Developing a conceptual model of reward-based crowdfunding platforms (RBCFP) in Islamic contexts: insights from bibliometric and content analysis
Zinat Shariati et al.
Abstract
Purpose Venture capital and financing are crucial for the growth of startups. With the advent of digital transformation, reward-based crowdfunding (RBCF) has emerged as an innovative method for financing startups within a startup ecosystem. Therefore, to provide a clear and comprehensive understanding of these digital platforms in Islamic contexts, this study aims to develop a conceptual framework and process model for RBCFP. This paper also conducts a comparison between Islamic and conventional reward-based crowdfunding platforms. Design/methodology/approach This study adopts an exploratory approach. Initially, a bibliometric analysis is performed using VOSviewer software. Following this, the data are examined through content analysis. After extracting insights from the papers via text mining, open and axial coding procedures are implemented to identify the primary and axial key features impacting RBCF platforms using MAXQDA software. These findings are then visualized in a conceptual process model. Findings Based on the identified components, the Islamic RBCFP process model is presented. The results indicate that the RBCFP comprises four main components, nine core components and 145 subcomponents. Finally, the relationships among the models, theories, research clusters and key features of RBCF are presented. Originality/value To the best of the authors’ knowledge, this study is one of the first attempts to conceptualize RBCFP with an Islamic perspective. It uncovers the gap in the literature regarding the compatibility of crowdfunding mechanisms with Islamic principles, paving the way for further research in this area.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.