IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v4y2022i4p42-786d923606.html
   My bibliography  Save this article

Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)

Author

Listed:
  • Yili Chen

    (School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao 999078, China)

  • Congdong Li

    (School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao 999078, China
    The School of Management, Jinan University, Zhuhai 519000, China)

  • Han Wang

    (The Faculty of Data Science, City University of Macau, Macao 999078, China
    The Department of Artificial Intelligence and Big Data Applications, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai 519000, China
    The School of Computer, College of Beijing University of Technology Zhuhai, Zhuhai 519000, China)

Abstract

Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.

Suggested Citation

  • Yili Chen & Congdong Li & Han Wang, 2022. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)," Forecasting, MDPI, vol. 4(4), pages 1-20, September.
  • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:42-786:d:923606
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/4/4/42/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-9394/4/4/42/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Vivek Kumar Singh & Prashasti Singh & Mousumi Karmakar & Jacqueline Leta & Philipp Mayr, 2021. "The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5113-5142, June.
    3. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    4. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
    5. Chulmo Koo & Zheng Xiang & Ulrike Gretzel & Marianna Sigala, 2021. "Artificial intelligence (AI) and robotics in travel, hospitality and leisure," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 473-476, September.
    6. Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
    7. Grazia Vicario & Shirley Coleman, 2020. "A review of data science in business and industry and a future view," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 6-18, January.
    8. Ziaul Haque Munim & Mariia Dushenko & Veronica Jaramillo Jimenez & Mohammad Hassan Shakil & Marius Imset, 2020. "Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 577-597, July.
    9. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    10. Tereza Semerádová & Petr Weinlich, 2020. "Website Quality and Shopping Behavior," SpringerBriefs in Business, Springer, number 978-3-030-44440-2, January.
    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. Ana-Maria Ionescu & Flavius Aurelian Sârbu, 2024. "Exploring the Impact of Smart Technologies on the Tourism Industry," Sustainability, MDPI, vol. 16(8), pages 1-23, April.
    2. Klaudia Hillebrandt-Szymanska & Dorota Piotrowska & Artur Blaszczyk & Jakub Statucki, 2023. "Strategic Insights: Navigating Business Intelligence Implementation - Phases, Tasks, and Risks: A Case Study on an International Manufacturing Company," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 865-888.

    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. Dušan Nikolić & Dragan Ivanović & Lidija Ivanović, 2024. "An open-source tool for merging data from multiple citation databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4573-4595, July.
    2. Nieminen, Paavo & Pölönen, Ilkka & Sipola, Tuomo, 2013. "Research literature clustering using diffusion maps," Journal of Informetrics, Elsevier, vol. 7(4), pages 874-886.
    3. Han Zhang & Yongbo Lv & Jianwei Guo, 2022. "New Development Direction of Underground Logistics from the Perspective of Public Transport: A Systematic Review Based on Scientometrics," Sustainability, MDPI, vol. 14(6), pages 1-31, March.
    4. Haochen Qian & Fan Zhang & Bing Qiu, 2023. "Deciphering the Evolution, Frontier, and Knowledge Clustering in Sustainable City Planning: A 60-Year Interdisciplinary Review," Sustainability, MDPI, vol. 15(24), pages 1-27, December.
    5. Liu, Aiping & Urquía-Grande, Elena & López-Sánchez, Pilar & Rodríguez-López, Ángel, 2023. "Research into microfinance and ICTs: A bibliometric analysis," Evaluation and Program Planning, Elsevier, vol. 97(C).
    6. Judith Nyulas & Ștefan Dezsi & Adrian Niță & Raluca-Andreea Toma & Ana-Maria Lazăr, 2024. "Trends and Future Directions in Analysing Attractiveness of Geoparks Using an Automated Merging Method of Multiple Databases—R-Based Bibliometric Analysis," Land, MDPI, vol. 13(10), pages 1-30, October.
    7. Zheng-Dong Li & Bei Zhang, 2023. "Family-friendly policy evolution: a bibliometric study," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    8. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
    9. Hong-Hua Qiu & Lu-Ge Liu, 2018. "A Study on the Evolution of Carbon Capture and Storage Technology Based on Knowledge Mapping," Energies, MDPI, vol. 11(5), pages 1-25, May.
    10. Hou, Jianhua & Wang, Dongyi & Li, Jing, 2022. "A new method for measuring the originality of academic articles based on knowledge units in semantic networks," Journal of Informetrics, Elsevier, vol. 16(3).
    11. Belussi, Fiorenza & Orsi, Luigi & Savarese, Maria, 2019. "Mapping Business Model Research: A Document Bibliometric Analysis," Scandinavian Journal of Management, Elsevier, vol. 35(3).
    12. Giudici, Paolo & Raffinetti, Emanuela, 2023. "SAFE Artificial Intelligence in finance," Finance Research Letters, Elsevier, vol. 56(C).
    13. Yunlong Niu & Mastura Adam & Hazreena Hussein, 2022. "Connecting Urban Green Spaces with Children: A Scientometric Analysis Using CiteSpace," Land, MDPI, vol. 11(8), pages 1-23, August.
    14. Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022. "Artificial intelligence in the field of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
    15. Yin Junjia & Aidi Hizami Alias & Nuzul Azam Haron & Nabilah Abu Bakar, 2023. "A Bibliometric Review on Safety Risk Assessment of Construction Based on CiteSpace Software and WoS Database," Sustainability, MDPI, vol. 15(15), pages 1-24, August.
    16. Floris Goerlandt & Jie Li & Genserik Reniers, 2021. "The Landscape of Risk Perception Research: A Scientometric Analysis," Sustainability, MDPI, vol. 13(23), pages 1-26, November.
    17. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    18. Floris Goerlandt & Jie Li & Genserik Reniers, 2020. "The Landscape of Risk Communication Research: A Scientometric Analysis," IJERPH, MDPI, vol. 17(9), pages 1-31, May.
    19. Minxi Wang & Ping Liu & Rui Zhang & Zhi Li & Xin Li, 2020. "A Scientometric Analysis of Global Health Research," IJERPH, MDPI, vol. 17(8), pages 1-19, April.
    20. Gisleine Carmo & Luiz Flávio Felizardo & Valderí Castro Alcântara & Cristiane Aparecida Silva & José Willer Prado, 2023. "The impact of Jürgen Habermas’s scientific production: a scientometric review," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1853-1875, March.

    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:jforec:v:4:y:2022:i:4:p:42-786:d:923606. 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.