IDEAS home Printed from https://ideas.repec.org/a/zag/busexc/v13y2019i2p131-154.html

Bibliometric Analyis Of Artificial Intelligence In Business Economics

Author

Listed:
  • Ivan Novak

    (Department of International Economics, Faculty of Economics and Business, University of Zagreb, Croatia)

Abstract

Artificial intelligence is one of the most promising technological advancements of the modern economy. While narrow AI has already been widely applied, this paper aims to explore the research area of artificial intelligence with an emphasis on business economics. Data were derived from records extracted from the Web of Science, one of the most relevant databases of scientific publications. A total of 1,369 records published from 1963 to 2019 were analyzed. The results provide a systemic overview of the most influential authors, seminal papers, and the most important sources for AI publication, while MCA results display an intellectual map of the research field.

Suggested Citation

  • Ivan Novak, 2019. "Bibliometric Analyis Of Artificial Intelligence In Business Economics," Poslovna izvrsnost-Business Excellence, University of Zagreb Faculty of Economics & Business, vol. 13(2), pages 131-154.
    • Handle: RePEc:zag:busexc:v:13:y:2019:i:2:p:131-154
    as

    Download full text from publisher

    File URL: https://hrcak.srce.hr/file/333923
    Download Restriction: None
    ---><---

    References listed on IDEAS

    as
    1. Kulkarni, Uday R. & Kiang, Melody Y., 1995. "Dynamic grouping of parts in flexible manufacturing systems -- a self-organizing neural networks approach," European Journal of Operational Research, Elsevier, vol. 84(1), pages 192-212, July.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    4. Grewal, Dhruv & Roggeveen, Anne L. & Nordfält, Jens, 2017. "The Future of Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 1-6.
    5. Nam, Taewoo, 2019. "Technology usage, expected job sustainability, and perceived job insecurity," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 155-165.
    6. Giacomo Marzi & Marina Dabić & Tugrul Daim & Edwin Garces, 2017. "Product and process innovation in manufacturing firms: a 30-year bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 673-704, November.
    7. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    8. Dabirian, Amir & Kietzmann, Jan & Diba, Hoda, 2017. "A great place to work!? Understanding crowdsourced employer branding," Business Horizons, Elsevier, vol. 60(2), pages 197-205.
    9. Siniša Sremac & Edmundas Kazimieras Zavadskas & Bojan Matić & Miloš Kopić & Željko Stević, 2019. "Neuro-fuzzy inference systems approach to decision support system for economic order quantity," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 1114-1137, January.
    10. Arntz, Melanie & Gregory, Terry & Zierahn, Ulrich, 2017. "Revisiting the risk of automation," Economics Letters, Elsevier, vol. 159(C), pages 157-160.
    11. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    12. Robert H. Storer & S. David Wu & Renzo Vaccari, 1992. "New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling," Management Science, INFORMS, vol. 38(10), pages 1495-1509, October.
    13. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    14. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    15. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    16. Kar Yan Tam, 1992. "Genetic algorithms, function optimization, and facility layout design," European Journal of Operational Research, Elsevier, vol. 63(2), pages 322-346, December.
    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. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    3. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    4. Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
    5. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    6. Su-Han Woo & Min-Su Kwon & Kum Fai Yuen, 2021. "Financial determinants of credit risk in the logistics and shipping industries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 268-290, June.
    7. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    8. Malhotra, Rashmi & Malhotra, D. K., 2002. "Differentiating between good credits and bad credits using neuro-fuzzy systems," European Journal of Operational Research, Elsevier, vol. 136(1), pages 190-211, January.
    9. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
    10. Mohamed Khaldi & Ghaith Manita & Amit Chhabra & Ramzi Guesmi & Tarek Hamrouni, 2025. "Predicting Corporate Financial Failure Using Sigmoidal Opposition-Based Arithmetic Optimization Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 517-569, July.
    11. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    12. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    13. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    14. Haider A. Khan, 2004. "General Conclusions: From Crisis to a Global Political Economy of Freedom," Palgrave Macmillan Books, in: Global Markets and Financial Crises in Asia, chapter 9, pages 193-211, Palgrave Macmillan.
    15. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    16. Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
    17. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    18. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    19. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    20. Atik, Atilla & Aslan, Fürüzan, 2015. "Estimation of Annual Industrial Wood Production Level in Forestry Operations with the Artificial Neural Network," Asian Journal of Agricultural Extension, Economics & Sociology, Asian Journal of Agricultural Extension, Economics & Sociology, vol. 7(4).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:zag:busexc:v:13:y:2019:i:2:p:131-154. 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: Dario Dunković (email available below). General contact details of provider: https://edirc.repec.org/data/fefzghr.html .

    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.