Alliances within Industry – how to discern what number of partners will achieve optimum benefit

To keep apace of a swift-moving era of innovation and to mitigate the effects of technological uncertainty, defined here as the difficulty of accurately predicting the future state of the technological environment in which they operate, firms often seek to enter into alliances with other firms.

These alliances allow firms to learn from their partners about relevant technologies being developed externally. They can also help firms stay informed about the general progress and direction of an industry's technology, and tip them off to future development opportunities. However, alliances do not prove equally effective in reducing technological uncertainty in all circumstances. The formation of such alliances can also induce costs. Therefore, it is important to discern the optimal number of alliances to produce a balance between benefits and costs - so there are enough alliances to reduce uncertainty, but not so many that the costs then outweigh the benefits. Empirical evidence thus shows that learning-related outcomes tend to be greatest at intermediate alliance portfolio size.

Existing theory and research in this field has centred on firms' relationships with direct partners. However, findings suggest that alliances may also serve as conduits through which firms learn from indirect partners. These are the set of firms that direct partners have access to through their own alliances. This research therefore also looks at how these indirect partners shape a firm's alliance portfolio, an area that has received significantly less focus in prior research.

The researchers developed a theoretical Bayesian learning model to examine how a firm's learning horizon, or the maximum distance in a network of alliances across which the firm learns from other firms, influences its optimal number of direct alliance partners. A 'close' learning horizon, where a firm learns only from direct partners, and a 'distant' learning horizon, where a firm learns from both direct and indirect alliances, were considered.

The model demonstrated that in a high tech industry, where technological uncertainty is comparatively high, residual uncertainty is costly, and where alliances are comparatively affordable and effective, firms with a distant learning horizon can substitute alliance ties to indirect partners for those with direct partners.

In contrast, in a low tech industry, where technological uncertainty is comparatively low, residual uncertainty is less costly, and where alliances are a comparatively costly and ineffective solution to technological uncertainty, indirect partners complement direct partners.

As part of this research, the technological performance of 347 firms engaged in alliances within the information technology industry between 1970 and 1999 was also analysed. Results consistently showed that firms well-connected to a broad range of indirect partners achieved higher performance with a relatively smaller number of direct partners. This finding suggests that to achieve a better grasp of development opportunities within the industry, indirect partners prove most cost-effective, and therefore identifying which direct partners are the best-connected becomes key.

A draft version of the research paper Learning Horizon and Optimal Alliance Formation is available below. The final version is forthcoming in Computational and Mathematical Organization Theory

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