Multidimensional Model Research Articles

Uncovering green brand equity and word-of-mouth intentions in Vietnam

The urgent situation of global climate change and the increasing concern for environmental issues across nations are putting pressure on all industries towards green transformation. In this context, green brand equity (GBE) is becoming a trend that attracts the attention of researchers, managers, and policymakers, especially in developing countries like Vietnam. This study aims to investigate the constituents of GBE and evaluate their respective degrees of impact on consumers' word-of-mouth intention (WOMI) in Vietnam. The dataset encompasses 528 consumers of green electronic products. The research employs the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique for data analysis. The results of the study unveil the ascending levels of positive influence that GBE components exert on WOMI, ranking as follows: Emotional value, functional value, green trust, and green satisfaction. Within the internal framework of GBE, the ascending levels of positive impact unfold as follows: Green perceived quality influencing emotional value, green brand image influencing green satisfaction, green perceived quality influencing emotional value, green brand image influencing green trust, and green satisfaction influencing green trust. This research contributes significantly, both from a scientific and practical perspective, by pioneering a multi-dimensional GBE model that incorporates various factors and examines their effects on consumers' WOMI within the unique context of an emerging market.

THE ROLE OF EXPLICIT INSTRUCTION IN DEVELOPING ENGLISH LEARNERS’ MULTIMODAL LITERACY

In today's context, successful communication requires individuals to grasp, engage with, and construct multimodal texts in various formats. The creation of knowledge artifacts is central to developing multimodal literacy and communicative competence. This action research study sought to investigate the influence of explicit instruction on enhancing students’ overall semiotic awareness. A multi-dimensional instructional model was tested for its effectiveness, with students' feedback serving as the primary evaluative tool. The study sample consisted of sixteen students, comprising seven males and nine females, all aged 17 or 18 years. A post-instruction questionnaire was conducted to collect students' reflections and feedback on the strategies used during the class session. The intervention lasted approximately two hours. Data from the qualitative questionnaire reported a high level of affective and cognitive engagement. The findings also indicated a noticeable increase in students’ overall semiotic awareness due to explicit instruction. These results could serve as a foundation for refining and implementing instructional methodologies that continue to empower students in their understanding and utilization of multimodal communication in diverse contexts.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0871/a.php" alt="Hit counter" /></p>

Third order interactions shift the critical coupling in multidimensional Kuramoto models

The study of higher order interactions in the dynamics of Kuramoto oscillators has been a topic of intense research. Previous works have demonstrated that such interactions can give rise to interesting new phenomena such as multi-stability and synchronization even if the interaction between oscillators is repulsive. Here we consider higher order interactions in the multidimensional Kuramoto model where pairs (1-simplex), triplets (2-simplex) and quadruples (3-simplex) of oscillators interact simultaneously with different coupling strengths, k1, k2 and k3, respectively. For the types of asymmetric interactions considered, we show that three body terms shift the critical coupling for synchronization towards higher values, except in 2 dimensions where a cancellation occurs. However, after the transition, three and four body interactions combine to facilitate synchronization. We also show that, for fixed values of k2 and k3, and fully connected networks, the behavior of the order parameter r(k1) is described by a universal curve given by its value at k2=k3=0, shifted along the k1 axis. Similar to the 2-dimensional case with asymmetric interactions, bi-stability and hysteresis develop for large enough higher order interactions. Multi-stability, typical of symmetric higher order interactions, is not found. We show simulations in three and four dimensions to illustrate the dynamics.

Escala de actitudes hacia las personas mayores como aprendices y usuarias de tecnologías: evidencias psicométricas

[Objective] Develop and validate a psychometric scale to assess the general population's attitude towards older adults as learners and users of Information and Communication Technologies (PAMTIC scale). [Methodology] A preliminary version of the scale was built based on a systematic review of the literature. In Study 1, the initial version was reviewed by experts and by cognitive interviews. In Study 2, PAMTIC and the validation scales were applied in digital format to 196 people aged between 18 and 85 years. Exploratory factor analysis (EFA), reliability and validity tests were performed. In studies 3 and 4, the final version of the scale and the validation scales in print and digital formats were applied to 1,041 people aged 18-82 years. In Studies 3 and 4, confirmatory factor analysis (CFA) and reliability and validity tests were performed. [Results] EFA showed 6 factors with good internal consistency coefficients ranging between .80 and .88. CFA confirmed the 6-factor structure and indicated a good fit of the multidimensional model to the data. Correlations between PAMTIC and validation scales provided evidence of nomological validity. [Conclusions] PAMTIC has satisfactory psychometric properties to measure attitude towards older adults as learners and users of Information and Communication Technologies in a reliable and valid way.

Diffusion Correction in Fricke Hydrogel Dosimeters: A Deep Learning Approach with 2D and 3D Physics-Informed Neural Network Models.

In this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range 1×10-6-1×10-4, while the gamma analysis results in a 90-100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters.

Mandatory environmental disclosure policy in the largest carbon emission country

PurposeThis paper aims to examine the impact of corporate environmental transparency (CET) on corporate financial performance under a mandatory environmental disclosure policy in China, the largest carbon-emitting country. It aims to clarify the concept of CET and investigate its short-term financial implications for key pollutant-discharging entities (KPEs).Design/methodology/approachA multidimensional model is used to construct a comprehensive CET index for KPEs in China. Empirical tests are conducted to assess the relationship between CET and corporate financial performance.FindingsThe study finds a negative relationship between CET and corporate financial performance in the short term. Increased environmental transparency necessitates higher environmental resource allocation, adversely affecting profits. The results remain unchanged from a battery of robustness tests. Despite mandatory disclosure, companies tend to provide general and vague information rather than specific and meaningful environmental data.Research limitations/implicationsThe findings provide rich practical implications for policymakers to improve a mandatory environmental disclosure policy. The paper also contributes to the existing knowledge by developing a measure of CET and presenting new evidence to the debate on whether corporate environmental disclosure can be regarded as transparency.Practical implicationsPolicymakers are advised to refine mandatory environmental disclosure regulations to ensure genuine transparency and to implement policy measures that alleviate the financial burdens of companies with high CET levels, thereby encouraging sustainable practices.Originality/valueThis paper contributes to the existing knowledge by developing a measure of CET and providing new evidence on the debate over whether environmental, social and governance (ESG) disclosure equates to transparency. It emphasizes the complexity of transparency and the inadequacy of current environmental disclosure practices among KPEs. The study underscores the need for financial support for companies with high CET levels to alleviate short-term financial strains and promote long-term sustainability.

Investigation on concentration detection of turbid solution based on hemisphere sample cell and multidimensional spectroscopy

The study developed a hemisphere sample cell and constructed a multidimensional spectroscopy acquisition system to enhance the accuracy of detecting turbid solution with scattering properties. The system simultaneously captures absorption and scattering information from the tested samples. Monte Carlo simulation was employed to model the transmission light intensity distribution data from sample cells of various shapes. It was found that the hemisphere sample cell increases the dimensionality of transmission light intensity information and enables the acquisition of more scattering-related data. Furthermore, 46 samples of intralipid-20% solution with varying concentrations were examined. Models were constructed using partial least squares (PLS) regression with one-dimensional and two-dimensional light intensity distribution data. The results indicate that the model using two-dimensional light intensity distribution data significantly outperforms the model using one-dimensional data, reducing the root mean square error by 39.96% and increasing the correlation coefficient by 0.332%. Experimental results demonstrate that the multidimensional spectroscopic modeling method employing the hemisphere sample cell can significantly enhance the accuracy and speed of detecting chemical composition concentrations in turbid solution.

Bridging the green gap: How digital financial inclusion affects corporate ESG greenwashing

We examine whether and how the development of digital financial inclusion (DFI) affects firm environmental, social, and governance (ESG) greenwashing. Our findings indicate that local DFI prohibits firms' greenwashing behaviors. These conclusions are supported through robust analysis using a multidimensional fixed-effects model, alternative measures, and an instrumental variable approach. Our research provides compelling evidence that DFI dampens corporate greenwashing by easing financial constraints and reducing information asymmetry. Additionally, DFI promotes genuine corporate engagement in ESG activities, though it does not influence ESG disclosure practices. Notably, the effects are more pronounced in firms with limited analyst coverage and increased government supervision.

Treatment engagement in comorbid alcohol use disorder and alcohol-related liver disease: A qualitative exploration of barriers and facilitators with service users.

Effective interventions to improve patient outcomes in comorbid alcohol use disorder (AUD) and alcohol-related liver disease (ARLD) remain a clinical unmet need. While the choice of abstinence is the cornerstone for the prevention of disease progression and mortality, evidence suggests a suboptimal engagement with treatment supporting recovery. This qualitative investigation aims to understand barriers and facilitators to treatment as experienced by this clinical population by applying a multidimensional adherence model proposed by the World Health Organization. Twenty-four participants with comorbid AUD and ARLD were recruited from an inpatient clinical setting. Data for this study were collected through semistructured, in-depth interviews. Deductive analysis was organized by the Framework method, and theory-driven themes were identified according to the multidimensional adherence model. This included factors across the social and economic, patient, condition, treatment, and healthcare system levels. The findings in this study indicate systematic challenges in maintaining continuity between primary, secondary, and community care. Aspects related to social and economic context, treatment, and healthcare systems were found to hinder engagement. Identified facilitators to engagement included the participatory role of family, shared lived experience of addiction/recovery, and therapeutic alliance with healthcare providers. The understanding of these barriers and facilitators from a service user's perspective can bridge the treatment gap for this clinical population. This can provide an opportunity for the implementation of effective interventions and inform the development of policies promoting accessible care. Government and public health bodies have fundamental roles in shifting treatment paradigms in comorbid AUDand ARLD.

Person explanatory multidimensional item response theory with the instrument package in R.

We present the new R package instrument to perform Bayesian estimation of person explanatory multidimensional item response theory. The package implements an exploratory multidimensional item response theory model and a higher-order multidimensional item response theory model, a type of confirmatory multidimensional item response theory. Explanation of person parameters is accomplished by fixed and random effect linear regression models. Estimation is carried out using Hamiltonian Monte Carlo in Stan. In this article, we provide a detailed description of the models; we use the instrument package to demonstrate fitting explanatory item response models with fixed and random effects (i.e., mixed modeling) of person parameters in R; and, we perform a simulation study to evaluate the performance of our implementation of the models.

Reconceptualizing open schooling: towards a multidimensional model of school openness

ABSTRACT ‘Open schooling’ has become in recent years a burgeoning theme in the discourse on how to rethink education for the 21st century. This paper addresses a gap between calls for implementing an open schooling approach in policy papers and international reports and the scarcity of rigorous academic discourse on what open schooling theoretically means and practically entails in terms of school organization and curriculum. To this end, the paper presents an ecological model of school openness that is composed of eight interrelated dimensions: shared governance, ‘open’ curriculum, inner-school communities, learning communities, student participation, social engagement, parental involvement, and community collaborations. These dimensions are organized into three categories, accounting for organizational, pedagogical and communal aspects of school openness. The multidimensional nature of the model presented here provides a more intricate and nuanced account of open schooling that acknowledges the complexities and challenges that the movement towards greater openness yields for school communities. From an educational research perspective, this model functions to inform the understanding and examination of the multidimensionality of opening schools to their community. From an educational practice perspective, it can instigate in-depth and meaningful dialogue within school teams on what open schooling is and its ensuing merits.

Assessment of Customers’ Evaluations of Service Quality in Live-Streaming Commerce: Conceptualizing and Testing a Multidimensional and Hierarchical Model

Assessment of Customers’ Evaluations of Service Quality in Live-Streaming Commerce: Conceptualizing and Testing a Multidimensional and Hierarchical Model

Florence Bullying-Victimization Scales: Validation Study and Victimization Associations With Well-Being and Social Self-Efficacy

This study provides a thorough psychometric evaluation of construct and criterion validity and measurement invariance of the promising Florence Bullying-Victimization Scales (FBVS). A special focus was devoted to the concurrent criterion validity of the victimization scale with regard to well-being and social self-efficacy. Exploratory and confirmatory multidimensional item response theory and structural equation modeling were applied to cross-sectional data retrieved from 3rd to 6th-grade Czech primary school students ( N = 1795; 49% female; M age = 10.42, SD = 1.25). The results supported the use of unidimensional factor structure that demonstrated acceptable model fit and measurement invariance across genders and grades. Moderate to high correlations of the FBVS scores with bullying and victimization measured by the Olweus Bully/Victim Questionnaire and other instruments indicated very good convergent validity. Regarding criterion validity, higher victimization was associated with lower levels of well-being and social self-efficacy.

Global classical solutions to a multidimensional radiation hydrodynamics model with symmetry and large initial data

AbstractAs a first stage to study the global large solutions of the radiation hydrodynamics model with viscosity and thermal conductivity in the high‐dimensional space, we study the problems in high dimensions with some symmetry, such as the spherically or cylindrically symmetric solutions. Specifically, we will study the global classical large solutions to the radiation hydrodynamics model with spherically or cylindrically symmetric initial data. The key point is to obtain the strict positive lower and upper bounds of the density and the lower bound of the temperature . Compared with the Navier–Stokes equations, these estimates in the present paper are more complicated due to the influence of the radiation. To overcome the difficulties caused by the radiation, we construct a pointwise estimate between the radiative heat flux and the temperature by studying the boundary value problem of the corresponding ordinary differential equation. And we consider a general heat conductivity: if ; if . This can be viewed as the first result about the global classical large solutions of the radiation hydrodynamics model with some symmetry in the high‐dimensional space.

Bearing Dynamics Modeling Based on the Virtual State-Space and Hammerstein-Wiener Model.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein-Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model's input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems.

Safety evaluation method for operational shield tunnels based on semi-supervised learning and a stacking algorithm

The safety assessment of structural defects in operational shield tunnels is crucial for ensuring their serviceability and safe operation. This study developed a novel comprehensive evaluation method for tunnel safety assessment based on semi-supervised learning and a stacking ensemble algorithm. First, in the membership degree calculation of the multidimensional normal cloud model, the importance coefficients of the tunnel safety evaluation indicators were used instead of their weights to improve the cloud model. This allowed generating unlabeled samples with patterns consistent with those of the collected samples with structural defects. Three classifiers, namely, random forest, extra trees, and gradient boosting, were employed for semi-supervised learning. This process converted unlabeled samples into pseudo-labeled samples, expanded the structural defects database, and ultimately optimized the classifier performance. Subsequently, a stacking algorithm was used to integrate the three optimized classifiers. This resulted in the creation of three stacking models, each containing multiple metamodels. Finally, the optimal metamodels were selected based on accuracy, precision, recall, and F1 score for a voting scheme to determine the tunnel safety level. The developed evaluation method was applied to a real-world engineering project. The evaluation results demonstrated consistency with those obtained from actual field conditions, thus validating the reliability and rationality of the proposed method.

Modelling the comparative influence of conjugation and transformation on plasmid spread in biofilms

In this work, we propose a multidimensional continuum model for plasmid dissemination in biofilms via horizontal gene transfer. The model is formulated as a system of nonlocal partial differential equations derived from mass conservation laws and reaction kinetics principles. Biofilm is modelled as a homogeneous, viscous, incompressible fluid with a velocity given by Darcy’s law. The model considers plasmid-carrying cells as distinct volume fractions and their vertical and horizontal gene transfer via conjugation and natural transformation. The model encompasses local detoxification of biofilm due to plasmid-borne resistance gene and its effect at the community scale. The equations are solved numerically and simulations are performed to investigate how transformation and conjugation regulate the dynamics and the ecology of plasmid spread in both a multidimensional and one-dimensional biofilm system. Model results are able to predict relevant experimentally observed results in plasmid spread, such as the respective intensity of different horizontal gene transfer mechanisms and the importance of selective pressure. Moreover, model results predict coexistence of plasmid-carrying and plasmid-free bacteria even in conditions when one should out-compete the other, offering a simple modelling explanation on global plasmid persistence in bacterial communities.

Asymptotically Correct Person Fit z-Statistics For the Rasch Testlet Model.

A well-known person fit statistic in the item response theory (IRT) literature is the statistic (Drasgow et al. in Br J Math Stat Psychol 38(1):67-86, 1985). Snijders (Psychometrika 66(3):331-342, 2001) derived , which is the asymptotically correct version of when the ability parameter is estimated. However, both statistics and other extensions later developed concern either only the unidimensional IRT models or multidimensional models that require a joint estimate of latent traits across all the dimensions. Considering a marginalized maximum likelihood ability estimator, this paper proposes and , which are extensions of and , respectively, for the Rasch testlet model. The computation of relies on several extensions of the Lord-Wingersky algorithm (1984) that are additional contributions of this paper. Simulation results show that has close-to-nominal Type I error rates and satisfactory power for detecting aberrant responses. For unidimensional models, and reduce to and , respectively, and therefore allows for the evaluation of person fit with a wider range of IRT models. A real data application is presented to show the utility of the proposed statistics for a test with an underlying structure that consists of both the traditional unidimensional component and the Rasch testlet component.

The emergence of a tailored multidimensional model for evaluating efficiency in teaching hospitals: main challenges and proposition of a theoretical framework

Background: Teaching hospitals serve as training grounds for students and residents across various specialties, while also providing more complex healthcare services that contribute to research production and advancements in health technologies. Considering all their functions, the efficiency evaluation of these institutions warrants special attention in the literature. This study aims to review the existing literature, identify the main challenges faced by previous studies, and establish new guidelines to address flaws in models assessing teaching hospitals' efficiency. Methods: An integrative review was conducted across five major databases: Springer Link, Scopus, ScienceDirect, Web of Science, and Inderscience. The search yielded 384 records, and after removing duplicates and following PRISMA guidelines, 28 studies were deemed eligible for inclusion. Results: The review reveals a scarcity of evaluation models specifically tailored to teaching hospitals. Data envelopment analysis is the most frequently used method in this field but exhibits several weaknesses. Key challenges identified include the lack of literature on the positive relationship between increased inputs and quality, proving the reliability of DEA, addressing its limitations in handling numerous input and output variables, improving communication with hospital managers, refining the categorization of healthcare professionals, and enhancing the understanding of productivity differences between medical residents and senior physicians. Conclusions: We propose a theoretical framework to guide future studies on the efficiency assessment of teaching hospitals, emphasizing the need for a multidimensional approach. This model should incorporate economic-financial, quality, and educational dimensions, alongside commonly applied dimensions such as healthcare, staff, and physical and technological infrastructure. Moreover, the challenges pointed out based on the integrative review suggest a rich agenda for upcoming studies, encourage the development of management tools to assess efficiency in teaching hospitals and contribute to formulating an agenda to orient public policies related to efficient resource management in teaching hospitals and educational and health systems.

Validation of a Questionnaire to Assess Patient Satisfaction with an Automated Drug Dispensing System.

Automated drug dispensing systems (ADDs) have been introduced to improve the efficiency of dispensing and patient safety. The available questionnaires measure patient satisfaction with particular aspects of ADDs. Also, the level of patient satisfaction with ADDs is not widely established. This study aimed to develop and validate a novel questionnaire to assess patient satisfaction with ADDs. Content and construct validity procedures were used to validate the 20-item questionnaire with four domains, including pharmacy administration, dispensing practice, patient education, and the dispensing system. Two hundred consenting participants took part in this study, from those who visited the outpatient pharmacy in a government hospital. The internal consistency of all four scale items shows acceptable reliability (>0.7). In the exploratory factor analysis, three items were removed due to poor factor loading and cross-loading. In the confirmatory factor analysis, the model has acceptable fit indices, including the comparative fit index (0.937), Tucker-Lewis's index (0.924), standardized root mean square residual (0.051), root mean square error of approximation (0.057), and χ2/df (1.67). The convergent and discriminant validity were established, since the average variance extracted (AVE) was ≥0.5 and the squared correlation (SC) values of one construct with other constructs were less than the AVE of the specific construct. This study offered a reliable and valid 17-item questionnaire incorporating a multi-dimensional four-factor model to evaluate patient satisfaction with ADDs. The validated questionnaire can be utilized to explore patients' perspectives on ADDs.