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Modeling Organizational Cognition: The Case of Impact Factor

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Abstract

This article offers an alternative perspective on organizational cognition based on e-cognition whereby appeal to systemic cognition replaces the traditional computational model of the mind that is still extremely popular in organizational research. It uses information processing, not to explore inner processes, but as the basis for pursuing organizational matters. To develop a theory of organizational cognition, the current work presents an agent-based simulation model based on the case of how individual perception of scientific value is affected by and affects organizational intelligence units' (e.g., research groups', departmental) framing of the notorious impact factor. Results show that organizational cognition cannot be described without an intermediate meso scale – called here social organizing – that both filters and enables the many kinds of socially enabled perception, action and behavior that are so characteristic of human cognition.

Suggested Citation

  • Davide Secchi & Stephen J. Cowley, 2018. "Modeling Organizational Cognition: The Case of Impact Factor," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-13.
    • Handle: RePEc:jas:jasssj:2017-48-2
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    1. Simon, Herbert A, 1993. "Altruism and Economics," American Economic Review, American Economic Association, vol. 83(2), pages 156-161, May.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    4. Davide Secchi & Martin Neumann (ed.), 2016. "Agent-Based Simulation of Organizational Behavior," Springer Books, Springer, edition 1, number 978-3-319-18153-0, July.
    5. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    6. Herbert A. Simon, 1991. "Bounded Rationality and Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 125-134, February.
    7. Henry Small, 2004. "On the shoulders of Robert Merton: Towards a normative theory of citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(1), pages 71-79, May.
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    1. Peer-Olaf Siebers & Dinuka B. Herath & Emanuele Bardone & Siavash Farahbakhsh & Peter Gloggengiehser Knudsen & Jens Koed Madsen & Mehwish Mufti & Martin Neumann & Dale Richards & Raffaello Seri & Davi, 2020. "On the quest for defining organisational plasticity: a community modelling experiment," Evidence-based HRM, Emerald Group Publishing Limited, vol. 9(2), pages 126-138, September.

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