IDEAS home Printed from https://ideas.repec.org/a/pkp/hassle/v9y2021i1p24-33id879.html
   My bibliography  Save this article

Employee Motivation and Industrial Output in Nigeria

Author

Listed:
  • Esther Elomien
  • Friday Francis Nchuchuwe
  • Oluwatobiloba Ademola Idowu
  • Ademola Onabote
  • Romanus Osabohien

Abstract

This study applied the autoregressive distribution lag (ARDL) econometric approach to examine the long-run effect of employee motivation on industrial output in Nigeria. Data used for the study was sourced from the World Development Indicators (WDI) of the World Bank from 1981 to 2018. Industrial output was proxied by industrial value added (annual % growth) and was used as the dependent variable, while the independent variable is motivation, which was proxied by wage and salaried workers, total (% of total employment) and was modelled on the ILO estimate. Results from the analysis showed that employee motivation is statistically significant and positive in determining the level of industrial output in Nigeria. This result implies that an increase in the level of employee motivation has the potential to increase industrial output by 98%. Therefore, based on the findings, the study recommends that employees should be motivated through regular payment, increased wages and salaries and other bonuses (fringe benefits) to increase industrial productivity.

Suggested Citation

  • Esther Elomien & Friday Francis Nchuchuwe & Oluwatobiloba Ademola Idowu & Ademola Onabote & Romanus Osabohien, 2021. "Employee Motivation and Industrial Output in Nigeria," Humanities and Social Sciences Letters, Conscientia Beam, vol. 9(1), pages 24-33.
    • Handle: RePEc:pkp:hassle:v:9:y:2021:i:1:p:24-33:id:879
    as

    Download full text from publisher

    File URL: https://archive.conscientiabeam.com/index.php/73/article/view/879/1259
    Download Restriction: no
    File URL: https://archive.conscientiabeam.com/index.php/73/article/view/879/5997
    Download Restriction: no
    ---><---

    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:pkp:hassle:v:9:y:2021:i:1:p:24-33:id:879. 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: Dim Michael (email available below). General contact details of provider: https://archive.conscientiabeam.com/index.php/73/ .

    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.