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University of New England

Identifying Complex Adaptive Systems Using Quantitative Approaches At A Midsized Biotechnology Firm

Abstract

dc:description.abstract

<p>Rapid technological progress is becoming more challenging for organizations to implement and manage. The traditional, hierarchical leadership models are often inadequate to cope with continuous change, and the inability to keep up with the latest advances can quickly imperil a company. In particular, the field of biotechnology is currently experiencing revolutionary advances. Where hierarchical leadership models lapse, complexity theory and complexity leadership theory may provide an alternative leadership model for successful organizational adaptation. However, much of the research surrounding complexity theory remains academic. Historical data from a biotechnology company was analyzed during a computer hardware and software upgrade to detect the presence of a complex adaptive system, the fundamental component of complexity. Results showed that after the upgrade, animal care technicians did not significantly increase their collective efficiency; instead, they appeared to significantly increase their collective accuracy. This might indicate that the animal care technicians acted as a complex adaptive system in response to an environmental change. Insights into aggregate employee behavior through the lens of complexity theory might be useful to leadership seeking to successfully implement organizational change. Additionally, the adoption of complexity leadership doctrines by management might help create enhanced conditions to cultivate increased innovation and growth.</p>

Degree

thesis:*
Name thesis:degree_name
Doctor of Education (EdD)
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Education
Year dc:date.available
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Sullivan, Sean M.
Contributors dc:contributor
  • Laura Bertonazzi
  • Darren Akerman
  • Michael Walden

Subjects

dc:subject × 1

Identifiers

dc:identifier.*
Repository record dc:identifier
https://dune.une.edu/theses/418
OAI identifier oai:identifier
oai:dune.une.edu:theses-1417

Chain of custody

source
Harvested from
University of New England
Base URL
dune.une.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Sullivan, Sean M.. Identifying Complex Adaptive Systems Using Quantitative Approaches At A Midsized Biotechnology Firm. Dissertation thesis, 2022. https://dune.une.edu/theses/418