Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM
Partial Least Squares Structural Equation Modeling (PLS-SEM) is well-known as the second generation of multivariate statistical analysis to correlate the relationship between multiple variables namely the latent construct. Lately, the popularity using PLS-SEM is growing within the Variance-Based (VB...
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my.uum.repo.269732020-05-12T03:45:52Z http://repo.uum.edu.my/26973/ Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM Tan, Kar Hooi Abu, Noor Hidayah Abdul Rahim, Mohd Kamarul Irwan QA76 Computer software Partial Least Squares Structural Equation Modeling (PLS-SEM) is well-known as the second generation of multivariate statistical analysis to correlate the relationship between multiple variables namely the latent construct. Lately, the popularity using PLS-SEM is growing within the Variance-Based (VB) SEM community. There is still a great number of researcher finding VB-SEM results reporting a daunting task. Ultimately, an advanced PLS-SEM analysis utilizing product innovation performance example with SmartPLS 3.2.6 tool. Higher order construct or hierarchical component modelling is seen as an advanced tool towards the parsimony of the research variables conceptualization. ExcelingTech Publishers 2018 Article PeerReviewed application/pdf en http://repo.uum.edu.my/26973/1/IJSCM%207%201%202018%2051%2069.pdf Tan, Kar Hooi and Abu, Noor Hidayah and Abdul Rahim, Mohd Kamarul Irwan (2018) Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM. International Journal of Supply Chain Management (IJSCM), 7 (1). pp. 51-64. ISSN 2050-7399 https://ojs.excelingtech.co.uk/index.php/IJSCM/article/view/1838 |
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Partial Least Squares Structural Equation Modeling (PLS-SEM) is well-known as the second generation of multivariate statistical analysis to correlate the relationship between multiple variables namely the latent construct. Lately, the popularity using PLS-SEM is growing within the Variance-Based (VB) SEM community. There is still a great number of researcher finding VB-SEM results reporting a daunting task. Ultimately, an advanced PLS-SEM analysis utilizing product innovation performance example with SmartPLS 3.2.6 tool. Higher order construct or hierarchical component modelling is seen as an advanced tool towards the parsimony of the research variables conceptualization. |
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Article |
author |
Tan, Kar Hooi Abu, Noor Hidayah Abdul Rahim, Mohd Kamarul Irwan |
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Tan, Kar Hooi Abu, Noor Hidayah Abdul Rahim, Mohd Kamarul Irwan |
author_sort |
Tan, Kar Hooi |
title |
Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM |
title_short |
Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM |
title_full |
Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM |
title_fullStr |
Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM |
title_full_unstemmed |
Relationship of big data analytics capability and product innovation performance using SmartPLS 3.2.6: hierarchical component modelling in PLS-SEM |
title_sort |
relationship of big data analytics capability and product innovation performance using smartpls 3.2.6: hierarchical component modelling in pls-sem |
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ExcelingTech Publishers |
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2018 |
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http://repo.uum.edu.my/26973/1/IJSCM%207%201%202018%2051%2069.pdf http://repo.uum.edu.my/26973/ https://ojs.excelingtech.co.uk/index.php/IJSCM/article/view/1838 |
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