The Situation Awareness Weighted Network (SAWN) model and method: Theory and application.

Area of Science:

• Organizational Psychology
• Information Science
• Military Science

Background:

• Situation Awareness (SA) is crucial for organizational performance, especially in distributed military settings.
• Existing models of SA focus on individual cognitive processes or network structures, but lack integration.
• Understanding SA generation in complex military operations requires novel modeling approaches.

Purpose of the Study:

• To introduce and validate the Situation Awareness Weighted Network (SAWN) model for analyzing SA generation in distributed organizations.
• To develop an empirical data collection method to support the SAWN model.
• To quantify the flow of SA within military staff networks during different operational contexts.

Main Methods:

• Developed the empirically derived Situation Awareness Weighted Network (SAWN) model, integrating Endsley's and Stanton et al.'s SA theories.
• Created a data collection method to capture interactions between human nodes and product nodes (information artifacts).
• Applied the SAWN model to aggregated empirical data from Australian military staff in steady-state and crisis scenarios.

Main Results:

• The SAWN model represents SA as a weighted semi-bipartite network, with link weights indicating SA levels.
• Analysis quantified the flow of SA through the organization as staff added value.
• The model effectively illustrated SA dynamics in both steady-state and crisis military contexts.

Conclusions:

• The SAWN model provides a novel, quantifiable method to understand SA generation and flow in distributed organizations.
• The SAWN approach enhances the analysis of organizational performance by visualizing SA dynamics.
• This model offers valuable insights for improving information sharing and decision-making in military and other complex environments.