Anna Wulf and Lynne Butel
The sharing of knowledge between partners in collaborative relationships is widely accepted to be fundamental to supporting strategic decision making, particularly in relation to…
Abstract
Purpose
The sharing of knowledge between partners in collaborative relationships is widely accepted to be fundamental to supporting strategic decision making, particularly in relation to innovation management and business sustainability. The purpose of this paper is to focus on how the structure of collaborative relationships in business networks may determine successful knowledge sharing and thus improve decision making and business performance.
Design/methodology/approach
Expert interviews were conducted with participants operating in networks and business ecosystem in four different sectors in Italy and Germany, exploring the process of knowledge sharing, organisational learning and decision making within collaborative relationships. A qualitative textual analysis was used to analyse the experts’ responses.
Findings
The research found that an organisation’s network position and the network structure, as well as the governance and richness of the business ecosystem in which it operates, influence its ability to share knowledge, to innovate and therefore to compete sustainably.
Research limitations/implications
The research demonstrates that innovative strategic decision making, based on access to appropriate knowledge, occurs within the context of social and business network relations operating within a broader more diverse business ecosystem. Closer dyadic or small working group ties best facilitate trust and sharing of the most valuable knowledge. Appropriate participation in and management of such structures is therefore essential to support knowledge-based decision making, and critical to sustained competitive advantage.
Originality/value
The research focusses on how interfirm relationships are established and maintained, how firms establish trust and facilitate knowledge sharing forming the basis of organisational learning.
Details
Keywords
Lynne Butel and Alison Watkins
Entrepreneurs operate in conditions of dynamic uncertainty; identifying and exploiting opportunities presented by the business environment. Opportunistic search is core to…
Abstract
Purpose
Entrepreneurs operate in conditions of dynamic uncertainty; identifying and exploiting opportunities presented by the business environment. Opportunistic search is core to entrepreneurial activity, but its dynamics are rarely explored. Groups of entrepreneurs are attracted to the same potential business opportunities. They have no incentive to cooperate, they may not even know of the existence of others. However, over time, clusters of entrepreneurs interested in the same opportunities develop. Aims to discuss the issues.
Design/methodology/approach
Ant colony optimisation modelling is used to simulate the activities of entrepreneurs in an opportunity rich environment. The entrepreneurs must identify the locations of the appropriate resources. Three simulations were run to observe entrepreneurial success in different environments.
Findings
A random search of the business environment for resources by individual entrepreneurs was unproductive. Once the entrepreneurs learned to read the business environment and so refine their search, they were increasingly efficient. This was even more pronounced when time allowed for search was constrained and weaker entrepreneurs had little influence.
Research limitations/implications
The computer simulations demonstrate how a cluster of entrepreneurial activity may begin. The results raise questions about the appropriateness of policies supporting entrepreneurial activity and about the path dependency of cluster development. Empirical research is now needed to test these research implications.
Originality/value
Focusing on the little explored dynamics of opportunistic search by would‐be entrepreneurs in a spatially defined business environment combines previous research in the fields of entrepreneurial outcomes and cluster development. Using a multi‐agent search model to simulate the dynamic interaction of a number of entrepreneurs in the same business environment demonstrates early cluster formation without the protagonists relying on cooperative, competitive or value chain interaction.