Paper Info
Title
Multi-Level Systems Engineering Analyzer Dashboard: A Semi-Automated Content Analysis for Interview Data
Abstract
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.
Year
DOI
Venue
2020
10.1109/SysCon47679.2020.9275905
2020 IEEE International Systems Conference (SysCon)
Keywords
DocType
ISSN
Systems engineering,systems engineers,workforce development,organizational model,data modeling and visualization,content analysis,text analysis,sentiment analysis
Conference
1944-7620
ISBN
Citations 
PageRank 
978-1-7281-5366-7
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Zhongyuan Yu100.34
Hoong Yan See Tao200.34
Yao Xiao3649.74
Pamela Burke400.34
Nicole Hutchison500.34
Deep Makwana600.34