Volume 6, Issue 1 -

The purpose of the research is to demonstrate how recognition of complex systems or complexity science enhances recognition and achievement of entrepreneurial opportunities. A further objective is to provide an integrated source of readily available information about a number of research papers that demonstrate the role of complex systems in entrepreneurial activities. Papers reviewed include those by McKelvey, Andriani, Boisot, Dooley, Siggelkow, Chiles, Plowman, Lichtenstein, Carbonara, Crawford, and others. These papers illustrate how complex systems operate, and how this can be used to develop entrepreneurial emergence. The research also seeks to identify an integrated model of emergence to provide a framework for entrepreneurship researchers and provide assistance to entrepreneurs. Lichtenstein’s Generative Emergence provides the most comprehensive model found to structure opportunity achievement. While Lichtenstein’s comprehensive model has not been tested, vis-a-vis other comprehensive entrepreneurial models, the components of the model have been tested. Overall Lichtenstein’s Generative Emergence model is strongly endorsed.

Emergence is at the nexus of entrepreneurship and complexity science because the former studies how and why new organizations emerge, and complexity examines the emergence of new order in dynamic systems. As a means of summarizing work in emergence, this invited paper presents six insights that complexity science has brought to entrepreneurship: An emergence process generates an emergent outcome; emergence is driven by agency; emergence increases the capacity of a system; emergence occurs in cycles; emergence follows a distinct logic; and entrepreneurial emergents follow an 80/20 rule.

This “comment” begins with a summary of the various complexity concepts (ingredients) that relate to the creation and development of effective startup companies. No single complexity concept is sufficient to create an effective entrepreneurial startup, but a random mix of organizational components and behaviors is also ineffective. As the competitive environments of entrepreneurial firms change, they also need to continually change their mix of complexity ingredients to remain competitive. In moving from biology to digital business , the most dominant evolutionary difference for companies is the much higher speed at which complexity dynamics – emergence, self-organization, and the creation of new order – occur. In a dynamic business ecosystem, firms will quickly disappear that don’t recognize digital-business effects and adapt to the high speed of coevolution amongst competitors.

Despite widespread recognition that an enterprise’s critical resources may extend beyond the enterprise’s traditional boundaries, with the focal enterprise drawing upon the resources of other firms and institutions through networks, there is a dearth of empirical research on knowledge mobility and appropriability patterns among innovative Australian small and medium-sized enterprises (SMEs) through the lens of complexity science. We address this gap, by examining what, how, and why innovation-related knowledge flows from networks into SMEs, and how SMEs protect intellectual property (IP) and appropriate value. Based on a survey of 838 SMEs, we find patterns of internal and external knowledge flows with SMEs searching for ideas internally, and via market-based networks, with internally sourced ideas having the strongest impact on innovativeness. The results also show SMEs are most likely to network with market-based agents relative to localised learning networks. Further, networking with suppliers increases innovativeness, as does sourcing knowledge as part of a package with the purchase of new equipment, underscoring the importance of the vertical supply chain network. Despite limited interaction with localised learning networks, outsourcing R&D to these networks increases innovativeness. We also find that informal IP, in particular, secrecy, complexity of product design, and frequent and rapid changes to products/services increases innovativeness, as do formal copyrights and trademarks. In addition to protecting IP, these practices are product market strategies, enabling SMEs to commercialise innovations and appropriate value. But while appropriability mechanisms provide innovation benefits to individual agents, from the perspective of complexity science, IP mechanisms act as barriers to effective knowledge flows (e.g. information sharing) preventing innovative networking through the mechanism of a positive feedback loop to evolve to the state where distributed intelligence comes into play and facilitates break-through innovations.

This paper proposes that social entrepreneurial ventures (SEVs) operate as systems of systems (SoS), a collection of complex systems working together to achieve an overall outcome in line with its mission. Previous literature shows that complexity theory is applicable to the unique structure of SEVs and the dual goals of creating economic and social value. We argue that not only are SEVs complex systems, but also operate as a SoS. As such, SEVs could greatly benefit from a general systemic risk management approach that is often applied to SoS. In order to explain our position, we consider an example of an existing SEV and apply these concepts to their operations. We also identify systemic risks in the example and show how the application of a systemic risk management approach can benefit SEVs.