Abstract
As technology has become indispensable in consumers’ daily life and economic growth, understanding how and why consumers decide to accept and use a new technology has become essential to both academic researchers and practice. This article provides a detailed dataset based on a questionnaire that utilizes an extended technology acceptance model (TAM), incorporating the theory of consumer values and the innovation diffusion theory. Data collection was done with an online survey among French consumers, resulting in a sample size of 174. The dataset contains measures on various consumer attitudes and perceptions (e.g., consumption values) that influence intention and behaviors (adoption intention and technology use). This article supplements a published research article by Richter, Schubring, Hauff, Ringle and Sarstedt [1] which provides a detailed guide on how to combine partial least squares structural equation modeling (PLS-SEM) with necessary condition analysis (NCA) and a related illustration in a standard software published by Richter, Hauff, Ringle, Sarstedt, Kolev and Schubring [2].
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 109190 |
| Tidsskrift | Data in Brief |
| Vol/bind | 48 |
| Udgave nummer | June |
| Sider (fra-til) | 109190 |
| Antal sider | 9 |
| ISSN | 2352-3409 |
| DOI | |
| Status | Udgivet - jun. 2023 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Dataset on an extended technology acceptance model: A combined application of PLS-SEM and NCA'. Sammen danner de et unikt fingeraftryk.-
How to apply necessary condition analysis in PLS-SEM
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Necessary conditions in international business research: advancing the field with a new perspective on causality and data analysis
Richter, N. F. & Hauff, S., aug. 2022, I: Journal of World Business. 57, 5, 27 s., 101310.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA
Richter, N. F., Schubing, S., Hauff, S., Ringle, C. M. & Sarstedt, M., 2020, I: Industrial Management & Data Systems. 120, 12, s. 2243-2267Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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