A Self‐organizing System for Innovation in Large Organizations

Foss, Richard A.

doi:10.1002/sres.2503

Cited by 8 publications

(3 citation statements)

References 84 publications

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“…Through formalisation into models, these processes can then be represented and explained in the context of the social macro–micro level (Anzola et al, 2017, p. 244; Skår, 2003, p. 1054). In empirical work, the use of the concept of self‐organisation varies according to the goal and methodology (e.g., Chaves‐Maza & Martel, 2020; Focardi et al, 2002; Foss, 2018; McDowell et al, 2023).…”

Section: Self‐organisation In Social Sciencesmentioning

confidence: 99%

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Šerá,

Doubravský,

Schüller

2023

Syst Res Behav Sci

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The aim of this paper is to show how Niklas Luhmann's concept of self‐organization can be formalized using fuzzy logic. This approach is based on the shared assumptions of systems theory and fuzzy logic and focuses on natural language expression and the complexity of social processes. It involves modelling the operations of the system in relation to the fuzziness of the environment, that is, the transformation of environmental stimuli in gradual uncertainty to binary codes of communication. Based on these operations, it is possible to formalize their unified pattern, which shows the observation of past operations and the simultaneous observation of the environment. An example is given of the formalization of restaurant self‐organization in the Czech Republic during the Covid‐19 pandemic. The model illustrates and explains how the restaurant system reduces lockdown as an environment, in other words, how the system stabilizes its behaviour in a complex pandemic period.

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Section: Self‐organisation In Social Sciencesmentioning

confidence: 99%

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Šerá,

Doubravský,

Schüller

2023

Syst Res Behav Sci

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“…Foss et al (21) designed a collective intelligence platform to propose an innovation system based on bee swarm logic. The platform is driven by six key functions needed to establish a collective intelligence system, mainly: Power decentralization, solutions diversity, opinion independence, Natural solution selection, Evidence gathering, and quorum-based decision-making.…”

Section: Summary Of Selected Studiesmentioning

confidence: 99%

Fostering innovation through collective intelligence: a literature review

Ibrahimi,

Merioumi,

Benchekroun

2023

Data and Metadata

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In the twenty-first century, Collective intelligence (CI) arose as a social phenomenon to assist organizations in managing future uncertainty. It pushes a broad diverse group to come up with new solutions that outperform those uncovered within the organization itself. Accordingly, CI has been widely acknowledged as a means to foster innovation, and develop, and sustain an organization's creative potential. This paper aims to conduct a literature review to examine the existing body of literature regarding the ways collective intelligence improves innovation. The findings emphasized the importance of collective intelligence in fueling a firm’s knowledge and innovation in all of its forms to overcome public and private organizational challenges. Furthermore, our review underlined the mediating role of information technology in taking full advantage of collective intelligence via digital platforms. In addition, our analysis pointed out the multifaceted traits of collective intelligence as reflected in the literature under several terms, including crowdsourcing. Our research revealed several gaps in the current literature, including insufficient analysis and modeling of the relationship between the two concepts. Finally, we concluded our paper by identifying the limits of our research and suggesting avenues for future studies on collective intelligence and innovation.

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“…In other words, team members that are software agents can be included in the model, as long as a style of communication for human-agent communication can be estimated. While there have been multiple studies within the field of computer science to create and support such human-agent hybrids, the efforts to model performance and coordination of self-organizing knowledge networks [7,49] incorporating both human and software agent team members are less well developed.…”

Section: Comparing Diversity Coordination Algorithmsmentioning

confidence: 99%

Supporting Advances in Human-Systems Coordination through Simulation of Diverse, Distributed Expertise

Nyre-Yu

Caldwell

2018

Systems

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Distributed expertise task environments represent a critical, but challenging, area of team performance. As teams work together to perform complex tasks, they share much information and expertise to efficiently and effectively coordinate activities. Information coordination and alignment is affected by many factors, including communication styles and distributions of domain and interaction expertise. This study was part of a series of work performed in the authors’ lab to explore feasibility of using software simulation methods as a complement to other human factors methods to explore information alignment in teams. More specifically, this study aimed to operationalize specific parameters identified in group dynamics, management, and cognitive psychology literatures. Such research can provide an operationalized model that incorporates some of these key factors in information alignment and how these factors impact overall task performance of teams in complex environments. Simulation methods were applied to explore time-based performance outcomes. Model convergence and functionality were established through a series of model-based statistical analyses, which can be later validated with supplementary field studies. Results indicate that this style of simulation modeling is feasible, and provides directions for additional examination of factors affecting team configuration, process, and performance in complex systems.

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A Self‐organizing System for Innovation in Large Organizations

Cited by 8 publications

References 84 publications

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Fostering innovation through collective intelligence: a literature review

Supporting Advances in Human-Systems Coordination through Simulation of Diverse, Distributed Expertise

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