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Mining survey data for SWOT analysis

Mining survey data for SWOT analysis
Mining survey data for SWOT analysis
Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is one of the most important tools for strategic planning. The traditional method of conducting SWOT analysis does not prioritize and is likely to hold subjective views that may result in an improper strategic action. Accordingly, this research exploits Importance-Performance Analysis (IPA), a technique for measuring customers’ satisfaction based on survey data, to systematically generate prioritized SWOT factors based on customers’ perspectives which in turn produces more accurate information for strategic planning. This proposed approach is called IPA based SWOT analysis and its development issues discussed in this report are: (1) selecting a technique for measuring importance which is one of the two main aspects of IPA since currently there are no well-established approaches for measuring importance; and (2) identifying opportunities and threats since only strengths and weaknesses can be inferred from the IPA result.
The first issue is addressed by conducting an empirical comparison to analyse the performance of various techniques for measuring importance. Specifically, this thesis considers two data mining techniques namely Naïve Bayes and Bayesian Networks for measuring importance and compares their performance with other techniques namely Multiple Linear Regressions, Ordinal Logistic Regression and Back Propagation Neural Networks that have been used to derive the importance from the survey data. The comparison result measured against the evaluation metrics suggests that Multiple Linear Regressions is the most suitable technique for measuring importance.
Regarding the second issue, opportunities and threats were identified by comparing the IPA result of the target organisation with that of its competitor. Through the use of IPA based SWOT analysis, it is expected that an organisation can efficiently formulate strategic planning as the SWOT factors that should be maintained or improved can be clearly identified based on customers’ viewpoints. The application of the IPA based SWOT analysis was illustrated and evaluated through a case study of Higher Education Institutions in Thailand. The evaluation results showed that SWOT analysis of the case study has a high face validity and its quality is considered acceptable, thereby demonstrating the validity of this study. Although the application of IPA based SWOT analysis was illustrated in the specific field, it can be argued that IPA based SWOT analysis can be used widely in the other business areas where SWOT analysis has been seen to be applicable and the customer satisfaction surveys are generally conducted.
University of Southampton
Phadermrod, Boonyarat
fcde4e03-7e97-4993-b5da-f61a2cc083a0
Phadermrod, Boonyarat
fcde4e03-7e97-4993-b5da-f61a2cc083a0
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023

Phadermrod, Boonyarat (2016) Mining survey data for SWOT analysis. University of Southampton, Faculty of Physical Science and Engineering, Doctoral Thesis, 256pp.

Record type: Thesis (Doctoral)

Abstract

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is one of the most important tools for strategic planning. The traditional method of conducting SWOT analysis does not prioritize and is likely to hold subjective views that may result in an improper strategic action. Accordingly, this research exploits Importance-Performance Analysis (IPA), a technique for measuring customers’ satisfaction based on survey data, to systematically generate prioritized SWOT factors based on customers’ perspectives which in turn produces more accurate information for strategic planning. This proposed approach is called IPA based SWOT analysis and its development issues discussed in this report are: (1) selecting a technique for measuring importance which is one of the two main aspects of IPA since currently there are no well-established approaches for measuring importance; and (2) identifying opportunities and threats since only strengths and weaknesses can be inferred from the IPA result.
The first issue is addressed by conducting an empirical comparison to analyse the performance of various techniques for measuring importance. Specifically, this thesis considers two data mining techniques namely Naïve Bayes and Bayesian Networks for measuring importance and compares their performance with other techniques namely Multiple Linear Regressions, Ordinal Logistic Regression and Back Propagation Neural Networks that have been used to derive the importance from the survey data. The comparison result measured against the evaluation metrics suggests that Multiple Linear Regressions is the most suitable technique for measuring importance.
Regarding the second issue, opportunities and threats were identified by comparing the IPA result of the target organisation with that of its competitor. Through the use of IPA based SWOT analysis, it is expected that an organisation can efficiently formulate strategic planning as the SWOT factors that should be maintained or improved can be clearly identified based on customers’ viewpoints. The application of the IPA based SWOT analysis was illustrated and evaluated through a case study of Higher Education Institutions in Thailand. The evaluation results showed that SWOT analysis of the case study has a high face validity and its quality is considered acceptable, thereby demonstrating the validity of this study. Although the application of IPA based SWOT analysis was illustrated in the specific field, it can be argued that IPA based SWOT analysis can be used widely in the other business areas where SWOT analysis has been seen to be applicable and the customer satisfaction surveys are generally conducted.

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Published date: November 2016
Organisations: University of Southampton, Electronic & Software Systems

Identifiers

Local EPrints ID: 404711
URI: http://eprints.soton.ac.uk/id/eprint/404711
PURE UUID: 732c8736-67a3-4a51-860f-1587ed72f9dc

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Date deposited: 18 Feb 2017 00:24
Last modified: 13 Mar 2019 20:18

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Contributors

Author: Boonyarat Phadermrod
Thesis advisor: Richard Crowder

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