Refining sector classification for economic impact analysis: Assessing COVID-19 effects on the experience economy with AI-enhanced models

Mika Naumanen

Refining sector classification for economic impact analysis: Assessing COVID-19 effects on the experience economy with AI-enhanced models

Mika Naumanen

Quantitative science and technology studies

Research output: Contribution to conference › Conference article › Scientific

Abstract

The findings reinforce the idea that a more nuanced approach to sector classification improves economic impact analysis. The traditional NACE-based classification alone may be too rigid, whereas the LLM-based models provide a more flexible and accurate representation of company activities. As a result, policy recommendations and economic impact assessments based on these refined models are likely to be more reliable and informative for decision-makers.

Original language	English
Publication status	Published - 2025
MoE publication type	Not Eligible
Event	Eu-SPRI Annual Conference 2025: Shaping Societal Futures with STI Policies - Dortmund, Germany Duration: 11 Jun 2025 → 13 Jun 2025

Conference

Conference	Eu-SPRI Annual Conference 2025
Country/Territory	Germany
City	Dortmund
Period	11/06/25 → 13/06/25

Funding

Only abstract reviewed

Metaverse meets the Experience Industry
Naumanen, M. (Manager), Valkokari, K. (Participant), Vainikainen, S. (Participant), Oksanen, J. (Participant) & Windrum, P. (Participant)
1/07/23 → 30/06/25
Project: Business Finland project

Cite this

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title = "Refining sector classification for economic impact analysis: Assessing COVID-19 effects on the experience economy with AI-enhanced models",

abstract = "The findings reinforce the idea that a more nuanced approach to sector classification improves economic impact analysis. The traditional NACE-based classification alone may be too rigid, whereas the LLM-based models provide a more flexible and accurate representation of company activities. As a result, policy recommendations and economic impact assessments based on these refined models are likely to be more reliable and informative for decision-makers.",

author = "Mika Naumanen",

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AB - The findings reinforce the idea that a more nuanced approach to sector classification improves economic impact analysis. The traditional NACE-based classification alone may be too rigid, whereas the LLM-based models provide a more flexible and accurate representation of company activities. As a result, policy recommendations and economic impact assessments based on these refined models are likely to be more reliable and informative for decision-makers.

M3 - Conference article

Y2 - 11 June 2025 through 13 June 2025

ER -

Refining sector classification for economic impact analysis: Assessing COVID-19 effects on the experience economy with AI-enhanced models

Abstract

Conference

Funding

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Projects

Metaverse meets the Experience Industry

Cite this