro-robot. To quantify the benefits of introducing porosity into the active piezoelectric element, a new analysis model was developed to inform material design and maximize the acoustic propulsion force. Detailed characterisation and modelling of the swimming robots demonstrated that a swimming robot based on a lead-free porous Ba0.85Ca0.15Zr0.1Ti0.9O3 (BCZT) ceramic exhibited a higher acoustic radiation propulsion force and a faster swimming speed compared to a robot fabricated using a dense ceramic element. These benefits were associated with the lower elastic modulus, density and acoustic impedance of the porous piezoelectric material. The lower dielectric constant, reduced device capacitance, and lower resonant frequency of the porous piezoelectric element also significantly reduced the driving current and power requirements of the robot. This work therefore provides new insights on the impact of hydrophobic and acoustically matched piezoelectric materials on the performance of swimming micro-robots, and successfully demonstrates the use of porosity to improve acoustic impedance matching of resonant piezoelectric devices, such as micro-robots and ultrasonic transducers.

Experiment and analysis on seismic performance of a self-centering Y-eccentrically braced frames structure

Conventional eccentrically braced frames (EBFs) exhibit significant residual deformations following major earthquakes, necessitating costly repairs for the damaged structures. This study proposed a novel self-centering Y-eccentrically braced frames (SC-YEBFs) system to enhance the seismic resilience. A theoretical mechanical model which takes into account its components is formulated to predict the lateral load behavior of SC-YEBFs. The pseudo-static experiments were conducted on four specimens, and the resulting observations and analyses demonstrate that the shear links effectively function as ductile fuses, dissipating a significant portion of the input energy to safeguard the main frame from damage. Furthermore, the self-centering joint exhibits a hinge-like behavior with remarkable self-centering characteristics. The structural reparability of all specimens was found to be exceptional during major seismic events. By increasing the initial prestress and sectional area of steel strands, the self-centering performance can be further enhanced. The damaged shear links could be easily detached by loosening bolts, and the consideration of prestress loss is nonnegligible to ensure an exceptional self-centering performance. The proposed theoretical model predicted the mechanical properties of SC-YEBFs, including lateral stiffness, energy dissipation, and self-centering mechanism, through a comparison with experimental results. Moreover, the theoretical model was utilized to propose an equivalent simulation method for simulating self-centering joint and shear link using Connector function in ABAQUS software, while establishing a simplified frame finite element model for further analysis.

A second-order kinetic model for global analysis of vibrational polariton dynamics.

The interaction between cavity photons and molecular vibrations leads to the formation of vibrational polaritons, which have demonstrated the ability to influence chemical reactivity and change material characteristics. Although ultrafast spectroscopy has been extensively applied to study vibrational polaritons, the nonlinear relationship between signal and quantum state population complicates the analysis of their kinetics. Here, we employ a second-order kinetic model and transform matrix method (TMM) to develop an effective model to capture the nonlinear relationship between the two-dimensional IR (or pump-probe) signal and excited state populations. We test this method on two types of kinetics: a sequential relaxation from the second to the first excited states of dark modes, and a Raman state relaxing into the first excited state. By globally fitting the simulated data, we demonstrate accurate extraction of relaxation rates and the ability to identify intermediate species by comparing the species spectra with theoretical ground truth, validating our method. This study demonstrates the efficacy of a second-order TMM approximation in capturing essential spectral features with up to 10% excited state population, simplifying global analysis and enabling straightforward extraction of kinetic parameters, thus empowering our methodology in understanding excited-state dynamics in polariton systems.

A comparative study on turbulent models for three-dimensional thermo-hydrodynamic analysis of textured thrust bearing

A comparative study on turbulent models for three-dimensional thermo-hydrodynamic analysis of textured thrust bearing

Experimental investigation on the residual strength of concrete under the multi-factor coupling action of chloride penetration, freeze-thaw, and fatigue effect

With the ongoing process of globalization and the rapid expansion of the global economy, the proliferation of coastal constructions and infrastructures has witnessed a notable surge. These structures often encounter a confluence of environmental stressors in practical engineering, including cyclic fatigue loading, chloride salt erosion, and freeze-thaw cycles. These synergistic factors accelerate concrete’s failure, resulting in accelerated deterioration and significantly reduced durability. A comprehensive understanding of the damage characteristics of concrete materials in offshore environments, under the multi-factor coupled action of freeze-thaw phenomena, along with an assessment of their residual strength, is crucial for the development of relevant structural analysis and lifetime prediction models. To address this, twenty-seven dedicated specimens were designed and fabricated for this investigation. They were employed to scrutinize the residual strength of concrete under the combined influences of fatigue, freeze-thaw, and chloride penetration. Nine distinct multi-factor coupling actions were meticulously devised and characterized by different parameters. Following the multi-factor coupling action, tests were conducted to assess the residual flexural and compressive strengths, as well as the chloride ion content near the specimen edges. The findings revealed a substantial negative correlation between the residual flexural and compressive strengths with both the number of freeze-thaw cycles and fatigue loading iterations. In contrast, chloride ion concentration and the stress level of fatigue loading (stress ratio: 0.1–0.3) exhibited a comparatively marginal influence. Despite the relatively short test duration, it was evident that both freeze-thaw cycles and fatigue loading exerted a discernible promotional effect on chloride ion ingress in the concrete of the specimens.

Harmonizing sentiments: Analyzing user reviews of Spotify through sentiment analysis

This research investigates the sentiment of user reviews on Spotify, with a particular focus on the Indonesian market, leveraging advanced sentiment analysis techniques. We employed three prominent classification models—Naïve Bayes, Support Vector Machine (SVM), and Random Forest—to analyze a dataset of 14,296 user reviews extracted from the Google Play Store and App Store. These findings reveal that the SVM model achieved the highest performance, with an F1-score of 0.875 and an accuracy of 0.874, outperforming Naïve Bayes and Random Forest, which scored accuracies of 0.857 and 0.856 respectively. These results highlight not only the significance of this research which offers valuable contributions to the broader academic discourse on digital marketing, sentiment analysis, and consumer behavior. Additionally, it also showcases the robustness and superior performance of SVM and Random Forest in various sentiment analysis contexts. This study not only provides valuable insights for Spotify’s future development strategies but also contributes to the broader academic discourse on sentiment analysis and machine learning model performance in digital marketing. By highlighting the efficacy of specific models, this research underscores the importance of model selection in sentiment analysis, paving the way for more accurate and effective sentiment analysis applications in the music streaming industry.

Depression, anxiety, and student satisfaction with university life among college students: a cross-lagged study

Previous studies have shown that a high prevalence of depression and anxiety is a key factor leading to a decrease in student satisfaction with university life. Therefore, this study used two waves of longitudinal data to investigate the longitudinal relationships among depression, anxiety, and student satisfaction with university life among college students. We employed correlation analysis and cross-lagged models to analyze the correlation and cross-lagged relationships among depression, anxiety, and student satisfaction with university life. The results indicate a significant negative correlation between depression and student satisfaction with university life. The cross-lagged models indicate that depression (Time 1) negatively predicts student satisfaction with university life (Time 2). Anxiety (Time 1) does not have a significant predictive effect on student satisfaction with university life (Time 2). Moreover, student satisfaction with university life negatively predicts both depression (Time 2) and anxiety (Time 2). Improving student satisfaction with university life has a significant impact on reducing levels of depression and anxiety among college students. The research results can provide valuable information for mental health professionals, school administrators, and policymakers, enabling them to take more targeted measures to reduce depression and anxiety symptoms among university students and enhance student satisfaction with university life.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Behavior of FRP-interlayer-steel confined concrete columns subjected to eccentric compression

Fiber reinforced polymer (FRP)-interlayer-steel confined concrete (FISC) columns are proposed by bonding a filament-wound FRP tube with a concrete-filled steel tube (CFST) using grouting material to increase the corrosion resistance of CFST columns. FISC columns have high bearing capacity, benefiting from the dual confinement provided by both FRP and steel tubes on the core concrete. Despite this, the current research on the behaviors of FISC columns is limited, particularly regarding the confinement mechanism in FISC columns under eccentric compression. In this paper, a total of twenty short specimens were tested under eccentric compression, including seventeen FISC specimens, two FRP confined concrete-filled steel tubular (CCFT) columns, and one CFST specimen. Failure modes, load versus deflection curves and strain development for FISC specimens were presented and discussed, considering variations in eccentricity, FRP types, FRP thicknesses, and the ratio of FRP and steel confining indexes. Additionally, confining stresses of the FRP and steel tubes were obtained, revealing a non-uniform distribution of confinement on concrete along the sectional circumference, which decreased with increasing eccentricity. Furthermore, by regarding the concrete and grouting material as a whole unity and accounting for the influence of eccentricity, a sectional numerical analysis model for FISC columns was established and validated against test results, and the influences of steel thickness, FRP thickness, and concrete-grouting strength on the N-M correlation curves of FISC columns were investigated.

Seismic Performance Analysis of the Internal Joint in the New Demountable Fabricated Concrete Frame with Prestressed Mortise–Tenon Connections

This paper proposed a novel demountable fabricated joint in frame, which is connected by the prestressed and mortise–tenon connection. The prefabricated components of the demountable structures are designed to be reused, and the joint presented in this paper will promote the sustainable application of prefabricated components in future. The damage process and damage pattern of the internal joints under the horizontal load were analyzed using the refined numerical analysis model based on ABAQUS 6.14. Parametric analyses were conducted simultaneously for five parameters: axial compression ratio, the area and effective initial stress of unbonded prestressed strands (UPSs), the local reinforcement ratio in the core zone of the demountable joint, and the friction coefficient between the interface of concrete. The results showed that the demountable joint exhibits excellent energy dissipation potential under horizontal loads, but the damage was concentrated in the core zone. The deformation of the joint mainly consisted of the self-deformation of the prefabricated components, including bending, bearing and shear, as well as the relative slip deformation between the prefabricated components. The axial compression ratio has a more significant effect on the hysteresis performance compared to the areas of the UPSs and the reinforcement ratio. The initial effective stress of the UPS and the friction coefficient have relatively minor influence on the hysteretic performance of the joint. Finally, this paper recommends the design parameter values (axial compression ratio should not exceed 0.4, area of unbonded prestressed reinforcement should be not lower than Asn=0.02 and not higher than Asn=0.1, the initial stress of the UPS takes the value of 0.75fpu) and outlines optimization measures.

Multidepth quantitative analysis of liver cell viscoelastic properties: Fusion of nanoindentation and finite element modeling techniques.

Liver cells are the basic functional unit of the liver. However, repeated or sustained injury leads to structural disorders of liver lobules, proliferation of fibrous tissue and changes in structure, thus increasing scar tissue. Cellular fibrosis affects tissue stiffness, shear force, and other cellular mechanical forces. Mechanical force characteristics can serve as important indicators of cell damage and cirrhosis. Atomic force microscopy (AFM) has been widely used to study cell surface mechanics. However, characterization of the deep mechanical properties inside liver cells remains an underdeveloped field. In this work, cell nanoindentation was combined with finite element analysis to simulate and analyze the mechanical responses of liver cells at different depths in vitro and their internal responses and stress diffusion distributions after being subjected to normal stress. The sensitivities of the visco-hyperelastic parameters of the finite element model to the effects of the peak force and equilibrium force were compared. The force curves of alcohol-damaged liver cells at different depths were measured and compared with those of undamaged liver cells. The inverse analysis method was used to simulate the finite element model in vitro. Changes in the parameters of the cell model after injury were explored and analyzed, and their potential for characterizing hepatocellular injury and related treatments was evaluated. RESEARCH HIGHLIGHTS: This study aims to establish an in vitro hyperelastic model of liver cells and analyze the mechanical changes of cells in vitro. An analysis method combining finite element analysis model and nanoindentation was used to obtain the key parameters of the model. The multi-depth mechanical differences and internal structural changes of injured liver cells were analyzed.

A short video sentiment analysis model based on multimodal feature fusion

With the development of the internet, the number of short video platform users has increased quickly. People's social entertainment mode has gradually changed from text to short video, generating many multimodal data. Therefore, traditional single-modal sentiment analysis can no longer fully adapt to multimodal data. To address this issue, this study proposes a short video sentiment analysis model based on multimodal feature fusion. This model analyzes the text, speech, and visual content in the video. Meanwhile, the information of the three modalities is integrated through a multi-head attention mechanism to analyze and classify emotions. The experimental results showed that when the training set size was 500, the recognition accuracy of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.96. The F1 score was 98, and the average absolute error value was 0.21. When the validation set size was 400, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 2.1 s. When the iterations were 60, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.9 s. The experimental results show that the proposed multimodal sentiment analysis model based on modal contribution recognition and multi-task learning has good model performance and can accurately identify emotions in short videos.

Spatial heterogeneity analysis of biased land resource supply policies on housing prices and innovation efficiency

To rationally allocate land resources, the government has formulated biased land resource supply policies. However, this policy will promote the fluctuation of regional housing prices, thereby affecting regional innovation efficiency. To elucidate the intrinsic logical relationship among these factors, this study investigates the influence of biased land resource supply policies on housing prices and innovation across 31 Chinese provinces over a 16-year period. It places particular emphasis on probing into the spatial disparities in this impact. The innovation of this paper lies in the development of a theoretical analysis model, which creatively utilizes the regional land supply status as an instrumental variable to address the endogeneity problem. At the same time, a double robustness test was conducted by substituting the explanatory variables and employing the fixed effects model. The results of the TSLS regression indicate: (1) The land resource supply policy, which prioritizes the central and western regions, has resulted in an increase in housing prices across the majority of regions in China, with the eastern region experiencing a more rapid rise compared to the central and western regions. (2) The accelerated growth of housing prices in the eastern region is likely to exert a more pronounced inhibitory effect on innovation compared to the central and western regions. In summary, China’s land resource supply policy, which favors the central and western regions, has exacerbated the rise in housing prices in the eastern region, and rising housing prices will inhibit the innovation efficiency of the region. Hence, it is imperative for the government to continuously optimize and adjust land resource supply policies, ensuring their alignment with population migration patterns, stabilizing the real estate market, while also considering the innovation efficiency of regional development.

Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models

The escalating frequency of environmental pollution incidents has raised significant concerns regarding the potential health impacts of pollutant fluctuations. Consequently, a comprehensive study on the role of pollutants in the prevalence of viral hepatitis is indispensable for the advancement of innovative prevention strategies. Monthly incidence rates of viral hepatitis from 2005 to 2020 were sourced from the Chinese Center for Disease Control and Prevention Infectious Disease Surveillance Information System. Pollution data spanning 2014–2020 were obtained from the National Oceanic and Atmospheric Administration (NOAA), encompassing pollutants such as CO, NO2, and O3. Time series analysis models, including seasonal auto-regressive integrated moving average (SARIMA), Holt-Winters model, and Generalized Additive Model (GAM), were employed to explore prediction and synergistic effects related to viral hepatitis. Spearman correlation analysis was utilized to identify pollutants suitable for inclusion in these models. Concurrently, machine learning (ML) algorithms were leveraged to refine the prediction of environmental pollutant levels. Finally, a weighted quantile sum (WQS) regression framework was developed to evaluate the singular and combined impacts of pollutants on viral hepatitis cases across different demographics, age groups, and environmental strata. The incidence of viral hepatitis in Beijing exhibited a declining trend, primarily characterized by HBV and HCV types. In predicting hepatitis prevalence trends, the Holt-Winters additive seasonal model outperformed the SARIMA multiplicative model ((1,1,0) (2,1,0) [12]). In the prediction of environmental pollutants, the SVM model demonstrated superior performance over the GPR model, particularly with Polynomial and Besseldot kernel functions. The combined pollutant risk effect on viral hepatitis was quantified as βWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM2.5 emerged as the most sensitive risk factor, notably impacting patients with HCV and HEV, as well as individuals aged 35–64. CO predominantly affected HAV patients, showing a risk effect of βWQS (95% CI) = − 0.0355 (− 0.0695, − 0.0016). Lower levels of PM2.5 and PM10 were associated with heightened risk of viral hepatitis incidence with a lag of five months, whereas elevated levels of PM2.5 (100–120 μg/m3) and CO correlated with increased hepatitis incidence risk with a lag of six months. The Holt-Winters model outperformed the SARIMA model in predicting the incidence of viral hepatitis. Among machine learning algorithms, SVM and GPR models demonstrated superior performance for analyzing pollutant data. Patients infected with HAV and HEV were primarily influenced by PM10 and CO, whereas SO2 and PM2.5 significantly impacted others. Individuals aged 35–64 years appeared particularly susceptible to these pollutants. Mixed pollutant exposures were found to affect the development of viral hepatitis with a notable lag of 5–6 months. These findings underscore the importance of long-term monitoring of pollutants in relation to viral hepatitis incidence.

Efficiency Comparison between Simplified and Advanced Evacuation Analysis Models: A Case Study of Guryong Station, Republic of Korea

Modern subway systems have increased in size and complexity, and this growth presents significant challenges for planners of emergency evacuations. In this study, the effectiveness of the simplified and advanced evacuation analysis methods recommended by the International Maritime Organization (IMO) are evaluated for Guryong Station in Seoul, South Korea. The simplified evacuation analysis method facilitates rapid assessments by using general parameters, while the advanced evacuation analysis entails performing detailed simulations of human behavior and physical interactions. Our findings indicate that the results of the simplified evacuation analysis method are reasonably close to those of the more time-consuming advanced evacuation analysis method, thereby demonstrating the practical applicability of the former method for conducting initial evacuation safety assessments. Specifically, both the simplified and advanced methods showed a 20% reduction in Total Evacuation Time when tunnel evacuation routes were utilized. This finding demonstrates that the simplified method can produce results comparable to the advanced method, making it a reliable tool for initial assessments and for evaluating alternative strategies to reduce evacuation time. By demonstrating that the simplified evacuation analysis method can yield reliable results, we provide valuable insights for developing smart, resilient cities with efficient emergency-response capabilities.

System Structural Error Analysis in Binocular Vision Measurement Systems

A binocular stereo vision measurement system is widely used in fields such as industrial inspection and marine engineering due to its high accuracy, low cost, and ease of deployment. An unreasonable structural design can lead to difficulties in image matching and inaccuracies in depth computation during subsequent processing, thereby limiting the system’s performance and applicability. This paper establishes a systemic error analysis model to enable the validation of changes in structural parameters on the performance of the binocular vision measurement. Specifically, the impact of structural parameters such as baseline distance and object distance on measurement error is analyzed. Extensive experiments reveal that when the ratio of baseline length to object distance is between 1 and 1.5, and the angle between the baseline and the optical axis is between 30 and 40 degrees, the system measurement error is minimized. The experimental conclusions provide guidance for subsequent measurement system research and parameter design.

Lateral stiffness of modular steel joint with semi-rigid bolted intra-module connection

In contrast to the beam-column joints found in traditional assembled steel structures, modular steel structures feature inter-module and intra-module connections within their joints. While existing research has largely focused on the mechanical properties of inter-module connections, the impact of intra-module connection stiffness on the performance of modular steel joints remains unclear. This study introduces a novel corner-fitting-reinforced fully bolted joint specifically designed for modular steel structures. A series of experiments were conducted, including a flexural test on bolted intra-module connection to determine its initial rotational stiffness, and four groups of lateral static tests on full-scale modular steel joints with varying intra-module connection stiffnesses. These tests aimed to characterize mechanical properties such as load-carrying capacity, lateral stiffness, strain development, and ductility. The study elucidates the influence of intra-module connection stiffness on the lateral stiffness of modular steel joints. Furthermore, a refined finite-element (FE) model of the corner-fitting-reinforced fully bolted joint was developed. Simulation results showed good agreement with experimental findings., with an average error of less than 10 % for ultimate load-carrying capacity prediction. The FE model also, analyzed the stress-strain development of the corner fitting throughout the process. The study establishes a theoretical analysis model for the corner-fitting-reinforced fully bolted joint and derives a theoretical formula for the initial lateral stiffness of the modular steel joint, considering semi-rigid intra-module connections. This formula aligns well with both experimental and FE results, with a maximum error of 15 %. Finally, the study delves into the force-transfer mechanism of the corner-fitting-reinforced fully bolted joint, providing valuable insights for its design.

Assessment of influencing factors on the flight performance of J-series fighter jets based on the entropy weight-TOPSIS model

To enhance the scientific approach to fighter jet design and improve performance in areas such as supersonic cruise, long-range strike, and high maneuverability, this paper first collects, organizes, and thoroughly analyzes relevant data on factors affecting the flight performance of existing J-series fighter jets, including indicators such as engine thrust, aspect ratio, root-to-tip ratio, and wing shape. Using the entropy weight-TOPSIS evaluation analysis model, the performance factors influencing J-series fighter jets are quantitatively analyzed and ranked, avoiding subjective assumptions. Ultimately, the flight performance scores of eight J-series fighter jets under ten influencing factors are obtained. The results show that the top four scoring fighter jets are J-31 > J-20 > J-10 > J-15. This outcome provides directional guidance for the future development of fighter jets, indicating that by selecting appropriate engines, root-to-tip ratios, aspect ratios, and tail fin types, fighter jets can achieve better flight performance. This has significant implications for enhancing national defense capabilities, promoting military technological progress, and fostering economic development.

Development Of Pedagogical Competence of Indonesian Primary School Teachers: Merdeka Learning Implementers

This research describes the development of driving teacher pedagogical competence. This study aims to describe teachers efforts in developing pedagogical competence, supporting and inhibiting factors in developing pedagogical competence for teachers driving Indonesian elementary schools using the IA (Appreciative Inquiry) approach. In this study, the subjects studied were 37 driving teachers in 25 public and private elementary schools in Klaten Regency. This research uses a descriptive qualitative approach. Data collection techniques through observation, interviews, field notes, and documentation. To check the validity of the data, triangulation of sources and research techniques was used. Data analysis techniques using Cresswell's 4-step analysis model. The pedagogical competency profile of driving teachers to explore pedagogical knowledge based on philosophy and psychology of the development of elementary school age children, based on reflective abilities, understanding and resilience of emotional intelligence has shown a fairly good classification for 42.3% of teachers and a good classification for 57.6% of teachers.