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The Dynamics of Contractual Design: Determinants of Contract Duration in Franchising Networks

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Interfirm Networks

Abstract

This paper deals with the contractual design in franchising networks. We investigate the determinants and the evolution of contracts duration on the basis of a multidisciplinary approach using law, management and economics. Taking into account the traditional explanation of franchise contracts duration in terms of specific investments, this paper focuses on the dynamics of contractual design. The empirical analysis is based on franchise French data, coupled with financial data. We use descriptive statistics and econometrics. A dynamic panel data model for contract length is estimated. Our results highlight specific dynamics and adjustment costs in the determination of contracts duration.

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Notes

  1. 1.

    During the launching phase of a franchise network, the franchisor and the first franchisees together test and co-built several network elements (Boulay and Chanut 2010). It is then likely that the first franchisees have a real bargaining power on the content of the contract.

  2. 2.

    See for example the judgment of the Commercial Court of Paris on 5 December 1997, against LVT SA Lafont and son and another, Les relations franchiseur-franchisé: au-delà du droit, la recherche d’une parfaite moralisation, Petites affiches, February 5 1999, No. 26, p. 16–18. For further references, see Boulay and Chanut (2010, p. 99).

  3. 3.

    Franchisors’ websites, listings in directories of franchise like the one published in France by the FFF (Toute la Franchise, les textes, les chiffres, les réseaux; annual publication) or inserts in the general and/or specialized press dealing with franchising (annual specific publication of L'Express, L’officiel de la franchise or Franchise magazine in France for example).

  4. 4.

    The hold-up problem results from an opportunistic behavior: a contractor tries to capture the value of investments made by the partner. In a situation of hold-up, one of the contractors does not get the full marginal return on its investment.

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Author information

Authors and Affiliations

  1. CRET-LOG, University of Aix-Marseille, Aix-en-Provence Cedex 1, France

    Odile Chanut

  2. GATE Lyon-St Etienne, 6, Rue Basse des Rives, 42023, Saint-Etienne cedex 2, France

    Magali Chaudey & Muriel Fadairo

  3. University of Lyon, Lyon, France

    Magali Chaudey, Muriel Fadairo & Frédéric Perdreau

  4. COACTIS Lyon St. Etienne, 6, Rue Basse des Rives, 42023, Saint-Etienne cedex 2, France

    Frédéric Perdreau

Authors
  1. Odile Chanut
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  2. Magali Chaudey
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  3. Muriel Fadairo
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  4. Frédéric Perdreau
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Corresponding author

Correspondence to Magali Chaudey .

Editor information

Editors and Affiliations

  1. Department of Management, University of Vienna, Vienna, Austria

    Josef Windsperger

  2. Centre Recherche en Economic et Management (CREM), Universite de Rennes 1, Rennes Cédex 7, France

    Gérard Cliquet

  3. Institut für Strategisches Management, University of Münster, Münster, Germany

    Thomas Ehrmann

  4. Department of Organization, Erasmus University, Rotterdam, The Netherlands

    Georg Hendrikse

Appendix: Tests for the Estimations Models

Appendix: Tests for the Estimations Models

NB: in all the following models, the instruments used are:

Instruments for first differences equation

  • Standard

  •  Difference of (SECTOR2 SECTOR3 SECTOR4 SECTOR5 YEAR_1996

  •  YEAR_1997 YEAR_1998 YEAR_1999 YEAR_2000 YEAR_2001

  •  YEAR_2002 YEAR_2003 RETURN TURNOVER ln(AGE)

  •  Ln(SIZE))

  • GMM-type (missing=0, separate instruments for each period unless collapsed)

  •  Lag2.(CONTRACT LENGTH Investment ownership_rate)

Instruments for levels equation

  • Standard

  •  constant

  •  SECTOR2 SECTOR3 SECTOR4 SECTOR5 YEAR_1996

  •  YEAR_1997 YEAR_1998 YEAR_1999 YEAR_2000 YEAR_2001

  •  YEAR_2002 YEAR_2003 RETURN TURNOVER ln(AGE)

  •  Ln(SIZE)

  • GMM-type (missing=0, separate instruments for each period unless collapsed)

  •  Difference of Lag.( CONTRACT LENGTH Investment ownership_rate)

1.1 Model 1: Dynamic Panel-Data Estimation Results for the Full Sample (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.031

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.871

  • Hansen test of overid. Prob > chi2 = 0.586

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.488

  • Difference (null H = exogenous): Prob > chi2 = 0.581

  • Hansen test excluding group: Prob > chi2 = 0.361

  • Difference (null H = exogenous): Prob > chi2 = 0.692

1.2 Model 2: Dynamic Panel-Data Estimation Results for the Subsample Retail (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.040

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.769

  • Hansen test of overid. Prob > chi2 = 0.720

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.741

  • Difference (null H = exogenous): Prob > chi2 = 0.531

  • Hansen test excluding group: Prob > chi2 = 0.766

  • Difference (null H = exogenous): Prob > chi2 = 0.493

1.3 Model 3: Dynamic Panel-Data Estimation Results for the Subsample Services (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.081

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.901

  • Hansen test of overid. Prob > chi2 = 0.601

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.451

  • Difference (null H = exogenous): Prob > chi2 = 0.645

  • Hansen test excluding group: Prob > chi2 = 0.540

  • Difference (null H = exogenous): Prob > chi2 = 0.547

1.4 Model 4: Dynamic Panel-Data Estimation Results for the Subsample of Outperforming Franchisors (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.042

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.526

  • Hansen test of overid. restrictions: Prob > chi2 = 0.690

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.583

  • Difference (null H = exogenous): Prob > chi2 = 0.636

  • Hansen test excluding group: Prob > chi2 = 0.360

  • Difference (null H = exogenous): Prob > chi2 = 0.817

1.5 Model 5: Dynamic Panel-Data Estimation Results for the Subsample of Underperforming Franchisors (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.162

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.461

  • Hansen test of overid. Prob > chi2 = 0.917

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.869

  • Difference (null H = exogenous): Prob > chi2 = 0.762

  • Hansen test excluding group: Prob > chi2 = 0.801

  • Difference (null H = exogenous): Prob > chi2 = 0.834

1.6 Model 6: Dynamic Panel-Data Estimation Results for the Subsample of Low-Risk Networks (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.124

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.218

  • Hansen test of overid. restrictions: Prob > chi2 = 0.326

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.313

  • Difference (null H = exogenous): Prob > chi2 = 0.388

  • Hansen test excluding group: Prob > chi2 = 0.451

  • Difference (null H = exogenous): Prob > chi2 = 0.274

1.7 Model 7: Dynamic Panel-Data Estimation Results for the Subsample of High-Risk Networks (Two-Step System GMM)

  • Arellano-Bond test for AR (1) in first differences: Pr > z = 0.115

  • Arellano-Bond test for AR (2) in first differences: Pr > z = 0.224

  • Hansen test of overid. restrictions: Prob > chi2 = 0.675

  • Difference-in-Hansen tests of exogeneity of instrument subsets:

  • GMM instruments for levels

  • Hansen test excluding group: Prob > chi2 = 0.629

  • Difference (null H = exogenous): Prob > chi2 = 0.567

  • Hansen test excluding group: Prob > chi2 = 0.341

  • Difference (null H = exogenous): Prob > chi2 = 0.797

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Chanut, O., Chaudey, M., Fadairo, M., Perdreau, F. (2015). The Dynamics of Contractual Design: Determinants of Contract Duration in Franchising Networks. In: Windsperger, J., Cliquet, G., Ehrmann, T., Hendrikse, G. (eds) Interfirm Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-10184-2_2

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