By Josef Windsperger, Gérard Cliquet, Thomas Ehrmann, Georg Hendrikse

The association of interfirm networks, similar to alliances, cooperatives, franchise and retail chains, has develop into a huge examine subject within the box of economics, advertising, strategic administration, and association concept. This ebook contributes to the literature on formal and casual inter-organizational governance through supplying new insights on agreement layout, possession, evolution of cooperation, function of social capital and function in franchising networks; contains subject matters of loyalty, attractiveness and organizational shape in addition to functionality of cooperatives, and discusses the connection among formal and relational governance in alliances, governance constructions of innovation actions, dynamics of interfirm conflicts, and community externalities and alliance formation.

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Additional info for Interfirm Networks: Franchising, Cooperatives and Strategic Alliances

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Recently, by applying transaction cost theory, Hussain et al. (2012, 2013) show that high environmental uncertainty favors single-unit franchising because of the stronger entrepreneurial orientation of the single-unit franchisees compared to multi-unit outlet managers, and high transaction-specific investments increase the proportion of MUF due to bonding and economies of scale effects. We can conclude that resource scarcity, agency and transaction cost theories explain MUF as governance mode that helps to mitigate franchisor’s resource scarcity problems and to reduce agency and transaction costs.

Integrating the dynamic process, we are interested in estimating: À Á yi, t À yi, tÀ1 ¼ α yÃi, t À yi, tÀ1 ð2Þ Where α is a (inverse) measure of the adjustment costs. e. α ¼ 1) the adjustment is immediate, if costs are high (α near to 0) the adjustment is very slow. Once developed, the model aims to estimate: yi, t ¼ ð1 À αÞyi, tÀ1 þ αβXi, t þ γ i þ δt þ εi, t ð3Þ Panel data analysis allows studying the dynamic process at the firm level, but the classical fixed or random effect models lead to inconsistent and biased estimations when there is, as in our model, a lagged independent variable.

2 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix: Measures of Variables . . . . . . . . . . . . . . . . . .

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