Abstract
Cooperation can benefit and hurt firms at the same time. An important question then is: when is it better to cooperate? And, once the decision to cooperate is made, how can an appropriate partner be selected? In this paper we present a model of inter-firm cooperation driven by cognitive distance, appropriability conditions and external knowledge. Absorptive capacity of firms develops as an outcome of the interaction between absorptive R&D and cognitive distance from voluntary and involuntary knowledge spillovers. Thus, we offer a revision of the original model by Cohen and Levinthal (Econ J 99(397):569–596, 1989), accounting for recent empirical findings and explicitly modeling absorptive capacity within the framework of interactive learning. We apply that to the analysis of firms’ cooperation and R&D investment preferences. The results show that cognitive distance and appropriability conditions between a firm and its cooperation partner have an ambiguous effect on the profit generated by the firm. Thus, a firm chooses to cooperate and selects a partner conditional on the investments in absorptive capacity it is willing to make to solve the understandability/novelty trade-off.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Henceforth, knowledge in this sense includes technologies that firms use in innovation. Innovation refers to a technically new product which develops as an outcome of R&D (see the Oslo Manual, OECD 2005). Consequently, by R&D profit we imply profit due to innovation.
Although recent studies have argued that absorptive capacity, being a multidimensional concept, is not fully proxied by R&D or staff quality alone (Flatten et al. 2011; Zahra and George 2002), we assume that a significant portion of it is embodied in R&D performance. Therefore, our conceptualisation of absorptive capacity in this paper derives mainly from a firm’s R&D investments.
Some studies (e.g., Cantner and Meder 2007, Mowery et al 1996) have also shown that cognitive proximity reduces over time. This affects the learning and innovation potential of an alliance and reduces the likelihood that the same partners will cooperate in the next period. This dynamic is important and we address it in a subsequent paper.
This argument is important for our model and will be applied later in modeling the firm’s profit.
In our model we are concerned with firms competing on the same technological trajectory. In the extreme case that the cooperating partners operate in different industries, competition between them is mostly negligible. In this case, spillovers do not constitute a disincentive to cooperation and R&D investments (Cantner and Pyka 1998, p. 374).
Distance, in this sense, includes not only cognitive distance but also organisational, social, institutional and geographical ones (Boschma 2005). For instance, Dettmann and von Proff (2010) demonstrated that organisational and institutional proximity facilitate patenting collaboration over large geographical distances. Wuyts et al. (2005) demonstrated that, depending on the industry, organisational and strategic proximity are sometimes more important in the formation of alliances. And the literature on economic geography is coherent on the relevance of geographic distance in knowledge transfer; the greater the distance, the more knowledge decays (Boschma 2005). Nevertheless, since our study is concerned with knowledge sharing, it is more appropriate to concentrate on cognitive distance.
In a dynamic sense, cognitive overlap tends to increase with cooperation intensity Mowery et al. (1998). Thus, it is expected that a firm will reconsider its cooperation decisions depending on cognitive distance. Alliances may be discontinued when partners become too close and previously discontinued alliances may be re-formed if the partners have become sufficiently distant in terms of their knowledge endowment.
Even in this framework the understandability–novelty trade-off exists. In the context of exploitation, wherein firms are concerned with improving their performance along the same technological trajectory, a high level of mutual understanding is required to reduce transaction costs (Drejer and Vindig 2007; Cantner and Meder 2007). Notwithstanding, since technological opportunities within a certain trajectory tend to decrease continuously according to Wolff’s law (Cantner and Pyka 1998), firms seek for more explorative or extensive opportunities, the aim of which is to generate novelty. Consequently, increasing cognitive distance positively influences the value of interactive learning because it raises the novelty value of technological opportunities as well as the possibility of novel combinations of complementary resources. This is, however, only possible as long as the partners are close enough to understand each other.
This means that technological fit, rather than social capital factors like trustworthiness and embeddedness, is a major causal force behind alliance formation (Baum et al. 2010). Firms will select partners from whom they can learn significantly and for specific (short-term) purposes. In this sense, multiple partnerships may not be necessary and firms stop their partnership search once they find a technologically fit partner.
In the dynamic setting that we analyse in Savin and Egbetokun (2013), cognitive distance changes according to the innovation success and learning of the firms.
This does not necessarily eliminate the risks associated with innovation. First, firms need to be able to understand the information available, an endeavour which is by itself costly and risky. Then, innovation still runs the risk of failing, irrespective of how well-informed firm’s cooperation decisions are.
We abstract from production and the market by treating the R&D budgets as exogenous. In this way, the focus of the model is narrowed to the firm’s investment and cooperation decisions, and innovation.
This follows partly from our focus on dyadic partnerships. In this sense, knowledge spillovers from other firms not in the dyad and from public organisations together constitute technological opportunities for the dyad.
The exact number of periods constituting a reasonable expectation is best validated in a simulation model (Savin and Egbetokun 2013).
However, in the dynamic setting that we simulate subsequently, Eq. (4) necessarily introduces some uncertainty as the expectation of firm i will not necessarily coincide with the actual investment decision of firm j, which, in turn, is based on its own expectation about firm’s i decision: E i(ρ j,t )≠ρ j =f(E j(ρ i,t )).
This fraction is determined by the appropriability conditions which include the patent system in a particular industry and the efficacy of secrecy or other forms of protection of firm j’s internal knowledge.
Note that while cognitive distance is symmetric (i.e. d i j =d j i ), a n i,j and a c i,j are asymmetric. This is because the investment trade-off is not solved by the two companies identically (i.e. absorptive R&D investments are not necessarily the same for the two companies).
This is similar to the conceptutalisation by Lane and Lubatkin (1998) of absorptive capacity as ‘a learning dyad-level construct’.
As in Eq. (9), a c i,e k =f(d i e k ).
Recall that in CL \(\frac {\partial \Pi _{i}}{\partial k_{i}}>0\), \(\frac {\partial \Pi _{i}}{\partial k_{j}}<0\) and \(\frac {\partial \Pi _{i}}{\partial k_{i}\partial k_{j}}<0\).
Once we address the dynamics of firms in the knowledge space, the notion of radical innovation will also be required. However, for the sake of brevity we do not include its discussion in this study.
A deterministic iterative solution (e.g., according to the fixed-point theorem) is also not applicable as the function does not necessarily always converge to a ρ i ∈[0,1] for all possible combinations of parameters.
For illustrative reasons we take a single set of parameter values for two firms satisfying their constraints. In particular, \(\alpha =\sqrt {2}/50\), \(\beta _{1}=\sqrt {2}\), β 2=1, ψ=ξ=0.4, R D i =R D j =0.2, δ c =0.5, e k=1, ρ j =0.5, \(d_{iek}=\sqrt {2}/1.001\) and \(d_{ij}=\sqrt {2}/1.01\). These values were chosen to demonstrate on a single set of graphs the complex shape of the ρ and π functions in response to changes in the variables of interest.
‘a stable network is one in which for each agent (or pair of agents) there is a payoff maximizing decision about which link to form’ (Cowan et al. 2007, p. 1052)
For the sake of comparison, CL’s ease of learning is analogous to our cognitive distance, intra-industry spillovers - to appropriability conditions and technological opportunities - to external knowledge.
Note that by construction, in CL firm i’s marginal returns to R&D have the same effect on marginal returns generated by the firm in terms of profit.
For instance, setting investment decision of the partner ρ j =0.75, π i in the cooperating scenario shows only a small downturn and then rises consistently outperforming the non-cooperating scenario.
References
Arrow K (1962) Economic welfare and the allocation of resources for invention. In: Nelson R (ed) The rate and direction of inventive activity. Princeteon University Press, Princeteon, pp 609–626
Atallah G (2003) Information sharing and the stability of cooperation in research joint ventures. Econ Innov New Tech 12(6):531–554
Bamford J, Ernst D (2002) Managing an alliance portfolio. McKinsey Q 3:29–39
Baum JAC, Cowan R, Jonard N (2010) Network-independent partner selection and the evolution of innovation networks. Manag Sci 56(11):2094–2110. doi:10.1287/mnsc.1100.1229
Blueschke D, Blueschke-Nikolaeva V, Savin I (2013) New insights into optimal control of nonlinear dynamic econometric models: application of a heuristic approach. J Econ Dyn Control 37(4):821–837
Boschma R (2005) Proximity and innovation: a critical assessment. Reg Stud 39(1):61–74
Brusoni S, Prencipe A, Pavitt K (2001) Knowledge specialization, organizational coupling, and the boundaries of the firm: why do firms know more than they make? Adm Sci Q 46(4):597–621
Cantner U, Meder A (2007) Technological proximity and the choice of cooperation partner. J Econ Interac Coord 2:45–65
Cantner U, Pyka A (1998) Absorbing technological spillovers: simulations in an evolutionary framework. Ind Corp Chang 7(2):369–397
Cassiman B, Pérez-Castrillo D, Veugelers R (2002) Endogenizing know-how flows through the nature of R&D investments. Int J Ind Organ 20(6):775–799
Cohen W, Levinthal D (1989) Innovation and learning: the two faces of R&D. Econ J 99(397):569–596
Cowan R, Jonard N, Zimmermann J (2007) Bilateral collaboration and the emergence of innovation networks. Manag Sci 53(7):1051–1067
De Bondt R (1996) Spillovers and innovative activities. Int J Ind Organ 15(1):1–28
Dettmann A, von Proff S (2010) Inventor collaboration over distance – a comparison of academic and corporate patents. Tech. Rep. 2010–01, Working Papers on Innovation and Space
Dosi G (1982) Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Res Policy 11(3):147–162
Drejer I, Vindig A (2007) Searching near and far: determinants of innovative firms’ propensity to collaborate across geographical distance. Ind Innov 14(3):259–275
Egbetokun E, Savin I (2014) Absorptive capacity and innovation: when is it better to cooperate? In: Foster J, Pyka A (eds) Co-evolution and complex adaptive systems in evolutionary economics, Springer series economic complexity and evolution. Springer, Berlin, Heidelberg
Fehr E, Gächter S (2000) Fairness and retaliation: the economics of reciprocity. J Econ Perspect 14:159–181
Flatten T, Engelen A, Zahra S, Brettel M (2011) A measure of absorptive capacity: scale development and validation. Eur Manag J 29(2):98–116
de Fraja G (1993) Strategic spillovers in patent-races. Int J Ind Organ 11(1):139–146
de Jong J, Freel M (2010) Absorptive capacity and the reach of collaboration in high technology small firms. Res Policy 39(1):47–54
de Man AP, Duysters G (2005) Collaboration and innovation: a review of the effects of mergers, acquisitions and alliances on innovation. Technovation 25(12):1377–1387
Gilsing V, Nooteboom B, Vanhaverbeke W, Duysters G, van den Oord A (2008) Network embeddedness and the exploration of novel technologies: technological distance, betweenness centrality and density. Res Policy 37(10):1717–1731
Gulati R (1998) Alliances and networks. Strat Manag J 19(4):293–317
Hall B, Jaffe A, Trajtenberg M (2005) Market value and patent citations. RAND J Econ 36(1):16–38
Hammerschmidt A (2009) No pain, no gain: an R&D model with endogenous absorptive capacity. J Inst Theor Econ 165(3):418–437
Kamien M, Zang I (2000) Meet me halfway: research joint ventures and absorptive capacity. Int J Ind Organ 18(7):228–240
Kim L (1998) Crisis construction and organizational learning: capability building in catching-up at Hyundai motor. Organ Sci 9(4):506–521
Klepper S (1996) Entry, exit, growth, and innovation over the product life cycle. Am Econ Rev 86(3):562–583
Lane PJ, Lubatkin M (1998) Relative absorptive capacity and interorganizational learning. Strat Manag J 19(5):461–477
Lin C, Wu YJ, Chang C, Wang W, Lee CY (2012) The alliance innovation performance of R&D alliances: the absorptive capacity perspective. Technovation 32(5):282–292
March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2(1):71–87
Mowery DC, Oxley JE, Silverman BS (1996) Strategic alliances and interfirm knowledge transfer. Strat Manag J 17:77–91
Mowery DC, Oxley JE, Silverman BS (1998) Technological overlap and interfirm cooperation: implications for the resource-based view of the firm. Res Policy 27(5): 07–523
Nooteboom B (1999) Inter-firm alliances: analysis and design. Routledge, London
Nooteboom B, Haverbeke WV, Duysters G, Gilsling V, van den Oord A (2007) Optimal cognitive distance and absorptive capacity. Res Policy 36(7):1016–1034
OECD (2005) Proposed guidelines for collecting and interpreting technological innovation data: Oslo manual. Organisation for economic cooperation and development, Paris
Oxley J (1997) Appropriability hazards and governance in strategic alliances: a transaction cost approach. J Law Econ Org 13(2):387–409
Powell W (1998) Learning from collaboration: knowledge and networks in the biotechnology and pharmaceutical industries. Calif Manag Rev 40(3):228–240
Powell WW, Grodal S (2005) Networks of innovators. In: Fagerberg J, Mowery DC, Nelson RR (eds) Oxford handook of innovation, vol 3. Oxford University Press, Oxford, pp 56–85
Savin I, Egbetokun A (2013) Emergence of innovation networks from R&D cooperation with endogenous absorptive capacity. Tech. Rep. 13/022, CEB Working Paper
Storn R, Price K (1997) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Wiethaus L (2005) Absorptive capacity and connectedness: why competing firms also adopt identical R&D approaches. Int J Ind Organ 23(5–6):467–481
Wuyts S, Colombo M, Dutta S, Noteboom B (2005) Empirical tests of optimal cognitive distance. J Econ Behav Organ 58(2):277–302
Zahra S, George G (2002) Absorptive capacity: a review, reconceptualization, and extension. Acad Manag Rev 27(2):185–203
Author information
Authors and Affiliations
Corresponding author
Additional information
Financial support from the German Science Foundation (DFG RTG 1411) is gratefully acknowledged. Thanks are due to very helpful comments and suggestions from Uwe Cantner and Robin Cowan as well as from participants at the SPRU 18th DPhil Day in Brighton, 14th International J. A. Schumpeter Society Conference in Brisbane and 6th Summer Academy on Innovation and Uncertainty in Jena. The usual disclaimers apply.
Rights and permissions
About this article
Cite this article
Egbetokun, A., Savin, I. Absorptive capacity and innovation: when is it better to cooperate?. J Evol Econ 24, 399–420 (2014). https://doi.org/10.1007/s00191-014-0344-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00191-014-0344-x