TY - GEN
T1 - Multi-level systems engineering analyzer dashboard
T2 - 14th Annual IEEE International Systems Conference, SYSCON 2020
AU - Yu, Zhongyuan
AU - See Tao, Hoong Yan
AU - Xiao, Yao
AU - Burke, Pamela
AU - Hutchison, Nicole
AU - Makwana, Deep
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/8/24
Y1 - 2020/8/24
N2 - Helix is a multi-year project that aims to develop an understanding of systems engineers and organizational systems engineering (SE) effectiveness. Since 2013, the Helix team has conducted over 180 interview sessions with over 480 individuals from 31 organizations and resulted in over 6,500 pages of text data. As the team continued to gather more interview data, the manual qualitative content analysis methods originally employed was no longer adequate to keep pace with the expanding dataset. In this study, the team proposes a computer assisted method - a multi-level SE analyzer dashboard - to extract, analyze and visualize interview data customized specifically for SE knowledge. The dashboard developed in this study is able to “stay true” to the interviewees' actual feedback, while automating some aspects of the content analysis to generate useful insights. The multi-level SE analyzer dashboard enables: (1) exploring how critical factors contributing to SE effectiveness in the literature appear in the Helix interviews; (2) comparing SE implementations and processes among various industry sectors; (3) deriving an organizational profile on SE capability; and (4) gathering major concerns and accomplishments from individual systems engineers and senior leadership.
AB - Helix is a multi-year project that aims to develop an understanding of systems engineers and organizational systems engineering (SE) effectiveness. Since 2013, the Helix team has conducted over 180 interview sessions with over 480 individuals from 31 organizations and resulted in over 6,500 pages of text data. As the team continued to gather more interview data, the manual qualitative content analysis methods originally employed was no longer adequate to keep pace with the expanding dataset. In this study, the team proposes a computer assisted method - a multi-level SE analyzer dashboard - to extract, analyze and visualize interview data customized specifically for SE knowledge. The dashboard developed in this study is able to “stay true” to the interviewees' actual feedback, while automating some aspects of the content analysis to generate useful insights. The multi-level SE analyzer dashboard enables: (1) exploring how critical factors contributing to SE effectiveness in the literature appear in the Helix interviews; (2) comparing SE implementations and processes among various industry sectors; (3) deriving an organizational profile on SE capability; and (4) gathering major concerns and accomplishments from individual systems engineers and senior leadership.
KW - Content analysis
KW - Data modeling and visualization
KW - Organizational model
KW - Sentiment analysis
KW - Systems engineering
KW - Systems engineers
KW - Text analysis
KW - Workforce development
UR - http://www.scopus.com/inward/record.url?scp=85098918836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098918836&partnerID=8YFLogxK
U2 - 10.1109/SysCon47679.2020.9275905
DO - 10.1109/SysCon47679.2020.9275905
M3 - Conference contribution
AN - SCOPUS:85098918836
T3 - SYSCON 2020 - 14th Annual IEEE International Systems Conference, Proceedings
BT - SYSCON 2020 - 14th Annual IEEE International Systems Conference, Proceedings
Y2 - 24 August 2020 through 27 August 2020
ER -