Technological endowments in entrepreneurial partnerships


This paper discusses a novel argument to interpret the importance of thinking of collaborative partnerships in pre-competitive agreements. To do so, we adopt a dynamic iterative process to model technology diffusion between the partners of an agreement. We find that the success of an agreement of a given length hinges around identifying the suitable efficient combinations of the initial technological endowments of partners. As the time horizon of the agreement expands, the probability of identifying a suitable partner decreases, thus justifying the prevalence of short-horizon R&D agreements.

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  1. 1.


  2. 2.

    Note that we assume ξ i >0. This is so because ξ i =0 would mean that firm i has already exhausted its possibilities to lower costs, thus pre-emptying its participation in any agreement.

  3. 3.

    As it appears, for instance, in the Operating procedures of the HDP Inc at

  4. 4.

    As we will argue in the next section, in that case our results would be even more stringent because of the structure of our approach.

  5. 5.

    This finds empirical and theoretical support in the studies by Mansfield (1961), Stoneman (1981) Karshenas and Stoneman (1993), or De Palma et al. (1991).

  6. 6.

    We are implicitly assuming the absence of any kind of free-riding behavior, as anticipated at the beginning of this section.

  7. 7.

    Examples of these partnerships are agreements like Chamalon, Aider, or Chauffeur involving partners form the rubber, automobile, aircraft, electronics, and telecom industries among others.

  8. 8.

    We do not explicitly model punishments for deviations from the agreements. This would go beyond the main objective of the analysis. Remember that we are assuming that the model satisfies the condition for an optimal contract to exist. Accordingly, the design of the contract already takes into account those penalties.

  9. 9.

    In general, this is the kind of cost-benefit analysis that firms carry out when they evaluate the convenience of joining an agreement. Firms look at the evolution of profits over a finite horizon from the actual situation by computing the present (discounted) value of the flow of future profits. In addition, we compare stock variables at different moments in time and implicitly discount them at the same rate. It is important to remember that we are considering extreme cases where the agreement must be profitable during every single period. Milder assumptions would consider comparing aggregate discounted profits over a certain number of periods. Then opportunities for successful collaboration should appear more easily.

  10. 10.

    Remember that firms can exploit the benefits they get from the agreement only at the end of period two.

  11. 11.

    Carsense is a consortium of 12 European car manufacturers, suppliers and research institutes, sponsored by the EC to develop a sensor system, that shall give sufficient information on the car environment at low speeds in order to allow low speed driving. See Cartalk is another consortium of 7 European car manufacturers, suppliers and research institutes, sponsored by the EC and focusing on new driver assistance systems based on inter-vehicle communication. See


  1. Aghion Ph, Dewatripont M, Rey P (2002) On partial contracting. Eur Econ Rev 46:745–753

    Article  Google Scholar 

  2. Baptista R (1999) The diffusion of process innovations: a selective review. Int. J. Econ. Bus. 6:107–129

    Article  Google Scholar 

  3. Blackmore D, Rahman A, Shah J (2009) Discrete modeling and analysis of the R-S flip-flop circuit. Chaos Solitons Fractals 42:951–963

    Article  Google Scholar 

  4. Canepa A, Stoneman P (2004) Comparative international diffusion: patterns, determinants and policies. Econ. Innov. New Technol. 13:279–298

    Article  Google Scholar 

  5. Chesbrough HW (2003) The era of open innovation. MIT Sloan Manag Rev 44:35–41

    Google Scholar 

  6. Chipman JS (1970) External economies of scale and competitive equilibrium. Q J Econ 84:347–385

    Article  Google Scholar 

  7. De Palma A, Droesbeke JJ, Lefevre C (1991) Modèles de diffusion en marketing. Presse Universitaire de France, Paris

    Google Scholar 

  8. Devaney RL (1985) An introduction to the chaotic dynamical systems. Benjamin/Cummings, Menlo Park

    Google Scholar 

  9. Frankort H, Hagedoorn J, Letterie W (2012) R&D partnership portfolios and the inflow of technological knowledge. Ind Corp Change 21:507–537

    Article  Google Scholar 

  10. Gillier Th, Kazakci AO, Piat G (2012) The generation of common purpose in innovation partnerships. Eur J Innov Manag 15:372–392

    Article  Google Scholar 

  11. Hagedoorn J (2002) Inter-firm R&D partnership: an overview of major trends and patterns since 1960. Res Policy 31:477–492

    Article  Google Scholar 

  12. Harrigan KR (1986) Managing for joint venture success. Lexington Books, Lexington

    Google Scholar 

  13. Jayaraman V, Bhatti MI, Saber H (2004) Towards an optimal testing of an hypothesis based on dynamic technology transfer model. Appl Math Comput 147:115–129

    Article  Google Scholar 

  14. Karshenas M, Stoneman P (1993) Rank, stock, order and epidemic effects in the diffusion of new process technology. Rand J Econ 24:503–519

    Article  Google Scholar 

  15. Kraft RL (1999) Chaos, Cantor sets and hyperbolicity for the logistic maps. Am Math Mon 105:400–409

    Article  Google Scholar 

  16. Li T-Y, Yorke J (1975) Period three implies chaos. Am Math Mon 82:985–992

    Article  Google Scholar 

  17. Lokshin B, Hagerdoorn J, Letterie W (2011) The bumpy road of technology partnership: understanding causes and consequences of partnership mal-functioning. Res Policy 40:297–308

    Article  Google Scholar 

  18. Mansfield E (1961) Technical change and the rate of imitation. Econometrica 29:741–766

    Article  Google Scholar 

  19. May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261:459–467

    Article  Google Scholar 

  20. Narula R, Hagedoorn J (1999) Innovating through strategic alliances: moving towards international partnership and contractual agreements. Technovation 19:283–294

    Article  Google Scholar 

  21. Nicolini R, Artige L (2008) Investigación y Desarrollo más Innovación: Recomendaciones para políticas públicas más cercanas a las necesidades de las empresas. CES Edition, Madrid

    Google Scholar 

  22. Pérez-Castrillo D, Sandonís J (1996) Disclosure of know-how in research joint ventures. Int J Ind Organ 15:51–75

    Article  Google Scholar 

  23. Roijakkers N, Hagedoorn J (2006) Inter-firm R&D partnering in pharmaceutical biotechnology since 1975: trends, patterns and networks. Res Policy 35:431–446

    Article  Google Scholar 

  24. Segrestin B (2005) Partnering to explore: the Renault-Nissan Alliance as a forerunner of new cooperative patterns. Res Policy 35:657–672

    Article  Google Scholar 

  25. Singh N, Vives X (1984) Price and quantity competition in a differentiated duopoly. Rand J Econ 15:546–554

    Article  Google Scholar 

  26. Stoneman P (1981) Intrafirm diffusion, Bayesian learning and profitability. Econ J 91:375–388

    Article  Google Scholar 

  27. UNCTAD (2005) World investment report 2005: Transnational corporation and internationalization of R&D, Geneva

  28. Veugelers R, Kesteloot K (1994) On the design of stable joint ventures. Eur Econ Rev 38:1799–1815

    Article  Google Scholar 

  29. Vives X (1999) Oligopoly pricing. Old ideas and new tools. The MIT Press, Cambridge

    Google Scholar 

  30. Zeller Ch (2004) North Atlantic innovative relations of Swiss pharmaceuticals and the proximities with regional biotech arenas. Econ. Geogr. 80:83–111

    Article  Google Scholar 

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We are grateful to R. Devaney, X. Jarque, D. Pérez-Castrillo, J. Sandonís, two anonymous referees, and participants at several conferences and seminars for their useful suggestions and discussions. We gratefully acknowledge the financial support from research projects 2009SGR-169, ECO2009-7616, and Consolider-Ingenio 2010 (Xavier Martinez-Giralt), 2009SGR-00600 and SEJ2008-01850/ECON (Rosella Nicolini). Xavier Martinez-Giralt is a research fellow of MOVE (Markets, Organizations and Votes in Economics). The usual disclaimer applies.

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Correspondence to Xavier Martinez-Giralt.


Appendix A: Proof of Proposition 2

Here, firms compete à la Bertrand in the market. The equilibrium prices, quantities, and profits are,


where \(\overline{a}_{1}=a_{1}-\mu \lambda_{0}(1-\lambda_{0})[b_{1}-c\xi ]\) and \(\overline{a}_{2}=a_{2}-\mu \lambda_{0}(1-\lambda_{0})[b_{2}\xi -c]\).

As before, given the symmetry of the problem, we concentrate on the behavior of firm 1. Firm 1 evaluates the benefits it can get from the agreement and compares the level of profits with and without the agreement. That is, it compares profits in (6) and (16). Participating in an agreement will be profitable if and only if,

$$ b_{1} \biggl( \frac{2b_{2}\overline{a}_{1}+ c\overline{a}_{2}}{4b_{1}b_{2}-c^{2} } \biggr)^{2}> b_{1} \biggl( \frac{2b_{2}\hat{a}_{1}+c\hat{a}_{2}}{ 4b_{1}b_{2}-c^{2}} \biggr)^{2}. $$

After some algebraic computations, the previous inequality reduces to,

$$ b_1\bigl[ 1-\mu \lambda_{0}(1-\lambda_{0})\bigr] \bigl(2b_{1}b_{2}-b_{2}c\xi -c^{2} \bigr)>0. $$

Note that (18) differs from (12) in the second term in (round) brackets. Note also that this term is independent of λ 0. The second term in round brackets is concave in c, and has one positive and one negative root. Therefore, for positive values of c smaller than the positive root, the term (2b 1 b 2b 2 c 2) is positive, and inequality (18) behaves as (12). Thus, we obtain the same result as in the monopoly case. In contrast, for large enough values of c (beyond the positive root), the term (2b 1 b 2b 2 c 2) is negative, so that the inequality is fulfilled when [1−μλ 0(1−λ 0)]<0 that is, for \(\lambda_{0}\in (\frac{1-\overline{\mu }}{2},\frac{1+\overline{\mu }}{2})\), where \(\overline{\mu }= (\frac{\mu -4}{\mu } )^{1/2}\in (0,1)\), for μ>4.

When 0<μ≤4 the first term of (18) in square brackets is always positive. Hence, a solution exists only if the second term in round brackets is positive, i.e. for positive values of c smaller than the positive root.

Appendix B: Proof of Proposition 3

We need to prove that Λ is a Cantor set, namely, that it is a closed, perfect and totally disconnected subset of I. Following Devaney (1985), we structure the proof in three steps.

  1. 1.

    Λ is a closed set. Let us define G(λ 0)=1−F(λ 0) and re-write it as G≡1−F. By construction A i is an open interval centered around 1/2 (see Fig. 4). Let us concentrate on A 0. In that case, the function G maps both the intervals I 0=[0,λ 1] and I 1=[λ 2,1] monotonically onto I. Moreover, G is decreasing on the first interval and increasing on the second. Since G(I 0)=G(I 1)=I there is a pair of intervals (one in I 0 and the other in I 1) which are mapped onto A 0 by G. These intervals define the set A 1. Next, let us consider Λ 1=I−(A 0A 1). This set consists of four closed intervals (see Fig. 4) and G maps them monotonically onto either I 0 or I 1, but as before, each of the four intervals contains an open subinterval which is mapped by G 2 onto A 0, i.e., the points of this interval escape from I after the third iteration of G. By applying this iterative process, we note that A t consists of 2t disjoint open intervals and Λ t =I−(A 0∪…∪A t ) consists of 2t+1 closed intervals. Hence, Λ is a nested intersection of closed intervals, and is thus a closed set.

  2. 2.

    Λ is a perfect set. Note that all endpoints of A t , (t=1,…) are contained in Λ. Such points are eventually mapped to the fixed point of G at 1, and they stay in I under iteration. If a point xΛ, were isolated, each nearby point must leave I under iteration, and therefore these points must belong to some A t . Two possibilities arise. We can think of a sequence of endpoints of A t converging to x. In this case the endpoints of A t map to 1 and so, they are in Λ. Alternatively, all points in a deleted area nearby x are mapped out of I by some iteration of G. In this case, we may assume that G τ maps x to 1 and all the other nearby points are mapped in the positive axis above 1. Then G τ has a minimum at x, i.e., \(G_{\tau }^{\prime }(x)=0\). This iterative process ensures that it must be so for some t<τ. Hence, G t (x)=1/2, but then G t+1(x)∉I and G τ (x)→−∞, contradicting the fact that G τ (x)=1.

  3. 3.

    Λ is a totally disconnected set. Let us focus in the first iteration and assume μ is large enough so that |G′(x)|>1 for all xI 0I 1. For those values of μ, there exists γ>1 such that |G′(x)|>γ for all xΛ. Our iterative process yields \(\vert G_{\tau }^{\prime}(x)\vert >\gamma_{\tau }\). We want to prove that Λ does not contain any interval. Let us proceed by contradiction and assume that there is a closed interval [x,y]∈Λ, x,yI 0I 1, xy. In this case, \(\vert G_{\tau }^{\prime }(z)\vert>\gamma_{\tau }\), for all z∈[x,y]. Choose τ so that λ τ |yx|>1. Applying the Mean Value Theorem, it follows that |G τ (y)−G τ (x)|≥γ τ |yx|>1, implying that either G τ (y) or G τ (x) lies outside of I. This contradicts with our main hypothesis, and thus Λ does not contain intervals. It remains to be determined the μ-values for which the previous argument holds. Finding the values of μ allowing |G′(x)|>1 means to identify μ-values for which [−μ(1−2x)]2>1. When G=0, this inequality holds for \(\mu >2+\sqrt{5}\). Thus, we have proven that Λ is totally disconnected for \(\mu >2+\sqrt{5}\). Recall from Lemma 1 that we already know that μ>4. Hence, we need to verify whether Λ is also totally disconnected for \(\mu\in (4, 2+\sqrt{5}]\). We appeal to Kraft (1999) who establishes that Λ is a Cantor set for μ>4. The idea behind the proof is that for \(\mu\in (4, 2+\sqrt{5}]\) it turns out that |G′(x)|⪋1. Kraft argues that the iteration process shrinks some components of I, and stretches some others. His proof thus consists in showing that in the interval \((4, 2+\sqrt{5})\) the stretching is dominated by the shrinking. To this end, he proves that Λ is an hyperbolic set, namely that \(\vert G_{\tau }^{\prime}(x)\vert>k\delta_{\tau }>1\) for xΛ, k>0, δ>1.

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Martinez-Giralt, X., Nicolini, R. Technological endowments in entrepreneurial partnerships. Comput Math Organ Theory 19, 601–621 (2013).

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  • Cantor set
  • Technology diffusion
  • Logistic function
  • Discrete time