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Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process

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  • Merhi, Mohammad I.

Abstract

This study aims to fill a gap in the literature by identifying, defining, and evaluating the critical success factors that impact the implementation of data intelligence in the public sector. Fourteen factors were identified, and then divided into three categories: organization, process, and technology. We used the analytical hierarchy process, a quantitative method of decision-making, to evaluate the importance of the factors presented in the study using data collected from nine experts. The results showed that technology, as a category, is the most important. The analysis also indicated that project management, information systems & data, and data quality are the most important factors among all fourteen critical success factors. We discuss the implications of the analysis for practitioners and researchers in the paper.

Suggested Citation

  • Merhi, Mohammad I., 2021. "Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521006132
    DOI: 10.1016/j.techfore.2021.121180
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    References listed on IDEAS

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    1. Asma I. Magaireah* & HidayahSulaiman & Nor’ashikin Ali, 2019. "Identifying the Most Critical Factors to Business Intelligence Implementation Success in the Public Sector Organizations," The Journal of Social Sciences Research, Academic Research Publishing Group, vol. 5(2), pages 450-462, 02-2019.
    2. Thomas L. Saaty, 1986. "Axiomatic Foundation of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 32(7), pages 841-855, July.
    3. 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.
    4. Azzone, Giovanni, 2018. "Big data and public policies: Opportunities and challenges," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 116-120.
    5. William Yeoh & Aleš Popovič, 2016. "Extending the understanding of critical success factors for implementing business intelligence systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(1), pages 134-147, January.
    6. Iqbal, Rahat & Doctor, Faiyaz & More, Brian & Mahmud, Shahid & Yousuf, Usman, 2020. "Big data analytics: Computational intelligence techniques and application areas," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    7. Samaneh Salehi Nasab & Farhang Jaryani & Harihodin Bin Selamat & Maslin Masrom, 2017. "Critical success factors for business intelligence system implementation in public sector organisation," International Journal of Information Systems and Change Management, Inderscience Enterprises Ltd, vol. 9(1), pages 22-43.
    8. Warrick, D.D., 2017. "What leaders need to know about organizational culture," Business Horizons, Elsevier, vol. 60(3), pages 395-404.
    9. 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.
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