Value expansion and sense making

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.