Beyond design and delivery: developing a model to measure end-user satisfaction (delight) in projects
PurposeThis research aims to develop and test a model for measuring end-user satisfaction (EUS) in a practical manner and then statistically verify its reliability and validity.Design/methodology/approachA comprehensive list of attributes using extensive literature review, focus group and brainstorm...
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| Veröffentlicht in: | Built Environment Project and Asset Management 2023-06, Vol.13 (4), p.509-534 |
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| container_title | Built Environment Project and Asset Management |
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| creator | Abu Arqoub, Muath Ghanbaripour, Amir Naser Langston, Craig Skulmoski, Greg |
| description | PurposeThis research aims to develop and test a model for measuring end-user satisfaction (EUS) in a practical manner and then statistically verify its reliability and validity.Design/methodology/approachA comprehensive list of attributes using extensive literature review, focus group and brainstorming meetings were used to create a set of attributes for the model. These attributes were then used in a survey among the end-users (N = 687) of seven case studies with different characteristics (type, size and location) to assess the reliability and validity of the model. The statistical methods included reliability tests (Cronbach's alpha), confirmatory factor analysis (CFA) and correlation analysis), canonical discriminant analysis (CDA), item response theory (IRT) and model specification tests.FindingsEUS must be quantified before determining a project's overall performance. The analysis of repeatability and consistency (reliability and validity) performed on case studies (data collected from the end-users) strongly suggests that the EUS model is robust for a range of project types.Originality/valueAlthough many studies have proposed customer satisfaction models in the project success context, research on quantitative measurement tools is scarce. The paper departs from past research and develops and validates a new EUS model independent of project characteristics (while the study's limitations are acknowledged). |
| doi_str_mv | 10.1108/BEPAM-07-2022-0106 |
| format | Article |
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These attributes were then used in a survey among the end-users (N = 687) of seven case studies with different characteristics (type, size and location) to assess the reliability and validity of the model. The statistical methods included reliability tests (Cronbach's alpha), confirmatory factor analysis (CFA) and correlation analysis), canonical discriminant analysis (CDA), item response theory (IRT) and model specification tests.FindingsEUS must be quantified before determining a project's overall performance. The analysis of repeatability and consistency (reliability and validity) performed on case studies (data collected from the end-users) strongly suggests that the EUS model is robust for a range of project types.Originality/valueAlthough many studies have proposed customer satisfaction models in the project success context, research on quantitative measurement tools is scarce. 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These attributes were then used in a survey among the end-users (N = 687) of seven case studies with different characteristics (type, size and location) to assess the reliability and validity of the model. The statistical methods included reliability tests (Cronbach's alpha), confirmatory factor analysis (CFA) and correlation analysis), canonical discriminant analysis (CDA), item response theory (IRT) and model specification tests.FindingsEUS must be quantified before determining a project's overall performance. The analysis of repeatability and consistency (reliability and validity) performed on case studies (data collected from the end-users) strongly suggests that the EUS model is robust for a range of project types.Originality/valueAlthough many studies have proposed customer satisfaction models in the project success context, research on quantitative measurement tools is scarce. 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| ispartof | Built Environment Project and Asset Management, 2023-06, Vol.13 (4), p.509-534 |
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| subjects | Case studies Consumer behavior Correlation analysis Customer feedback Customer satisfaction Customer services Decision making Discriminant analysis Factor analysis Indoor air quality Information systems Literature reviews Mathematical models Model testing Product development Reliability analysis Stakeholders Statistical analysis Statistical methods Success factors User satisfaction |
| title | Beyond design and delivery: developing a model to measure end-user satisfaction (delight) in projects |
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