This study addresses the lack of theoretical grounding and definitions for the Aviation Safety Reporting System (ASRS) anomaly codes, particularly those related to pilot communication and coordination errors. To improve the conceptual consistency and analytical usability of ASRS data, the research developed a framework that maps ASRS codes to three established theory-based human factors taxonomies: the Human Factors Analysis and Classification System (HFACS), Threat and Error Management (TEM), and Aviation Causal Contributors for Error Reporting Systems (ACCERS). The study used qualitative text mining to systematically code communication and coordination errors submitted by Part 121 pilots for a stratified sample of 365 ASRS reports from 2007 to 2022. Additional analysis examined trends related to changes in pilot training and safety programs, while demographic data ensured sample representativeness.
The resulting taxonomy framework revealed both overlapping and distinct emphases in how the taxonomies classify events, with each taxonomy offering unique strengths, TEM emphasizing error detail, ACCERS capturing influencing factors, HFACS focusing on human error, and ASRS documenting event types. Despite these differences, substantial alignment was found, with approximately 85% of interactively coded taxonomy results matching those derived through the framework, supporting its validity for cross-database analysis. Trends also showed increases in taxonomy coding rates and communication-related codes following the introduction of key safety initiatives like AQP and SMS, suggesting some influence on reporting practices.
This framework contributes a structured, theory-informed method for aligning safety data across disparate taxonomies, enhancing the accuracy, efficiency, and depth of aviation safety analysis. It provides practical tools for analysts conducting cross-database searches and offers a foundation for developing a standardized industry taxonomy. The study recommends further expansion to include more error types and taxonomies, ultimately supporting future automation and machine learning applications for predictive safety analysis.
","abstract_html":"<p>This study addresses the lack of theoretical grounding and definitions for the Aviation Safety Reporting System (ASRS) anomaly codes, particularly those related to pilot communication and coordination errors. To improve the conceptual consistency and analytical usability of ASRS data, the research developed a framework that maps ASRS codes to three established theory-based human factors taxonomies: the Human Factors Analysis and Classification System (HFACS), Threat and Error Management (TEM), and Aviation Causal Contributors for Error Reporting Systems (ACCERS). The study used qualitative text mining to systematically code communication and coordination errors submitted by Part 121 pilots for a stratified sample of 365 ASRS reports from 2007 to 2022. Additional analysis examined trends related to changes in pilot training and safety programs, while demographic data ensured sample representativeness.</p> <p>The resulting taxonomy framework revealed both overlapping and distinct emphases in how the taxonomies classify events, with each taxonomy offering unique strengths, TEM emphasizing error detail, ACCERS capturing influencing factors, HFACS focusing on human error, and ASRS documenting event types. Despite these differences, substantial alignment was found, with approximately 85% of interactively coded taxonomy results matching those derived through the framework, supporting its validity for cross-database analysis. Trends also showed increases in taxonomy coding rates and communication-related codes following the introduction of key safety initiatives like AQP and SMS, suggesting some influence on reporting practices.</p> <p>This framework contributes a structured, theory-informed method for aligning safety data across disparate taxonomies, enhancing the accuracy, efficiency, and depth of aviation safety analysis. It provides practical tools for analysts conducting cross-database searches and offers a foundation for developing a standardized industry taxonomy. The study recommends further expansion to include more error types and taxonomies, ultimately supporting future automation and machine learning applications for predictive safety analysis.</p>","abstract_has_math":false,"creators":["Herr, Jennifer Rose"],"institution":null,"degree_name":"Doctor of Philosophy in Aviation","degree_level":"Dissertation - Open Access","degree_discipline":"College of Aviation","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-10-01T07:00:00Z","date_published":"2025-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["communication and coordination","taxonomy framework","qualitative research","error classification","aviation safety","pilot reporting","cross-database analysis","ASRS","Aviation Safety and Security"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/929","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Herr, Jennifer Rose"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["College of Aviation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Aviation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["communication and coordination","taxonomy framework","qualitative research","error classification","aviation safety","pilot reporting","cross-database analysis","ASRS","Aviation Safety and Security"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/929"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study addresses the lack of theoretical grounding and definitions for the Aviation Safety Reporting System (ASRS) anomaly codes, particularly those related to pilot communication and coordination errors. To improve the conceptual consistency and analytical usability of ASRS data, the research developed a framework that maps ASRS codes to three established theory-based human factors taxonomies: the Human Factors Analysis and Classification System (HFACS), Threat and Error Management (TEM), and Aviation Causal Contributors for Error Reporting Systems (ACCERS). The study used qualitative text mining to systematically code communication and coordination errors submitted by Part 121 pilots for a stratified sample of 365 ASRS reports from 2007 to 2022. Additional analysis examined trends related to changes in pilot training and safety programs, while demographic data ensured sample representativeness.
The resulting taxonomy framework revealed both overlapping and distinct emphases in how the taxonomies classify events, with each taxonomy offering unique strengths, TEM emphasizing error detail, ACCERS capturing influencing factors, HFACS focusing on human error, and ASRS documenting event types. Despite these differences, substantial alignment was found, with approximately 85% of interactively coded taxonomy results matching those derived through the framework, supporting its validity for cross-database analysis. Trends also showed increases in taxonomy coding rates and communication-related codes following the introduction of key safety initiatives like AQP and SMS, suggesting some influence on reporting practices.
This framework contributes a structured, theory-informed method for aligning safety data across disparate taxonomies, enhancing the accuracy, efficiency, and depth of aviation safety analysis. It provides practical tools for analysts conducting cross-database searches and offers a foundation for developing a standardized industry taxonomy. The study recommends further expansion to include more error types and taxonomies, ultimately supporting future automation and machine learning applications for predictive safety analysis.
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The resulting taxonomy framework revealed both overlapping and distinct emphases in how the taxonomies classify events, with each taxonomy offering unique strengths, TEM emphasizing error detail, ACCERS capturing influencing factors, HFACS focusing on human error, and ASRS documenting event types. Despite these differences, substantial alignment was found, with approximately 85% of interactively coded taxonomy results matching those derived through the framework, supporting its validity for cross-database analysis. Trends also showed increases in taxonomy coding rates and communication-related codes following the introduction of key safety initiatives like AQP and SMS, suggesting some influence on reporting practices.
This framework contributes a structured, theory-informed method for aligning safety data across disparate taxonomies, enhancing the accuracy, efficiency, and depth of aviation safety analysis. It provides practical tools for analysts conducting cross-database searches and offers a foundation for developing a standardized industry taxonomy. The study recommends further expansion to include more error types and taxonomies, ultimately supporting future automation and machine learning applications for predictive safety analysis.
"],"dc:identifier":["https://commons.erau.edu/edt/929"],"dc:subject":["communication and coordination","taxonomy framework","qualitative research","error classification","aviation safety","pilot reporting","cross-database analysis","ASRS","Aviation Safety and Security"],"dc:title":["Aligning Aviation Safety Reporting System Anomaly Codes for Crew Communication and Coordination with Human Factors Taxonomies"],"thesis:degree_discipline":["College of Aviation"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Aviation"]},"updated_at":"2026-07-27T19:26:22Z"}