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Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry

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  • Emmanuel P. Paulino

    (DBA, LPT, PLM Business Graduate School, General Luna corner Muralla Streets, Intramuros, Manila, Philippines 1002, e-mail: eppaulino@plm.edu.ph)

Abstract

PURPOSE: The concept of business analytics (BA) and business intelligence (BI) is just emerging in the Philippines. Since these are new concepts, it is important to investigate their impact on organizational performance and the performance metrics in business industry. The aim of this study is to examine the impact of business analytics generating business intelligence and how it affects organizational performance by developing a structural model. Consequently, the impact of organizational performance on other performance metrics was also established. METHODOLOGY: The partial least squares – structural equation modeling was utilized, which proposed a model that shows how business intelligence, generated by business BA, affects organizational performance, which consequently leads to improved marketing, financial, and business process performance. A survey was conducted on business analysts and executive managers of retail companies that have already been implementing BA for at least three years. FINDINGS: BA capabilities have a significant positive effect on the level of BI. BI has a significant positive effect on organizational performance. However, the result of the moderation analysis indicated that the level of readiness for BA implementation could not be considered a moderating factor on the relationship between BI and organizational performance. IMPLICATIONS: Out of the different BA capabilities, the decision support system and business process management were found to be the most beneficial functions in generating BI. BI amplifies organizational performance and consequently improves the marketing and business process performance of retail firms. However, the readiness for BA implementation does not significantly affect how BI improves organizational performance. Overall, it is recommended that in order to enhance marketing and business process performance, retail firms should focus on the BA capabilities of decision support system and business process management. ORIGINALITY AND VALUE: This would be the first empirical study in the Philippines which has assessed how business analytics and business intelligence impact organizational performance. This study is original in determining what BA capabilities generate BI, which translates to improved organizational performance. This study is also unique in defining what key performance metrics are much improved as a result of its implementation. This may serve as a viable reference for other researchers interested in business analytics and other technology about data management applied in business operations.

Suggested Citation

  • Emmanuel P. Paulino, 2022. "Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 18(2), pages 69-104.
  • Handle: RePEc:aae:journl:v:18:y:2022:i:2:p:69-104
    DOI: 10.7341/20221823
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    References listed on IDEAS

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    1. Martin Kowalczyk & Peter Buxmann, 2014. "Big Data and Information Processing in Organizational Decision Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 267-278, October.
    2. Troilo, Michael & Bouchet, Adrien & Urban, Timothy L. & Sutton, William A., 2016. "Perception, reality, and the adoption of business analytics: Evidence from North American professional sport organizations," Omega, Elsevier, vol. 59(PA), pages 72-83.
    3. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    4. Côrte-Real, Nadine & Oliveira, Tiago & Ruivo, Pedro, 2017. "Assessing business value of Big Data Analytics in European firms," Journal of Business Research, Elsevier, vol. 70(C), pages 379-390.
    5. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    6. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    7. Kowalczyk, Martin & Buxmann, Peter, 2014. "Big Data and Information Processing in Organizational Decision Processes: A Multiple Case Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65730, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. Zabaleta, Mercedes Elena Martínez & Luna, Raúl Enrique Rodríguez, 2023. "Inteligencia empresarial y su rol en la generación de valor en los procesos de negocios," Revista Tendencias, Universidad de Narino, vol. 24(1), pages 226-251, January.
    2. Agnieszka Zakrzewska-Bielawska & Anna M. Lis & Anna Ujwary-Gil, 2022. "Use of structural equation modeling in quantitative research in the field of management and economics: A bibliometric analysis in the systematic literature review," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 18(2), pages 7-40.

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