Hsu, L., & Jang, S. (2009), Effects of Restaurant Franchising: Does an optimal franchise proportion exist?

Loading...

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

0 comments

Post a comment

    Post a comment
    Embed Video
    Edit your comment Cancel

    Favorites, Groups & Events

    Hsu, L., & Jang, S. (2009), Effects of Restaurant Franchising: Does an optimal franchise proportion exist? - Presentation Transcript

    1. Effects of Restaurant Franchising: Does an optimal franchise proportion exist?
      January 22
      Suh-hee Choi
      HTM 681 2nd Article
      Spring 2009
      Hsu, L.,& Jang, S. (in press)
    2. Spinelli et al., 2003; Aliouche and Schlentrich, 2005
      Research
      Questions
      Alon et al., 2004
      Does franchise influence a restaurant firm’s performance?
      franchise
      Vs.
      non-franchise
      profitability
      intangible value
      Research based on
      the empirical evidence
      Does optimal franchise proportion exist?
      • reduces investment cost / risk
      • disadvantages
      Other things being equal
      Bradach, 1997
      “marginal costs
      = marginal benefits”
      Organizational learning theory (Sorenson & Sorenson, 2001)
      Resource scarcity theory (Hunt, 1973; Norton, 1988; Oxenfeldt & Thompson, 1968, Oxenfeldt & Kelly, 1969)
      Theories
      Agency theory (Rubin, 1978)
    3. Methodology
    4. Methodology
      Dependent variables
      ROA
      ROE
      Tobin’s Q
    5. Methodology (cont.)
      Independent variables
      franchise proportion
      Controlled variables
      leverage
      firm size (economy of scale)
      advertising
      profitability (in Tobin’s Q model)
    6. Methodology
      T-test
      compare the means between
      franchise / non-franchise
      ROA(ROE, Tobin’s Q
      Regression analysis
    7. Methodology
      Regression analysis
      • normality  natural log transformation
      • multicollinearity • heteroskedasticity
    8. Resultsdescriptive information
    9. Results franchise vs. non-franchise
      profitability and intangible values are significantly higher in franchise firms than non-franchise firms
    10. Results franchise vs. non-franchise
      H1: Franchise restaurant firms have higher profitability and create more intangible value than non-franchise restaurant firms.
    11. Results effects of franchise proportion
      H2: There is non-linear relationship between franchise proportion and profitability and intangible value.
    12. Results effects of franchise proportion
      maximum ROA (ROE, Tobin’s Q)?
      shaped
    13. Results geography matters
      not significant
      sq% coefficient:
      negative linear
      relationship
      • less dispersed
      max: 35% 36%
    14. Results geography matters
      • dispersed hard to monitor  more franchising
      max: 52% 55% 43%
    15. Conclusions
      Franchising betters financial performance / intangible value.
      Optimal proportions of franchising can be found (to maximize financial performance / intangible value).
      other factors of optimal proportions might be significant : geographical dispersion was tested.
    16. limitations
      Didn’t include positioning and marketing concepts.
      Focused only on restaurant industry.
      Limited data available (what about small franchisors?)
    17. Further studies
      Develop methodologies to find optimal points that consider both maximizing profits and reducing risks at the same time (e.g. Agency Theory (Rubin, 1978))
      What happens if OTHER THINGS ARE NOT EQUAL?
      Other benefits of franchising?
      social values
      sustainable relationships between the firms
    18. appendix: White's test
      Assumption: V(ui) = s 2 + g(E(Yi))2
      i.e. the variance of u depends on the expected value of Y (and hence on at least one of the x variables).
       V(ui) = s2 + g(b1 + b2x2 + ... + bkxk)2
      H0: g = 0  homoscedasticity
      otherwise heteroscedasticity.
      Regress e2 on x22, x32, etc and x2x3, x3x4, etc.
      Obtain nR2 ~ c2(k-1) as an LM statistic.
      www.sussex.ac.uk/economics/documents/lecture_4.ppt

    + Suh-hee ChoiSuh-hee Choi, 2 months ago

    custom

    101 views, 0 favs, 0 embeds more stats

    This is the presentation file I created for an HTM more

    More info about this document

    © All Rights Reserved

    Go to text version

    • Total Views 101
      • 101 on SlideShare
      • 0 from embeds
    • Comments 0
    • Favorites 0
    • Downloads 2
    Most viewed embeds

    more

    All embeds

    less

    Flagged as inappropriate Flag as inappropriate
    Flag as inappropriate

    Select your reason for flagging this presentation as inappropriate. If needed, use the feedback form to let us know more details.

    Cancel
    File a copyright complaint
    Having problems? Go to our helpdesk?

    Categories