scholarly journals Patterns of Learning in Dynamic Technological System Lifecycles—What Automotive Managers Can Learn from the Aerospace Industry?

Author(s):  
Daniel Guffarth ◽  
Mathias Knappe

Not only with respect to the common overlaps within the market of urban air mobility, but also in terms of their requirement profile with regard to the systemic core, all mobility industries are converging. This article focuses on the required patterns of learning in order to cope with these changes, and what automotive managers can learn from the aerospace industry in this context. As organizational learning is the central parameter of economic evolution, and technology develops over trajectory shifts, companies are, at the very least, cyclically forced to learn ambidextrously, or are squeezed out of the market. They have to act and react as complex adaptive systems in their changing environment. Especially in these dynamics, ambidextrous learning is identified to be a conditio sine qua non for organizational success. Especially the combination of efficiency-oriented internal exploitation with an explorative and external-oriented open innovation network turns out to be a superior strategy. By combining patent data, patent citation analysis and data on the European Framework Programs, we show that there are temporal differences, i.e., position of the product in the product, technique, technology, and industry life cycle. Furthermore, we draw a conclusion dependent on the systemic product character, which enforces different learning requirements concerning supply chain position and, as an overarching conclusion, we identify product structure to be decisive for how organizational learning should be styled.

Agent based modeling is one of many tools, from the complexity sciences, available to investigate complex policy problems. Complexity science investigates the non-linear behavior of complex adaptive systems. Complex adaptive systems can be found across a broad spectrum of the natural and human created world. Examples of complex adaptive systems include various ecosystems, economic markets, immune response, and most importantly for this research, human social organization and competition / cooperation. The common thread among these types of systems is that they do not behave in a mechanistic way which has led to problems in utilizing traditional methods for studying them. Complex adaptive systems do not follow the Newtonian paradigm of systems that behave like a clock works whereby understanding the workings of each of the parts provides an understanding of the whole. By understanding the workings of the parts and a few external rules, predictions can be made about the behavior of the system as a whole under varying circumstances. Such systems are labeled deterministic (Zimmerman, Lindberg, & Plsek, 1998).


Author(s):  
A. Faye Bres

This chapter is based on a design-based research study of organizational learning and on a subsequent integral analysis of how and why organizational learning did, and did not, occur in the study. Integral theory is applied to deepen the understanding of how human organizations learn and adapt as complex adaptive systems made up of nested, operationally closed groups and individuals. The level of development and learning potential of an organization, as holon, can be understood as an emergent property resulting from the coordination of function and action of the unities that make up the system, even given that the levels of development and learning potentials of the groups and individuals in an organization are not consistent across the organization. The advantages of combining complexity and integral theory are explored, as both are understood to provide different, complementary interpretations of whole human systems.


2013 ◽  
Vol 5 (1) ◽  
pp. 32-52 ◽  
Author(s):  
Eugenio Dante Suarez ◽  
Manuel Castañón-Puga

Distributed Agency is the name of a conceptual framework for describing complex adaptive systems that this paper develops. To understand the complexity of the world in a holistic fashion, the field of Modeling and Simulation is currently lacking a common terminology in which different bodies of knowledge can communicate with each other in a general language. In this work, agency is proposed as the common link between the different dimensions of reality, expressing the influence of one dimension on another. This conceptualization is based on a process of backwards induction where nested actors such as an evolved organism or a human choice can be represented as the resulting force of intertwined aims and constraints. The theoretical framework can serve as a point of reference for the social and computational researcher by communicating structural and emergent properties that are essential for the understanding of social and evolutionary phenomena such as companies, economies, governments, and ecosystems.


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