Organizational adaptation to
ABSTRACT A computational model of organizational adaptation in which change occurs at both the strategic and the operational level is presented. In this model, simulated annealing is used to alter the organization's structure even as the agents within the organization learn. Using this model a virtual experiment is run to generate hypotheses which can be tested in multiple venues. The results suggest that, although it may not be possible for organizations of complex adaptive agents to locate the optimal form, they can improve their performance by altering their structure. Moreover, organizations that most successfully adapt over time come to be larger, less dense, with fewer isolated agents, and fewer overlooked decision factors. These results have implications for organizations of both humans and non-humans. For example, they suggest that organizational learning resides not just in the minds of the personnel within the organization, but in the connections among personnel, and among personnel and tasks. These results suggest that collections of non-humans may come to seem more intelligent (i.e., show improved performance) even if the agents remain unchanged if the system simply develops duplicate copies of some of the artificial agents and if the connections among agents are dynamically altered.
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ABSTRACT: It is argued that diverse complex adaptive systems, such as proteins, cells, organisms, organizations, societies and ecosystems, all together constitute one developing, multiscale continuum-economy composed of interacting and interdependent adaptive organizational forms that co-exist and co-evolve at different spatiotemporal scales, forming a nested set of interdependent organizational hierarchies. When reconceptualized in equivalent terms of self- organizing adaptive networks of energy/matter/information exchanges, complex systems of different scales appear to exhibit universal scale-invariant patterns in their organization and dynamics, suggesting the self-similarity of spatiotemporal scales and fractal organization of the living matter continuum. The self-organization of biomolecules into cells, cells into organisms and organisms into societies and ecosystems is presented here in terms of a universal scale-invariant organizational process driven by economy and assisted by memory and innovation. It is driven by economy as individual adaptive organizations compete and cooperate at every scale in their efforts to maximize the rate and efficiency of energy/matter/information extraction from their environments and the rate and efficiency of negative entropy production. Evolutionary memory, manifested as organizational structure balancing economic efficiency and adaptability, and innovation, manifested as stochastic generation of new organizational forms, facilitate economy-driven self- organization. Self-organization is proposed to be an ever-expanding process covering increasingly larger spatiotemporal scales through formation of interdependent organizational hierarchies. The process of self-organization blends Darwinian phases dominated by diversification, competition, and selection and organizational phases dominated by specialization, cooperation, and organization. It is argued and illustrated that the self-similarity of spatiotemporal scales in the organization and dynamics of living matter can be exploited both for scientific discovery within specialized disciplines and the unification of individual sciences within one and the same conceptual framework of self-organization. This is achieved by 1) defining scale-invariant organizational concepts, patterns and measures; 2) reconceptualizing organizational phenomena of different scales in the same scale-invariant terms and 3) mapping the knowledge structures of different scales onto each other, using overlapping patterns for alignment, filling in missing parts, and re-structuring misaligned patterns on the assumption of spatiotemporal self-similarity of scales.
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ABSTRACT: Although their advantages are well known, technology alliances may not always positively affect innovative performance. Previous studies have found several explanations for this problem. Technology alliances often require excessive resources and capabilities to create and maintain relationships with partners. In addition, they divert managerial attention and functions from internal research and development (R&D) activities. In this study, we hypothesize that firms often execute inefficient technology alliance strategies, thus affecting their innovative capabilities negatively and consequently reducing their subsequent innovation performance. Specifically, we test whether firms with greater prior experience in technology alliance are more likely to execute inefficient technology alliance strategies. Second, we attempt to investigate the negative effects of technology alliances on firms' internal R&D capabilities. To test our hypotheses, we employ data from 1,036 technology alliances in the US nanobiotechnology sector. Implications from the analyses are offered for executives and technology alliance strategies. Specifically, we propose that firms should adopt technology alliance but with due consideration of its negative aspects and the firms' limited resources.Creativity and Innovation Management 03/2013;
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ABSTRACT: This paper describes a research into the adaptation property of complex organizations. The research is focused on the development of a methodology for identifying and appraising loops that can allow for organizational adaptation. The proposed methodology draws a parallel between the nature of adaptation in complex organizations and the process of adaptive decision- making in human behavior. From this perspective, the adaptive loop in complex organizations can be divided into four steps adapted from the OODA loop (Observe-Orient-Decide-Act). The extension of the OODA loop to an organizational scale is incorporated with an assumption that flow of information, involved in adaptation processes, can be formed by different organizational components. Subsequently, the OODA loop can be presented as a chain of actions created by independent components of both the organization and its environment. Applying this approach to complex organizations necessitates mapping a functional definition of different organizational components within each step of the adaptive loop. Thus, while the functional definition of an organization can be done by using existing tools of organizational analysis (organizational structure, functional decomposition, architecture frameworks, etc.), the main goal of this proposed methodology is the determination of adaptive loops on an organizational scale.Procedia Computer Science 01/2012; 12:56–62.