First-order Variables Research Articles

University or Personal Sustainability— What Shapes Sustainable Entrepreneurship Intentions of Students?

The intentions and behaviour of entrepreneurs have been well researched and explored and now the limelight has shifted to sustainable entrepreneurship. The present study aims to examine the personal and institutional sustainability inclination that may help students develop their intent for sustainable entrepreneurship. Quantitative analysis with partial least squares structural equation modelling (PLS-SEM) was performed with 422 samples collected from students at Indian universities. The model tested the first-order variables for university and personal sustainability and further the effect on the intentions to start sustainable entrepreneurship. The first-order variables of university sustainability are education on sustainability and campus sustainability and the students’ personal consumption profile and moral obligations for the first-order variables of personal sustainability. The findings indicated that the tendency towards sustainable entrepreneurship was more strongly influenced by personal than university sustainability inclination. Further, the results of the importance–performance map analysis (IPMA) highlighted the importance of personal sustainability as well as further advancements in ethical standards and campus sustainability to nurture students’ intentions for sustainable entrepreneurship. The findings of this study offer policymakers, researchers and higher education institutes insights to establish campuses that contribute to sustainability and reframe the educational curriculum to focus on students’ innate propensities to engage in sustainable endeavours. This article outlines the pioneering research effort into the personal and university sustainability inclination in consideration for shaping sustainable entrepreneurship intentions among students.

Numerical simulation and coupling mechanism study of acoustic-inertial micromixer

Acoustic-inertial micromixers exhibit a wider operating range and higher mixing efficiency than conventional mixers. We have carried out experimental studies and numerical simulations of acoustic-inertial micromixers. It is found that the acoustic streaming will be changed in morphology and intensity by the background flow, and the coupling mechanism between them is obtained. Specifically, we developed the numerical model of acoustic-inertial micromixer by using perturbation theory and the Generalized Lagrangian Mean (GLM) theory. By dividing the flow variables into zero-order, first-order and second-order variables, we obtained the control equations representing background flow, acoustic response and time-averaged streaming flow. Then realized the decoupling analysis of the acoustic-inertial coupling phenomenon. It is found that in terms of acoustic streaming flow, the intensity extreme of it increases as the background flow rate increases. The flow velocity at 5Vpp can reach 0.46 m/s under 500 μL/min, which is 30 times for the same condition under 10 μL/min. However, the streaming morphology no longer behaves as a symmetric vortex distributed on both sides of the sharp edge, which is stretched and twisted by the background flow and structure at a high flow rate. The percentage of acoustic contribution at different working conditions is also investigated quantitatively, and the acoustic contribution index (ACI) will generally decrease to less than 0.5 after the background flow dominates the flow field. The results of the study can provide a reference for the design of acoustic-inertial micromixers with larger flow ranges and higher efficiencies.

Millet yield estimations in Senegal: Unveiling the power of regional water stress analysis and advanced predictive modeling

Crop yield is usually affected by impending weather, climate conditions, and human interventions like irrigation. Hence, the prompt detection of regions experiencing water scarcity can aid in implementing effective mitigation strategies. Our study utilized a data-driven approach to compute a Water Demand Index (WDI), which incorporates crucial first-order geophysical variables like ambient temperature, vegetation status, and soil moisture, to identify water-stressed fields in Senegal’s agricultural regions during the millet planting, growing, and harvesting periods. We have also explored various scenarios for enhancing the accuracy of millet yield prediction by incorporating other drought indices, soil characteristics, and a bias correction factor. The novel aspects of this research include the development of regional crop yield predictive models based on sub-seasonal and unaggregated crop information to reflect local variances. To optimize the hyperparameters of machine learning (ML) models, various techniques were utilized. Meanwhile, the performance of these ML models was evaluated using a nested cross-validation approach. The outcomes of the analysis demonstrate that the Random Forest Regressor model exhibits superior predictive performance. The results imply that a holistic approach, encompassing diverse environmental factors and crop growth stages, could result in more precise and dependable millet yield predictions. These results mark a leap forward in crop yield prediction for data-poor regions, paving the way for more accurate and efficient agricultural management. By emphasizing the critical role of detailed, spatio-temporal data, this work substantially enhances model precision, informing targeted strategies to boost yields. This advancement stands to serve producers and consumers alike, fostering sustainable, high-yield agriculture.

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Exploration, management, and conservation of groundwater resources are critical stages toward potable water supply, driven by an expanding populace and the threat of a new norm posed by the distinctive coronavirus (COVID-19) pandemic. An in-depth assessment of the potential of groundwater reserves and susceptibility, using a multi-criteria evaluation, is required to aid in the planning of exploration programs for groundwater well location. Thirty (30) vertical electrical soundings (VES) were collected in Okerenkoko, Warri-Southwest, Delta State, to assess groundwater potential and vulnerability indicators. The VES data were used to obtain the first-order geoelectric variables, which were further exploited to calculate the geo-hydraulic parameters (hydraulic conductivity and transmissivity) and the vulnerability indices of the aquifer. For aquifer vulnerability appraisal, the AVI (aquifer vulnerability index), GOD (groundwater occurrence, overlying lithology, and depth to the aquifer), and GLSI (geoelectric layer susceptibility index) models were used. The groundwater characteristics in the area were evaluated using the aquifer resistivity, thickness, transmissivity and coefficient of anisotropy values of the aquifer layers defined from VES 1-30. The results show that aquifer layers with low resistivity favor more saturation due to immense porosity and therefore have greater groundwater potential than aquifers with high resistivity. The geoelectric structures defined by VES 1, 2 and 4 were consistent in their groundwater potential and yield judging from the multi-criteria assessments. The estimation of AVI, GOD, and GLSI models for aquifer threat assessment was facilitated by the multi-criteria evaluation of vulnerability indices utilizing hydro-geophysical parameters and index-based approaches. The models depend on the symbiotic effects of geologic array and thickness as the basis for the magnitude of conservation imparted to any particular aquifer involved. The AVI model map depicts that most of the VES locations were rated high (C between 1 and 2) to extremely high (C < 1), indicating that the aquifers at these locations are vulnerable to pollution. However, the extent of vulnerability observed in the GOD model is less than in the AVI model, as GOD accords much more inclination to the inherent properties of geologic entities. The GOD model map categorized the vulnerability index ratings in the area as negligible (0.0-0.1), low (0.1-0.3) and moderate (0.3-0.5), with most VES locations ranked low to moderate, which indicates that these locations are susceptible to vulnerability. In the GLSI model, individual overlying layer thicknesses were prioritized. The GLSI model map shows that the vulnerability index ratings in the area are ranked as moderate (2.00-2.99), high (3.00-3.99) and extremely high (≥ 4.00) with most of the VES locations ranked moderate to high with the exception of VES 27, which ranked extremely high in both AVI and GLSI indices. By correlating the results of vulnerability index valuation for the AVI, GOD and GLSI models, more correlation was observed between the AVI and GLSI models. These findings validate the adoption of a multi-criteria evaluation methodology for groundwater potential and aquifer vulnerability studies and are strongly recommended as practical criteria for locating subsurface aquifers and their protective measures for groundwater prospect development planning and management.

A hierarchical component model for sustainable performance measurement of maintenance practices: A fourth-order PLS-SEM approach

Despite the growing recognition of the significant role played by effective maintenance practices in pursuing sustainability goals, a rigorous and validated method to assess and monitor its activities’ impacts on the triple-bottom-line is still lacking. To address this gap, this paper proposes a fourth-order Hierarchical Component Model (HCM) for sustainable performance measurement of maintenance practices based on PLS-SEM approach. To validate the model, Oil & Gas industry is surveyed, where a total of 207 responses are obtained from maintenance experts in the Gulf region. The results of the study support the indicators’ reliability as well as the first-order constructs’ internal consistency, convergent validity and discriminant validity. An embedded multistage approach for the higher-order constructs measurement models evaluation reveals that they exhibit satisfactory levels of quality. The structural model evaluation supports the significance of the hypothesized paths, a strong degree of predictive relevance, and the hypothesized mediation relationships proved significant. Finally, the total effect analysis demonstrates that “Energy Use” has the highest effect among first-order variables followed by “Material use”, ‘Costs & Returns”, “Emission & Pollution”, and “Effluents & Waste”. Furthermore, “Energy Use” related indicators have the highest weights among all indicators. This study provides empirical evidence on the causal background of the proposed model’s implicated elements on sustainable performance of maintenance. The findings offer guidance to practitioners in this area and useful insights to decision-makers, as it enables them to determine the inherent impacts of the model components on the overall performance and allocate resources efficiently to increase the maintenance sustainability level.

Explore, eat and revisit: does local food consumption value influence the destination’s food image?

PurposeThis research investigates how foreign tourists' revisit intentions are influenced by their local food consumption (LFC) value by emphasizing their attitude towards the local food and the corresponding destination food image. It will also reveal the foreign tourists' food consumption value and explore its influences on the destination's food image.Design/methodology/approachThe data collection was performed from 433 foreign tourists who visited Delhi, India, using a structured survey instrument and chief constructs were measured as the first-order reflective variables. The 39 items associated with the LFC value underwent both exploratory and confirmatory evaluations. The authors employed partial least square structural equation modeling (PLS-SEM). The model's discriminant and convergent validity, consistency and overall fit were evaluated using confirmatory factor analysis (CFA).FindingsThe findings revealed that “tourists' attitudes and behaviors toward the local cuisine” had a significant and positive influence on their “intention to visit/revisit” and “intention to recommend”. Also, “destination food image” significantly and positively influenced the “intention of tourist to visit/revisit”; however, it negatively and insignificantly influenced the “intention of tourists to recommend”.Research limitations/implicationsThis research used five variables related to LFC—quality, health/nutrition, emotion, prestige and price—that impact tourists' attitudes and behaviors toward local foods in Delhi. However, in addition to these constructs, other factors or constructs may be involved that could affect the tourists' attitudes and behaviors. Future studies might explore and include these constructs to provide a more comprehensive image of Delhi's LFC value.Originality/valueUnderstanding tourists' food-linked behaviors is critical for effective market conduct. However, the interrelations between travelers' destination food image, LFC value, their perceptions of the local cuisine and behavioral intentions are still unknown, and this will be one of the first attempts to discuss these behaviors.

Active intensity vortex and stagnation point singularities in a shallow underwater waveguide.

Vector acoustic properties of a narrowband acoustic field are observed as a function of range from a source towed in waters of depth 77 m on the New England Mud Patch. At the source frequency (43 Hz), the waveguide supported three trapped modes, with mode 2 weakly excited owing to the towed source depth. The receiving sensor was positioned 1.45 m above the seafloor with a sampling range aperture of 2500 m. The vector acoustics observations enabled study of vortex regions that encompass two singular points for active acoustic intensity: the vortex point, which is co-located with a dislocation, and stagnation point. Interpretative modeling, based on the normal modes and using a geoacoustic model consistent with those emerging from studies conducted at this location, is in agreement with these measurements. Model-data comparisons were based on the first-order variables of acoustic pressure and velocity along with inverse Hankel transforms, which yield normalized horizontal wavenumber spectra, and second-order variables in the form of horizontal and vertical intensity as well as non-dimensional intensity-based ratios. These measures provide a degree of observational confirmation of some vortex region properties. Both observations and modeling point to a gradual deepening of such regions with increasing range owing to sediment attenuation.

Validation of the Arabic version of the cyberchondria severity scale 12 items (CSS-12-Ar) among a sample of Lebanese adults

BackgroundTo the best of our knowledge, the Cyberchondria Severity Scale-12 (CSS-12) has not been translated into Arabic; therefore, our objective was to assess the psychometric properties of the Arabic version of the CSS (CSS-12-Ar) among a sample of Lebanese adults.MethodsParticipants were enrolled in January 2021. A confirmatory factor analysis (CFA) was carried out using the MPlus software v.7.2, reporting several goodness-of-fit indicators: Relative Chi-square (χ2/df), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and Tucker Lewis Index (TLI). To evaluate measurement invariance across gender, we conducted higher-order multiple group confirmatory analysis using lavaan software.Results449 participants enrolled in this study (mean age: 24.34 ± 8.22 years, 70.6% females). Since the correlations between the four-factor model were very high (r > 0.8), we ran the higher-order CFA in which all first-order latent variables were loading a general factor. The analyzed model was well-fitted to the data χ2(50) = 173.34; p < 0.001; CFI = 0.926; RMSEA = 0.074 [0.062, 0.086]. The Cronbach’s alpha values were good for the total score (0.92), as well as for excessiveness (0.80), distress (0.77), reassurance (0.81) and compulsion (0.76). The results provided evidence of full scalar invariance across gender. The comparison of latent mean scores revealed no significant differences across gender, in either the cyberchondria total score or its facets. The CSS-12 score was positively associated with anxiety (r = 0.10; p = 0.003) (convergent validity), OCD (r = 0.11; p = 0.016) and stress (r = 0.35; p < 0.001) (concurrent validity).ConclusionThe CSS-12-Ar was deemed a suitable scale to measure the severity of cyberchondria among Lebanese university students. We hope that researchers and clinicians can benefit now from this scale.

Understanding the adoption of electric motorcycles among road users in Vietnam

Motorcycles with internal combustion engines using fossil fuels account for the majority of traffic flow in major Asian cities. The increase in the number of conventional motorcycles using a gas engine is one of the main causes leading to air and noise pollution in comparison with other lower-emitting transport modes (e.g., walking, cycling, and public transport). Thus, many Asian countries have promoted the adoption of electric motorcycles to significantly reduce air pollution and non-renewable energy consumption as a solution for motorcycles using internal combustion engines. However, the application of electric motorcycles still faces many limitations and challenges. This study explores factors influencing the adoption of electric motorcycles in Vietnam based on the extention of TAM model. Based on 751 valid responses in Ho Chi Minh and Da Nang cities, Vietnam, a structural equation model (SEM) with first-order and second-order latent variables is proposed to analyse the collected data. The results indicate that perceived of usefulness is the most significant positive effect on the adoption of electric motorcycles. In contrast, perceived risk is found to be a barrier of the adoption intention. In addition, the results of multigroup analysis show that age moderates the correlation between prceived easy to use and the adoption intention. Similarly, the correlation between perceived risk and the adoption of electric motorcycles is influenced by the difference in monthly income. Several policies and governance implications are also discussed in this study.

The Perspective of Community Members in the Assessment of the Social Value Generated by Non-profit and Voluntary Organizations

This study aimed at developing a scale to measure Social Added Value (SAV) according to the perspective of community members, building on the relational framework and complementing the work of Mannarini et al. (Voluntas: Int J Voluntary Nonprofit Organ 29(6):1315–1329, 2018). The study was conducted on 720 Italian citizens who were not directly involved in Nonprofit and Voluntary Organizations (NPVOs) as active members to examine the statistical validity and psychometric properties of the SAV scale. Confirmatory factor analyses showed that good and satisfactory fit indexes were obtained by a third-order factor model saturated by two second-order variables (contribution to community and quality of external relationships) and eight first-order variables (services and tasks, vision, social responsibility, control mutuality, communality relationship, trust, commitment, capacity to build community connections). Additional analyses revealed that both perceived NPVOs target values and sense of belonging to the community were positively associated with SAV. Theoretical, methodological and practical implications of the findings are discussed.

Factors Influencing Adoption of IoT and Its Impact on CRM in Banks: Examining the Moderating Role of Gender, Age, and Bank Ownership Type

Internet is becoming a part of our lifestyle; however, the usage rate and application of the Internet are disparate in different parts of the world. In many emerging countries, the Internet is yet to penetrate ordinary households. The present study focuses on how IoT adoption impacts the banks’ customer relationship management (CRM) in an emerging market context. Furthermore, the moderating roles of gender, age, and bank ownership type on the relationship between the adoption of IoT and CRM have also been tested. Cost, convenience, social context, and privacy were studied as the predicting variables of IoT adoption, while IoT adoption was investigated as the antecedent of CRM. The CRM variable has been operationalized as a second-order latent construct consisting of three first-order latent variables: responsiveness, satisfaction, and assurance. A cross-sectional, non-probability-based survey was conducted from 467 bank customers of three public and three private sector banks in Aligarh city of India. Two CFA models were run to ensure reliability, validity, and model fit. Hypotheses were tested using structural equation modeling (SEM) on AMOS software, while PROCESS Macro v4.0 by Hayes (2009) was used to test the moderating effect of gender on the relationship between IoT adoption and CRM. The results indicate that cost, convenience, social context, and privacy are positively influencing IoT adoption, which in turn positively affects CRM. Gender and age were found to have a negative moderation effect on the path between IoT adoption and CRM, while bank ownership type positively moderated this link.

Study of Factors Influencing Sustainable Supply Chain Management (SSCM) in China

Sustainability in the transportation and supply chain industry has been a concern for decades. Conversations have been ongoing about how to reduce the carbon footprint, incorporate electric vehicles into fleets, and adopt alternative fuels. Now, however, we’re at a crossroad. The global climate crisis has reached a tipping point, highlighting transportation’s contribution to the problem in the boardrooms of most corporations. And for good reason. According to the U.S. Environmental Protection Agency, transportation is responsible for nearly a third (29%) of all greenhouse gas emissions. While passenger vehicles make up a significant portion of that number, ships, trains, planes and freight trucks are also in the mix.The purpose of this research is to study factors influencing Sustainable Supply Chain Management (SSCM) in China. These factors include seven first-order variables: independent variables: Carbon Footprint (CF), Organizational Practices (OP), Transportation Model (TM), Environmentally Responsible Packages (EP), Alternative Energy (AE), Partnership Initiative (PI), and Technology Development (TD); two second-order variables: Environmental, Social, and Governance (ESG) and Operating Model (OPER) and one dependent variable: Sustainable Supply Chain Management (SSCM). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .039 (&lt;.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .903 (&gt;.90), the model seems to fit well according to the descriptive measures of fit. Moreover, CFI, which is incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), its value equals .956 indicating an acceptable fit.

Study of Factors Influencing Digital Transformation Process in Bangkok

Most digital transformations fail. Various studies from academics, consultants, and analysts indicate that the rate of digital transformations failing to meet their original objectives ranges from 70% to 95%, with an average at 87.5%. Yet, digital transformation has been at the top of corporate agendas for at least a decade and shows no sign of slowing down. On the contrary, many commentators have highlighted the accelerating impact of the Covid-19 period on digital transformation. Digital transformation is something no management team should attempt alone. It takes deep into reality and a sense of ownership among people across the enterprise to make transformation a reality.The purpose of this research is to study factors influencing digital transformation process in Bangkok, Thailand. These factors include seven first-order independent variables: Leadership (LD), Employee (EP), Culture (CT), Work Environment (WE), Mindset (MS), Organizational Friction (OF), Management of Transformation (MT), and Talent Acquisition (TA); three second-order variables: Leadership &amp; Motivation (LDM), People (PPL), and Workplace Culture (WPC) and one dependent variable: Digital Transformation (DT). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .04 (&lt;.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .902 (&gt;.90), the model seems to fit well according to the descriptive measures of fit. Moreover, CFI, which is incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), its value equals .903 indicating an acceptable fit.More importantly, Talent Acquisition (TA), Leadership &amp; Motivation (LDM), and Workplace Culture (WPC) seem to have significant effects on Digital Transformation (DT) process due to their p-values are all less than .05. That means if corporates focus more on acquiring new talent and at the same time improve corporate leadership and motivation and workplace culture, they will be more likely to be successful in digital transformation which is necessary condition for all organizations to be competitively sustainable going forward.

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MP50-15 TESTICULAR RADIOMICS CORRELATED WITH PATHOLOGY AT TIME OF POST-CHEMOTHERAPY RETROPERITONEAL LYMPH NODE DISSECTION FOR NON-SEMINOMATOUS GERM CELL TUMOR

You have accessJournal of UrologyCME1 Apr 2023MP50-15 TESTICULAR RADIOMICS CORRELATED WITH PATHOLOGY AT TIME OF POST-CHEMOTHERAPY RETROPERITONEAL LYMPH NODE DISSECTION FOR NON-SEMINOMATOUS GERM CELL TUMOR Nikit Venishetty, Jacob Taylor, Yin Xi, Jeffrey Howard, Yeeseng Ng, and Aditya Bagrodia Nikit VenishettyNikit Venishetty More articles by this author , Jacob TaylorJacob Taylor More articles by this author , Yin XiYin Xi More articles by this author , Jeffrey HowardJeffrey Howard More articles by this author , Yeeseng NgYeeseng Ng More articles by this author , and Aditya BagrodiaAditya Bagrodia More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003298.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Testicular germ cell tumors are the most commonly diagnosed malignancy in men aged 20 to 39 years old, as a third of patients will have metastatic disease on presentation. Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) is performed to treat patients with metastatic non-seminomatous germ cell tumor (NSGCT). Although 50% of patients will viable GCT or teratoma, there is no accurate measurement to predict pre-operatively which patients will have residual disease after chemotherapy. Our aim was to use testicular radiomics data, a method that collects quantitative tumor imaging data from conventional imaging, to predict pathology after PC-RPLND. METHODS: Radiomics, clinical, and pathologic data were extracted from 45 patients with metastatic NSGCT undergoing PC-RPLND. An abdominal radiologist drew regions of interest (ROI) around metastatic notes, and PyRadiomics was used to extract first order, shape, and second order statistics from each ROI. T-Tests were performed to differentiate radiomics features between binary pathology type. P values were adjusted using the BH method to control false discovery rate. Python 3.7 and R 4.2.0 used for additional statistical analyses. RESULTS: There were 16 clinical stage II patients and 28 clinical stage III. 42% of patients had necrosis on PC-RPLND pathology, while 53% and 4% patients had teratoma and viable germ cell tumor, respectively. First order statistics mean, median, 90th percentile, and root mean squares were significant (Table 1). No significant differences was observed in other first order, shape, or texture features. We also show that the first order statistics above as significant, while the other radiomic statistics were not significant. CONCLUSIONS: Testicular radiomics is a tool that can help predict which patients with metastatic NSGCT are at higher risk of persistent disease after chemotherapy. We found relatively few first-order radiomic variables that were correlated with post-operative pathology. Results may have also been less predictive given small number of patients (4%) with residual GCT at time of RPLND. Further precision of extraction of the radiomics data may improve clinical decision-making in patients with metastatic NSGCT after chemotherapy prior to RPLND. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e690 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nikit Venishetty More articles by this author Jacob Taylor More articles by this author Yin Xi More articles by this author Jeffrey Howard More articles by this author Yeeseng Ng More articles by this author Aditya Bagrodia More articles by this author Expand All Advertisement PDF downloadLoading ...

Collective teacher culture and school goal structure: Associations with teacher self-efficacy and engagement

One purpose of this study was to test a model of a collective teacher culture (CTC) proposed by Skaalvik and Skaalvik (Skaalvik and Skaalvik, Social Psychology of Education 24:1389–1406, 2021). In this model, a second-order CTC variable was indicated by four first-order variables: positive and supportive social relations with colleagues, collective teacher efficacy, shared goals and values, and value consonance. A second purpose was to test how a CTC was associated with teachers’ perceptions of the school goal structure (learning and performance goal structures). A third purpose was to explore relations between the two dimensions of the school goal structure, CTC, teacher self-efficacy, and teacher engagement. Participants in the study were 1145 teachers in elementary school, middle school, and high school. The data were analyzed by means of confirmatory factor analyses and SEM analysis. The factor analyses supported the proposed model and revealed that a CTC was positively and strongly associated with a learning goal structure and positively and moderately associated with both teacher self-efficacy and teacher engagement. In contrast, a CTC was negatively associated with a performance goal structure. A learning goal structure was also positively associated with teacher self-efficacy and engagement. In the SEM model, CTC partly mediated the associations between a learning goal structure and teacher self-efficacy and engagement.

Comparing a Fuzzy Hybrid Approach with Invariant MGCFA to Study National Identity

National identity studies diverge on several issues, such as the number of factors and their respective items’ adscription. Multi-Group Confirmatory Factor Analysis (MGCFA) is the standard method applied to cross-national datasets. Differences between groups can be the result of measurement artefacts. We argue that these problems can be better addressed by an alternative approach that builds a synthetic indicator named Relative National Identity Synthetic Indicator (RNISI), based on a Fuzzy Hybrid Analysis (FHA). The study aims to shed some light on the study of the latent variable national identity by comparing two methodologies: the classic method most often used (MGCFA) and the Fuzzy-Hybrid Approach, which, to our knowledge, has not been previously applied. This empirical study was based on a dataset from across ten countries using two waves (2003 and 2013) of the International Social Survey Programme (ISSP). The FHA results were compared with those obtained by two MGCFA models in which national identity was built as a second-order construct that depends on the ethnic, ancestry and civic first-order latent variables. The comparison lets us conclude that FHA can be considered a valid tool to measure the national identity by groups, and to provide additional information in form of elasticity figures. These figures can be employed to analyse the indicator’s sensitivity by group and for each of the items included in the national identity construct.

Factors influencing Consumer’s Driving Behavior of Most Preferred Luxury Car Brand in Bangkok Thailand

The luxury car market in Thailand is expected to keep expanding with double-digit growth this year (2022), despite the impact of lockdown measures implemented to contain the spread of COVID-19. Demand for cars and the purchasing power of prospective buyers remain strong in these segments, making Thailand a target market for upper-end car manufacturers. Revenue in the Luxury Cars market segment is projected to reach US$201m in 2022. The purpose of this research is to study factors influencing consumer’s driving behavior of his/her most preferred luxury car brand in Bangkok, Thailand. These factors include six independent first-order variables: Social Status (SS), Social Pressure (SP), Brand Personality (BP), Brand Image (BI), Brand Trust (BT), and Product (PD); three second-order latent variables: Product (PD), Brand (BRN) and Social (SOC) and one dependent variable: Car Usage Behavior (CU). 400 sample were collected using electronic questionnaire through social media. We used second-order Structural Equation Models (SEM) for data analysis. The result shows For data analysis, we employed second-order Structural Equation Models (SEM). The result shows that the RMSEA for this model is.049 (.05), which strongly implies a "near fit," and the Goodness of Fit Index (GFI) value is.968 (&gt;. 90), indicating that the model fits well according to the descriptive measures of fit. CFI and TLI, which are incremental fit indices that compare the fit of our hypothesized model to that of a baseline model (i.e., the model with the worst fit), both have values more than.90 (CFI =.979, TLI =.900), indicating an acceptable fit. More importantly the second-order latent factors included in the model i.e. Product (PD) and Social (SOC) seem to have significant effects on influencing consumer’s driving behavior of his/her most preferred luxury car brand in Bangkok, Thailand due to their p-values are all both less than .05.

Mobile learning determinants that influence Indian university students' learning satisfaction during the COVID-19 pandemic

Due to the COVID-19 pandemic, the whole world went under strict lockdown, including educational institutions. This led to the quick reshaping of educational systems to provide uninterrupted education to the students. Preferably, both teachers and students switched from physical classrooms to online classrooms. This overnight change brought numerous challenges for a country like India. But the authors of this study see it as an opportunity and aim to explore mobile learning (m-learning) determinants that influence Indian university students' learning needs during the COVID-19. For this, the data were gathered using a web-based questionnaire from 557 students of seven different universities (both public and private) in India. Next, the data were quantitatively analysed using reliability analysis, confirmatory factor analysis, and multiple regression analysis. The results show that out of three first-order m-learning variables, only two (system and service quality items) have a positive impact on students' learning satisfaction in the Indian context. In the end, the implications of the study in the adoption of m-learning at different Indian universities have been discussed.

비동거 부모-자녀가구 간 결속 유형 분석: 일반화 구조방정식 모형(GSEM) 활용

This study aims to identify the types of intergenerational solidarity between parents and adult children and examine the significant factors of solidarity types. To this end, this study employs data from the 7th (2018) of Korean Longitudinal Study of Aging with 2,028 parents living apart from their children. The provision and reception of functional solidarity between parents and adult children are set as the first-order latent variables, associational solidarity and functional solidarity as the second-order latent variables, and intergenerational solidarity as the third-order latent variable. Four types are derived using a Generalized Structural Equations Model (GSEM): ‘Disconnected Type’ with low associational and functional solidarity, ‘Associational Solidarity Type’ with only minimal high-level associational solidarity, ‘Loose Solidarity Type’ with a medium level of associational and functional solidarity, and ‘Strong Solidarity Type’ with both high level of associational and functional solidarity. As a result of analyzing the influence factors focusing on the ‘Strong Solidarity Type’ - the highest intergenerational solidarity – the geographical proximity between parents and children, the income of parent households, and the residential areas are found to be important determinants. From these results, policy implications are presented, and suggestions are put forth for follow-up research.