IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p899-d1064159.html
   My bibliography  Save this article

A Review on Business Analytics: Definitions, Techniques, Applications and Challenges

Author

Listed:
  • Shiyu Liu

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Ou Liu

    (Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China)

  • Junyang Chen

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

Over the past few decades, business analytics has been widely used in various business sectors and has been effective in increasing enterprise value. With the advancement of science and technology in the Big Data era, business analytics techniques have been changing and evolving rapidly. Therefore, this paper reviews the latest techniques and applications of business analytics based on the existing literature. Meanwhile, many problems and challenges are inevitable in the progress of business analytics. Therefore, this review also presents the current challenges faced by business analytics and open research directions that need further consideration. All the research papers were obtained from the Web of Science and Google Scholar databases and were filtered with several selection rules. This paper will help to provide important insights for researchers in the field of business analytics, as it presents the latest techniques, various applications and several directions for future research.

Suggested Citation

  • Shiyu Liu & Ou Liu & Junyang Chen, 2023. "A Review on Business Analytics: Definitions, Techniques, Applications and Challenges," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:899-:d:1064159
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/899/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/899/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
    2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    3. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    4. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    5. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    6. Troilo, Michael & Bouchet, Adrien & Urban, Timothy L. & Sutton, William A., 2016. "Perception, reality, and the adoption of business analytics: Evidence from North American professional sport organizations," Omega, Elsevier, vol. 59(PA), pages 72-83.
    7. Pape, Tom, 2016. "Prioritising data items for business analytics: Framework and application to human resources," European Journal of Operational Research, Elsevier, vol. 252(2), pages 687-698.
    8. Anne-Wil Harzing & Satu Alakangas, 2016. "Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 787-804, February.
    9. Zhou, Wenwen & Chen, Mengyao & Yang, Zaoli & Song, Xiaobo, 2021. "Real estate risk measurement and early warning based on PSO-SVM," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    10. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    11. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    12. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
    13. Rikhardsson, Pall & Yigitbasioglu, Ogan, 2018. "Business intelligence & analytics in management accounting research: Status and future focus," International Journal of Accounting Information Systems, Elsevier, vol. 29(C), pages 37-58.
    14. Appelbaum, Deniz & Kogan, Alexander & Vasarhelyi, Miklos & Yan, Zhaokai, 2017. "Impact of business analytics and enterprise systems on managerial accounting," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 29-44.
    15. Kyoung-jae Kim & Kichun Lee & Hyunchul Ahn, 2018. "Predicting Corporate Financial Sustainability Using Novel Business Analytics," Sustainability, MDPI, vol. 11(1), pages 1-17, December.
    16. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    17. Steven Batt & Tara Grealis & Oskar Harmon & Paul Tomolonis, 2020. "Learning Tableau: A data visualization tool," The Journal of Economic Education, Taylor & Francis Journals, vol. 51(3-4), pages 317-328, August.
    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 Medina-López & Montserrat Jiménez-Partearroyo & Ángeles Cámara, 2023. "A Dynamic CGE Model for Optimization in Business Analytics: Simulating the Impact of Investment Shocks," Mathematics, MDPI, vol. 12(1), pages 1-42, December.

    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. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    2. Domenica Lavorato & Palmira Piedepalumbo, 2023. "How Smart Technologies Affect the Decision-Making and Control System of Food and Beverage Companies—A Case Study," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    3. Francis Aboagye‐Otchere & Cletus Agyenim‐Boateng & Abdulai Enusah & Theodora Ekua Aryee, 2021. "A Review of Big Data Research in Accounting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 268-283, October.
    4. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    5. Jochen Fähndrich, 2023. "A literature review on the impact of digitalisation on management control," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 34(1), pages 9-65, March.
    6. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.
    7. Constantin Zopounidis & Michalis Doumpos & Dimitrios Niklis, 2018. "Financial decision support: an overview of developments and recent trends," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 63-76, June.
    8. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    9. Adilson Carlos Yoshikuni & Rajeev Dwivedi & Ronaldo Gomes Dultra-de-Lima & Claudio Parisi & José Carlos Tiomatsu Oyadomari, 2023. "Role of Emerging Technologies in Accounting Information Systems for Achieving Strategic Flexibility through Decision-Making Performance: An Exploratory Study Based on North American and South American," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 199-218, June.
    10. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    11. Arianna Petrosino & Daniela Mancini & Stefano Garzella & Rita Lamboglia, 2018. "La Business Intelligence e la Business Analytics nell?era dei Big Data: una analisi della letteratura," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 31-58.
    12. Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
    13. Zhang, Chao & Zhu, Weidong & Dai, Jun & Wu, Yong & Chen, Xulong, 2023. "Ethical impact of artificial intelligence in managerial accounting," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    14. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    15. Klaus Möller & Utz Schäffer & Frank Verbeeten, 2020. "Digitalization in management accounting and control: an editorial," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 1-8, April.
    16. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    17. Robalo, Rui C. & Moreira, José António, 2020. "The influence of power strategies in AIS implementation processes," International Journal of Accounting Information Systems, Elsevier, vol. 39(C).
    18. Osman, Ibrahim H. & Anouze, Abdel Latef & Irani, Zahir & Lee, Habin & Medeni, Tunç D. & Weerakkody, Vishanth, 2019. "A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values," European Journal of Operational Research, Elsevier, vol. 278(2), pages 514-532.
    19. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    20. Burger, Katharina & White, Leroy & Yearworth, Mike, 2019. "Developing a smart operational research with hybrid practice theories," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1137-1150.

    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:jmathe:v:11:y:2023:i:4:p:899-:d:1064159. 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.