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AI-Driven Market Research Value Creation

Abstract
Startups often operate under tight timeframes and limited resources, which makes traditional market research difficult to manage effectively. With artificial intelligence becoming more widely available, there is growing interest in whether it can offer faster, more cost-efficient, and more insightful ways to collect and analyze market data. This study explores how artificial intelligence can support market research in startup environments by examining where it can be helpful, where it falls short, and what should be considered before adopting it. Drawing on a qualitative case study that includes interviews and observations, the study identifies four central themes that influence the value artificial intelligence can bring to market research: speed and efficiency, resource optimization, data accuracy and trust, and human and artificial intelligence collaboration. These findings were used to develop a practical framework that outlines both the potential benefits and the risks associated with adopting artificial intelligence in this context. Although artificial intelligence can reduce manual work and help startups act more quickly, the results also show that poor implementation may lead to inaccurate insights, confusion, and strategic missteps. The study applies an abductive approach, combining theoretical insights with empirical findings, and contributes to the field of knowledge-based entrepreneurship by highlighting how digital tools can support value creation. It also emphasizes that technology alone is not enough. Real value comes from how artificial intelligence is integrated into daily practices, how it is understood by those using it, and how well it fits with the organization’s overall goals.
Degree
Master 2-years
URI
https://hdl.handle.net/2077/89116
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  • Master theses
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Master thesis (742.5Kb)
Date
2025-08-05
Author
Nasseri Mood, Amir Reza
Keywords
Artificial Intelligence
Market Research
Startups
Value Creation
Service-Dominant Logic
Innovation Management
Digital Tools in Entrepreneurship
Series/Report no.
2025:12
Language
eng
Metadata
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