IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v96y2024ipcs1059056024006737.html
   My bibliography  Save this article

How does data factor utilization stimulate corporate total factor productivity: A discussion of the productivity paradox

Author

Listed:
  • Ren, Yuheng
  • Zhang, Jue
  • Wang, Xin

Abstract

Stimulating total factor productivity through the utilization of data factors is a crucial concern for companies operating in the digital era. This study employs data from A-share listed firms from 2011 to 2022 to examine the impact and mechanisms of data utilization on corporate total factor productivity. The findings reveal that effective data utilization can significantly improve corporate total factor productivity, but no evidence supports the productivity dilemma. The stimulating effects are achieved by alleviating investment inefficiency and resource redundancy. Further analysis verifies that firms’ financial performance and life cycle can lead to heterogeneous effects. Specifically, the positive impact is more pronounced in firms with lower operational performance and in periods of maturity and recession.

Suggested Citation

  • Ren, Yuheng & Zhang, Jue & Wang, Xin, 2024. "How does data factor utilization stimulate corporate total factor productivity: A discussion of the productivity paradox," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    • Handle: RePEc:eee:reveco:v:96:y:2024:i:pc:s1059056024006737
    DOI: 10.1016/j.iref.2024.103681
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056024006737
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2024.103681?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Du, Longzheng & Lin, Weifen, 2022. "Does the application of industrial robots overcome the Solow paradox? Evidence from China," Technology in Society, Elsevier, vol. 68(C).
    2. Chang, Lei & Taghizadeh-Hesary, Farhad & Mohsin, Muhammad, 2023. "Role of artificial intelligence on green economic development: Joint determinates of natural resources and green total factor productivity," Resources Policy, Elsevier, vol. 82(C).
    3. Zhai, Shaoxuan & Liu, Zhenpeng, 2023. "Artificial intelligence technology innovation and firm productivity: Evidence from China," Finance Research Letters, Elsevier, vol. 58(PB).
    4. Wannakrairoj, Wit & Velu, Chander, 2021. "Productivity growth and business model innovation," Economics Letters, Elsevier, vol. 199(C).
    5. Guo, Xiaochuan & Li, Mengmeng & Wang, Yanlin & Mardani, Abbas, 2023. "Does digital transformation improve the firm’s performance? From the perspective of digitalization paradox and managerial myopia," Journal of Business Research, Elsevier, vol. 163(C).
    6. Pan, Wenrong & Xie, Tao & Wang, Zhuwang & Ma, Lisha, 2022. "Digital economy: An innovation driver for total factor productivity," Journal of Business Research, Elsevier, vol. 139(C), pages 303-311.
    7. Zhang, Haiyan & Dong, Shizheng, 2023. "Digital transformation and firms' total factor productivity: The role of internal control quality," Finance Research Letters, Elsevier, vol. 57(C).
    8. Lee, Chi-Chuan & Lee, Chien-Chiang, 2022. "How does green finance affect green total factor productivity? Evidence from China," Energy Economics, Elsevier, vol. 107(C).
    9. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    10. Yang, Chih-Hai, 2022. "How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan," Research Policy, Elsevier, vol. 51(6).
    11. Zhao Li & Bingyang Lv, 2021. "Total factor productivity of Chinese industrial firms: evidence from 2007 to 2017," Applied Economics, Taylor & Francis Journals, vol. 53(60), pages 6910-6926, December.
    12. Chen, Yang & Yang, Shengping & Li, Quan, 2022. "How does the development of digital financial inclusion affect the total factor productivity of listed companies? Evidence from China," Finance Research Letters, Elsevier, vol. 47(PB).
    13. Zhonghao Lei & Dongmei Wang, 2023. "Digital transformation and total factor productivity: Empirical evidence from China," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-18, October.
    14. Prakash, Navendu & Singh, Shveta & Sharma, Seema, 2021. "Technological diffusion, banking efficiency and Solow's paradox: A frontier-based parametric and non-parametric analysis," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 534-551.
    15. Ben Lahouel, Béchir & Taleb, Lotfi & Ben Zaied, Younes & Managi, Shunsuke, 2021. "Does ICT change the relationship between total factor productivity and CO2 emissions? Evidence based on a nonlinear model," Energy Economics, Elsevier, vol. 101(C).
    16. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    17. Wen, Huwei & Zhong, Qiming & Lee, Chien-Chiang, 2022. "Digitalization, competition strategy and corporate innovation: Evidence from Chinese manufacturing listed companies," International Review of Financial Analysis, Elsevier, vol. 82(C).
    18. Wang, Lan, 2023. "Digital transformation and total factor productivity," Finance Research Letters, Elsevier, vol. 58(PA).
    19. Yilin Zhong & Feng Xu & Longpeng Zhang, 2024. "Influence of artificial intelligence applications on total factor productivity of enterprises—evidence from textual analysis of annual reports of Chinese-listed companies," Applied Economics, Taylor & Francis Journals, vol. 56(43), pages 5205-5223, September.
    20. Cheng, Yiran & Zhou, Xiaorui & Li, Yongjian, 2023. "The effect of digital transformation on real economy enterprises’ total factor productivity," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 488-501.
    21. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    22. Kong, Qunxi & Peng, Dan & Ruijia, Zhang & Wong, Zoey, 2021. "Resource misallocation, production efficiency and outward foreign direct investment decisions of Chinese enterprises," Research in International Business and Finance, Elsevier, vol. 55(C).
    23. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dongri Han & Ke Lu & Peisen Liu, 2025. "Multiplication of number and quality rise: data elements enable low-carbon innovation," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-17, December.
    2. Wang, Hongyan & Cheng, Jiandong & Zhou, Hang, 2025. "Tax administration informatization, financial tightened regulation, and firm productivity: A quasi-natural experiment based on the "Golden Tax Phase III project"," International Review of Economics & Finance, Elsevier, vol. 98(C).
    3. Chen, Rongda & Mao, Weidao & Wang, Shengnan & Jin, Chenglu, 2024. "Can companies' input of data factor eliminate investors' home biases?," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    4. He, Zheng & Liu, Zhengkai & Zhang, Congzhi & Zhao, Yuanjun, 2025. "How do carbon pricing spillover effects impact green asset price volatility? An empirical study based on the TVP-VAR-DY model," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 2162-2179.

    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. Tang, Li & Zhang, Teng & Wang, Jinglang & Liu, Baoliu & Huang, Yujie, 2025. "“Dual synergistic” transformation and corporate total factor productivity: Empirical evidence from China," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 717-732.
    2. Wen, Huwei & Wen, Changyong & Lee, Chien-Chiang, 2022. "Impact of digitalization and environmental regulation on total factor productivity," Information Economics and Policy, Elsevier, vol. 61(C).
    3. Sun, Zhiyao & Che, Shuai & Wang, Jie, 2024. "Deconstruct artificial intelligence's productivity impact: A new technological insight," Technology in Society, Elsevier, vol. 79(C).
    4. Lu, Shan & Peng, Sihan & Shi, Jiaxin & Zhang, Ci & Feng, Yanchao, 2024. "How does digital transformation affect the total factor productivity of China's A-share listed enterprises in the mineral resource-based sector?," Resources Policy, Elsevier, vol. 94(C).
    5. Jiaqi Liu & Yiyang Cheng & Yamei Fu & Fei Xue, 2024. "The Impact of Digital Economy on TFP of Industries: Empirical Analysis Based on the Extension of Schumpeterian Model to Complex Economic Systems," Mathematics, MDPI, vol. 12(17), pages 1-24, August.
    6. Jiang, Weijie & Li, Jiaye, 2024. "Digital transformation and its effect on resource allocation efficiency and productivity in Chinese corporations," Technology in Society, Elsevier, vol. 78(C).
    7. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    8. Ge, Shilong & Luo, Xiaodan & Li, Yuangang & Zheng, Lanxing, 2024. "The impact of green credit policy on total factor productivity of enterprises," International Review of Economics & Finance, Elsevier, vol. 95(C).
    9. Suo, Xuekun & Zhang, Longting & Guo, Rong & Lin, Han & Yu, Mingchuan & Du, Xiuhong, 2024. "The inverted U-shaped association between digital economy and corporate total factor productivity: A knowledge-based perspective," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    10. Ma, Xuxia & Zhu, Shanshan, 2025. "Impact of venture capital on total factor productivity: Insights from enterprise-investment institution factor flows," Finance Research Letters, Elsevier, vol. 74(C).
    11. Zhang, Longpeng & Zhang, Xingye, 2025. "Impact of digital government construction on the intelligent transformation of enterprises: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    12. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    13. Ryota Nakatani, 2024. "Multifactor productivity growth enhancers across industries and countries: firm-level evidence," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 14(2), pages 401-446, June.
    14. Zhang, Haiyan & Dong, Shizheng, 2023. "Digital transformation and firms' total factor productivity: The role of internal control quality," Finance Research Letters, Elsevier, vol. 57(C).
    15. Xu, Ru-Yu & Wang, Ke-Liang & Miao, Zhuang, 2024. "The impact of digital technology innovation on green total-factor energy efficiency in China: Does economic development matter?," Energy Policy, Elsevier, vol. 194(C).
    16. Zhang, Jian & Yu, Chin-Hsien & Zhao, Jinsong & Lee, Chi-Chuan, 2025. "How does corporate digital transformation affect green innovation? Evidence from China's enterprise data," Energy Economics, Elsevier, vol. 142(C).
    17. Liu, Yunxin & Cao, Yuqiang & Lu, Meiting & Shan, Yaowen & Xu, Jiangang, 2024. "Automating efficiency: The impact of industrial robots on labor investment in China," Economic Modelling, Elsevier, vol. 140(C).
    18. Xiao, Bingyu & Gong, Guangming & Xiao, Liang, 2025. "Does the work experience of regulatory leaders at local companies affect the productivity of state-owned enterprises? Evidence from China," Economic Modelling, Elsevier, vol. 145(C).
    19. Yang, Liuhui & Di, Jianguo, 2025. "Green supply chains and high-quality development of enterprises under tax reduction and fee decrease," International Review of Economics & Finance, Elsevier, vol. 98(C).
    20. Jin, Huanhuan & Jiang, Nanyue & Su, Weihua & Dalia, Streimikiene, 2025. "How does customer enterprise digitalization improve the green total factor productivity of state-owned suppliers: From the supply chain perspective," Omega, Elsevier, vol. 133(C).

    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:eee:reveco:v:96:y:2024:i:pc:s1059056024006737. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.