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Determinants of Reward Crowdfunding Success for Technology Projects: The Moderating Role of Platform Age

Author

Listed:
  • Ichrak Dridi

    (ESC-Business School of Tunis, QuAnLab LR24ES21, Manouba University, Manouba 2010, Tunisia)

  • Oussama Gafrej

    (Laboratoire de Management de l'Innovation et de Développement Durable (LAMIDED), Higher Institute of Commercial Studies, University of Sousse, Sousse, Tunisia)

  • Jabeur Salhi

    (Laboratoire de Management de l'Innovation et de Développement Durable (LAMIDED), Higher Institute of Management of Sousse, University of Sousse, Sousse, Tunisia)

Abstract

[Purpose] Moving beyond the average effects identified in prior literature, this study investigates the differential drivers of technology crowdfunding success and how platform maturity reshapes the competitive landscape. Specifically, it examines whether strategies ensuring basic viability can secure top-tier success and how key drivers evolve as a platform ages. [Design/methodology/approach] The study analyzes 600 technology projects from Kickstarter (2009-2023) using a complementary analytical approach. Generalized Least Squares regression and subsample analysis establish baseline relationships and test the moderating role of platform age. Quantile regression then uncovers how the influence of key determinants shifts across the entire conditional distribution of campaign success. [Findings] The results confirm the significance of fundamental determinants, with subsample analysis showing their influence is contingent on a project's final outcome. A key finding is that platform age positively moderates the relationship between media richness and success. Quantile regression further reveals a performance-level hierarchy, showing that influential criteria evolve from establishing baseline viability for lower-performing campaigns to signaling superior credibility and comprehensive development for top performers. [Originality/value] This study advances decision sciences by demonstrating context-dependent crowdfunding strategies and employing quantile regression to reveal a hierarchical decision model, with platform age as a key moderator. In contrast to earlier studies that overlook decision heterogeneity, this research proposes an original quantile regression approach that incorporates platform age as a moderator, enabling a differentiated analysis of reward-based crowdfunding success across performance levels. It identifies the key determinants of success in reward-based crowdfunding, offering a decision-oriented framework to understand how creators' strategic choices and backers' responses jointly drive funding outcomes. [Practical implications] For decision sciences in practice, the findings prescribe distinct contingent strategies for creators based on performance targets and platform maturity. They further advocate for platform administrators to develop level-specific decision support systems, moving beyond one-size-fits-all analytics to improve strategic outcomes.

Suggested Citation

  • Ichrak Dridi & Oussama Gafrej & Jabeur Salhi, 2026. "Determinants of Reward Crowdfunding Success for Technology Projects: The Moderating Role of Platform Age," Advances in Decision Sciences, Asia University, Taiwan, vol. 30(2), pages 68-113, June.
    • Handle: RePEc:aag:wpaper:v:30:y:2026:i:2:p:68-113
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    References listed on IDEAS

    as
    1. Lin Peng & Linyi Zhang, 2025. "Unleashing the Crowd: The Effect of Social Networks in Crowdfunding Markets," Management Science, INFORMS, vol. 71(6), pages 4942-4976, June.
    2. Audretsch, David B. & Keilbach, Max C. & Lehmann, Erik E., 2006. "Entrepreneurship and Economic Growth," OUP Catalogue, Oxford University Press, number 9780195183511.
    3. David B. Audretsch (ed.), 2006. "Entrepreneurship, Innovation and Economic Growth," Books, Edward Elgar Publishing, number 4130.
    4. Ali Saleh Alshebami, 2022. "Crowdfunding Platforms as a Substitute Financing Source for Young Saudi Entrepreneurs: Empirical Evidence," SAGE Open, , vol. 12(3), pages 21582440221, September.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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