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Knowledge-Based Dynamic Capabilities and Competitive Advantage – Data Analysis and Interpretations

In: Knowledge-Based Dynamic Capabilities

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  • Vaneet Kaur

    (The University of Texas at Dallas)

Abstract

The chapter discusses the results of pre-testing as well as presents the descriptive and inferential statistical data analysis of the present study. The chapter ensures the adequacy of survey response and gives the background of the respondents by analyzing the demographic profiles. This is followed by data purification and analysis by using descriptive statistics. For the purpose of ensuring the application of multivariate techniques for analyzing data, the survey responses have been screened to ensure normality, linearity, homoscedasticity and multicollinearity. A panoply of research techniques like correlation, regression, analysis of variance, exploratory factor analysis, confirmatory factor analysis, structural equation modelling have been used to analyze the data. Thereafter, the results of these techniques are presented in a meaningful manner to arrive at relevant conclusions. Interpretations have been done under three broad themes namely Knowledge-Management Process Capabilities, Higher-Order Dynamic Capabilities and Competitive Advantage.

Suggested Citation

  • Vaneet Kaur, 2019. "Knowledge-Based Dynamic Capabilities and Competitive Advantage – Data Analysis and Interpretations," Innovation, Technology, and Knowledge Management, in: Knowledge-Based Dynamic Capabilities, chapter 0, pages 145-208, Springer.
    • Handle: RePEc:spr:innchp:978-3-030-21649-8_5
    DOI: 10.1007/978-3-030-21649-8_5
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    Cited by:

    1. Suqin Liao & Qianying Hu & Jingjing Wei, 2023. "How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    2. Summer K. Mohamed & Sandra Haddad & Mahmoud Barakat & Bojan Rosi, 2023. "Blockchain Technology Adoption for Improved Environmental Supply Chain Performance: The Mediation Effect of Supply Chain Resilience, Customer Integration, and Green Customer Information Sharing," Sustainability, MDPI, vol. 15(10), pages 1-20, May.

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