Measurement Model Research Articles

How Financial Literacy Impacts Financial Well-Being: The Influence of Financial and Technical Efficacy

The fundamental human resource that influences financial well-being is financial literacy. People are more likely to save and invest if they understand the time value of money, credit, insurance, and investments. Having a sound financial understanding lessens stress and increases financial well-being. This study used a structured survey questionnaire and back-translated it into Bangla to ease readability for the respondents. Four hundred thirty-five invitations were sent mainly to the Dhaka city dwellers’ and only 253 complete responses were retained for analysis. Partial least squares structural equation modeling (PLS-SEM) is used to assess the intercorrelations and validate the measurement model among the constructs (financial attitude, behavior, knowledge, self-efficacy, technological self-efficacy, and financial well-being) using SmartPLS version 4. The result found a full mediation effect among financial behavior, financial self-efficacy, and financial well-being. Partial mediation effects are found among financial attitudes toward financial self-efficacy, technological self-efficacy, and financial well-being.

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.

Unleashing the power of perceived enjoyment: exploring Chinese undergraduate EFL learners' intention to use ChatGPT for English learning

PurposeThis study intends to extend the unified theory of acceptance and use of technology (UTAUT) model by integrating perceived enjoyment as an intrinsic motivation so as to investigate factors influencing Chinese undergraduate English as a Foreign Language (EFL) students' intention to use ChatGPT for English learning.Design/methodology/approachA cross-sectional quantitative survey method research design was used in this study. Data were collected from 432 undergraduate students at two Chinese universities. The data analysis was carried out using SmartPLS 4, a computer software that employs the partial least squares structural equation modelling (PLS-SEM) technique. The analysis of the model was performed in two stages, including the assessment of reflective measurement model and structural model. The PLS predict was utilized to assess the model’s predicting power.FindingsFindings showed that effort expectancy, performance expectancy, social influence and perceived enjoyment were positively related to Chinese undergraduate EFL learners' intention to use ChatGPT for English learning. Perceived enjoyment mediated the relationships between effort expectancy, performance expectancy, social influence and intention to use ChatGPT for English learning respectively.Originality/valueThrough incorporating the perceived enjoyment as an intrinsic motivation into the UTAUT model to explore factors that impact Chinese undergraduate EFL learners' intention to use ChatGPT for English learning, this study has extended the applicability of the UTAUT model and provide insights into factors affecting students' intention to utilize ChatGPT or other AI-based technologies for English learning.

Construction of Measurement Model for Ultimate Carrying Capacity of Medium Voltage Distribution Network Based on Genetic Neural Network

Construction of Measurement Model for Ultimate Carrying Capacity of Medium Voltage Distribution Network Based on Genetic Neural Network

Research on Improved Differential Evolution Particle Swarm Hybrid Optimization Method and Its Application in Camera Calibration

The calibration of cameras plays a critical role in close-range photogrammetry because the precision of calibration has a direct effect on the quality of results. When handling image capture using a camera, traditional swarm intelligence algorithms such as genetic algorithms and particle swarm optimization, in conjunction with Zhang’s calibration method, frequently face difficulties regarding local optima and sluggish convergence. This study presents an enhanced hybrid optimization approach utilizing both the principles of differential evolution and particle swarm optimization, which is then employed in the context of camera calibration. Initially, we establish a measurement model specific to the camera in close-range photogrammetry and determine its interior orientation parameters. Subsequently, employing these parameters as initial values, we perform global optimization and iteration using the improved hybrid optimization algorithm. The effectiveness of the proposed approach is subsequently validated through simulation and comparative experiments. Compared to alternative approaches, the proposed algorithm enhances both the accuracy of camera calibration and the convergence speed. It effectively addresses the issue of other algorithms getting trapped in local optima due to image distortion. These research findings provide theoretical support for practical engineering applications in the field of control theory and optimization to a certain extent.

Design of corporate human capital value measurement model based on binary tree

The measurement process of corporate human capital value ignores the in-depth analysis of the influencing factors of value measurement, which leads to the inaccuracy of the measurement result of corporate human capital value. Therefore, taking an enterprise in Heilongjiang Province as the research object, a measurement model of enterprise human capital value based on binary tree is designed. Establish the corporate human capital value measurement index system, and initially fix the human capital value. Calculate the floating value of human capital under the complete information, and determine the influencing factors of human capital value measurement. The human capital value of enterprise under incomplete information is calculated to complete the construction of the measurement model of human capital value of enterprise based on binary tree. Taking the actual human resource performance score of an enterprise from 2019 to 2022 as an example, the experiment proved that the design model and the traditional measurement model evaluated the enterprise human resource performance, and proved that the measurement model based on binary tree was more accurate than the traditional model, and the final measurement accuracy of the design model reached 93.5%. Compared with the traditional measurement model ahead of 18.5%–10.3%, compared with the current advanced measurement model ahead of 5.2%–5.7%. The evaluation Angle in the research is slightly lacking of practical significance, and further research can carry out further evaluation on income to maximize the practical significance of the research. However, the model is evaluated from the perspective of pricing, but in real life, the value of enterprise human capital is reflected in income distribution. Therefore, if the measurement of human capital value is combined with income distribution in the next step, the research results will be closer to the actual situation and will have more practical significance.

THE EFFECT OF SELF-AWARENESS, SELF-CONTROL, SELF-MOTIVATION, SOCIAL AWARENESS, AND SOCIAL SKILLS ON PERFORMANCE OF MSME WOMEN ENTERPRISES

Purpose: The research aims to measure the relationship between self-awareness, self-control, self-motivation, social awareness, and social skills and business performance of female entrepreneurs. Design/methodology/approach: The total population in this study was 248 businesses. The analysis used in this research used several PLS methods, which included several analyses, including Factor Analysis, Designing a Structural Measurement Model (Inner Model), Designing a Measurement Model (Outer Model), and Hypothesis Testing. Findings: The effect of self-awareness on the performance of MSMEs shows a coefficient value of 0.435. The effect of self-control on the performance of MSMEs shows a coefficient value of 0.054. The effect of self-motivation on the performance of MSMEs shows a coefficient value of 0.273. The effect of social awareness on the performance of MSMEs shows a coefficient value of 0.048, and the effect of social skills on the performance of MSMEs shows a coefficient value of 0.246. Research implications: This research shows that self-awareness, self-control, self-motivation, social awareness, and social skills have a positive and significant effect on MSME performance.

A guide to measuring expert performance in forensic pattern matching.

Decisions in forensic science are often binary. A firearms expert must decide whether a bullet was fired from a particular gun or not. A face comparison expert must decide whether a photograph matches a suspect or not. A fingerprint examiner must decide whether a crime scene fingerprint belongs to a suspect or not. Researchers who study these decisions have therefore quantified expert performance using measurement models derived largely from signal detection theory. Here we demonstrate that the design and measurement choices researchers make can have a dramatic effect on the conclusions drawn about the performance of forensic examiners. We introduce several performance models - proportion correct, diagnosticity ratio, and parametric and non-parametric signal detection measures - and apply them to forensic decisions. We use data from expert and novice fingerprint comparison decisions along with a resampling method to demonstrate how experimental results can change as a function of the task, case materials, and measurement model chosen. We also graphically show how response bias, prevalence, inconclusive responses, floor and ceiling effects, case sampling, and number of trials might affect one's interpretation of expert performance in forensics. Finally, we discuss several considerations for experimental and diagnostic accuracy studies: (1) include an equal number of same-source and different-source trials; (2) record inconclusive responses separately from forced choices; (3) include a control comparison group; (4) counterbalance or randomly sample trials for each participant; and (5) present as many trials to participants as is practical.

A validity test for a multivariate linear measurement error model

A criterion is proposed for testing the hypothesis about the nature of the error variance in the dependent variable in a linear model, which separates correctly and incorrectly specified models. In the former one, only the measurement errors determine the variance (i.e., the dependent variable is correctly explained by the independent ones, up to measurement errors), while the latter model lacks some independent covariates (or has a nonlinear structure). The proposed MEMV (Measurement Error Model Validity) test checks the validity of the model when both dependent and independent covariates are measured with errors. The criterion has an asymptotic character, but numerical simulations outlined approximate boundaries where estimates make sense. A practical example of the test’s implementation is discussed in detail – it shows the test’s ability to detect wrong specifications even in seemingly perfect models. Estimations of the errors due to the omission of the variables in the model are provided in the simulation study. The relation between measurement errors and model specification has not been studied earlier, and the proposed criterion may stimulate future research in this important area.

Assessment of overlapping triple-frequency BDS-3/BDS-2/INS tightly coupled integration model in kinematic surveying

The China BeiDou Navigation Satellite System (BDS-3) is recognized for its advantages compared to BDS-2. However, the enhancement of performance through the simultaneous utilization of BDS-2 and BDS-3 in real-time kinematic (RTK) applications remains insufficiently investigated. Herein, we developed an overlapping triple-frequency (TF) BDS-3/BDS-2/inertial navigation system (INS) tightly coupled (TC) integration model that takes advantage of the BDS-3/BDS-2 overlapping frequencies of B1I/B2b(B2I)/B3I for intersystem combination and INS-assisted positioning. An analytical formula for ambiguity dilution of precision (ADOP) was derived, serving as the foundation for an exploration into how multi-frequency measurements and INS assistance affect ambiguity resolution (AR). A vehicle experiment was conducted in a city to evaluate the performance of the measurement models for various frequencies, available satellites, and INS assistance. Analysis of the double differencing errors of the pseudorange and carrier phases revealed that a robust model in kinematic situations is preferred over static situations. AR ability was assessed regarding ADOP, ratio test, and success rates, and the positioning and attitude determination results were examined. Overall, the characteristics of the ADOP analytic formula and processing results produce a similar conclusion regarding the contribution of multi-frequency, available measurements, and INS assistance on AR; further, they provide reference values for moving measuring system users to implement the optimal model in kinematic situations.

Research on the Shock Wave Overpressure Peak Measurement Method Based on Equilateral Ternary Array.

The measurement process of ground shock wave overpressure is influenced by complex field conditions, leading to notable errors in peak measurements. This study introduces a novel pressure measurement model that utilizes the Rankine-Hugoniot relation and an equilateral ternary array. The research delves into examining the influence of three key parameters (array size, shock wave incidence angle, and velocity) on the precision of pressure measurement through detailed simulations. The accuracy is compared with that of a dual-sensor array under the same conditions. Static explosion tests were conducted using bare charges of 0.3 kg and 3 kg TNT to verify the numerical simulation results. The findings indicate that the equilateral ternary array shock wave pressure measurement method demonstrates a strong anti-interference capability. It effectively reduces the peak overpressure error measured directly by the shock wave pressure sensor from 17.73% to 1.25% in the test environment. Furthermore, this method allows for velocity-based measurement of shock wave overpressure peaks in all propagation direction, with a maximum measurement error of 3.59% for shock wave overpressure peaks ≤ 9.08 MPa.

The effect of government-public relationships on residents’ support in mega sport events: a moderating effect of government crisis response

ABSTRACT The study seeks to investigate the role of host governments’ policy public relations and crisis response strategies in shaping residents’ support for mega sport events. Specifically, the research focuses on the moderation effects of crisis response strategies on the relationships between the government-public relationships (GPR) dimensions (i.e., control mutuality, trust, and satisfaction) and residents’ support. The research targeted residents of Tokyo, a host city for the 2020 Olympic and Paralympic Games. Online surveys were disseminated via an international survey company between 23rd to 29th October 2020, obtaining 500 complete responses. Then, a two-step approach was utilised to ensure the tenability of the hypothesised model: CFA for testing the measurement model and SEM for testing the hypothetical relationships. There was a positive association between perceived control mutuality and satisfaction with residents’ support for the mega sport event. The study reaffirmed the importance of control mutuality and satisfaction as GPR indicators for residents’ support, introducing this concept into the mega sport event context. In addition, the relationship between control mutuality and residents’ support was moderated by their perception of the appropriateness of crisis response. Based on the results, theoretical and practical implications were presented.

Do positive emotions prompt students to be more active? Unraveling the role of hope, pride, and enjoyment in predicting Chinese and Iranian EFL students’ academic engagement

ABSTRACT Drawing on the control-value theory, this cross-cultural research inspected the association between positive activating emotions, namely pride, hope, and enjoyment, and academic engagement among Chinese and Iranian EFL students. This study also looked into the role of these positive emotions in predicting Chinese and Iranian students’ academic engagement. Using a maximum variation sampling technique, 346 Chinese and 353 Iranian EFL students were chosen from various colleges and universities. Afterward, an online survey was sent to participants via email and common messaging apps. Following that, structural equation modelling and confirmatory factor analysis were conducted through AMOS software to analyse participants’ answers. The results obtained from CFA and SEM indicated that Chinese and Iranian students’ academic engagement is closely related to their feelings of pride, hope, and enjoyment. The results also demonstrated that pride, hope, and enjoyment can meaningfully and favourably predict Chinese and Iranian students’ academic engagement. Interestingly, no significant difference was discovered between the Chinese and Iranian measurement models. These outcomes add to the existing body of literature on the interplay between positive activating emotions and students’ academic behaviours.

Nurses' organizational climate of perceived organizational support and its relationships with psychosocial working conditions and psychological contracts: A longitudinal questionnaire study.

The aim of the present study was to investigate the longitudinal relationships between nurses' organizational climate of perceived organizational support (POS-climate) and their psychosocial working conditions and psychological contracts. A two-wave longitudinal cohort questionnaire study was carried out among registered nurses employed within six hospitals in two regions in Sweden (n = 711). Two cross-lagged panel models were tested after ensuring scalar factorial invariance of the measurement models. The first model investigated longitudinal relationships between psychosocial working conditions and the POS-climate, while the second model investigated such relationships between the psychological contracts and the POS-climate. The results indicated that influence at work and an ideology-infused psychological contract had positive effects on the nurses' POS-climate. These results highlight the importance of providing nurses with such influence, and of a shared ideology within the entire health-care organization, centered on the ethical values of the health-care professions.

The role of destination personality in the relationship between destination image and behavioral intention among ski tourists

PurposeThe aim of this study was (1) to investigate the relationship between destination image (DI), destination personality (DP) and behavioral intention (BI) in the context of ski tourism and (2) especially the role of DP in the relationship between DI and BI among ski tourists.Design/methodology/approachWe collected data using WJX.CN (N = 400) to test the hypothesized model. Confirmatory factor analysis (CFA) was used to examine the psychometric properties of the measurement model and partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses.FindingsThe results show that DI directly affects DP and partially affects BI, while DP directly affects ski tourists' BI. In addition, the indirect effect of DP between affective image and BI was significant, showing full mediation, and the indirect effect of DP between cognitive image and BI was significant, showing a partial mediation effect.Originality/valueThe findings enrich the ski tourism literature, contribute to the development of ski tourism in destination cities and the strategic marketing of ski resorts and provide recommendations for ski tourism researchers and marketers.

Personal Competencies for Work–Family Integration and Its Relationship with Productivity and Comprehensive Health in Salaried Professionals

Work–Family Integration (WFI) is the decision-making process that enables an individual to effectively balance work, family, and personal responsibilities, generating a level of personal satisfaction aligned with the management of these demands. This research aims to explore the potential links between personal competencies facilitating work and family integration (WFI Competencies), employer-provided support (WFI Support), perceived satisfaction in role integration (WFI Satisfaction), and their association with organizational performance indicators and the overall health of professionals in dependent employment. Data were obtained via an online questionnaire administered to 270 professionals possessing a university education or higher, employed in public or private organizations spanning various sectors in Venezuela. The data were subsequently analyzed utilizing Structural Equation Modeling (SEM). The study was divided into two main parts: the factorial analysis (both exploratory and confirmatory) of measurement models and the analysis of the relationships and modeling inherent to the structural model. Initially, two diagnostic instruments were developed, one for WFI Competencies and another for WFI Indicators; though applied simultaneously, their structuring and validation were conducted separately. In the subsequent phase, conceptual models for structural analysis were defined. A positive relationship was observed between WFI Support and WFI Satisfaction, corroborating findings from previous research. The relationships between WFI Competencies and Satisfaction led to insights into the necessity of training to strengthen the personal decision-making process under the dual pressures of work and family roles. Future longitudinal studies could elucidate the effects of relationships within such programs on WFI Satisfaction. Concerning organizational indicators, this study found that WFI Satisfaction positively correlates with organizational commitment, enhancing work productivity and mitigating negative health effects. This research presents a model that could be replicated in other countries and with various sample types, facilitating comparative analyses that enrich the body of knowledge on this subject.

Social competence in autism: A structural equation modeling approach.

Autistic individuals present with difficulties in social competence (e.g., navigating social interactions and fostering relationships). Clinical interventions widely target social cognition and social behavior, but there is inconsistent understanding of the underlying components of social competence. The present study used structural equation modeling to examine social cognition and social behavior and explore the relationship between these latent constructs. Autistic youth (ages 10-17; n = 219) and their caregivers participated in this study. Constructs of social cognition and social behavior were captured using caregiver-report and self-report rating scales, as well as observational measures and direct clinical assessments (e.g., NEPSY-II). Measurement models of social cognition and social behavior demonstrated adequate to good fit. Correlational models demonstrated adequate to poor fit, indicating latent constructs of social cognition and social behavior are not closely related in autistic youth. Exploratory examination of a subsample of male youth (n = 157) evidenced improved model fit of social behavior, specifically. Findings tease apart social cognition and social behavior as cohesive and separable constructs; results do not support a structural relationship between social cognition and social behavior. Noted treatment implications include consideration of how targeting social cognition and social behavior together or separately may support autistic youth's progress toward reaching their identified therapeutic goals and supporting their self-directed social development.

Green strategic leadership capability: Construct development and measurement validation

Green strategic leadership capability (GSLC) has emerged as a major study topic in strategic management in light of urgent global crises such as climate change. There is, however, a paucity of theoretically conceptualised and empirically validated measurement models evaluating the various leadership capabilities of top managers. GSLC implies that top managers endorse green management practices in organisational operations to minimise their environmental impact. Our research conceptualises GSLC from a natural-resource-based view by considering top managers’ capabilities to develop GSLC measurement models. We used a multi-study, multi-method approach to develop GSLC multidimensional scales using field interviews, thematic analysis and bulk surveys. GSLC is operationalised as a hierarchical and multidimensional scale consisting of three core dimensions, namely green foresight capability, green adaptive capability and green absorptive capability, along with nine subdimensions – resulting in 31 measurement items. The developed and validated scales may be applied to extend the theory and practice of green management, while offering a valuable source for organisations to assess their GSLC and identify and prioritise areas for green growth.

Why teachers do (or do not) implement recommended teaching practices? An application of the theory of planned behavior.

In Luxembourg, competency-based practices (CBP), differentiated instruction (DI), and formative assessment (FA) have been imposed by the 2009 school law. Referring to the Theory of Planned Behavior (TPB), this study examined factors influencing the implementation of these practices in classrooms. Teachers participated in an online survey assessing their attitudes, subjective norm, perception of behavioral control, intention, and pedagogical practices regarding CBP, DI, or FA. Measurement models were used in structural equation models testing the TPB. If the main relationships postulated by the theory were confirmed, some inconstancies were observed depending on the targeted practices. Structural equation TPB models controlling for gender, experience, teaching level, and socio-economic level of the school population explained between 20 and 45% of the variance in teachers' practices, and between 65 and 75% of the variance in teachers' intention to use these practices. The relevance of the TPB for studying teaching practices and implications for professional training are discussed.

Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning.

Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models.