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Understanding compliance requires integrating multiple theories. Complexity science and network analysis reveal interconnected variables, offering a new perspective beyond traditional approaches to compliance behavior.

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Area of Science:

  • Behavioral Science
  • Complexity Science
  • Network Analysis

Background:

  • Existing compliance research often isolates theories (rational choice, social, legitimacy, capacity, opportunity).
  • A comprehensive understanding of how diverse mechanisms influence compliant and non-compliant behavior is lacking.
  • Previous studies fail to capture the interconnectedness of factors shaping compliance.

Purpose of the Study:

  • To develop an integrated understanding of compliance using complexity science.
  • To explore the simultaneous relationships between key compliance theories and their variables.
  • To analyze compliance behavior through a network analysis lens.

Main Methods:

  • Online survey data (N=562) on COVID-19 mitigation measures compliance.
  • Application of network analysis to explore variable interconnections.
  • Comparison with traditional regression analysis.

Main Results:

  • Regression analysis confirmed associations between compliance and elements from most major theories (excluding social).
  • Network analysis identified novel groupings and interconnections of variables.
  • Findings revealed a complexity in compliance not captured by existing, non-networked theories.

Conclusions:

  • Compliance is best understood as a complex network of interacting variables from diverse theories.
  • A shift from narrow, traditional approaches to a complexity science perspective is needed.
  • Future research should focus on mapping compliance networks and modeling intervention impacts.