IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p4839-d1091813.html
   My bibliography  Save this article

Sustainability Analysis of Enterprise Performance Management Driven by Big Data and Internet of Things

Author

Listed:
  • Ying Yang

    (Business School, Beijing Normal University, Beijing 100088, China)

Abstract

Today’s society has entered the information technology era as early as possible. The Internet of Things (IoT) technology and big data (BD) technology are the products of this era and are also important features of the new era. Today, when various fields enter the era of great integration, the Internet of Things and BD analysis technology are of great significance to the development of traditional enterprises, and also herald the arrival of the era of intelligence. Performance management plays an important role in modern enterprise management, especially in small and medium-sized enterprises. Through the implementation of performance management, it can effectively promote the development of enterprises and enhance their vitality. Based on this, this paper discusses the application of the Internet of Things and its BD analysis technology in the enterprise performance management system (PMS) and the sustainability of this application. At the same time, this paper conducts relevant empirical analysis, and the results show that the standardized path coefficient value (FP) of BD capability to financial performance is 0.421. The p value of the significance test was 0.008, which was less than 0.05, indicating that BD capability has a significant impact on the FP of enterprises. The standardized path coefficient value of the IoT on FP was 0.387, the significance test p value was 0.007, and the p value was less than 0.05, indicating that the IoT has a significant impact on the FP of enterprises.

Suggested Citation

  • Ying Yang, 2023. "Sustainability Analysis of Enterprise Performance Management Driven by Big Data and Internet of Things," Sustainability, MDPI, vol. 15(6), pages 1-11, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4839-:d:1091813
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/4839/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/4839/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nachiappan Subramanian & Angappa Gunasekaran & Lin Wu & Tinghua Shen, 2019. "Role of traditional Chinese philosophies and new product development under circular economy in private manufacturing enterprise performance," International Journal of Production Research, Taylor & Francis Journals, vol. 57(23), pages 7219-7234, December.
    2. Thomas Niebel & Fabienne Rasel & Steffen Viete, 2019. "BIG data – BIG gains? Understanding the link between big data analytics and innovation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 28(3), pages 296-316, April.
    3. Rizwan Ullah Khan & Yashar Salamzadeh & Hiroko Kawamorita & Gabor Rethi, 2021. "Entrepreneurial Orientation and Small and Medium-sized Enterprises’ Performance; Does ‘Access to Finance’ Moderate the Relation in Emerging Economies?," Vision, , vol. 25(1), pages 88-102, March.
    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. Kiwon Lee & Suchul Lee, 2023. "Enhancing R&D Performance Management: A Case of R&D Projects in South Korea," Sustainability, MDPI, vol. 15(15), pages 1-14, July.
    2. Andra-Teodora Gorski & Elena-Diana Ranf & Dorel Badea & Elisabeta-Emilia Halmaghi & Hortensia Gorski, 2023. "Education for Sustainability—Some Bibliometric Insights," Sustainability, MDPI, vol. 15(20), pages 1-17, October.

    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. Irene Bertschek & Joern Block & Alexander S. Kritikos & Caroline Stiel, 2024. "German financial state aid during Covid-19 pandemic: Higher impact among digitalized self-employed," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 36(1-2), pages 76-97, January.
    2. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    3. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    4. Erdsiek, Daniel & Rost, Vincent, 2022. "Datenbewirtschaftung in deutschen Unternehmen: Umfrageergebnisse zu Status-quo und mittelfristigem Ausblick," ZEW Expert Briefs 22-09, ZEW - Leibniz Centre for European Economic Research.
    5. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    6. Delera, Michele & Pietrobelli, Carlo & Calza, Elisa & Lavopa, Alejandro, 2022. "Does value chain participation facilitate the adoption of Industry 4.0 technologies in developing countries?," World Development, Elsevier, vol. 152(C).
    7. Genghua Tang & Hongxun Mai, 2022. "How Does Manufacturing Intelligentization Influence Innovation in China from a Nonlinear Perspective and Economic Servitization Background?," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    8. Wang, Li & Wu, Yuhan & Huang, Zeyu & Wang, Yanan, 2024. "Big data application and corporate investment decisions: Evidence from A-share listed companies in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
    9. Sarbu, Miruna, 2022. "The impact of industry 4.0 on innovation performance: Insights from German manufacturing and service firms," Technovation, Elsevier, vol. 113(C).
    10. Daniel Jugend & Hugo Henrique dos Santos & Susana Garrido & Regiane Máximo Siqueira & Jaime A. Mesa, 2024. "Circular product design challenges: An exploratory study on critical barriers," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 4825-4842, July.
    11. Mihai BOGDAN & Anca BORZA, 2019. "Big Data Analytics and Organizational Performance: A Meta-Analysis Study," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 4(2), pages 1-13, June.
    12. Mihai BOGDAN & Anca BORZA, 2020. "Big Data Analytics And Firm Performance: A Text Mining Approach," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 549-560, November.
    13. Lahcene Makhloufi & László Vasa & Joanna Rosak-Szyrocka & Farouk Djermani, 2023. "Understanding the Impact of Big Data Analytics and Knowledge Management on Green Innovation Practices and Organizational Performance: The Moderating Effect of Government Support," Sustainability, MDPI, vol. 15(11), pages 1-22, May.
    14. Hua Zhang & Shaofeng Yuan, 2023. "How and When Does Big Data Analytics Capability Boost Innovation Performance?," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    15. K. E. K. Vimal & Ming-Lang Tseng & Samanyu Raju & Mahesh Cherukuri & Amith Ashwithi & Jayakrishna Kandasamy, 2022. "Circular function deployment: a novel mathematical model to identify design factors for circular economy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9068-9101, July.
    16. Oduro, Stephen & De Nisco, Alessandro & Mainolfi, Giada, 2023. "Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus," Technovation, Elsevier, vol. 128(C).
    17. Bäck, Asta & Hajikhani, Arash & Jäger, Angela & Schubert, Torben & Suominen, Arho, 2022. "Return of the Solow-paradox in AI? AI-adoption and firm productivity," Papers in Innovation Studies 2022/1, Lund University, CIRCLE - Centre for Innovation Research.
    18. Rammer, Christian & Es-Sadki, Nordine, 2023. "Using big data for generating firm-level innovation indicators - a literature review," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    19. Radicic, Dragana & Petković, Saša, 2023. "Impact of digitalization on technological innovations in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    20. Koski, Heli & Fornaro, Paolo, 2024. "Digitalization and Resilience: Data Assets and Firm Productivity Growth During the COVID-19 Pandemic," ETLA Working Papers 113, The Research Institute of the Finnish Economy.

    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:gam:jsusta:v:15:y:2023:i:6:p:4839-:d:1091813. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.