IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2401.05395.html
   My bibliography  Save this paper

SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market

Author

Listed:
  • Ruixin Ding
  • Bowei Chen
  • James M. Wilson
  • Zhi Yan
  • Yufei Huang

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.

Suggested Citation

  • Ruixin Ding & Bowei Chen & James M. Wilson & Zhi Yan & Yufei Huang, 2023. "SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market," Papers 2401.05395, arXiv.org.
    • Handle: RePEc:arx:papers:2401.05395
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2401.05395
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. João L. Assunção & Robert J. Meyer, 1993. "The Rational Effect of Price Promotions on Sales and Consumption," Management Science, INFORMS, vol. 39(5), pages 517-535, May.
    2. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    3. Jan R. Landwehr & Aparna A. Labroo & Andreas Herrmann, 2011. "Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts," Marketing Science, INFORMS, vol. 30(3), pages 416-429, 05-06.
    4. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    5. Glen L. Urban & John R. Hauser & John H. Roberts, 1990. "Prelaunch Forecasting of New Automobiles," Management Science, INFORMS, vol. 36(4), pages 401-421, April.
    6. Christian Homburg & Dominik M. Wielgos, 2022. "The value relevance of digital marketing capabilities to firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(4), pages 666-688, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    2. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    3. Konstantakis, Konstantinos N. & Milioti, Christina & Michaelides, Panayotis G., 2017. "Modeling the dynamic response of automobile sales in troubled times: A real-time Vector Autoregressive analysis with causality testing for Greece," Transport Policy, Elsevier, vol. 59(C), pages 75-81.
    4. Ert, Eyal & Raz, Ornit & Heiman, Amir, 2016. "(Poor) seeing is believing: When direct experience impairs product promotion," International Journal of Research in Marketing, Elsevier, vol. 33(4), pages 881-895.
    5. David R. Bell & Jeongwen Chiang & V. Padmanabhan, 1999. "The Decomposition of Promotional Response: An Empirical Generalization," Marketing Science, INFORMS, vol. 18(4), pages 504-526.
    6. George Doorn & Bryan Paton & Charles Spence, 2016. "Is J the new K? Initial letters and brand names," Journal of Brand Management, Palgrave Macmillan, vol. 23(6), pages 666-678, November.
    7. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    8. Joe Cox & Daniel Kaimann, 2013. "The Signaling Effect of Critics - Evidence from a Market for Experience Goods," Working Papers CIE 68, Paderborn University, CIE Center for International Economics.
    9. Ludivine Ravat & Aurélie Hemonnet-Goujot & Sandrine Hollet-Haudebert, 2023. "Data-driven innovation capability of marketing: an exploratory study of its components and underlying processes," Post-Print hal-04151199, HAL.
    10. Liuan Wang & Lu (Lucy) Yan & Tongxin Zhou & Xitong Guo & Gregory R. Heim, 2020. "Understanding Physicians’ Online-Offline Behavior Dynamics: An Empirical Study," Information Systems Research, INFORMS, vol. 31(2), pages 537-555, June.
    11. M. Narciso, 2022. "The Unreliability of Online Review Mechanisms," Journal of Consumer Policy, Springer, vol. 45(3), pages 349-368, September.
    12. Eugene C.X. Ikejemba & Peter C. Schuur, 2018. "Analyzing the Impact of Theft and Vandalism in Relation to the Sustainability of Renewable Energy Development Projects in Sub-Saharan Africa," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    13. Yuanfang Lin & Amit Pazgal, 2016. "Hide Supremacy or Admit Inferiority—Market Entry Strategies in Response to Consumer Informational Needs," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(2), pages 94-103, June.
    14. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    15. Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(11), pages 60-70, November.
    16. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    17. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    18. Minnema, Alec & Bijmolt, Tammo H.A. & Gensler, Sonja & Wiesel, Thorsten, 2016. "To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns," Journal of Retailing, Elsevier, vol. 92(3), pages 253-267.
    19. Giovanni Bernardo & Massimo Ruberti & Roberto Verona, 2022. "Image is everything! Professional football players' visibility and wages: evidence from the Italian Serie A," Applied Economics, Taylor & Francis Journals, vol. 54(5), pages 595-614, January.
    20. Heiman, Amir & Ofir, Chezy, 2010. "The effects of imbalanced competition on demonstration strategies," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 175-187.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2401.05395. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.