The Impact of Big Data Analytics Capabilities on the Organizational Skillfulness: Field Study: The Ministries of The Hashemite Kingdom of Jordan

Authors

  • Shatha M.S. AlShraideh Researcher, Jordan
  • Samer A.M Bashabsheh Prof., Mu’tah University, Karak

DOI:

https://doi.org/10.59759/business.v5i1.1568

Keywords:

Big data analytics capabilities, Big Data, organizational ambidexterity

Abstract

This study aimed to investigate the effect of big data analytics capabilities On The organizational ambidexterity: Field Study: The Ministries Of The Hashemite Kingdom Of Jordan .

To achieve that goal, the discriptive analytical approach was used through distributing a questionnaire to (335) employees in the top and middle managerial level in the ministries of the Hashemite Kingdom of Jordan, in addition to analyzing the data using the (SmartPls.4) software.

The results revealed that the level of big data analytics capabilities was medium, and a medium level of organizational ambidexterity. In addition to the existence of a statistical significant effect for the big data analytics capabilities on organizational ambidexterity.

Lastly the study had recommended the necessity of ensuring the availabily of the big data analytics capabilities due to their significant effect in achieving the organizaional ambidexterity.

 

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References

المراجع العربية:

أبو رمان، إسراء رضوان عبدالفتاح؛ و الشورة، محمد سليم خليف. (2022). البيانات الضخمة وأثرها في البراعة المنظمية: الدور المعدل لرأس المال البشري في مستشفيات عمان الحكومية المستخدمة لنظام "حكيم" (رسالة دكتوراه غير منشورة). جامعة العلوم الإسلامية العالمية، عمان.

إلياس، أحمد فاروق. (2022). متطلبات تقنية البيانات الضخمة وتأثيرها على ذكاء الأعمال وانعكاسه على البراعة التنظيمية: دراسة تطبيقية على البنوك التجارية المصرية. المجلة العلمية للبحوث التجارية، س9, ع3 ، ص(635-681).

حسين، هدى عبد الرحيم؛ العاني، الاء عبد الموجود (2018). التوافق بين مدخل البيانات الكبيره والبراعة التنظيمية: دراسة استطلاعية لاراء عينة من المدراء في شركه اسيا سيل للاتصالات المتنقله في العراق. مجلة العلوم الاقتصادية والإدارية، عدد 105، مجلد 24، ص (216 – 293).

المراجع الأجنبية:

Akter, S., Wamba, S., Gunasekaran, A., Dubey, R., & Childe, S. (2016). How to improve firm performance using big data analytics capability and business strategy alignment. International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018

Alaskar, T. H., Alsadi, A. K., Aloulou, W. J., & Ayadi, F. (2024). Big data analytics, strategic capabilities, and innovation performance: Mediation approach of organizational ambidexterity. Sustainability, 16(12), 5111. https://doi.org/10.3390/su16125111

Aziz, N., & Long, C. S. (2023). Big data analytics capabilities and dynamic capabilities: Evidence from Malaysian banks. Information Systems Frontiers, 25(3), 1018–1035. https://doi.org/10.1007/s10796-022-10245-9

Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2020). Statistical methods for speech and audio processing: Theory and applications. Springer.

Benitez, J., Castillo, A., Llorens, J., & Braojos, J. (2018). IT-enabled knowledge ambidexterity and innovation performance in small U.S. firms: The moderator role of social media capability. Information & Management, 55(1), 131–143. https://doi.org/10.1016/j.im.2017.09.004

Camarrone, F., & M., M. (2018). Fast multiway partial least squares regression. IEEE Transactions on Biomedical Engineering, 66(4), 433–443.

Cetindamar, D., Shdifat, B., & Erfani, E. (2021). Understanding big data analytics capability and sustainable supply chains. Information Systems Management, 39(1), 19–33. https://doi.org/10.1080/10580530.2021.1900464

Chen, Y., Wang, Y., Nevo, S., Benitez, J., & Kou, G. (2017). Improving strategic flexibility with information technologies: Insights for firm performance in an emerging economy. Journal of Information Technology, 32(1), 10–25. https://doi.org/10.1057/jit.2015.26

Conboy, K., Dennehy, D., & O’Connor, M. (2020). Big time: An examination of temporal complexity and business value in analytics. Information & Management, 57(1). https://doi.org/10.1016/j.im.2018.05.010

Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: Impact on firm performance. Management Decision, 57(8), 1923–1936. https://doi.org/10.1108/MD-07-2018-0825

Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064.

Hair, M., Ringle, N., & Cohen, I. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

He, W., Wu, H., Yan, G., Akula, V., & Shen, J. C. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management, 52(7), 801–812. https://doi.org/10.1016/j.im.2015.04.006

Iivari, J., & Huisman, H. (2007). The relationship between organizational culture and the deployment of systems development methodologies. MIS Quarterly, 31(1), 35–58. https://doi.org/10.2307/25148780

Kearns, G., & Lederer, A. (2003). A resource-based view of strategic IT alignment: How knowledge sharing creates competitive advantage. Decision Sciences, 34(1), 1–29. https://doi.org/10.1111/1540-5915.02289

Kong, T., & Feng, T. (2025). Enhancing supply chain resilience: The role of big data analytics capability and organizational ambidexterity. Industrial Management & Data Systems, 125(7), 2348–2370. https://doi.org/10.1108/IMDS-07-2024-0674

Latan, H. (2018). Partial least squares structural equation modeling. International Series in Operations Research & Management Science. Springer International Publishing.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32. https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293–303.

Löfgren, K., & Webster, C. W. R. (2020). The value of big data in government: The case of 'smart cities'. Big Data and Society, 7(1), 1–14.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71

Nitzl, C. (2018). Management accounting and partial least squares-structural equation modeling (PLS-SEM): Some illustrative examples. International Series in Operations Research and Management Science. Springer International Publishing

Nwankpa, J. K., Roumani, Y., & Datta, P. (2022). Process innovation in the digital age of business: The role of digital business intensity and knowledge management. Journal of Knowledge Management, 26(5), 1319–1341. https://doi.org/10.1108/JKM-04-2021-0277

Owsky, D., & Dean, S. (2021). OpenStax statistics. OpenStax.

Pentland, A. (2013). The data-driven society. Scientific American, 309(4), 78–83.

Ranjan, J., & Foropon, C. (2021). Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, 1–13.

Ratner, B. (2022). Statistical and data sciences (2nd ed.). CENGAGE Learning.

Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758–790. https://doi.org/10.1016/j.ipm.2018.01.010

Sekaran, U., & Bougie, R. (2022). Research methods for business: A skill building approach (7th ed.). John Wiley & Sons Inc.

Shamim, S., Zeng, J., Choksy, U. S., & Shariq, S. M. (2020). Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level. International Business Review, 29(6), 1–12.

Sharpe, N. (2024). Understanding statistics: An introduction to statistics and data analysis. Academic Press.

Siegel, S., & Castellan, N. J. (2020). Nonparametric statistics for the behavioral sciences (2nd ed.). McGraw-Hill.

Sun, Y., Liu, J., & Ding, Y. (2019). Analysis of the relationship between open innovation, knowledge management capability and dual innovation. Technology Analysis & Strategic Management, 32(1), 15–28. https://doi.org/10.1080/09537325.2019.1635003

Tosi, D., Kokaj, R., & Roccetti, M. (2024). 15 years of big data: A systematic literature review. Journal of Big Data, 11, 73, 1–39.

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031

Wang, N., Chen, B., Wang, L., Ma, Z., & Pan, S. (2024). Big data analytics capability and social innovation: The mediating role of knowledge exploration and exploitation. Humanities and Social Sciences Communications, 11, 864, 1–18. https://doi.org/10.1057/s41599-024-03288-8

Published

2026-03-30

How to Cite

M.S. AlShraideh, S., & A.M Bashabsheh, S. (2026). The Impact of Big Data Analytics Capabilities on the Organizational Skillfulness: Field Study: The Ministries of The Hashemite Kingdom of Jordan. Business Series, 5(1), 1–54. https://doi.org/10.59759/business.v5i1.1568

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