Focusing Limited Web3 Startup Resources for Higher Impact Product Development

Early-stage startups often have more ideas than resources and require targeted resource deployment to ensure suitable product delivery to market. Early-stage Web3 startups have an inherent advantage compared to non-Web3 early-stage startups in that they have access to granular, user-level, data via open-source ledgers or, commonly known as the blockchain. By utilizing this readily available user-level data, early-stage Web3 startups can concentrate their resources on high-impact product development to minimize unproductive resource deployment. This focused approach can improve early-stage startup financial success and reduce the likelihood of depleting raised funding. Additionally, Web3 early-stage startups can work to retain high-quality and economically beneficial end users to accelerate growth and financial stability. The intent of this praxisis to utilize machine learning to identify and rank high-quality products to ensure targeted resourced development towards their improvement for a single early-stage Web3 startup.

Author

• Stewart, Matthew

Language

• en

Keyword

• Startup
• Machine Learning
• TOPSIS
• Web3
• Multi-criteria Decision Making

Date created

• 2023

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Biemer, Steven

Committee Member(s)

• Etemadi, Amir
• Blackford, Joseph P

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/1v53jx91g