Title: A computer-assisted qualitative data analysis framework for the engineering management domain

Authors: Amirali Saeedi; Toni L. Doolen

Addresses: School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University (OSU), 204 Rogers Hall, Corvallis, OR 97331-6001, USA. ' School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University (OSU), 204 Rogers Hall, Corvallis, OR 97331-6001, USA

Abstract: In this research, a computer assisted framework was developed to help researchers in conducting qualitative research. This framework leveraged the GATE platform, along with natural language processing and knowledge extraction techniques, to develop an automatic text annotation and summarisation system. A performance model, developed from the literature on lean manufacturing implementation strategies was employed, and a lexicon database for lean implementation practices was also developed. A unique dataset from a previous research study focusing on lean implementation practices was used to conduct this development and testing. A number of different summarisation techniques were developed and tested. A customised sensitivity analysis method was developed and used to systematically perform summarisation algorithms comparisons. The results of this study showed that using this framework at the early stages of a qualitative study has a great potential to reduce the time spent by researchers in annotating large datasets.

Keywords: qualitative data analysis; QDA; natural language processing; NLP; GATE; general architecture; text engineering; engineering management; computer-assisted framework; knowledge extraction; automatic text annotation; automatic text summarisation; performance models; lean manufacturing; lexicon databases; lean implementation; datasets; sensitivity analysis; summarisation algorithms; algorithm comparisons; researchers; data analysis techniques; data analysis strategies.

DOI: 10.1504/IJDATS.2012.045119

International Journal of Data Analysis Techniques and Strategies, 2012 Vol.4 No.1, pp.1 - 20

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 24 Jan 2012 *

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