NONE 20260422104140148 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 1109-9526 2224-2899 Sevil Isik Ozder Faculty of Business Administration, Gebze Technical University, Gebze 41400, Kocaeli, TÜRKIYE Hüseyin Ince Faculty of Business Administration, Gebze Technical University, Gebze 41400, Kocaeli, TÜRKIYE Salih Zeki Imamoglu Faculty of Business Administration, Gebze Technical University, Gebze 41400, Kocaeli, TÜRKIYE Mehmet Naci Efe Faculty of Economics and Administrative Sciences, Istanbul Beykent University, 34398, Istanbul, TÜRKIYE Big data is one of the most important driving forces for firms seeking a competitive advantage. This study examines the effects of system quality, information quality, perceived benefits, perceived ease of use, and perceived risks on the adoption of Big Data Analytics (BDA) by firms. The proposed model was tested using survey data collected from 210 firms that employ BDA tools. First, the validity and reliability of the constructs were assessed, and then the hypotheses were tested using both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results reveal that (i) information and system quality positively affect perceived benefits, (ii) perceived risk negatively affects perceived benefits, (iii) perceived benefits positively influence perceived usefulness, and (iv) perceived ease of use and perceived usefulness positively affect the intention to adopt BDA systems. This study contributes to the big data literature by developing a comprehensive and empirically grounded research model. While extending the Technology Acceptance Model (TAM), it highlights the role of external factors, specifically information and system quality, in shaping perceived benefits and influencing BDA adoption. Furthermore, by incorporating system and information quality into the TAM framework and applying a dual-method approach (SEM and fsQCA), this study provides novel theoretical and methodological insights into BDA adoption. 04 22 2026 04 22 2026 277 21 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23207.2026.23.21 https://wseas.com/journals/bae/2026/a425107-3492.pdf Verma, S.; Bhattacharyya, S.S.; Kumar, S. An Extension of the Technology Acceptance Model in the Big Data Analytics System Implementation Environment. Inf. Process. Manag. 2018, 54, 791–806, doi:10.1016/j.ipm.2018.01.004. Park, J.H.; Kim, Y.B. Factors Activating Big Data Adoption by Korean Firms. J. Comput. Inf. Syst. 2021, 61, 285–293, doi:10.1080/08874417.2019.1631133. Lai, Y.; Sun, H.; Ren, J. Understanding the Determinants of Big Data Analytics (BDA) Adoption in Logistics and Supply Chain Management: An Empirical Investigation. Int. J. Logist. Manag. 2018, 29, 676–703, doi:10.1108/IJLM-06-2017-0153. 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