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AI-empowered scale development: Testing the potential of ChatGPT
Technological Forecasting and Social Change, Volume: 205, Start page: 123488
Swansea University Author: Yogesh Dwivedi
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DOI (Published version): 10.1016/j.techfore.2024.123488
Abstract
AI-tools such as ChatGPT can assist researchers to improve the performance of the research process. This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. The study describes a process how to prompt ChatGPT to assist the scale development...
Published in: | Technological Forecasting and Social Change |
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ISSN: | 0040-1625 |
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Elsevier BV
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa66530 |
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2024-06-10T11:49:04.2631944 v2 66530 2024-05-29 AI-empowered scale development: Testing the potential of ChatGPT d154596e71b99ad1285563c8fdd373d7 Yogesh Dwivedi Yogesh Dwivedi true false 2024-05-29 AI-tools such as ChatGPT can assist researchers to improve the performance of the research process. This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. The study describes a process how to prompt ChatGPT to assist the scale development of a new construct, using the example of the construct of perceived value of ChatGPT-supported consumer behavior. The paper reports four main empirical studies (US: N = 148; Australia: N = 317; UK: N = 108; Germany: N = 51) that have been employed to validate the newly developed scale. The first study purifies the scale. The following studies confirm the adjusted factorial validity of the reduced scale. Although the empirical data imply a simplification of the initial multi-dimensional scale, the final three-dimensional operationalization is highly reliable and valid. The paper outlines the shortcomings and several critical notes to stimulate more research and discussion in this area. Journal Article Technological Forecasting and Social Change 205 123488 Elsevier BV 0040-1625 Artificial intelligence; ChatGPT; ChatGPT-supported consumer behavior; Scale development; Validation 1 8 2024 2024-08-01 10.1016/j.techfore.2024.123488 COLLEGE NANME COLLEGE CODE Swansea University Another institution paid the OA fee This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 2024-06-10T11:49:04.2631944 2024-05-29T18:54:27.1095938 Faculty of Humanities and Social Sciences School of Management - Business Management Stefan Hoffmann 1 Wassili Lasarov 2 Yogesh Dwivedi 3 66530__30582__e81f30d61a2649128369ca9134d2e480.pdf 66530.VoR.pdf 2024-06-10T11:27:34.4500309 Output 1403426 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/ |
title |
AI-empowered scale development: Testing the potential of ChatGPT |
spellingShingle |
AI-empowered scale development: Testing the potential of ChatGPT Yogesh Dwivedi |
title_short |
AI-empowered scale development: Testing the potential of ChatGPT |
title_full |
AI-empowered scale development: Testing the potential of ChatGPT |
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AI-empowered scale development: Testing the potential of ChatGPT |
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AI-empowered scale development: Testing the potential of ChatGPT |
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AI-empowered scale development: Testing the potential of ChatGPT |
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Stefan Hoffmann Wassili Lasarov Yogesh Dwivedi |
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Technological Forecasting and Social Change |
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description |
AI-tools such as ChatGPT can assist researchers to improve the performance of the research process. This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. The study describes a process how to prompt ChatGPT to assist the scale development of a new construct, using the example of the construct of perceived value of ChatGPT-supported consumer behavior. The paper reports four main empirical studies (US: N = 148; Australia: N = 317; UK: N = 108; Germany: N = 51) that have been employed to validate the newly developed scale. The first study purifies the scale. The following studies confirm the adjusted factorial validity of the reduced scale. Although the empirical data imply a simplification of the initial multi-dimensional scale, the final three-dimensional operationalization is highly reliable and valid. The paper outlines the shortcomings and several critical notes to stimulate more research and discussion in this area. |
published_date |
2024-08-01T14:35:47Z |
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11.048149 |