IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v18y2019i04ns0219622019300040.html
   My bibliography  Save this article

Understanding Big Data Through a Systematic Literature Review: The ITMI Model

Author

Listed:
  • Andrea De Mauro

    (Department of Enterprise Engineering, University of Rome Tor Vergata, Rome, Italy)

  • Marco Greco

    (#x2020;Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy)

  • Michele Grimaldi

    (#x2020;Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy)

Abstract

The concept of Big Data in academic and professional literature has developed in a euphoric, chaotic, and unstructured manner. Decision-making is increasingly relying on Big Data, resorting to novel analytic methodologies that are applied in many different industries. This study aims to provide clarity over the Big Data phenomenon by means of a comprehensive and systematic literature review, able to produce a clear description of what Big Data is today, a structured classification of the various streams of current research, and a list of promising emerging trends. This study analyses a corpus of 4,327 articles through a novel combination of unsupervised algorithms that produces a hierarchical topic structure which empirically validates and enhances the “Information,” “Technology,” “Methods,” and “Impact” conceptual model of Big Data, identifying 17 fundamental topics and providing researchers and practitioners with a meaningful overview of the body of knowledge and a proposed research agenda.

Suggested Citation

  • Andrea De Mauro & Marco Greco & Michele Grimaldi, 2019. "Understanding Big Data Through a Systematic Literature Review: The ITMI Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1433-1461, July.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:04:n:s0219622019300040
    DOI: 10.1142/S0219622019300040
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622019300040
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622019300040?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. O. H. Salman & A. A. Zaidan & B. B. Zaidan & Naserkalid & M. Hashim, 2017. "Novel Methodology for Triage and Prioritizing Using “Big Data” Patients with Chronic Heart Diseases Through Telemedicine Environmental," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1211-1245, September.
    2. Martin Hilbert, 2016. "Big Data for Development: A Review of Promises and Challenges," Development Policy Review, Overseas Development Institute, vol. 34(1), pages 135-174, January.
    3. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    4. Mazzei, Matthew J. & Noble, David, 2017. "Big data dreams: A framework for corporate strategy," Business Horizons, Elsevier, vol. 60(3), pages 405-414.
    5. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    6. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    7. Yi Peng & Gang Kou & Yong Shi & Zhengxin Chen, 2008. "A Descriptive Framework For The Field Of Data Mining And Knowledge Discovery," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 639-682.
    8. Agostino La Bella & Andrea Fronzetti Colladon & Elisa Battistoni & Silvia Castellan & Matteo Francucci, 2018. "Assessing perceived organizational leadership styles through twitter text mining," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(1), pages 21-31, January.
    9. Xingsen Li & Yingjie Tian & Florentin Smarandache & Rajan Alex, 2015. "An Extension Collaborative Innovation Model in the Context of Big Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 69-91.
    10. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    11. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    12. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    13. 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.
    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. Teena Goyal & Piyush K. Rai & Sandeep K. Maurya, 2020. "Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 7(2), pages 307-345, June.
    2. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    3. Sestino, Andrea & Prete, Maria Irene & Piper, Luigi & Guido, Gianluigi, 2020. "Internet of Things and Big Data as enablers for business digitalization strategies," Technovation, Elsevier, vol. 98(C).

    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. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    2. Alberto Bertello & Alberto Ferraris & Stefano Bresciani & Paola Bernardi, 2021. "Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1035-1055, December.
    3. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    4. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    5. Jing Xu & Huijun Zhang, 2020. "Environmental Activism and Big Data: Building Green Social Capital in China," Sustainability, MDPI, vol. 12(8), pages 1-24, April.
    6. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    7. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    8. Pan, Qiaohong & Luo, Wenping & Fu, Yi, 2022. "A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China," Technology in Society, Elsevier, vol. 71(C).
    9. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    10. Kayabay, Kerem & Gökalp, Mert Onuralp & Gökalp, Ebru & Erhan Eren, P. & Koçyiğit, Altan, 2022. "Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    11. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.
    12. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    13. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    14. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    15. Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    16. Christoph Keding, 2021. "Understanding the interplay of artificial intelligence and strategic management: four decades of research in review," Management Review Quarterly, Springer, vol. 71(1), pages 91-134, February.
    17. Stefan Sleep & John Hulland & Richard A. Gooner, 2019. "THE DATA HIERARCHY: factors influencing the adoption and implementation of data-driven decision making," AMS Review, Springer;Academy of Marketing Science, vol. 9(3), pages 230-248, December.
    18. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    19. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    20. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.

    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:wsi:ijitdm:v:18:y:2019:i:04:n:s0219622019300040. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    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.