SRNI-CAR: A Comprehensive Dataset for Analyzing the Chinese Automotive Market

Ding, R., Chen, B. , Wilson, J. M. , Yan, Z. and Huang, Y. (2024) SRNI-CAR: A Comprehensive Dataset for Analyzing the Chinese Automotive Market. In: 2023 IEEE International Conference on Big Data (IEEE BigData 2023), Sorrento, Italy, 15-18 December, pp. 3405-3412. ISBN 9798350324457 (doi: 10.1109/BigData59044.2023.10386203)

Abstract

The automotive industry plays a critical role in the global economy, and particularly important is the expanding Chinese automobile market due to its immense scale and influence. However, existing automotive sector datasets are limited in their coverage, failing to adequately consider the growing demand for more and diverse variables. This paper aims to bridge this data gap by introducing a comprehensive dataset spanning the years from 2016 to 2022, encompassing sales data, online reviews, and a wealth of information related to the Chinese automotive industry. This dataset serves as a valuable resource, significantly expanding the available data. Its impact extends to various dimensions, including improving forecasting accuracy, expanding the scope of business applications, informing policy development and regulation, and advancing academic research within the automotive sector. To illustrate the dataset’s potential applications in both business and academic contexts, we present two application examples. Our developed dataset enhances our understanding of the Chinese automotive market and offers a valuable tool for researchers, policymakers, and industry stakeholders worldwide.

• Item Type: Conference Proceedings
• Additional Information: The first author acknowledges the University of Glasgow Adam Smith Business School for Student Research Internship funding support. The second author would like to thank the funding support of Nvidia Accelerated Data Science Grant and University of Glasgow Adam Smith Business School.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wilson, Dr James and Ding, Mr Ruixin and Chen, Dr Bowei
• Authors: Ding, R., Chen, B., Wilson, J. M., Yan, Z., and Huang, Y.
• College/School: College of Social Sciences > Adam Smith Business School; College of Social Sciences > Adam Smith Business School > Management
• ISBN: 9798350324457
• Copyright Holders: Copyright © 2023, IEEE
• First Published: First published in 2023 IEEE International Conference on Big Data (BigData)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy