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The role of Industry 4.0 on the association between customers' and suppliers' involvement and performance improvement
Published in Carolina Machado, J. Paulo Davim, Industry 4.0, 2020
Guilherme Luz Tortorella, Alejandro Mac Cawley Vergara, Rogério Miorando, Rapinder Sawhney
Initially, supported by IBM® SPSS Statistics, we performed an EFA to verify the best combination among the 11 customer- and supplier-related practices included in the questionnaire. Thus, a principal component analysis (PCA) with varimax rotation was carried out to extract orthogonal components (Hair et al., 2014). Factor loadings whose values were larger than 0.50 were considered accepted (Tabachnick and Fidell, 2007). We were able to replicate results through oblique rotation in order to verify orthogonality, whose results indicated that the components were quite similar. Table 5.2 shows that all measures reasonably loaded into two constructs. The first construct, denoted as [INV_SUP], represents practices that foster suppliers' involvement, such as “feedback on quality and delivery performance” and “involvement in new product development process”. The second one, [INV_CUST], refers to practices that enhance customers' involvement, such as “involvement of customers in current and future product offerings” and “sharing current and future demand information with marketing department” (Shah and Ward, 2007). Reliability of both constructs was evidenced, since they demonstrated a Cronbach's alpha larger that 0.7 (Meyers et al., 2006).
Validation of pedestrian behaviour scale in Belgrade
Published in Gianluca Dell’Acqua, Fred Wegman, Transport Infrastructure and Systems, 2017
B. Antić, D. Pešić, N. Milutinović, M. Maslać
This paper examined internal consistency and principal component structure of the questionnaire. The questionnaire had an exceptional internal consistency (Cronbach’s alpha 0.72). The internal consistency (Cronbach’s alpha) was calculated for ordinary violations (.70), errors (.66), lapses (.72), aggressive behaviour (.71) and positive behaviour (.76). Cronbach’s alpha test showed an acceptable internal consistency for all groups of items, except for errors. Principal Component Analysis (PCA) with orthogonal Varimax rotation was carried out on all 25 items of the scale. The scree plot indicated that the data best fitted a five-factor solution, which accounted for 60.6% of the total variance. The Kaiser–Meyer–Olkin measure of sampling adequacy was satisfactory (0.76) and Bartlett’s test of sphericity was significant (0.0001). The factor loadings responded well for twenty items, while five items had factor loadings <.33, but they were expelled from further analysis.
Driving and the Environment
Published in Rich C. McIlroy, Neville A. Stanton, Eco-Driving, 2017
Rich C. McIlroy, Neville A. Stanton
As aforementioned, the environmental attitudes section was taken directly from Harvey et al. (2013), the questions and results for which can be found in Table 3.4. Each of the 26 items invited the respondent to indicate, on a 7-point Likert scale, the extent to which they agreed with a given statement (from 1, strongly disagree, to 7, strongly agree). Principal component analysis with varimax rotation was undertaken, resulting in the identification of four factors. Although Harvey et al. (2013) also reported four factors, the analysis presented here resulted in different item groupings: F1, general energy use attitudes; F2, energy conservation attitudes; F3, incentives for energy use reductions; and F4, motivation to use public transport.
Clustering pedestrians’ perceptions towards road infrastructure and traffic characteristics
Published in International Journal of Injury Control and Safety Promotion, 2023
Aditya Saxena, Ankit Kumar Yadav
After the collection of samples, data analysis was undertaken using SPSS version 22. The data obtained for the selected 14 parameters were then subjected to factor analysis (a dimension reduction method) using the principal component analysis technique with varimax rotation. Varimax rotation involves using a mathematical algorithm that maximizes high- and low-value factor loadings and minimizes mid-value factor loadings. Using factor analysis, factor loadings were obtained which were used to derive the number of factors to be considered for dimension reduction. Factors with an eigenvalue above 1 were only retained. Four factors were acquired from component variances. The correlation between factors and parameters was found from component loading and only those parameters were retained whose correlation value was above 0.5 (Jolliffe & Cadima, 2016; Maskey et al., 2018; Venkatramanan et al., 2019). The following equation was used for computing covariance (Mishra et al., 2017):
Statistical study of Khibiny Alkaline Massif (Kola Peninsula) groundwater quality with respect to elevated aluminum concentrations
Published in Environmental Technology, 2022
Daria Popugaeva, Konstantin Kreyman, Ajay K. Ray
FA/PCA, a powerful data reduction tool that uses the extraction of eigenvalues and eigenvectors from the correlation matrix, was applied to the dataset 1999–2018 [20]. Varimax rotation was used in the analysis to maximize the variation among the variables under each factor. The Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests [20,28] were performed to check the adequacy of the data for the FA/PCA. The Euclidean distance as a measure of distance and Ward's method as a linkage rule were used in HCA to obtain the most distinctive clusters between the variables [22]. The data were standardized by z-scale transformation [29] for the multivariate statistical analysis. Z-scale transformation for n variables each with N observations was obtained using the following equation:
Empirical Investigation of Lean Six Sigma Enablers and Barriers in Indian MSMEs by Using Multi-Criteria Decision Making Approach
Published in Engineering Management Journal, 2022
The grouping through expert’s input has been tested by statistical analysis using Exploratory Factor Analysis (EFA). Factor analysis is a data reduction technique which reduces a large number of factors into significant numbers for modeling purpose (Hair et al., 2010). EFA was used to determine the minimum number of factors to represent the covariation found among all elements. Only factors that represented a variance greater than one were extracted (Eigen values > 1). It has been applied for factor loading of 25 enablers and 21 barriers with a sample size of 250 (n = 250) by the Statistical Package for Social Sciences (SPSS 21) software. In this analysis, Kaiser-Meyer-Olkin (KMO) and Bartlett test of sphericity of items were estimated using Principal Component Analysis (PCA) through Varimax rotation (De Freitas et al., 2017). The Varimax rotation reduces the number of variables used to strengthen the interpretability that has wide loading on orthogonal factors (Rehman et al., 2016). The factor extraction is based on Eigen value, it might be more than 1 and minimum 3 items loaded in individual factor with factor loading value greater than 0.40 (Jain & Raj, 2016). The identified 25 enablers of LSS were formulated in six logical groups and 21 barriers were framed in five groups as shown in Exhibits 9 and 10.