The SO-What Analytical Analysis for Virtual Decision Teams

Rashmi Solanki; Chad Kennedy; Yash Shirke

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: ERM: Teamwork makes the dream work!
Page Count: 14
DOI: 10.18260/1-2--40392
Permanent URL: https://peer.asee.org/40392
Download Count: 215

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Paper Authors

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Rashmi Solanki Arizona State University, Polytechnic campus

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Master's student at Arizona State University

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Chad Kennedy Arizona State University, Polytechnic Campus

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Chad Kennedy’s experience spans entrepreneurship, engineering research, project management and advanced technology application in industry. His expertise stems from spending the last 25+ years working in the field of engineering. His early career began working in various engineering design, testing, and astronaut training capacities at NASA Johnson Space Center. After, Kennedy joined the start-up, VI Technology Inc., an automation and testing systems firm, and helped establish the Silicon Valley office and operations. VI Technology was later acquired by Aeroflex, Inc. Kennedy was the co-founder and CEO of the start-up company, Restorative Biosciences Inc., an early-stage company that focused on developing anti-fouling, anti-inflammatory coatings and therapeutics for ophthalmic applications.

Kennedy is currently a Senior Lecturer and Graduate Chair in the technology entrepreneurship and management (TEM) program in the Polytechnic School, one of the Ira A. Fulton Schools of Engineering at Arizona State University. In addition, Dr. Kennedy is one of the founding faculty of the MS Innovation and Venture Development program, a joint partnership with FSE, WPC, and HIDA. Formerly, he was the national chair and Professor of biomedical engineering technology at DeVry University. Kennedy sat on the national Association for Medical Instrumentation (AAMI) educational committee and the American Institute for Medical and Biological Engineering (AIMBE) Academic Council. Kennedy holds a bachelor’s degree in mechanical engineering from the University of Texas at Austin in addition to master’s and doctorate degrees in Bioengineering from Arizona State University.
Outside of academics, Kennedy mentors and consults with many technology start-up companies such as (Invoytech, Picmonics, OraVu LLC, Riviulet, Hat-tac, etc.).

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Yash Shirke Arizona State University, Polytechnic campus

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I am interested in learning new technology and work on new projects.
Passionate about electronics and automation also in SPC ( statistical process control).
Interested in WCM - World Class Manufacturing, Supply change management.

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Abstract

The traditional Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis has been used by all manner of enterprises, such as companies, non-profits, and governments, to make strategic decisions that can have major implications on entrepreneurial business models, investments, and policy. However historical research shows that, team bias, subjectivity, and lack of reliable quantitative metrics limit the effectiveness of the traditional SWOT analysis, thus leading to high variability of outcomes low fidelity decision making. In addition, complex algorithmic implementations of SWOT are not likely to be implemented as a tool for start-ups, virtual teams, and small business. In this study, we re-engineer the SWOT analysis for virtual teams to include, easy to implement quantitative measures and developed the ‘SO-WhaT’ analysis which includes a novel 1-D ‘SO-WhaT Index’ and 2D normalized metrics (‘Internal’ Y-axis component, ‘External’ X-axis component, and ‘Consensus Vector’, |C|), to evaluate and facilitate decision making. This paper focuses on the binary decision situation (i.e. Yes or No, Go or No-Go decision). Results for three different groups (12 teams each with 5 students or n = 60 students each group; 180 students total); Virtual Team SOWT (VTS), Group Weighted SOWT (GWS), and Traditional Unweighted SWOT (TUS), were evaluated and normalized for statistical comparison. The Virtual Team SOWT showed more conservative and less variable results and minimized bias toward opportunities relative to the TUS group and the GWS groups, respectively. In addition to passive statistical analysis, live calculations, plotting, and decision debriefing in the classroom environment was used to show student teams conflicts in intuition (subjectivity) versus using data driven metrics (objectivity) to help make team based decisions. ANOVA and t-tests were performed on the data collected from the 3 groups to conclude the hypothesis. Based on 95% significance (p-value 0.5), the results displayed better outcomes in terms of majority for scored method as compared to the traditional SWOT method.

Keywords: SWOT, SO-WT, SOWT, SOWT analysis, decision analysis, virtual teams, decision bias mitigation, quantitative decision making.

Citation
Format

Solanki, R., & Kennedy, C., & Shirke, Y. (2022, August), The SO-What Analytical Analysis for Virtual Decision Teams Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40392

TY  - CPAPER
AB  - The traditional Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis has been used by all manner of enterprises, such as companies, non-profits, and governments, to make strategic decisions that can have major implications on entrepreneurial business models, investments, and policy. However historical research shows that, team bias, subjectivity, and lack of reliable quantitative metrics limit the effectiveness of the traditional SWOT analysis, thus leading to high variability of outcomes low fidelity decision making. In addition, complex algorithmic implementations of SWOT are not likely to be implemented as a tool for start-ups, virtual teams, and small business. In this study, we re-engineer the SWOT analysis for virtual teams to include, easy to implement quantitative measures and developed the ‘SO-WhaT’ analysis which includes a novel 1-D ‘SO-WhaT Index’ and 2D normalized metrics (‘Internal’ Y-axis component, ‘External’ X-axis component, and ‘Consensus Vector’, |C|), to evaluate and facilitate decision making. This paper focuses on the binary decision situation (i.e. Yes or No, Go or No-Go decision). Results for three different groups (12 teams each with 5 students or n = 60 students each group; 180 students total); Virtual Team SOWT (VTS), Group Weighted SOWT (GWS), and Traditional Unweighted SWOT (TUS), were evaluated and normalized for statistical comparison. The Virtual Team SOWT showed more conservative and less variable results and minimized bias toward opportunities relative to the TUS group and the GWS groups, respectively. In addition to passive statistical analysis, live calculations, plotting, and decision debriefing in the classroom environment was used to show student teams conflicts in intuition (subjectivity) versus using data driven metrics (objectivity) to help make team based decisions. ANOVA and t-tests were performed on the data collected from the 3 groups to conclude the hypothesis. Based on 95% significance (p-value 0.5), the results displayed better outcomes in terms of majority for scored method as compared to the traditional SWOT method.

Keywords: SWOT, SO-WT, SOWT, SOWT analysis, decision analysis, virtual teams, decision bias mitigation, quantitative decision making. 
AU  - Rashmi Solanki
AU  - Chad Kennedy
AU  - Yash Shirke
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - The SO-What Analytical Analysis for Virtual Decision Teams
UR  - https://peer.asee.org/40392
DO  - 10.18260/1-2--40392
ER  -