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Big Data Analytics, Strategic Capabilities, and Innovation Performance: Mediation Approach of Organizational Ambidexterity

Author

Listed:
  • Thamir H. Alaskar

    (Business Administration Department, College of Business, Imam Mohammad Ibn Saud Islamic University, Riyadh 31441, Saudi Arabia)

  • Amin K. Alsadi

    (Business Administration Department, College of Business, Imam Mohammad Ibn Saud Islamic University, Riyadh 31441, Saudi Arabia)

  • Wassim J. Aloulou

    (Business Administration Department, College of Business, Imam Mohammad Ibn Saud Islamic University, Riyadh 31441, Saudi Arabia)

  • Faouzi M. Ayadi

    (Business Administration Department, College of Business, Imam Mohammad Ibn Saud Islamic University, Riyadh 31441, Saudi Arabia)

Abstract

Our study explores the critical role played by organizational ambidexterity capabilities in the link between big data analytics, strategic innovation capabilities, and innovation performance. We developed a conceptual framework based on resource-based and dynamic capability views to examine the direct and indirect relationships among main variables. We used a quantitative approach to collect data from 172 Saudi IT and Telecom firms. We then employed structural equation modeling through Smart-PLS to test the study hypotheses. Our findings revealed that big data analytics and strategic innovation capabilities have a significant impact on organizational ambidexterity and then on innovation performance. Ambidexterity capability mediates between big data analytics capabilities and innovation performance and between strategic innovation capabilities and innovation performance. Our study contributes to the literature on big data and innovation. It offers valuable insights into the potential impacts of big data analytics, strategic innovation, and ambidexterity capabilities on innovation performance. It demonstrates how significantly boosting a firm’s capabilities for improved firms’ innovation performance can potentially enhance performance outcomes (e.g., competitiveness and sustainability). These findings provide managers with meaningful implications regarding the innovation performance that can be achieved by leveraging these important resources and capabilities.

Suggested Citation

  • Thamir H. Alaskar & Amin K. Alsadi & Wassim J. Aloulou & Faouzi M. Ayadi, 2024. "Big Data Analytics, Strategic Capabilities, and Innovation Performance: Mediation Approach of Organizational Ambidexterity," Sustainability, MDPI, vol. 16(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5111-:d:1415695
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    References listed on IDEAS

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    1. 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.
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    3. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Organizational Performance and Capabilities to Analyze Big Data: Do the Ambidexterity and Business Value of Big Data Analytics Matter?," OSF Preprints an8er, Center for Open Science.
    4. Lysander Weiss & Dominik Kanbach, 2022. "Toward an integrated framework of corporate venturing for organizational ambidexterity as a dynamic capability," Management Review Quarterly, Springer, vol. 72(4), pages 1129-1170, December.
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    8. Omar. A. Alghamdi & Gomaa Agag, 2023. "Boosting Innovation Performance through Big Data Analytics Powered by Artificial Intelligence Use: An Empirical Exploration of the Role of Strategic Agility and Market Turbulence," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    9. Hua Zhang & Shaofeng Yuan, 2023. "How and When Does Big Data Analytics Capability Boost Innovation Performance?," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
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    11. Thamir Hamad Alaskar, 2023. "Innovation Capabilities as a Mediator between Business Analytics and Firm Performance," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    12. Zeki Simsek, 2009. "Organizational Ambidexterity: Towards a Multilevel Understanding," Journal of Management Studies, Wiley Blackwell, vol. 46(4), pages 597-624, June.
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    Cited by:

    1. Aleksandar M. Damnjanović & Milan Rašković & Svetozar D. Janković & Boris Jevtić & Volodymyr N. Skoropad & Zoran D. Marković & Violeta Lukić-Vujadinović & Zoran Injac & Srđan Marinković, 2025. "AI-Enabled Strategic Transformation and Sustainable Outcomes in Serbian SMEs," Sustainability, MDPI, vol. 17(19), pages 1-33, September.
    2. Xuemei Liu & Assad Latif & Mohammed Maray & Ansar Munir Shah & Muhammad Ramzan, 2025. "Big Data Meets Jugaad: Cultural Innovation Strategies for Sustainable Performance in Resource-Constrained Developing Economies," Sustainability, MDPI, vol. 17(15), pages 1-43, August.
    3. Deyin Zhang & Shijian Luo & Yue Liu & Xiyuan Zhang & Yuqi Hu & Jianan Shen & Peiqi Yi & Kayu Zhao & Wei Liu, 2026. "Large language model tools as catalysts for collective cognition in collaborative new-product development: a quasi-experimental study," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-20, December.
    4. Thamir Hamad Alaskar, 2025. "Integrated AI and Business Analytics for Sustaining Data-Driven and Technological Innovation: The Mediating Role of Integration Capabilities and Digital Platform," Sustainability, MDPI, vol. 17(19), pages 1-30, September.

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