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Value expansion and sense making.

Olga Zervina1

  • 1Transport and Telecommunication Institute, Lomonosova 1, Riga, 1019 Latvia.

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Summary
This summary is machine-generated.

Automating the identification and categorization of a company's value proposition is crucial for business strategy. This research explores a novel, data-driven approach to systematically analyze and classify these values, setting a baseline for future automation.

Keywords:
AviationMachine learningTaxonomyValue proposition

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Area of Science:

  • Business Strategy
  • Computational Linguistics
  • Data Science

Background:

  • Companies aim to create value, using competitive analysis for value proposition development.
  • Manual analysis is time-consuming; automation offers significant industrial benefits.
  • This study addresses the novel task of automating value proposition identification and categorization.

Purpose of the Study:

  • To summarize and systematize prior research on automated value proposition identification mechanisms.
  • To demonstrate the feasibility and establish a baseline for this novel task.
  • To gain insights and systematize values within the air transportation domain.

Main Methods:

  • Utilized a bottom-up, data-driven approach.
  • Created a corpus of values through open-ended survey questions (96 respondents).
  • Annotated 796 start-ups' landing pages by 96 annotators to identify and label values, followed by taxonomy creation.

Main Results:

  • Systematized a corpus of company values from the air transportation sector.
  • Developed a structured dataset of values, organized into taxonomies.
  • Generated machine learning training material for automating value-related tasks.

Conclusions:

  • The research demonstrates the feasibility of a data-driven approach to systematize company values.
  • The findings provide a foundation for developing automated systems for value proposition analysis.
  • The generated dataset and taxonomies serve as a practical resource for machine learning applications.