Advancing Startup Ecosystems Through AI-Driven Matchmaking: A Comprehensive Bibliometric Analysis - Lutfiani, Ninda; Wijono, Sutarto; Rahardja, Untung; Purnomo, Hindriyanto Dwi (International Journal of Engineering, Science and Information Technology, 2025)
This study investigates the integration of AI in streamlining the alignment process between startups and potential collaborators and partners, particularly in the Indonesian startup ecosystem. The motivation behind this research lies in the gaps and challenges startups face in efficiently connecting with suitable partners or investors. We employed a bibliometric analysis approach. This study sourced data from Scopus, analysing 515 articles and 59,412 citations published from 2018 to 2023. Key findings provide insights into the predominant role of AI technologies, notably machine learning methods like deep learning and data mining, and the significance of recommendation systems that incorporate collaborative filtering. Furthermore, the results underscore the increasing importance of AI as an indispensable tool in the startup landscape, enhancing the efficiency and productivity of collaborations. We assessed publications from several countries, authors, and citations through the bibliometric measures to comprehensively understand the current trends and trajectories. The study concludes by recognising the transformative potential of AI in fostering tighter and more efficient alliances within the startup ecosystem, laying the groundwork for future research into refining AI-driven collaborative processes.