This research evaluates the psychometric properties of a modified instrument designed to examine green logistics implementation, organizational outcomes, and the regulatory influence of government. Drawing from validated 5-point Likert scales in current literature, the study utilized a pilot sample of 50 participants, with data processed via SPSS v30.0. The refinement of the scale was achieved through internal consistency checks and exploratory factor analysis (EFA). Findings confirmed robust reliability, as Cronbach's alpha coefficients for all constructs surpassed the 0.7 threshold. The final 40-item scale, organized into five theoretically aligned dimensions, demonstrated strong factor loadings and sampling adequacy (KMO > 0.7). By validating these core constructs, this work establishes a rigorous foundation for future empirical inquiries into the environmental performance of the trucking industry.

This study investigates the benefits of Generative Artificial Intelligence (GAI) in human resources operations. It focuses on several key outcomes that include reliability, validity, digitalization, relative advantage, online response, computability, and the time and money costs. Basically, the study seeks an understanding of the less-consistent benefits of using GAI in HR. Data from 290 information technology professionals in Amman Town were collected during a well-planned public examination. The analysis uses a new approach to the method with IBM SPSS version 21 integrated with smart PLS. The findings reveal that determinants such as reliability, validity, digitalization, and relative advantage are strong in terms of efficiency, while computability and online response collaboration are weak determinants. The importance of the research is that it identifies the glitches when GAI skills are used in human resource management; hence, focusing on some vital areas of reliability, validity, digitalization, relative advantage, online responsiveness, and computability, one will have a detailed analysis of the potential issues while using GAI within HR, along with the possible solutions.

Structural Equation Modeling (SEM) includes measurement and structural model for hypothesis testing. The results yielded from structural model is unlikely to be valid if a poor loading of an indicator is selected. The impact of these erroneous result on standardized loading is disregard. Thus, knowing how poor loading can affect the validity of measurement model is a crucial issue. This paper attempts to compare the standardized loadings result between two prominent SEM methods (CBSEM and PLS-SEM) using three varied of simulation models (TRA, Loyalty and UTAUT model) to investigate their effects on reliability and validity of measurement model. The data for each model were generated using R software by setting the value of standardized loading and the construct correlations (N=50, 100, 200 and 500). The value of standardized loadings was set to 0.60 for each construct in the model while the construct correlations were set in the range between 0.45 to 0.65. Then, the AMOS 21.0 and ADANCO 2.0 were used to perform the statistical analysis. It shows that good standardized loading can increase the reliability and validity of construct representation. CBSEM is particularly yielded valid and unbiased estimation under confirmatory condition (established theory) compared with PLS-SEM. The results are illustrated with empirical examples. This paper provides updated evidence about CBSEM and PLS-SEM when assessing the measurement model.