This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big Data External and Internal, Innovative Usage, Indexing, and Sources Accuracy. In addition, Artificial intelligence positively affects business performance, including Data Accuracy, Data Transparency, Data Speed, and Creative Thinking and Learning. Moreover, business intelligence has a direct and positive impact on business performance, including Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. In addition, the findings indicate that e-learning which represents system quality, information quality, and self-efficacy has a positive relationship on enhancing business performance. Interestingly, the present findings are inconsistent with those of previous studies showing the variables of interest which have no effect on e-learning and business performance. Taken together, the findings of this study suggest that firms should begin to apply processes related with applying e-learning and developing business performance. The novelty of the present study lies in highlighting the key dimensions of big data, artificial intelligence, and business intelligence when it comes to enhancing e-learning and business performance at Jordanian telecommunications industry.
Two simple methods for the determination of eugenol were developed. The first depends on the oxidative coupling of eugenol with p-amino-N,N-dimethylaniline (PADA) in the presence of K3[Fe(CN)6]. A linear regression calibration plot for eugenol was constructed at 600 nm, within a concentration range of 0.25-2.50 μg.mL–1 and a correlation coefficient (r) value of 0.9988. The limits of detection (LOD) and quantitation (LOQ) were 0.086 and 0.284 μg.mL–1, respectively. The second method is based on the dispersive liquid-liquid microextraction of the derivatized oxidative coupling product of eugenol with PADA. Under the optimized extraction procedure, the extracted colored product was determined spectrophotometrically at 618 nm. A l
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