Číslo 1 (2024)
Permanent URI for this collection
Browse
Recent Submissions
Item The impact of the effective tax rate change on financial assets of commercial banks: The case of Visegrad group countries(Technická univerzita v Liberci, 2024) Andrejovska, Alena; Glova, Jozef; Regaskova, Martina; Slyvkanyc, Natalia
Item DuPont analysis among European dentistry companies to measure the impact of the COVID-19 pandemic(Technická univerzita v Liberci, 2024) Heryan, Tomáš; Gajdova, KarinItem Effectiveness factors of small and medium-sized enterprises from the perspective of corporate culture: A case study in Slovakia(Technická univerzita v Liberci, 2024) Lorincova, Silvia; Hitka, Milos; Durian, Jozef; Rauser, DanielItem The impact of environmental, social and governance policies on companies’ financial and economic performance: A comprehensive approach and new empirical evidence(Technická univerzita v Liberci, 2024) Noja, Gratiela Georgiana; Baditoiu, Bianca Raluca; Buglea, Alexandru; Munteanu, Valentin Partenie; Gligor, Diana CorinaItem Determinants of the impact of ESG policy and corporate governance on employee rights(Technická univerzita v Liberci, 2024) Li, Chiao-Ming; Lee, Joe-MingItem Educational attainment as a predictor of poverty and social exclusion: Empirical analysis of Serbian case(Technická univerzita v Liberci, 2024) Dzunic, Marija; Golubovic, Natasa; Jankovic-Milic, VesnaItem Examining climate change awareness and climate-friendly activities of urban residents: A case study in Košice(Technická univerzita v Liberci, 2024) Toth, Veronika; Sebova, MiriamItem Exploring the relationship between usage of social networking sites, cyberbullying and academic performance: Evidence from the higher education sector of Saudi Arabia(Technická univerzita v Liberci, 2024) Rasool, Samma Faiz; Raza, Hamid; Zubr, Vaclav; Asghar, Muhammad Zaheer; Sultana, RaziaItem The nexus of a regional competitiveness and economic resilience: The evidence-based on V4+4 NUTS 2 regions(Technická univerzita v Liberci, 2024) Svoboda, Ondrej; Melecky, Lukas; Stanickova, MichaelaItem The influence of the COVID-19 pandemic on managerial functions: Theory verified by Delphi method(Technická univerzita v Liberci, 2024) Nosková, Marta; Kutlák, JiříItem Optimization of inventory cost control for SMEs in supply chain transformation: A case study and discussion(Technická univerzita v Liberci, 2024) Zheng, Xiaosong; Chen, YilinItem A comparative analysis of multivariate approaches for data analysis in management sciences(Technická univerzita v Liberci, 2024) Ahmed, Rizwan Raheem; Streimikiene, Dalia; Streimikis, Justas; Siksnelyte-Butkiene, IndreThe researchers use the SEM-based multivariate approach to analyze the data in different fields, including management sciences and economics. Partial least square structural equation modeling (PLS-SEM) and covariance-based structural equation modeling (CB-SEM) are powerful data analysis techniques. This paper aims to compare both models, their efficiencies and deficiencies, methodologies, procedures, and how to employ the models. The outcomes of this paper exhibited that the PLS-SEM is a technique that combines the strengths of structural equation modeling and partial least squares. It is imperative to know that the PLS-SEM is a powerful technique that can handle measurement error at the highest levels, trim and unbalanced datasets, and latent variables. It is beneficial for analyzing relationships among latent constructs that may not be candidly witnessed and might not be applied in situations where traditional SEM would be infeasible. However, the CB-SEM approach is a procedure that pools the strengths of both structural equation modeling and confirmatory factor analysis. The CB-SEM is a dominant multivariate technique that can grip multiple groups and indicators; it is beneficial for analyzing relationships among latent variables and multiple manifest variables, which can be directly observed. The paper concluded that the PLS-SEM is a more suitable technique for analyzing relations among latent constructs, generally for a small dataset, and the measurement error is high. However, the CB-SEM is suitable for analyzing compound latent and manifest constructs, mainly when the goal is to generalize results to specific population subgroups. The PLS-SEM and CB-SEM have specific efficiencies and deficiencies that determine which technique to use depending on resource availability, the research question, the dataset, and the available time.
