Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship

Sutarto Wijono; Untung Rahardja; Hindriyanto Dwi Purnomo; Ninda Lutfiani; Natasya Aprila Yusuf

doi:10.34306/att.v6i3.462

Authors

Sutarto Wijono Satya Wacana Christian University https://orcid.org/0000-0003-2154-6056
Untung Rahardja Universiti Teknologi Malaysia https://orcid.org/0000-0002-2166-2412
Hindriyanto Dwi Purnomo Satya Wacana Christian University https://orcid.org/0000-0001-6728-7868
Ninda Lutfiani University of Raharja https://orcid.org/0000-0001-7019-0020
Natasya Aprila Yusuf University of Raharja https://orcid.org/0009-0007-5422-675X

DOI:

https://doi.org/10.34306/att.v6i3.462

Keywords:

Machine Learning Models, Historical Data, Startup Collaboration, Long-term Partnership Success

Abstract

In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.

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