Success factors for teams in business game Dynama

  • Received September 29, 2017;
    Accepted January 12, 2018;
    Published March 20, 2018
  • Author(s)
    Ruth Alas , Peeter Kross , Anto Liivat , Jaanika Saar
  • DOI
    http://dx.doi.org/10.21511/ppm.16(1).2018.30
  • Article Info
    Volume 16 2018, Issue #1, pp. 309-319
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  • Cited by
    2 articles
  • 1566 Views
  • 266 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

The current article is based on data from Dynama business games conducted in two Estonian institutions of higher education (Estonian Business School (EBS) and Estonian Entrepreneurship University of Applies Sciences) between years 2012 and 2016. The research questions that this paper attempts to explore look at the relationship between the results obtained by different teams in business game Dynama and their teams’ characteristics. The paper also shows how the volume of market research information used affects the results obtained by different teams in the business game. This research of implementational nature primarily helps to develop the teaching methods and basis for team composition in business game Dynama with the purpose of sup¬porting the course and achieving the learning outcomes in a more effective way. The research results may also be of interest to those who use and research business games as a teaching tool. The variables employed in this study enabled us to compare our research with that conducted by A.-E. Lerviks and M. Paltschik at Hanken School of Economics and Business Administration in 1982.

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    • Table 1. Descriptive statistics for teams ment
    • Table 2. Comparison of teams on the 1st and 5th place
    • Table 3. Comparison of 10 most successful (Success group 1) and 10 least successful (Success group 2) teams based on accumulated profits
    • Table 4. Comparison of 10 most successful and 10 least successful teams based on normalized accumulated profits
    • Table 5. Comparison of upper and lower quartiles based on normalized accumulated profits
    • Table 6. Models for game results based on team characteristics and market information acquired
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