IDEAS home Printed from https://ideas.repec.org/p/ags/ifma13/345685.html
   My bibliography  Save this paper

PR - Experience, Learning, And Innovativness In Beef Production: Results From A Cluster Analysis

Author

Listed:
  • Micheels, Eric T.

Abstract

Research in agriculture and other industries has shown that innovativeness is a key driver of improved performance measures of small and medium-sized enterprises. The willingness to change current practice may be a function of the level of experience of the manager as well as the manager’s commitment to learning. Firms with more experience may suffer from confirmation bias and therefore may not see the performance benefits that stem from innovative activities. Using data from a survey of beef producers, this study employs cluster analysis to segment firms along experience and learning variables. Using a non-hierarchical clustering procedure, three clusters emerge which represent younger firms with high and low levels of learning and older firms with moderate learning scores. The study employs one-way ANOVA tests to examine differences in innovativeness and performance across clusters. Results indicate firms with a commitment to learning have a greater willingness to accept innovations and are more satisfied with overall performance. The paper concludes with some implications for managers and policy makers.

Suggested Citation

  • Micheels, Eric T., 2013. "PR - Experience, Learning, And Innovativness In Beef Production: Results From A Cluster Analysis," 19th Congress, Warsaw, Poland, 2013 345685, International Farm Management Association.
  • Handle: RePEc:ags:ifma13:345685
    DOI: 10.22004/ag.econ.345685
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/345685/files/13_Micheels_P301-308v3.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.345685?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    Research Methods/ Statistical Methods;

    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:ags:ifma13:345685. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/ifmaaea.html .

    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.