IDEAS home Printed from https://ideas.repec.org/a/zib/zbnamm/v1y2018i1p16-17.html
   My bibliography  Save this article

Theoretical Retical Aspect In Formulatting Assesment Model Of Big Data Analytics Environment

Author

Listed:
  • Cecilia Adrian

    (Department of Software Engineering and Information Systems, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)

  • Rusli Abdullah

    (Department of Information System and Software Engineering, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)

  • Rodziah Atan

    (Department of Information System and Software Engineering, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)

  • Yusmadi Yah Jusoh

    (Department of Information System and Software Engineering, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)

Abstract

This paper explains the interrelationship of organization, people and technology dimensions were considered in formulating the big data analytics (BDA) environment assessment model. The theoretical lenses used in the model development are included Resource-based View (RBV) and Information Systems Success Model (ISSM).

Suggested Citation

  • Cecilia Adrian & Rusli Abdullah & Rodziah Atan & Yusmadi Yah Jusoh, 2018. "Theoretical Retical Aspect In Formulatting Assesment Model Of Big Data Analytics Environment," Acta Mechanica Malaysia (AMM), Zibeline International Publishing, vol. 1(1), pages 16-17, February.
  • Handle: RePEc:zib:zbnamm:v:1:y:2018:i:1:p:16-17
    DOI: 10.26480/amm.01.2018.16.17
    as

    Download full text from publisher

    File URL: https://actamechanicamalaysia.com/download/646/
    Download Restriction: no

    File URL: https://libkey.io/10.26480/amm.01.2018.16.17?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
    ---><---

    References listed on IDEAS

    as
    1. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    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. Van Vang Le & Lan Huong Nguyen, 2019. "Design And Fabrication Of Distillation Equipment Of Fresh Water From The Seawater By The Use Of The Waste Heat From Diesel Engines," Journal of Mechanical Engineering Research & Developments (JMERD), Zibeline International Publishing, vol. 42(2), pages 79-83, March.
    2. Valery A. Konyavsky & Gennady V. Ross, 2019. "Computer With Changeable Architecture," Journal of Mechanical Engineering Research & Developments (JMERD), Zibeline International Publishing, vol. 42(3), pages 19-23, March.
    3. Van Viet Pham, 2019. "Correlation Of Overall Heat Transfer Coefficient In The Three Zones Of Wire And Tube Condenser," Journal of Mechanical Engineering Research & Developments (JMERD), Zibeline International Publishing, vol. 42(2), pages 87-97, March.

    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. 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.
    2. Mohammad Ali Yamin, 2021. "Investigating the Drivers of Supply Chain Resilience in the Wake of the COVID-19 Pandemic: Empirical Evidence from an Emerging Economy," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    3. Rosa Lombardi & Raffaele Trequattrini & Federico Schimperna & Myriam Cano-Rubio, 2021. "The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 11-30.
    4. Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    5. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    6. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    7. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    8. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    9. Ron Berman & Ayelet Israeli, 2022. "The Value of Descriptive Analytics: Evidence from Online Retailers," Marketing Science, INFORMS, vol. 41(6), pages 1074-1096, November.
    10. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    11. Bargoni, Augusto & Santoro, Gabriele & Messeni Petruzzelli, Antonio & Ferraris, Alberto, 2024. "Growth hacking: A critical review to clarify its meaning and guide its practical application," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    12. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    13. Teng, Yuanyang & Zheng, Jianzhuang & Li, Yicun & Wu, Dong, 2024. "Optimizing digital transformation paths for industrial clusters: Insights from a simulation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    14. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    15. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    16. Shah, Tushar R., 2022. "Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry," Technology in Society, Elsevier, vol. 68(C).
    17. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    18. Muhammad Imran & Waseem ul Hameed & Adnan ul Haque, 2018. "Influence of Industry 4.0 on the Production and Service Sectors in Pakistan: Evidence from Textile and Logistics Industries," Social Sciences, MDPI, vol. 7(12), pages 1-21, November.
    19. 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.
    20. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.

    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:zib:zbnamm:v:1:y:2018:i:1:p:16-17. 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: Zibeline International Publishing (email available below). General contact details of provider: https://actamechanicamalaysia.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.