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Measuring the Impact of Data Warehouse and Business Intelligence on Enterprise Performance in Peru: A Developing Country

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  • Rolando Gonzales
  • Jonathan Wareham
  • Jaime Serida

Abstract

The purpose of this research is to assess the impact of data warehouse and business intelligence on the business performance of enterprises in developing regions such as Peru. Two models were developed; the qualitative exploratory model employed 23 interviews from several business sectors that use data warehouse and business intelligence. This model was exploratory in nature and helped to identify specific factors relevant to data warehouse and business intelligence in developing countries. The quantitative model examined 110 survey responses from different business sectors that use data warehouse and business intelligence. The DeLone and McLean model from 2003 was applied to test the fit of a structural equation model. Through this model, several hypotheses were tested. Thereafter, insights were combined from a review of the literature and from qualitative and quantitative analysis to consider the extension of additional constructs relevant to studies of data warehouse and business intelligence in developing economies. The additional constructs include: financial investment, alignment between the business intelligence project and business, and technology and specific skills. Implications for the study of advanced technologies in developing regions are considered.

Suggested Citation

  • Rolando Gonzales & Jonathan Wareham & Jaime Serida, 2015. "Measuring the Impact of Data Warehouse and Business Intelligence on Enterprise Performance in Peru: A Developing Country," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 18(3), pages 162-187, July.
    • Handle: RePEc:taf:ugitxx:v:18:y:2015:i:3:p:162-187
    DOI: 10.1080/1097198X.2015.1070616
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    Cited by:

    1. Abdalwali Lutfi & Adi Alsyouf & Mohammed Amin Almaiah & Mahmaod Alrawad & Ahmed Abdullah Khalil Abdo & Akif Lutfi Al-Khasawneh & Nahla Ibrahim & Mohamed Saad, 2022. "Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs," Sustainability, MDPI, vol. 14(3), pages 1-17, February.
    2. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    3. Arwa Mohammed Asiri & Sabah Abdullah Al-Somali & Rozan Omar Maghrabi, 2024. "The Integration of Sustainable Technology and Big Data Analytics in Saudi Arabian SMEs: A Path to Improved Business Performance," Sustainability, MDPI, vol. 16(8), pages 1-28, April.

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