Mobilising networks to achieve strategic difference

This article examines the strategies for achieving differentiation through a network approach. The generic strategies models of positioning and the resource-based view have opened up the possibility of strategic difference, but, drawing on a social network approach, this article argues that, rather than accepting strategy as simply market given or internally driven, firms can draw on their unique social networks of relations to make a difference to their strategies. Illustrations from three successful Taiwanese computer firms seeking to secure differentiation of action show that one enjoys a policy-driven network advantage over its domestic rivals, another has developed both technological and familial networks, while the third exploits its established management and production links in the Taiwanese industrial culture. These comparative case studies illustrate that strategies for achieving differentiation can be based on a wide variety of external social networks of relationships (including political, familial, friendship and alumni links, as well as alliances via boards, trade unions and banks etc). The implications for managers are that firms need to expand their external networks of relationships to secure their survival and growth, and should seek to identify and develop structures that are resourceful, rare and inimitable.     

Resolution dependence of the maximal information coefficient for noiseless relationship

Reshef et al. (Science 334:1518–1523, 2011) introduce the maximal information coefficient, or MIC, which captures a wide range of relationships between pairs of variables. We derive a useful property which can be employed either to substantially reduce the computer time to determine MIC, or to obtain a series of MIC values for different resolutions. Through studying the dependence of the MIC scores on the maximal resolution, employed to partition the data, we show that relationships of different natures can be discerned more clearly. We also provide an iterative greedy algorithm, as an alternative to the ApproxMaxMI proposed by Reshef et al., to determine the value of MIC through iterative optimization, which can be conducted parallelly.