Using root cause data analysis for requirements and knowledge elicitation

Zhao Xia Jin; John Hajdukiewicz; Geoffrey Ho; Donny Chan; Yong-Ming Kow

doi:10.1007/978-3-540-73331-7_9

Using root cause data analysis for requirements and knowledge elicitation

Zhao Xia Jin
, John Hajdukiewicz
, Geoffrey Ho
, Donny Chan
, Yong-Ming Kow

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

The purpose of this paper is to present a technique, called Knowledge FMEA, for distilling textual raw data which is useful for requirements collection and knowledge elicitation. The authors first give some insights into the diverse characteristics of textual raw data which can lead to higher complexity in analysis and may result in some gaps in interpreting the interviewees' world view. We then outline a Knowledge FMEA procedure as it applies to qualitative data and its key benefits. Examples from a case study are presented to illustrate how to use the technique. Proposed Knowledge FMEA brings many advantages such as forcing the analysts to become deeply immersed in the raw data, identifying how the information is connected in causation, classifying the data according to why, what, how formulations and quantifying the findings for further quantitative analysis. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Engineering Psychology and Cognitive Ergonomics
Subtitle of host publication	7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings
Editors	Don Harris
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	79-88
ISBN (Electronic)	978-3-540-73331-7
ISBN (Print)	9783540733300
DOIs	https://doi.org/10.1007/978-3-540-73331-7_9
Publication status	Published - 2007
Externally published	Yes
Event	7th International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE 2007) - Beijing, China Duration: 22 Jul 2007 → 27 Jul 2007

Publication series

Name	Lecture Notes in Computer Science
Volume	4562
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE 2007)
Place	China
City	Beijing
Period	22/07/07 → 27/07/07

Research Keywords

Failure Modes and Effects Analysis (FMEA)
Qualitative research
Root cause analysis
Thematic analysis

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10.1007/978-3-540-73331-7_9

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Jin, Z. X., Hajdukiewicz, J., Ho, G., Chan, D., & Kow, Y.-M. (2007). Using root cause data analysis for requirements and knowledge elicitation. In D. Harris (Ed.), Engineering Psychology and Cognitive Ergonomics: 7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings (pp. 79-88). (Lecture Notes in Computer Science; Vol. 4562). Springer . https://doi.org/10.1007/978-3-540-73331-7_9

Jin, Zhao Xia ; Hajdukiewicz, John ; Ho, Geoffrey et al. / Using root cause data analysis for requirements and knowledge elicitation. Engineering Psychology and Cognitive Ergonomics: 7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings. editor / Don Harris. Berlin, Heidelberg : Springer , 2007. pp. 79-88 (Lecture Notes in Computer Science).

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title = "Using root cause data analysis for requirements and knowledge elicitation",

abstract = "The purpose of this paper is to present a technique, called Knowledge FMEA, for distilling textual raw data which is useful for requirements collection and knowledge elicitation. The authors first give some insights into the diverse characteristics of textual raw data which can lead to higher complexity in analysis and may result in some gaps in interpreting the interviewees' world view. We then outline a Knowledge FMEA procedure as it applies to qualitative data and its key benefits. Examples from a case study are presented to illustrate how to use the technique. Proposed Knowledge FMEA brings many advantages such as forcing the analysts to become deeply immersed in the raw data, identifying how the information is connected in causation, classifying the data according to why, what, how formulations and quantifying the findings for further quantitative analysis. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

keywords = "Failure Modes and Effects Analysis (FMEA), Qualitative research, Root cause analysis, Thematic analysis",

author = "Jin, \{Zhao Xia\} and John Hajdukiewicz and Geoffrey Ho and Donny Chan and Yong-Ming Kow",

year = "2007",

doi = "10.1007/978-3-540-73331-7\_9",

language = "English",

isbn = "9783540733300",

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pages = "79--88",

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Jin, ZX, Hajdukiewicz, J, Ho, G, Chan, D & Kow, Y-M 2007, Using root cause data analysis for requirements and knowledge elicitation. in D Harris (ed.), Engineering Psychology and Cognitive Ergonomics: 7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings. Lecture Notes in Computer Science, vol. 4562, Springer , Berlin, Heidelberg, pp. 79-88, 7th International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE 2007), Beijing, China, 22/07/07. https://doi.org/10.1007/978-3-540-73331-7_9

Using root cause data analysis for requirements and knowledge elicitation. / Jin, Zhao Xia; Hajdukiewicz, John; Ho, Geoffrey et al.
Engineering Psychology and Cognitive Ergonomics: 7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings. ed. / Don Harris. Berlin, Heidelberg: Springer , 2007. p. 79-88 (Lecture Notes in Computer Science; Vol. 4562).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Using root cause data analysis for requirements and knowledge elicitation

AU - Jin, Zhao Xia

AU - Hajdukiewicz, John

AU - Ho, Geoffrey

AU - Chan, Donny

AU - Kow, Yong-Ming

PY - 2007

Y1 - 2007

N2 - The purpose of this paper is to present a technique, called Knowledge FMEA, for distilling textual raw data which is useful for requirements collection and knowledge elicitation. The authors first give some insights into the diverse characteristics of textual raw data which can lead to higher complexity in analysis and may result in some gaps in interpreting the interviewees' world view. We then outline a Knowledge FMEA procedure as it applies to qualitative data and its key benefits. Examples from a case study are presented to illustrate how to use the technique. Proposed Knowledge FMEA brings many advantages such as forcing the analysts to become deeply immersed in the raw data, identifying how the information is connected in causation, classifying the data according to why, what, how formulations and quantifying the findings for further quantitative analysis. © Springer-Verlag Berlin Heidelberg 2007.

AB - The purpose of this paper is to present a technique, called Knowledge FMEA, for distilling textual raw data which is useful for requirements collection and knowledge elicitation. The authors first give some insights into the diverse characteristics of textual raw data which can lead to higher complexity in analysis and may result in some gaps in interpreting the interviewees' world view. We then outline a Knowledge FMEA procedure as it applies to qualitative data and its key benefits. Examples from a case study are presented to illustrate how to use the technique. Proposed Knowledge FMEA brings many advantages such as forcing the analysts to become deeply immersed in the raw data, identifying how the information is connected in causation, classifying the data according to why, what, how formulations and quantifying the findings for further quantitative analysis. © Springer-Verlag Berlin Heidelberg 2007.

KW - Failure Modes and Effects Analysis (FMEA)

KW - Qualitative research

KW - Root cause analysis

KW - Thematic analysis

UR - https://www.scopus.com/pages/publications/37249050930

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-37249050930&origin=recordpage

U2 - 10.1007/978-3-540-73331-7_9

DO - 10.1007/978-3-540-73331-7_9

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783540733300

T3 - Lecture Notes in Computer Science

SP - 79

EP - 88

BT - Engineering Psychology and Cognitive Ergonomics

A2 - Harris, Don

PB - Springer

CY - Berlin, Heidelberg

T2 - 7th International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE 2007)

Y2 - 22 July 2007 through 27 July 2007

ER -

Jin ZX, Hajdukiewicz J, Ho G, Chan D, Kow YM. Using root cause data analysis for requirements and knowledge elicitation. In Harris D, editor, Engineering Psychology and Cognitive Ergonomics: 7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings. Berlin, Heidelberg: Springer . 2007. p. 79-88. (Lecture Notes in Computer Science). Epub 2007 Aug 24. doi: 10.1007/978-3-540-73331-7_9

Using root cause data analysis for requirements and knowledge elicitation

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