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Traditional marketing analytics, big data analytics and big data system quality and the success of new product development

Author

Listed:
  • Ahmad Ibrahim Aljumah

    (AAU - Al Ain University)

  • Mohammed T. Nuseir

    (AAU - Al Ain University)

  • Md. Mahmudul Alam

    (UUM - Universiti Utara Malaysia)

Abstract

Purpose This study investigates the impact of traditional marketing analytics and big data analytics on the success of a new product. Moreover, it assesses the mediating effects of the quality of big data system. Design/methodology/approach This study is based on primary data that were collected through an online questionnaire survey from large manufacturing firms operating in UAE. Out of total distributed 421 samples, 327 samples were used for final data analysis. The survey was conducted from March–April 2020, and data analysis was done via Structural Equation Modelling (SEM-PLS). Findings It emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success. Meanwhile, the BDA and TMA significantly affect the BDSQ. Results of the mediating role of BDSQ in the relationship between the BDA and NPD, as well as TMA and NPD, are significant. Practical implications There are significant policy implications for practitioners and researchers concerning the role of analytics, particularly big data analytics and big data system quality, when attempting to achieve success in developing new products. Originality/value This is an original study based on primary data from UAE.

Suggested Citation

  • Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    • Handle: RePEc:hal:journl:hal-03538161
    DOI: 10.1108/BPMJ-11-2020-0527
    Note: View the original document on HAL open archive server: https://hal.science/hal-03538161
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    as
    1. Mikalef, Patrick & Pateli, Adamantia, 2017. "Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA," Journal of Business Research, Elsevier, vol. 70(C), pages 1-16.
    2. Cegarra-Navarro, Juan-Gabriel & Soto-Acosta, Pedro & Wensley, Anthony K.P., 2016. "Structured knowledge processes and firm performance: The role of organizational agility," Journal of Business Research, Elsevier, vol. 69(5), pages 1544-1549.
    3. 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.
    4. One-Ki (Daniel) Lee & Vallabh Sambamurthy & Kai H. Lim & Kwok Kee Wei, 2015. "How Does IT Ambidexterity Impact Organizational Agility?," Information Systems Research, INFORMS, vol. 26(2), pages 398-417, June.
    5. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    6. García-Morales, Víctor Jesús & Jiménez-Barrionuevo, María Magdalena & Gutiérrez-Gutiérrez, Leopoldo, 2012. "Transformational leadership influence on organizational performance through organizational learning and innovation," Journal of Business Research, Elsevier, vol. 65(7), pages 1040-1050.
    7. Mahda Garmaki & Imed Boughzala & Samuel Fosso Wamba, 2016. "The effect of big data analytics capability on firm performance," Grenoble Ecole de Management (Post-Print) hal-01414811, HAL.
    8. Rialti, Riccardo & Zollo, Lamberto & Ferraris, Alberto & Alon, Ilan, 2019. "Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    9. Elisabetta Raguseo & Claudio Vitari, 2018. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," International Journal of Production Research, Taylor & Francis Journals, vol. 56(15), pages 5206-5221, August.
    10. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    11. Akter, Shahriar & Bandara, Ruwan & Hani, Umme & Fosso Wamba, Samuel & Foropon, Cyril & Papadopoulos, Thanos, 2019. "Analytics-based decision-making for service systems: A qualitative study and agenda for future research," International Journal of Information Management, Elsevier, vol. 48(C), pages 85-95.
    12. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    13. Kathleen M. Eisenhardt & Jeffrey A. Martin, 2000. "Dynamic capabilities: what are they?," Strategic Management Journal, Wiley Blackwell, vol. 21(10‐11), pages 1105-1121, October.
    14. Angappa Gunasekaran & Yahaya Y. Yusuf & Ezekiel O. Adeleye & Thanos Papadopoulos, 2018. "Agile manufacturing practices: the role of big data and business analytics with multiple case studies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 385-397, January.
    15. 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.
    16. Ismagilova, Elvira & Hughes, Laurie & Dwivedi, Yogesh K. & Raman, K. Ravi, 2019. "Smart cities: Advances in research—An information systems perspective," International Journal of Information Management, Elsevier, vol. 47(C), pages 88-100.
    17. Amado, Alexandra & Cortez, Paulo & Rita, Paulo & Moro, Sérgio, 2018. "Research Trends On Big Data In Marketing: A Text Mining And Topic Modeling Based Literature Analysis," European Research on Management and Business Economics (ERMBE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 24(1), pages 1-7.
    18. Birger Wernerfelt, 1984. "A resource‐based view of the firm," Strategic Management Journal, Wiley Blackwell, vol. 5(2), pages 171-180, April.
    19. Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    20. David J. Teece, 2012. "Dynamic Capabilities: Routines versus Entrepreneurial Action," Journal of Management Studies, Wiley Blackwell, vol. 49(8), pages 1395-1401, December.
    21. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
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    Cited by:

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    2. Alshawawreh, Ali Ra’Ed & Liébana-Cabanillas, Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of big data analytics on telecom companies' competitive advantage," Technology in Society, Elsevier, vol. 76(C).
    3. Sabika Nasrim Pilathottathil & Abdul Rauf, 2024. "Barriers to the Use of Cross-Laminated Timber for Mid-Rise Residential Buildings in the UAE," Sustainability, MDPI, vol. 16(16), pages 1-26, August.

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    Keywords

    Big data; Big data analytics; Organizational performance; Manufacturing industry; Ambidexterity; Business value of big data; UAE;
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