Empirical Validation Research Articles

First Steps towards Ecosystemic Governance for Health and Recreational Tourism Destinations

The concept of the business ecosystem emerged at the turn of the century, drawing inspiration from biology to describe the economic community. Since its inception, this concept has found applications across various domains, including technology, innovation, and entrepreneurship, where it significantly contributes to strategic management. However, within the realm of tourism, there remains a notable dearth of literature on this subject. While it is acknowledged that tourism destinations embody many characteristics of a business ecosystem, concrete tools for adopting an ecosystemic approach in tourism management, particularly across different tourism types, are lacking. The current study seeks to address this gap by undertaking a theoretical exploration of health and recreational tourism. It aims to identify socio-economic aspects that could facilitate the adoption of an ecosystemic approach in the governance of tourism destinations, with a specific focus on these types of tourism. As a result of this endeavor, an initial model delineating the ecosystem of a health and recreational tourism destination is proposed. This model lays the groundwork for empirical validation and further refinement through subsequent studies.

Data-driven corporate growth: A dynamic financial modelling framework for strategic agility

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application

Electric Field Features and Charge Behavior in Oil-Pressboard Composite Insulation under Impulse Voltage

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers.

Accelerating decarbonized economic growth through net-zero entrepreneurship: Low-carbon economic development perspective

Net-zero entrepreneurship is a novel concept introduced in the context of carbon neutrality, and exploring whether it can catalyze decarbonized economic growth is a worthy pursuit. This study constructs a comprehensive, low-carbon endogenous economic growth model to scrutinize the intricate nexus between net-zero entrepreneurship and decarbonized economic growth. Empirical validation employs a series of multiple regression models to rigorously test the hypotheses derived from the theoretical framework using an extensive dataset spanning Chinese provinces. The results reveal a nuanced landscape. (i) Net-zero entrepreneurship plays a remarkable role in promoting decarbonization growth, with considerable regional heterogeneity. (ii) Green technology progress exhibits a notable mediating effect. (iii) Environmental regulation and industrial structure optimization have positive moderating effects. (iv) The results passed alternative dependent variable and one-phase lag regression robustness tests. In a distinct contribution to entrepreneurship literature, this study augments the discourse on strategies to steer low-carbon transitions. The research findings indicate that net-zero entrepreneurship can accelerate the global decarbonization process, and green technology progress is a significant driving mechanism in this process. Additionally, it is essential to strengthen environmental agencies’ regulatory oversight and optimize industrial structures to pave the way for transformative sustainable growth. In the future, more entrepreneurs should be encouraged to engage in green technology and business model innovation to contribute to global decarbonization efforts.

Linder hypothesis and India’s services

This study examines the empirical validity of the Linder hypothesis for India’s service sector exports from 2005 to 2021, focusing on 35 major importing countries. We use a gravity model trade, applying Feasible Generalised Least Squares (FGLS) and two-step system generalised method of moments (GMM), incorporating country- and time-fixed effects. Our results confirm that the Linder hypothesis does not hold for Indian service exports, revealing an increase in trade intensity between countries with dissimilar income levels. The study finds that distance has a positive and significant impact on Indian service exports. Exchange rates have a negative and significant impact on India’s service exports, while the results for the RTA dummy variable are inconclusive. Sharing a common border, a common colony, and a language has a positive and significant effect on Indian service exports.

Exploring Primary Education in China: Approaches to Improving Teachers' Job Satisfaction

The job satisfaction of primary school teachers in Shandong Province is an issue that demands urgent attention. Research has shown that teacher job satisfaction directly impacts their professional development and the quality of education, with the principal’s leadership style potentially exerting significant influence. Although transformational leadership is theoretically advantageous, its practical application within China’s basic education system requires further empirical validation. This study collected quantitative data from 389 primary school teachers in Shandong Province via an online survey to explore the relationship between transformational leadership and job satisfaction. The findings indicate that, first, significant differences exist between various dimensions of job satisfaction, suggesting that teachers have inconsistent perceptions regarding aspects such as working environment and compensation. Second, while certain aspects of transformational leadership, such as concern for the subordinates’ quality of life, received positive evaluations from teachers, other areas, like compensation and promotion opportunities, show considerable room for improvement. Lastly, the data reveal a strong positive correlation between transformational leadership and job satisfaction, particularly in leadership practices that promote teachers’ well-being and personal development. These results challenge the idealized view of transformational leadership as a panacea for all organizational challenges. The study suggests that while transformational leadership can motivate teachers, its effectiveness is limited when not aligned with broader organizational policies and practices. Therefore, for transformational leadership to be genuinely effective, it must be complemented by structural reforms, especially in addressing the specific needs and concerns of teachers.

Industry 4.0 and Sustainability: Empirical Validation of Constructs of Industry Technology and Sustainable Development

Industry 4.0 and Sustainability: Empirical Validation of Constructs of Industry Technology and Sustainable Development

Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method.

Exploring the factors driving the sustainable consumer intentions for over-the-air updates in electric vehicles

With the development of intelligent and connected vehicles, over-the-air (OTA) updates empower cars with continuous evolution capabilities, leading to an accelerated penetration rate and steady market share growth. This research endeavors to construct a comprehensive model to understand and predict sustainable consumer intentions to purchase electric vehicles (EVs) and their inclination to pay for OTA updates for EVs. Empirical validation of the model was conducted through partial least squares structural equation modeling (PLS-SEM), using responses from 504 Chinese participants gathered through an online survey. The study demonstrates that instrumental attributes, product innovativeness, and driving experience have indirect effects on the intention to purchase both EVs and OTA updates. This indirect effect is observed through the construct of relationship quality, which includes trust and satisfaction. Moreover, satisfaction and trust are found to have significant and positive associations with purchase intentions for both EVs and OTA updates. This research offers valuable insights for policymakers and marketers seeking to promote EV purchases and OTA updates. It also contributes to the academic implication of intention and behavior in the context of sustainable transportation.

Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes.

Multi-step prediction of offshore wind power based on Transformer network and Huber loss

In the context of the burgeoning expansion of renewable energy sources, the precise prediction of offshore wind power assumes a pivotal role in safeguarding the reliability, economic viability, and sustainable progression of offshore wind farms. The present study introduces a novel methodology for offshore wind power prediction, predicated upon the synergy of the Transformer network and Huber loss function. Empirical validation is conducted utilizing authentic data from a European offshore wind farm. The resulting analyses delineate a discernible superiority of the Transformer network over classical LSTM and GRU models in capturing the intricate long-term dependencies intrinsic to the time series. Furthermore, the inclusion of the Huber loss function effectively mitigates the challenges posed by the high volatility often characteristic of offshore wind power data. The study also demonstrates the beneficial integration of autoencoder reconstruction for denoising and slime mould optimization algorithm to augment prediction performance. Distinctively diverging from traditional single-step prediction paradigms, the multi-step prediction model constructed within this research offers a more comprehensive and precise prediction of wind power. Such an innovative approach represents a valuable contribution to the field, with tangible implications for the dependable operation and future advancement of wind power.

Enhancing public safety: examining the influence of micro-level physical characteristics in CPTED for evidence-based action

ABSTRACT Crime Prevention Through Environmental Design (CPTED) has long been regarded as a promising approach to enhancing community safety by leveraging the physical environment to deter criminal activity. However, most studies on CPTED largely characterized by theoretical propositions and speculative discussions, lacking empirical evidence to support its effectiveness. This lack of empirical validation has hindered the adoption and implementation of CPTED strategies in practice. Through a quantitative approach, this study facilitates a shift towards evidence-based practice, with a specific focus on micro-level physical characteristics. The derived probability equation demonstrates promising predictive capabilities, suggesting its potential for anticipating robbery occurrences.

Sound absorption performance of honeycomb metamaterials Inspired by Mortise-and-Tenon structures

To address the limitations of traditional honeycomb sandwich structures in attenuating mid to low-frequency sounds, particularly in configurations with minimal thickness and weight, this study introduces an innovative honeycomb acoustic metamaterial incorporating the traditional Chinese mortise-and-tenon joint. We systematically investigate the acoustic absorption characteristics of the modified honeycomb structure through theoretical analysis, empirical validation, and numerical simulations. Our experimental setup maintained consistent geometric parameters across all trials and demonstrated that the resonance frequency of the modified honeycomb structure decreased by 10 % relative to its conventional counterpart. We conducted detailed analyses on the influence of micropore positioning, tenon geometric dimensions, and micropore diameters on the acoustic performance. Notably, elongating the tenon from 2 mm to 6 mm resulted in a 15 % reduction in resonance frequency, whereas increasing the micropore-to-tenon distance from 0 mm to 4 mm led to a 30 % increase. The integration of the mortise-and-tenon joint significantly enhances the mid to low-frequency sound absorption performance of the honeycomb panels. This improvement is achieved while preserving the structural benefits of low panel thickness and shallow cavity depth, alongside simplified processing of micropores. Our findings elucidate a promising approach to augmenting the acoustic properties of lightweight structural materials, thereby extending their application potential in noise control engineering. This study not only contributes a novel perspective to the design and optimization of acoustic metamaterials but also highlights the potential for integrating traditional architectural techniques with modern material science to enhance noise control solutions.

GreenCompass - A self-assessment tool for fostering integration of green skills

In today's ever-evolving landscape, fostering green skills emerges as a paramount endeavour, a beacon of progress towards a green(er) future. Within this transformative journey, this paper unveils a blueprint for organizations and institutions seeking to embrace a green future. The Self-Assessment Tool, GreenCompass, emerges as a pivotal cornerstone of this research, designed to quantitatively assess the green innovation capabilities of VET organizations. This tool offers a comprehensive view of an organization's readiness and potential for fostering green skills for green innovation. The paper further unveils the insights garnered from the extensive pilot-testing of the GreenCompass across diverse geographical regions and within various types of vocational education and training institutions, validating its applicability for a broad spectrum of settings. In the last part, the paper emphasizes the creation of an environment, a nurturing environment wherein green skills can thrive. It's a conceptual framework dedicated to facilitating the growth of a sustainable and green mindset, in alignment with the evolving dynamics of the green transition. This journey underscores the significance of a comprehensive methodology, one that begins with self-assessment, evolves through empirical validation, and culminates in the establishment of an environment to nurture green innovation. Ultimately, this multifaceted approach propels organizations towards a future characterized by innovation, sustainability, and an un-wavering commitment to a greener, more eco-conscious world.

Death-birth adaptive dynamics: modeling trait evolution.

Here, we derive stochastic adaptive dynamics from the microscopic death-birth process by explicitly modeling the trait variation from offspring to parent in each reproductive event, thereby accounting for a highly polymorphic population. This generalization enables the construction of a quantitative model that can be subjected to empirical validation. Our mathematical analysis furnishes a formula for estimating the trait variation in the reproductive step by exclusively observing the current trait variation in the population. In addition, we provide a straightforward approach to obtain the fitness function associated with a particular trait by examining its actual evolutionary trajectory, which can be employed to forecast the continued evolution of the trait.

The development of a bullying victimisation scale: the experience of Pakistani adolescents

ABSTRACT This paper describes and evaluates the procedure for the development and validation of the bullying victimisation scale for Pakistani young people aged 12–18 years. During phase I, a total of 60 participants aged from 13 to 17 years were selected by using a multistage sampling strategy for item generation for bullying victimisation. In phase II, empirical validation was carried out by 10 experienced clinical psychologists for the cultural relevance of the scale. In phase III, a pilot study was carried out on 20 participants for testing the layout of the bullying victimisation scale (BVS). In the final phase, a total of 780 participants were selected through a multi-stage sampling within the age range of 12–18 years (M = 14.78, SD = 1.33) and were given a demographic Proforma, BVS, and School Children Problem Scale. Factor analysis identified a three-factor solution for bullying victimisation, consisting of emotional bullying, verbal bullying, and physical bullying. The current findings have implications for psychological and educational professionals for the identification and management of social and emotional problems in children resulting from bullying victimisation.

Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the MK-LDA (MeanShift Kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-K-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

Investigating the molecular mechanisms of Fuzheng Yiliu Shenji prescription in SH-SY5Y neuroblastoma cells.

Neuroblastoma is the most common extracranial solid tumor in childhood. Fuzheng Yiliu Shenji Prescription (FYSP) has shown potential in treating malignant pediatric tumors in clinical settings. This study aims to explore the molecular mechanisms behind its effects, specifically in the context of neuroblastoma cell lines. To elucidate the active compounds in FYSP and their mechanisms of action in inhibiting neuroblastoma cell viability, inducing apoptosis, and affecting the cell cycle in SH-SY5Y cells through network pharmacology and empirical validation. We identified the major compounds in FYSP and their predicted targets, constructing a protein-protein interaction (PPI) network and performing GO and KEGG pathway analyses. The effects of FYSP were empirically validated through assays on cell viability, cell cycle, apoptosis, and protein expression in SH-SY5Y cells. The study identified 172 active chemical components in FYSP, with 188 common targets related to neuroblastoma. Network analysis highlighted the PI3K-Akt pathway as a significant target. Experimental validation in SH-SY5Y cells confirmed that FYSP could inhibit cell viability, induce G2/M cell cycle arrest, and promote apoptosis through modulation of the PI3K-Akt pathway, specifically upregulating caspase-3 and downregulating Bcl-2/Bax expression. The study elucidates the molecular basis of FYSP's effects on neuroblastoma cells in vitro, demonstrating its ability to modulate key pathways involved in cell cycle and apoptosis. While these findings suggest a potential therapeutic role for FYSP, they are limited to in vitro observations, and further research, including in vivo studies, is necessary to explore its clinical applicability.

Category Mapping of Emergency Supplies Classification Standard Based on BERT-TextCNN

In recent years, the escalation in emergency occurrences has underscored the pressing need for expedient responses in delivering essential supplies. Efficient integration and precise allocation of emergency resources under joint government–enterprise stockpiling models are pivotal for enhancing emergency response effectiveness and minimizing economic repercussions. However, current research predominantly focuses on contract coordination and cost-sharing within these joint reserve modes, overlooking significant discrepancies in emergency supply classification standards between government and enterprise sectors, as well as the asymmetry in cross-sectoral and cross-regional supply information. This oversight critically impedes the timeliness and accuracy of emergency supply responses. In practice, manual judgment has been used to match the same materials under differing classification standards between government and enterprise reserves. Still, this approach is inefficient and prone to high error rates. To mitigate these challenges, this study proposes a methodology leveraging the BERT pre-trained language model and TextCNN neural network to establish a robust mapping relationship between these classification criteria. The approach involves abstracting textual representations of both taxonomical classes, generating comparable sentence vectors via average pooling, and calculating cosine similarity scores to facilitate precise classification mapping. Illustrated with China’s Classification and Coding of Emergency Supplies standards and Global Product Classification standards, empirical validation on annotated data demonstrates the BERT-TextCNN model’s exceptional accuracy of 98.22%, surpassing other neural network methodologies such as BERT-CNN, BERT-RNN, BERT-BiLSTM, etc. This underscores the potential of advanced neural network techniques in enhancing emergency supply management across diverse sectors and regions.

Collaboration or Hierarchy: Experimental Evidence on Governance Modes and Legitimacy Perceptions

ABSTRACTAmidst growing interest in collaborative governance as means to enhance the legitimacy of public governance, this article investigates public officials' perceptions of this governance mode. Despite theoretical propositions linking collaborative governance to enhanced legitimacy, empirical validation is scarce. Using a factorial survey experiment with 932 public officials in the Finnish central administration, the article investigates if collaborative governance promotes legitimacy compared with hierarchical bureaucracy. The results are clear: collaborative governance does not inherently boost perceived legitimacy, but rather undermines it. This study captures the causal relationships between governance modes, key governance traits (stakeholder opposition/support and majority opposition/support), and perceived legitimacy, thereby challenging prevailing theoretical assumptions about the merits of collaborative governance.