Published: Jul 17, 2024
Converted to Gold OA:
DOI: 10.4018/IJBIR.346371
Volume 15
Badr Harfoush, Omar F. El-Gayar, Noura Mansoura
Despite the widespread acquisition of business intelligence (BI) systems, their implementation has not always been successful. This study examines the critical success factors (CSFs) that impact the...
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Despite the widespread acquisition of business intelligence (BI) systems, their implementation has not always been successful. This study examines the critical success factors (CSFs) that impact the implementation of BI systems in organizations. The systematic literature review follows the guidelines of Kitchenham and Charter's research that was published in 2007. A total of 93 articles published between 2011 and 2021 were analyzed for CSF related to BI systems implementation and delivery. The study identified 56 CSFs linked to organization empowerment & operations, 52 CSFs related to system implementation, and 28 CSFs associated with user enablement. The study found a paucity of research on user enablement in the context of BI implementation and delivery, highlighting a gap in the literature. The findings of this study can help organizations better understand the factors that contribute to successful BI system implementation and delivery, and guide future research in this field.
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MLA
Harfoush, Badr, et al. "Critical Success Factors for BI Systems Implementation and Delivery: A Systematic Literature Review." IJBIR vol.15, no.1 2024: pp.1-22. http://doi.org/10.4018/IJBIR.346371
APA
Harfoush, B., El-Gayar, O. F., & Mansoura, N. (2024). Critical Success Factors for BI Systems Implementation and Delivery: A Systematic Literature Review. International Journal of Business Intelligence Research (IJBIR), 15(1), 1-22. http://doi.org/10.4018/IJBIR.346371
Chicago
Harfoush, Badr, Omar F. El-Gayar, and Noura Mansoura. "Critical Success Factors for BI Systems Implementation and Delivery: A Systematic Literature Review," International Journal of Business Intelligence Research (IJBIR) 15, no.1: 1-22. http://doi.org/10.4018/IJBIR.346371
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Published: Jul 16, 2024
Converted to Gold OA:
DOI: 10.4018/IJBIR.346977
Volume 15
Inzamam Khan, Wajid Shakeel Ahmed, Shafqat Shad, Chandan Kumar, Muhammad Usman, Milad Jasemi, Khuarm Shafi
The purpose of this study is to determine the intraday hourly trading trends of currencies using predictive modeling techniques. The study encompasses two distinct intraday time intervals of 30...
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The purpose of this study is to determine the intraday hourly trading trends of currencies using predictive modeling techniques. The study encompasses two distinct intraday time intervals of 30 minutes and 1 hour, analyzing currencies from 8 different countries. It incorporates the use of wavelets MODWT to identify trends and noise in intraday currency analysis. Three predictive models, namely Support Vector Regression, Recurrent Neural Network, and Long Short-Term Memory, are applied to relative time series data to predict intraday trading currency trends. The study reveals significant noise presence in three currencies based on MODWT analysis. Additionally, it demonstrates that deep learning techniques, such as LSTM, outperform traditional machine learning approaches in accurately predicting intraday currency trends. This study contributes substantially to the theoretical understanding of international finance and provides practical insights for real-time problem-solving in currency markets. Further, this research adds to the discourse on leveraging sophisticated analytical methods within the domain of business intelligence to enhance decision-making processes in organizations operating within dynamic and complex financial environments.
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MLA
Khan, Inzamam, et al. "Determine Intraday Trading Currency's Trend Framework Evidence From Machine Learning Techniques." IJBIR vol.15, no.1 2024: pp.1-15. http://doi.org/10.4018/IJBIR.346977
APA
Khan, I., Ahmed, W. S., Shad, S., Kumar, C., Usman, M., Jasemi, M., & Shafi, K. (2024). Determine Intraday Trading Currency's Trend Framework Evidence From Machine Learning Techniques. International Journal of Business Intelligence Research (IJBIR), 15(1), 1-15. http://doi.org/10.4018/IJBIR.346977
Chicago
Khan, Inzamam, et al. "Determine Intraday Trading Currency's Trend Framework Evidence From Machine Learning Techniques," International Journal of Business Intelligence Research (IJBIR) 15, no.1: 1-15. http://doi.org/10.4018/IJBIR.346977
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