Darwinism, probability and complexity : market-based organizational transformation and change explained through the theories of evolution

The study of transformation and change is one of the most important areas of social science research. This paper synthesizes and critically reviews the emerging traditions in the study of change dynamics. Three mainstream theories of evolution are introduced to explain change: the Darwinian concept of survival of the fittest, the Probability model and the Complexity approach. The literature review provides a basis for development of research questions that search for a more comprehensive understanding of organizational change. The paper concludes by arguing for the development of a complementary research tradition, which combines an evolutionary and organizational analysis of transformation and change

Derivative Process Model of Development Power in Industry: Empirical Research and Forecast for Chinese Software Industry and US Economy

Based on concept and theory of Development Power [1], this paper analyzes the transferability and the diffusibility of industrial development power, points out that the chaos is the extreme of DP releasing and order is the highest degree of DP accumulating, puts forward A-C strength, the index of adjusting and controlling strength, and sets up the derivative process model for industrial development power on the Partial Distribution [2]-[4]. By the derivative process model, a kind of time series model, we can describe the process of industrial development effectively, and can forecast the future direction of industry or economy on using with [7]. Finally, by making use of the actual data of Chinese software industry and data of USA GDP (chained) price index, we give the examples of empirical analysis, and forecast the future of Chinese software industry and USA economic development. The conclusions in this paper are believed to be valuable and significant to guide the establishment of the industrial policy and to control the industrial development.development power (DP), partial distribution, derivative process, industry and macroeconomy, empirical research, forecast analysis

"Open Innovation" and "Triple Helix" Models of Innovation: Can Synergy in Innovation Systems Be Measured?

The model of "Open Innovations" (OI) can be compared with the "Triple Helix of University-Industry-Government Relations" (TH) as attempts to find surplus value in bringing industrial innovation closer to public R&D. Whereas the firm is central in the model of OI, the TH adds multi-centeredness: in addition to firms, universities and (e.g., regional) governments can take leading roles in innovation eco-systems. In addition to the (transversal) technology transfer at each moment of time, one can focus on the dynamics in the feedback loops. Under specifiable conditions, feedback loops can be turned into feedforward ones that drive innovation eco-systems towards self-organization and the auto-catalytic generation of new options. The generation of options can be more important than historical realizations ("best practices") for the longer-term viability of knowledge-based innovation systems. A system without sufficient options, for example, is locked-in. The generation of redundancy -- the Triple Helix indicator -- can be used as a measure of unrealized but technologically feasible options given a historical configuration. Different coordination mechanisms (markets, policies, knowledge) provide different perspectives on the same information and thus generate redundancy. Increased redundancy not only stimulates innovation in an eco-system by reducing the prevailing uncertainty; it also enhances the synergy in and innovativeness of an innovation system.Comment: Journal of Open Innovations: Technology, Market and Complexity, 2(1) (2016) 1-12; doi:10.1186/s40852-016-0039-

An emergence perspective on entrepreneurship: processes, structure and methodology

This paper explores entrepreneurship from the perspective of emergence, drawing on literature in complexity theory, social theory and entrepreneurship. Entrepreneurship is conceptualised as the production of emergence, or emergent properties, via a simple model of initial conditions, processes of emergence that produces emergent properties at multiple levels (new phenomena such as products, services, firms, networks, patterns of behaviour, identities). Conceptualisation through emergence thus embraces actors, context, processes and (structural) outcomes. This paper builds on previous work that theorises the relationship between entrepreneurship and social change. We extend that work by considering the methodological implications of relating processes of entrepreneurship to the emergence of new phenomena