Machine Learning for the Next Generation: A Guide to Matchmaking at Startups

Rifqi Fahrudin; Muhammad Hatta; Yulianti Yulianti; Erwin Erwin; Aurelie Zelene

doi:10.34306/itsdi.v6i1.678

Authors

Rifqi Fahrudin Catur Insan Cendekia University
Muhammad Hatta Catur Insan Cendekia University https://orcid.org/0000-0002-1892-9008
Yulianti Yulianti ISB Atma Luhur
Erwin Erwin ISB Atma Luhur
Aurelie Zelene Pandawan Incorporation

DOI:

https://doi.org/10.34306/itsdi.v6i1.678

Keywords:

Matchmaking, Startups Expectancy, UTAUT2, Digital Transformation

Abstract

In todays rapidly evolving digital landscape, the success of startups depends heavily on their ability to innovate and form effective partnerships. The process of connecting startups with compatible business partners is crucial, and Machine Learning (ML) has emerged as a promising solution for enhancing this matchmaking. This study utilizes the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine attitudes and intentions toward adopting ML for startup matchmaking. Key factors assessed include Performance Expectancy (PE), Ease of Use (EU), Social Influence (SI), and Facilitating Conditions (FC) that affect ML adoption. Using Structural Equation Modeling (SEM), this research analyzes a diverse sample of startups, focusing on variables like Machine Learning Adoption, Data-Driven Matchmaking Strategies, Alignment with Startup Goals, Continuous Learning Integration, and Adaptable Partnerships to evaluate their impact on Matchmaking Efficiency. This study aims to shed light on ML role in enhancing the startup matching process and its overall impact on partnership effectiveness.

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