Measurement Model Research Articles

Effectiveness of spatial measurement model based on SDM-STIRPAT in measuring carbon emissions from transportation facilities

To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management.

An investigation of antecedents and consequences of green value internalisation among sampled UK enterprises

Despite the growing popularity of the concept of green value internalisation, research on how this concept is being accomplished at the enterprise level is still limited. The purpose of this study is to address this knowledge gap by drawing from previously known concepts of green value and value internalisation. It examines the antecedents and consequences of green value internalisation and evaluates how these environment-leaning approaches impact competitive advantage. This study uses the resource-based view and the stakeholder theory as theoretical lenses in linking green value internalisation to its antecedents and how these impact competitive advantage. A two-step approach involving a measurement model and a structural model was used to analyse survey data from 213 UK enterprises to validate the research hypotheses. Hypotheses testing shows that green value internalisation has a positive and significant impact on green criteria development. The results also show that external pressure positively and significantly affects green value internalisation. These findings extend prior knowledge by establishing the level of significance in the relationship among the antecedents and consequences in the research model. The research design for this study draws from a systematic literature review. The study offers rigorous empirical insights for implementing green value internalisation as a value-creating strategy. However, the antecedents and consequences examined in this study may not capture in detail all underlying constructs. Hence future studies should proffer valid and reliable instruments for these constructs. The findings provide managers from enterprises across a broad industry size range seeking to implement green value internalisation with resources for embedding an enterprise-level pro-environmental strategy.

Evaluasi Ticketing MRT Jakarta Mobile Apss dengan Menggunakan Model DeLone dan McLean

The MRT Jakarta has three methods of ticket payment, i.e. Ticket Vending Machine, E-Money Tap Card, and by apps using MRTJ mobile apps. Some issues came from the passengers complaining about the simplicity of purchasing MRTJ ticket using this app. Passengers are confused when using this apps, while using another payment method is faster and more efficient. This research was conducted by evaluating information system success on the MRTJ mobile apps application using the Delone & McLean model approach with the correlation of 6 variables, including information quality, system quality, service quality, use, user satisfaction, and net benefit with the structural equal model method using the SmartPLS application. The analysis was carried out in two stages: the measurement model (outer model) and the structural model (inner model). In the outer model test, two indicators in the system quality variable do not pass the validity requirements, so they need to be excluded from these variables. All variables are proven to have an influence on the application, but the information quality variable on the use variable and the information quality variable on user satisfaction have a tendency to negatively influence based on the path coefficient value determined by the p-value and t-statistic values so that it does not fulfil the predetermined hypothesis.

Should I voice to improve workplace practices?

PurposeThis study attempts to examine how employee orientations and paranoid arousal intersect and influence the voice behaviors of employees, as they significantly influence workplace practices.Design/methodology/approachDrawing on a multi-source and multi-time-based survey, the data were collected from 37 supervisors and 270 employees in 80 branches of 11 microfinance banks. SmartPLS-4.1 was used to test the measurement and structural model. A total of 307 questionnaires were used for the data analysis.FindingsThis research presents the contrasting results of employee orientations with voice behavior. Results support the direct relationship between paranoid arousal and promotive-prohibitive voice behaviors. However, no support was found for approach-avoidance orientation and promotive-prohibitive voice behaviors.Practical implicationsEmployees' ability to voice-out in organizations depends on several motivational factors, consolidated into three areas – organizational culture, leadership, and individual traits. Organizations must implement a culture of transparency and open communication. Participative and inclusive leadership styles increase the quality of relationships with employees, stimulating a promotive voice. A support system for employees is needed to advance in their careers, increasing promotive behaviors.Originality/valueThe study adds value to the existing body of knowledge by studying approach-avoidance orientations and voice behaviors through the lens of paranoid arousal in the framework.

Análise do grau de governança pública dos municípios do Cariri cearense

Due to the growing need for discussions about governance in public administration, this paper aims to verify the level of public governance in the municipalities of Cariri cearense, given the current scenario that demands management centered on accountability for society, committed to the quality, ethics and efficiency of services. Considering this, the present work sought to answer the following question: what is the level of public governance in the municipalities of Cariri cearense? Thus, for its fulfillment and development, a quantitative approach to the problem was used, using the public governance measurement model developed by Oliveira and Pisa (2015) and adapted by Aquino et al. (2021), in addition to descriptive analysis techniques. The results showed that most municipalities in Cariri presented average degrees (32,14%) and low (46,43%) levels of governance. Furthermore, it was evident that, on average, the equity and participation dimensions presented the best indexes among the five dimensions, while the dimension of the legality-ethics-integrity indexes presented the worst average, followed by transparency and effectiveness respectively.

Construction and evaluation of a practical model for measuring health-adjusted life expectancy (HALE) in China

BackgroundHALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning.MethodsFirst, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE).ResultsA three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%).ConclusionWe constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.

The conceptualisation implies the statistical model: implications for measuring domains of teaching quality

ABSTRACT Classroom observation rubrics are a widely adopted tool for measuring the quality of teaching and provide stable conceptualisations of teaching quality that facilitate empirical research. Here, we present four statistical approaches for analysing data from classroom observations: Factor analysis, Rasch modelling, latent class or profile analysis, and formative measurement models. Each statistical model conceptualises the latent variable differently, which may or may not align with the observation rubric’s conceptualisation of teaching quality. We discuss the differences across these models, focusing on the alignment between the rubric’s conceptualisation of teaching quality and the model’s modelling of the latent variable. We discuss the need to align model selection with observation rubric so that the measured teaching quality reflects the theoretically conceptualised teaching quality.

Wireless Local Area Network and Analytical Hierarchy Process: Technology Acceptance Model for Increasing Use of Online Learning

Online learning is currently very much needed for the development of science in schools, the covid 19 outbreak has made changes to learning methods. The change in the learning model must also be supported by supporting learning devices and media, including computer networks and the internet in schools, the network that is used to support online learning is currently widely used by schools using WLAN, because it can reach parts of the hard one. The use of WLAN learning devices and media as a support for online learning can be an obstacle for some teachers and students, this is because not all teachers and students can understand the online learning system and not all areas have a fast internet access network, with these constraints in need a technology acceptance measurement model (TAM) for online learning using WLAN, so that online learning can be carried out properly and the achievement of successful learning can be carried out.

Factor structure and measurement invariance of the Depression anxiety stress scale (DASS-21) in Chinese left-behind and non-left-behind children: an exploratory structural equation modeling approach

BackgroundComprehensive data has shown that adolescents often suffer from depression, anxiety, and low self-esteem, and are in a particularly fragile stage of psychological, physiological, and social development. Left-behind children in particular tend to have significantly higher, state anxiety and depression compared to non-left-behind children. The Depression, Anxiety, and Stress Scale (DASS-21) is an effective tool for evaluating depression, anxiety, and stress, and is used to measure levels of depression, anxiety, and stress in groups from a variety of backgrounds. The purpose of this study was to determine the effectiveness, reliability, and measurement invariance of the DASS-21 in Chinese left-behind children.MethodThe test and re-test method was used (N = 676), and the exploratory structural equation model (Mplus v.8.3) used to verify basic measurement models. For measurement invariance, the configural, weak, strong, and strict models were tested. The reliability of the DASS-21 was also tested using the collected data.ResultsAnalysis results showed that the DASS-21 had a stable three-factor structure in the sample of left-behind children in China. The measurement invariance test showed that gender and time not only had strong invariance, but also strict invariance. The results of cross left and non-left invariance indicated a lack of strict invariance. Finally, the McDonald’s omega coefficient of the DASS-21 total scale was 0.864, and the internal consistency of each subscale was also good.ConclusionsThe DASS-21 is shown to be an effective and reliable tool for measuring depression, anxiety and stress in Chinese left-behind children.

Input currents of CMOS SAR ADC in microcontroller, their measurement and behavior model from users point of view

The article introduces the possible issue of internal CMOS Successive Approximation Register microcontroller’s Analog-to-Digital Converters (ADC) input currents. It describes the nature of the ADC input current and its dependence on the sampling frequency but also explains the issue when the ADC input appears to the user as a voltage source. Methods for measurement of parameters such as input charge, residual voltage, and equivalent capacitance of sample-hold circuits are presented and used in the proposed simplified model of ADC input behavior. These parameters, which are not listed in the datasheets, if not respected in the data acquisition design, can adversely affect how the ADC is used when measuring signal sources with non-negligible internal resistance. The article also explains how to solve a voltage measurement on a source with high internal resistance even in a situation where, according to datasheets, it should already have limitations. If the microcontroller contains a dynamically switched multiplexer, it can lead to other consequences, such as changing the input current or residual voltage. The proposed simplified CMOS SAR ADC input behavior model in an MCU can help users in an MCU-based data acquisition system design.

Individual and Organizational Ductility: Conceptualization, Development, and Validation of a New Scale.

In this article, we conceptualize a new construct named "ductility" and propose a measurement instrument. We examine psychometric properties-the factorial validity and reliability of the Ductility Scale in Italy. The results of exploratory factor analysis showed that the scale has a two-factor structure, namely, individual and organizational ductility. The scale reliability was excellent for both dimensions (individual ω = 0.82; organizational ω = 0.85). The participants were employees from private and public organizations (n = 466). We tested the construct validity of the Ductility Scale. The invariance of the measurement model tested via multigroup confirmative factor analysis showed that the Ductility Scale was invariant across gender. In addition, we found ductility to be positively related to proactive personality and work engagement. These preliminary results show that the Ductility Scale is a reliable and valid measure. In addition, our findings illustrate the potential usefulness of the ductility construct via the newly developed scale.

Evaluasi Penggunaan Flowgorithm dalam Pembelajaran Algoritma Pemrograman menggunakan Technology Acceptance Model (TAM)

be able to automate work and software development. However, a programmer must understand the basics of computational thinking that often causes a problem for students who are desirous of becoming programmers. This extensive research aims to evaluate student's acceptance of the use of flow Flogorithm using the Technological Acceptance Model (TAM) method. This type of research is quantitative and is conducted by using a survey method which involves distributing questionnaires to around 96 respondents who have particularly studied programming algorithms. Furthermore, the random sampling technique was used in the sampling. The analysis was carried out using the PLS-SEM method which consists of a measurement model and structural model with five variables, namely ease of use, perceived usefulness, attitude towards use, intention, and actual use. The result of the research shows that the acceptance of the Flowgorithm has a positive influence on all the set of variables, except for the perceived ease of use on attitude toward using.

Assessment of an Adolescent Digital Citizenship Scale: Examining Dimensionality, Measurement Invariance and External Validity

The positive online behavior effects of digital citizenship have increasingly attracted the attention of scholars. This study designed and tested the psychometric properties of an Adolescent Digital Citizenship Scale (DCS-A) in two independent samples of Mexican secondary students (Sample 1, M age = 13.2 years, SD = 1.5 and Sample 2, M age = 13.4 years, SD = 1.4; N1 = 750, N2 = 750). We examined content, factorial, discriminant, concurrent validity, and reliability. We also tested the cross-sample and gender invariance. Confirmatory factor analysis (CFA) demonstrated goodness-of-fit on a second-order factorial model that displays three first-order factors (online ethic, online civic engagement, and online diversity acceptance). Cross-validation confirmed the factorial structure stability of the DCS-A across the independent sample. The result demonstrated the equivalence of the measurement model in both genders (configural, metric, and scalar invariance). The latent means comparison indicates that females held greater online ethics, online civic engagement, and online inclusive behaviors than males. Finally, the concurrent validity of the scale was supported by finding a positive relationship between DCS-A dimensions and defender behavior and a negative association with passive and reinforces interventions in cyberbullying events. These results suggest that the DCS-A is a theoretically and psychometrically grounded measure of digital citizenship in adolescents.

A two-stage electricity consumption forecasting method integrated hybrid algorithms and multiple factors

Electricity consumption forecasting provides a reference basis for electricity scheduling, it has become a research hotspot in the field of electric power. The fluctuation of electricity consumption is influenced by multiple factors, which has strong randomness and are strongly disturbed by noise. Single algorithms cannot ensure the stability and accuracy of electricity consumption forecasting. To address this problem, a two-stage framework for electricity consumption forecasting is proposed in this paper. In the first stage, multi-forecasting algorithms are considered to eliminate noise and forecast the electricity consumption trend. A hybrid model is constructed, which consists of three parts: Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Unit (GRU) and Gradient Boosting Regression Tree (GBRT). The EEMD method is employed to decompose the electricity consumption signal into different frequency components. GRU and GBRT are adopted to forecast high-frequency and low-frequency components respectively. In the second stage, multidimensional meteorological factors are considered to further improve the accuracy of the electricity consumption forecasting. A structure for feature extraction is constructed to reduce the dimension of meteorological factors. The experimental results indicate that the two-stage framework model outperforms mainstream measurement models by an average reduction of 35.7% and 43.2% in MAE and RMSE.

Testing the relationships among involvement, sponsorship perceived fit and intention to purchase sponsors’ products: the case of esports tournament viewers

PurposeDespite fast developments in esports sponsorship, limited research exists in the area of sponsorship evaluation in the esports context. The purpose of the present study was to test the relationships among esports involvement, sponsorship perceived fit and viewers’ intention to buy the sponsor’s products, and examine the degree to which perceived fit mediates the relationship between the involvement dimensions and intention.Design/methodology/approachThe study draws on the theoretical model of sponsorship effects proposed by Wakefield et al. (2020) and obtained quantitative data from sampling esports viewers (n = 285). Statistical analysis was carried out in three steps. Beyond the descriptive statistics, confirmatory factor analysis (CFA) was conducted to assess the goodness of fit of the measurement model. The mediation analysis was performed at the end of the study.FindingsThe results supported the impact of one of the esports involvement dimensions (i.e. self-expression) on both perceived fit and esports viewers’ intentions to buy sponsors’ products. Involvement (self-expression) was found to have both direct and indirect relationships, through perceived fit, on purchase intentions. The study provided support for the associations among esports involvement dimensions, sponsorship perceived fit and purchase intentions.Practical implicationsThe practitioners should first consider the involvement profile of esports viewers. The more involved viewers will be more likely to have positive perceptions about the fit between the esports tournament and the sponsor.Originality/valueIt is the first study to test a sponsorship evaluation model in the context of esports users. It does so by including a more detailed measurement of involvement (with three-dimensions) in the hypothesized model.

Dynamic Numerical Simulation and Transfer Learning-Based Rapid Rock Identification during Measurement While Drilling (MWD)

In constructing rapid rock identification models for measurement while drilling (MWD) via neural network methods, collecting actual drilling data to train the model is extremely time-consuming and labor-intensive. This requires extensive drilling experiments in various rock types, resulting in limited neural network training data for rock identification that covers a limited range of rock types. To suitably address this issue, a dynamic numerical simulation model for rock drilling is established that generates extensive drilling data. The input parameters for the simulations include torque, drill bit rotation speed, and drilling speed. A neural network model is then developed for rock classification using large datasets from dynamic numerical simulations, specifically those of granite, limestone, and sandstone. Building upon this model, transfer learning is appropriately applied to store the knowledge obtained in the rock identification based on the neural network model. Further training through transfer learning is conducted with smaller datasets obtained during actual drilling, making the model suitable for practical rock identification and prediction in the drilling processes. The neural network rock classification model, incorporating dynamic numerical simulation and transfer learning, achieves a prediction accuracy of 99.36% for granite, 99.53% for sandstone, and 99.82% for limestone. This reveals an enhancement in prediction accuracy of up to 22.94% compared to the models without transfer learning.

Research on soft measurement model of flow in bends of primary circuits of the nuclear power plant

When measuring the coolant flow in a nuclear power plant using the elbow flowmeter, the complex fluid-heat coupling environment at the measurement location and other factors will affect the accuracy of the flow measurement, and the uncertainty of the influencing factors on the flow measurement needs to be considered to improve the measurement accuracy. To address this problem, this paper adopts the finite element simulation method to simulate and analyze the flow-heat field of the bend section of the primary circuit of a nuclear power plant and optimizes the Optimal Cross-section selection of the pipeline for flow measurement. Based on the pressure values measured using the traditional method, temperature information is added, and a BP neural network bend pipe flow soft measurement model based on the whale optimization algorithm is established to quantify the effects of temperature and pressure on flow measurement. The experimental results show that compared with the traditional engineering empirical method, the average absolute percentage error measured by the soft measurement method is reduced from 2.57 % to 0.21 %, which realizes the accurate measurement of the coolant flow rate of the elbow pipe.

Are there possible issues that we overlook when selecting green infrastructure?

Promoting green infrastructure (GN) as a tool for reducing natural disaster risk is promising, particularly in depopulated areas worldwide. Images of GN as being multifunctional and environment friendly seem to enhance citizens’ positive attitudes toward it. While conventional types of disaster prevention infrastructure, such as concrete levees, can completely prevent damage, GN can relieve the effects of natural hazards. By switching from conventional infrastructure to GN, societies could reduce the costs of disaster prevention infrastructure. However, the risk of disaster damage may increase in some areas. This study aimed to better understand citizens’ preference for GN. It was hypothesized that those who prefer GN are those who do not take the initiative to learn about it and that some citizens are not fully aware of the heterogeneous risks when selecting GN. To test the hypotheses, structural and measurement models, including latent variables, were developed, and structural equation modeling was performed. Data for constructing the measurement and structural equations were obtained using a web questionnaire administered by a professional research company in 2022 with 1,200 participants across Japan. Data from the portal site for the official statistics of Japan (e-Stat) were also used. The results confirm the hypothesis that individuals who favor GN are those who do not proactively seek to acquire knowledge concerning it. The findings highlight the importance of considering, in future policymaking, the possibility that citizens who prefer GN without knowledge about it may face high risks as a result of its uninformed adoption and use.

How Does Perceived Innovation Importance Underpin the Association Between Deliberate Practice and Employee Creative Behavior

ABSTRACTIn the pursuit of innovation, the critical role of deliberate practice in enhancing employee creative behavior is examined, considering the influences of harmonious entrepreneurial passion and the importance employees perceive in innovation within their organizations. This investigation, grounded in self‐determination theory (SDT) and social cognitive theory, proposes a model where deliberate practice impacts employee creative behavior through the mediating effect of harmonious entrepreneurial passion and is further amplified by the level of perceived innovation importance. The study uses a multisource, three‐wave data collection technique to gather data from employees working in software houses in Pakistan. The study utilizes SmartPLS (v 4.0) to assess the measurement and structural models. The findings reveal that deliberate practice has a significant positive impact on employee creative behavior. Besides, harmonious entrepreneurial passion mediates the link between deliberate practice and employee creative behavior. In addition, the results confirm the interaction effect of perceived innovation importance. The positive influence of deliberate practice on employee creative behavior through the mediating role of harmonious entrepreneurial passion is more potent at higher levels of perceived innovation importance. The study presents several important theoretical and practical implications.

Trajectory Compression with Spatio-Temporal Semantic Constraints

Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature points with maximum spatio-temporal semantic distance from the original trajectory until the compression rate is satisfied. Experimental results show these methods closely resemble each other in maintaining geometric similarity of trajectories, but our method significantly outperforms DP, TD-TR, and CascadeSync methods in preserving semantic similarity of trajectories. This indicates that our method considers both geometric and semantic features during compression, resulting in the compressed trajectory becoming more interpretable.