Correlation of Reverse Logistics Performance to Solutions Using Structural Equation Modeling

2019 ◽  
Vol 18 (04) ◽  
pp. 511-525
Author(s):  
Pornwasin Sirisawat ◽  
Tossapol Kiatcharoenpol
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Reverse Logistics ◽  
Empirical Data ◽  
Sustainable Management ◽  
Structural Equation ◽  
Electronics Industry ◽  
Equation Modeling ◽  
Proposed Model ◽  
Confirmatory Factor

Nowadays, reverse logistics (RL) is one of the key strategies in many industries, especially in the electronics industry due to increasing environmental awareness and sustainable management. The main aim of this research is to investigate the correlation of RL performance to solutions for RL practice of the electronics industry in Thailand. In this research, questionnaires were distributed to 417 companies in the electronics industry of Thailand. A conceptual model was developed and the model examined by using structural equation modeling (SEM). Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used in this study. The hypotheses were tested in which RL performance was directly positively associated with the overall solutions for RL practices. RL performance was also indirectly positively associated with management & organization solutions, legal & technology solutions and collaboration and support solutions of the various solutions for RL practices. Empirical data was tested by using SEM and it was found that the proposed model could fit with the empirical data. The proposed results of this study will help to understand more about RL practices and could provide further direction for researchers and practitioners in the electronics industry and other related industries.

scholarly journals Modeling and Analysis of the Motivations of Fast Fashion Consumers in Relation to Innovativeness

2016 ◽  
Vol 16 (4) ◽  
pp. 205-213
Author(s):  
Canan Saricam ◽  
Nazan Erdumlu
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Motivational Factors ◽  
Equation Modeling ◽  
Fast Fashion ◽  
Modeling And Analysis ◽  
Confirmatory Factor ◽  
The Relationship

Abstract In this study, fast fashion concept is investigated in order to understand the motivations of the consumers that make them adopt these products because of their willingness for the innovativeness. The relationship between the motivational factors which were named as “Social or status image” and “Uniqueness” as expressions of individuality, “Conformity” and the willingness for “Innovativeness” is analyzed using a conceptual model. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling were used to analyze and validate the model. The data used for the study was obtained from 244 people living in Turkey. The findings showed that the motivational factors “Social or status image” and “Uniqueness” as expressions of individuality are influential on the consumers’ willingness for “Innovativeness”.

Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review

2006 ◽  
Vol 99 (6) ◽  
pp. 323-338 ◽  
Author(s):  
James B. Schreiber ◽  
Amaury Nora ◽  
Frances K. Stage ◽  
Elizabeth A. Barlow ◽  
Jamie King
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Confirmatory Factor

Investigating the Structure of the Pediatric Symptoms Checklist in the Preschool Setting

2016 ◽  
Vol 35 (5) ◽  
pp. 494-505 ◽  
Author(s):  
Christine DiStefano ◽  
Jin Liu ◽  
Yin Burgess
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Short Form ◽  
School Setting ◽  
Equation Modeling ◽  
Confirmatory Factor ◽  
Psychological Instruments ◽  
Preschool Setting

When using educational/psychological instruments, psychometric investigations should be conducted before adopting to new environments to ensure that an instrument measures the same constructs. Exploratory structural equation modeling and confirmatory factor analysis methods were used to examine the utility of the short form of the Pediatric Symptoms Checklist (PSC-17) in the school setting. Using a sample of 836 preschool children rated by teachers, three factors were identified across both techniques, with factors matching the hypothesized structure of the instrument. The PSC-17 may be an option for use in preschool settings when conducting behavioral and emotional screening.

scholarly journals Student Burnout in Higher Education: A Demand-Resource Model Approach

2022 ◽  
Author(s):  
Balázs Jagodics ◽  
Éva Szabó
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Correlation Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Resource Model ◽  
Confirmatory Factor ◽  
Student Burnout

AbstractStudent burnout is a serious problem in higher education. It is associated with harmful consequences, such as decreased engagement, performance, and motivation, which can lead to dropout. The job demand-resource model of burnout is a comprehensive framework to grasp the factors related to the emergence of burnout. Although numerous studies claim its suitability in explaining burnout in work environments, its applicability in the educational context is less explored. The study aimed to analyze the structure and reliability of the newly developed University Demand-Resource Questionnaire (UDRQ) and to explore the links between its subscales and symptoms of student burnout. Using the online survey method, 743 Hungarian undergraduate students participated in the data collection. The student version of the Maslach Burnout Inventory was used in addition to the UDRQ. In the data analysis procedure, confirmatory factor analysis, correlation analysis, and structural equation modeling were utilized. The confirmatory factor analysis identified a five-factor structure related to both demands and resources. Correlation analysis revealed burnout to be associated positively to the subscales of demands and negatively to resources. Structural equation modeling analysis indicated that all five demands and two resources subscales can be used to build a model that predicts a significant proportion of the variance of student burnout scores. The findings suggest the demand-resource theory is an appropriate framework to predict burnout in higher education. The newly developed UDRQ has stable structure and good reliability and can be a useful tool in subsequent research related to student burnout.

scholarly journals Assessing the Unidimensionality of Clayton’s Environmental Identity Scale Using Confirmatory Factor Analysis (CFA) and Bifactor Exploratory Structural Equation Modeling (bifactor-ESEM)

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Paulo Moreira ◽  
Ana Loureiro ◽  
Richard Inman ◽  
Pablo Olivos-Jara
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Bifactor Model ◽  
Equation Modeling ◽  
Environmental Identity ◽  
Order Model ◽  
Exploratory Structural Equation Modeling ◽  
Confirmatory Factor

A relevant intrapersonal characteristic for understanding intentions and behavior toward environmental sustainability is the degree to which nature is important for a person’s self-definition. Clayton’s Environmental Identity (EID) scale purports to measure this construct. However, a limited number of prior exploratory studies of this measure have supported different factor structures. Hence, our initial aim was to develop an understanding of the dimensionality of Clayton’s 24-item EID scale by testing competing latent structures using confirmatory factor analysis. We analyzed self-reported data from 458 adults (Mage = 26.7 years; 81% female). Four a priori models (a first-order model, a second-order model, a unidimensional model, and a bifactor model) did not show satisfactory fit to the data. An ancillary analysis using bifactor exploratory structural equation modeling (bifactor-ESEM) indicated a bifactor model with three specific factors had a good fit to the data. The factor loadings of this model and values for bifactor indices (Omega Hierarchical and Explained Common Variance [ECV]) indicated a single mean score across all EID scale items taps into an essentially unidimensional construct and is therefore appropriate to interpret. In sum, our study provides a critical insight into the dimensionality of Clayton’s EID scale that will be valuable when applying this measure for research and intervention purposes.

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