Predicting restaurants’ surplus food platform continuance: Insights from the combined use of PLS-SEM and NCA and predictive model comparisons

Surplus or anti-food waste platforms have emerged as opportunities for restaurants to sell their daily surplus food. However, restaurants’ commitment to these platforms often diminishes over time, which can result in discontinuance. Hence, it is necessary to explain and predict restaurant surplus food platform continuance intentions. For this purpose, we propose a model that integrates the technology acceptance model (TAM) and self-determination theory. We assessed our model by jointly applying partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to analyze data from 214 restaurants using surplus food platforms. The findings indicate that perceived ease of use, economic motivation, and environmental motivation are significant determinants of and necessary conditions for continuance intention. Moreover, model comparisons using BIC, Akaike weights, and CVPAT highlighted that our model’s predictive accuracy was higher than that of alternative models based solely on the TAM.