Self-determination theory (SDT) is a theory of human motivation that highlights the distinction between intrinsic and extrinsic motivations. Recent research has advocated its wider use in marketing studies, suggesting that it has strong predictive accuracy for consumer behaviors, and has proposed arguments about the necessity of both intrinsic and extrinsic motivations for marketing outcomes. However, these statements have not been empirically substantiated. We address this gap by studying the motivations for attitude and intention to use anti-food waste apps. Data from 141 users and 227 non-users of the app "Too Good To Go" are analyzed using partial least squares structural equation modeling (PLS-SEM) and several of its methodological extensions (e.g., multigroup analysis and the cross-validated predictive ability test), and necessary condition analysis (NCA). The findings support the argument that SDT accurately predicts consumer attitudes and behaviors, while NCA provides a nuanced view of the necessity of intrinsic and extrinsic motivations.
(2024). The value of self-determination theory in marketing studies: Insights from the application of PLS-SEM and NCA to anti-food waste apps [journal article - articolo]. In JOURNAL OF BUSINESS RESEARCH. Retrieved from https://hdl.handle.net/10446/291306
The value of self-determination theory in marketing studies: Insights from the application of PLS-SEM and NCA to anti-food waste apps
Magno, Francesca
2024-01-01
Abstract
Self-determination theory (SDT) is a theory of human motivation that highlights the distinction between intrinsic and extrinsic motivations. Recent research has advocated its wider use in marketing studies, suggesting that it has strong predictive accuracy for consumer behaviors, and has proposed arguments about the necessity of both intrinsic and extrinsic motivations for marketing outcomes. However, these statements have not been empirically substantiated. We address this gap by studying the motivations for attitude and intention to use anti-food waste apps. Data from 141 users and 227 non-users of the app "Too Good To Go" are analyzed using partial least squares structural equation modeling (PLS-SEM) and several of its methodological extensions (e.g., multigroup analysis and the cross-validated predictive ability test), and necessary condition analysis (NCA). The findings support the argument that SDT accurately predicts consumer attitudes and behaviors, while NCA provides a nuanced view of the necessity of intrinsic and extrinsic motivations.File | Dimensione del file | Formato | |
---|---|---|---|
1-s2.0-S0148296323008135-main.pdf
accesso aperto
Versione:
publisher's version - versione editoriale
Licenza:
Creative commons
Dimensione del file
4.86 MB
Formato
Adobe PDF
|
4.86 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo