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The evolution of data pricing: From economics to computational intelligence
Jun Hao1,2, Zeyu Deng1,2, Jianping Li1,2
1School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China.
Heliyon
|October 9, 2023
Summary
This study analyzes 373 data pricing publications from 1990-2023 using bibliometric methods. It reveals rapid progress in data pricing strategies, primarily economic and computer science approaches, over the last decade.
Area of Science:
- Economics
- Computer Science
- Bibliometrics
Background:
- Data pricing is crucial for data trading, enabling valuation, openness, and circulation.
- Existing research on data pricing is continuously evolving.
- A comprehensive bibliometric analysis of data pricing literature is lacking.
Purpose of the Study:
- To conduct a first-time bibliometric and statistical analysis of data pricing literature.
- To analyze the evolution, external characteristics, and network structures of data pricing research.
- To identify research trends and future directions in data pricing.
Main Methods:
- Bibliometric analysis of 373 publications from 1990 to 2023.
- Statistical analysis of publication data.
- Analysis of keyword co-occurrence and co-citation networks.
Main Results:
- Data pricing research has shown significant growth in the last decade.
- Two primary research approaches identified: economics-based and computer science-based algorithms.
- Analysis of external characteristics, keyword co-occurrence, and co-citation networks provides insights into research landscape.
Conclusions:
- Data pricing is a rapidly advancing field with distinct theoretical and algorithmic approaches.
- The study provides a comprehensive overview of the evolution of data pricing.
- Identified future research directions for the field of data pricing.
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