Knowledge workers as fractals in a complex adaptive organization

2005 ◽  
Vol 12 (3) ◽  
pp. 225-236 ◽  
Author(s):  
Snunith Shoham ◽  
Alon Hasgall
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.


Author(s):  
Andrew P. Sage ◽  
Cynthia T. Small

This chapter describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid Knowledge Management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.


Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.


Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.


2009 ◽  
pp. 217-236
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.


2019 ◽  
Vol 7 (2) ◽  
pp. 38-58
Author(s):  
Alon E. Hagsall ◽  
Niv Ahituv ◽  
Nili Naveh

Purpose: Most organizations seek for innovative solutions to address tabulate changes and competition. However, each organization is required for rapid and effective processes of assimilating technological innovation into its operational activities. Consistent with previous studies, the aim of this study was to understand whether an organization characterized as CAS (Complex Adaptive System) could positively affect the process of assimilating technological innovation through supporting the development of knowledge workers. Such workers deal mostly with changes. Hence, they possess the ability to combine personal benefits with organizational goals, they are sensible to changes in the environment, they understand the integration of information required for such a process, and they need the ability to socialize among themselves. Design/methodology/approach: A sample of 300 employees in organizations of different sectors responded to an online questionnaire, which examined their attitude towards technological innovation in correlation with the level of organization's CAS characteristics. Pearson and regression analyses were used to examine the relationships between the functioning of the workers as CAS fractals[1] and their attitudes toward the process of assimilation of technological innovation. Findings: Workers who function as “fractals”, namely as knowledge-worker in organizations having the characteristics of CAS, were able to combine personal benefits with organizational goals. They had sensitivity to changes in the environment, integration of the information required for the process and the ability to socialize among themselves. These abilities of knowledge workers have significantly influenced the development of positive attitudes towards the process of assimilation of technological innovation, a better understanding of the technology and the advantages they gain from it, which make them ready to be involved in the process. Practical implications: The practical contribution of this study is the ability to best portray the characteristics of an optimal work environment in an organization that wishes to undergo assimilation processes, technological innovation, management and dissemination of relevant knowledge for the organization's use. Such an organization is required to provide its employees with a degree of operational autonomy enabling them to interweave  personal interests and organizational goals, and to be involved and to influence the processes of assimilating technological innovation in the organization. The organization should also maintain a high level of updating, transparency, and transfer of knowledge from outside into the organization. In addition, investment in information systems for the information integration provides the employees with the possibility of social networking during their work. 


2012 ◽  
pp. 82-95 ◽  
Author(s):  
D. Foley

Mathematical methods are only one moment in a layered process of theory generation in political economy, which starts from Schumpeterian vision, progresses to the identification of relevant abstractions, the development of mathematical and quantitative models, and the confrontation of theories with empirical data through statistical methods. But today the relevant abstract problems of political economy are modified to fit available mathematical tools. The role of empirical research in disciplining theoretical speculation, on which the scientific traditions integrity rests, was undermined by specific limitations of nascent econometric methods, and usurped by ex cathedra methodological fiats of theorists. These developmentssystematically favored certain ideological predispositions of economicsas a discipline. There is abundant room for New Thinking in political economy starting from the vision of the capitalist economy as a complex, adaptive system far from equilibrium, including the development of the theory of statistical fluctuations for economic interactions, redirection of macroeconomics and financial economics from path prediction toward an understanding of the qualitative properties of the system, introduction of constructive and computable methods into economic modeling, and the critical reconstruction of econometric statistical methods.


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