IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-97-6359-7_5.html
   My bibliography  Save this book chapter

Data Analysis

In: Supply Chain Agility and Innovation

Author

Listed:
  • Eias Al Humdan

    (Rabdan Academy, Abu Dhabi, UAE)

  • Yangyan Shi

    (College of Business Administration, Capital University of Economics and Business
    Macquarie University)

  • Masud Behnia

    (Stanford University)

Abstract

This chapter serves a dual purpose: firstly, it delves into survey data using appropriate techniques for basic data analysis, and secondly, it explores the survey constructs within the research model using Partial Least Square based Structural Equation Modelling (PLS-SEM) and presents the empirical findings. The chapter begins with a brief overview of the data entry and preparation process, followed by a discussion on data management and preparation. It then proceeds to basic analysis, starting with demographic analysis to provide insights into respondent information and participating companies. Descriptive analysis follows, offering an overview of the research model variables by examining mean, standard deviation, skewness, and kurtosis in the sample population to assess normality. Additional statistical tests are conducted to evaluate normality, reliability, and identify common method bias in the collected data.

Suggested Citation

  • Eias Al Humdan & Yangyan Shi & Masud Behnia, 2024. "Data Analysis," Springer Books, in: Supply Chain Agility and Innovation, chapter 0, pages 167-228, Springer.
    • Handle: RePEc:spr:sprchp:978-981-97-6359-7_5
    DOI: 10.1007/978-981-97-6359-7_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-981-97-6359-7_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.