In recent years, crowdfunding has emerged as a new fundraising technique for start-up ventures; however, Moroccan small and medium-sized businesses are still wary of this novel source of funding. This is confirmed by the low adoption rate of this financial innovation as well as the limited number of crowdfunding platforms in Morocco. This study aims to identify the impact of performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR) on SMEs’s intention to use crowdfunding platforms using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Empirical data were collected from 241 respondents through a survey, and structural equation modelling was used to analyze the findings. The results show that performance expectancy (PE), effort expectancy (EE), and facilitating conditions (FE) affect SMEs’s intentions to use crowdfunding. However, social influences (SI) and perceived risk (PR) were not found to be significant determinants. Regarding the moderating effect of age, our study has highlighted that this variable has moderated the relationship between the three independents variables: performance expectancy, facilitating conditions and perceived risk. Finally, this paper offers recommendations for how to increase SMEs’s intention to use crowdfunding platforms.

Moroccan manufacturing companies investing in the metallurgical, mechanical, and electromechanical industries sector are among the contributors to the growth of the national economy. The projects they are awarded do not have the same specific features as those of operating activities within other companies. They share several common features, making them particularly complex to fund. In such circumstances, supervised machine learning seems to be a suitable instrument to help such enterprises in their funding decisions, especially given that linear regression methods are inadequate for predicting human decision making as human thinking is a complicated system and not linear. Based on 5198 industrial projects of 53 firms operating in the said sector, four machine learning models are used to predict the funding method for some industrial projects, including are decision tree, random forest, gradient boosting, and K-nearest neighbors (KNN). Among the four machine learning methods, the gradient boosting method appears to be most effective overall, with an accuracy of 99%.

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