The hierarchical construction model (HCM) may be used to minimize colinear formative indicators while increasing the statistical power of content-specific constructs. However, the present research discovered a strong correlation between intrinsic and extrinsic motivation and job satisfaction, indicating a lack of discriminating validity that limits the use of HCM. Thus, this research condensed data on job satisfaction for university staff by comparing a consistent partial least square (PLSc) model to a composite model without utilizing higher-order constructs. The sample consisted of 392 individuals working in a Thai university with a total of 1,042 staff. The results show that the composite model performs better than a consistent partial least square, generating bias. Intrinsic motivation is both a direct and indirect effect on job satisfaction. Extrinsic motivation is a complementary model mediator effect. The limitation of the study is an inherent relation between intrinsic and extrinsic motivation and indicators of job satisfaction, which can cause a common factor model bias. Further studies where the partial least square structural equation model (PLS-SEM) is compared early with the covariance-based structural equation model (CB-SEM) are needed, particularly studies using composite indicators. PLS-SEM can now be used to measure both confirmatory composite analysis and confirmatory factor analysis, while CB-SEM can only estimate confirmatory factor analysis. The method of condensing data may help eliminate discriminant validity issues.
A university’s primary duty is to provide essential skills to help students work well after graduation. The quality of a university’s students depends on the university’s value proposition. In the past, Rajabhat University’s value proposition may not have been sufficiently constructed with co-creation. Although co-creation may now be more emphasized, it is still insufficient. The primary objective of this study is to use a composite model based on the structural equation model of students’ experiences to assess the impacts of co-creation, student satisfaction, loyalty, and university image. The data were collected from a population of 500 students enrolled at an anonymous Rajabhat University using a questionnaire. The sample population comprised 125 students, randomly selected from four classes of the four years of education. The model used a reflective-formative type two-stage approach while the algorithm was composite. This study used the third analysis to form a three-level model of co-creation. The second-order results showed that coproduction was positively related to university image and student satisfaction. Value-in-use was positively associated with university image, student satisfaction, and loyalty. The third-order constructs showed that value co-creation was positively related to university image, student satisfaction, and loyalty.
The current study investigates the effect of a composite model of the marketing mix and brand equity on motorcycle purchase decisions. For second-order, the marketing mix and brand equity were constructed using a disjointed two-state approach and a formative-formative weighting scheme. The purchasing decision was measured as a latent variable using a consistent partial least square (PLSc) model. The data was collected from 148 people who have bought motorcycles in Thailand. The results show that the lower-order constructs demonstrate how well they are organized by the indicators, as determined by the model fit indexes. To begin, the hypothesis regarding the three model fitting indexes is rejected. Thus, all bias indicators with shallow values and a negative symbol were removed from the composite model. Furthermore, multicollinearities exist between PLACE indicators, and they are addressed by removing the indicator with the highest variance inflation factors (VIF). Concerning purchasing decisions, the PLSc model with an indicator loading of less than 0.708 was eliminated. As a result, all three total model fit hypotheses returned to be accepted. For the higher-order construct, the composite model created a marketing mix positively linked to brand equity and purchasing decisions, and brand equity positively correlates with purchasing decisions. The hybrid model can generate model fit indexes for both the first and second construct.
Partial least square structural equation modeling (PLS-SEM) is more commonly used in marketing research because small sample sizes can be used. The main advantage is that the rule of thumb is often used to determine sample size, but the results may be underpowered. Therefore, appropriate sample size is still required to reach the acquired power of 0.80, which should be adequate to avoid false positive and false adverse effects arising from sample sizes that are too large or too small. This research investigates the impact of different sample sizes on the power of analysis, the effect size, and the significance level of the model fitness and parameter estimation process. Many methods are used to generate study sample sizes, such as minimum R2, ten-time rule, inverse square root method, Marsh et al. method, Soper method, and Yamane method. The rule of thumb methods of minimum R2 and ten-time rule generate sample sizes that are too small and inappropriate for PLS-SEM. However, the findings have shown that PLS-SEM can be effective with small sample sizes, but the sample size should be more significant than that generated by the rule-of –thumb methods. The appropriate sample size for this study was 50, with a power of 0.81 and an effect size (f2) ranging between 0.437 and 0.506.
The current study employed a composite model to examine the factors affecting student satisfaction with online education (OE) and the relationship between it and student life quality during the COVID-19 pandemic. Additionally, the research reviewed the ADANCO 2.3.1 software for composite analysis. A sample of 257 management science students from anonymous Rajabhat University was used for this study. The findings indicate that only the factors of output and setup had a significant relationship with student satisfaction concerning OE. The relationship between student satisfaction with OE and the quality of student life was found to be significant. The ADANCO was extremely useful for doing confirmatory composite analysis (CCA) in modern partial least squares structural equation models (PLS-SEM). It was also a helpful tool for transforming latent and observable variables into emergent ones for CCA research. This study successfully resolved the standard bias method resulting in a better outcome.
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