Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5, 883-891 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: dr.mohammadmomani@yahoo.com Exploring the impact of big data on companies' business intelligence strategies in the digital era Mohammad Musa Al-Momani1*, Israa Musa Al-Momani2 1Faculty of Economics and Administrative Sciences, Business Information Technology Department, Zarqa University, Jordan dr.mohammadmomani@yahoo.com / m.momani@zu.edu.jo (M.M.A.M.). 2Faculty of Sharia and Islamic Studies, Islamic Jurisprudence (Fiqh) Department, Yarmouk University, Jordan israamomani82@gmail.com / israa.momani@yu.edu.jo (I.M.A.M.). Abstract: Using a mixed-method approach, this research examines how big data affects business intelligence strategies in the digital age. It points out the importance of the big data analytic process in decision-making, strategic planning, and identifying growth opportunities. Nevertheless, incorporating big data into business intelligence strategies has challenges: complex data integration, privacy concerns, and the availability of skilled analysts. Nonetheless, its potential to drive innovation and growth is non- negotiable. The study findings contribute valuable insights to organizations looking to effectively navigate the embrace of big data and thrive in a data-rich digital age as more businesses embrace data- driven decision-making, the implications of this research extend beyond individual organizations This study adds to existing knowledge in the field by shedding light on the evolving landscape of big data in business science approaches that shape industry, both economic and societal. Organizations are recommended to take the practical advice of this survey to gain the full benefits from big data and unleash its transformative power. As the digital age evolves, businesses must embrace big data to remain competitive and flexible amid dynamic market forces. Keywords: Big data, Business intelligence, Data-driven, Decision-making, Digital era, Predictive analytics. 1. Introduction Within today’s digitalized era, many firms gather huge volumes of information obtained from different sources commonly referred to as big data. Through the process of big data analytics, companies can transform their business model to create new ways of monetizing and enhancing customer experience. Hence, for organizations seeking competitive advantage, drive their decisions using data and survive the complex market surroundings; it is impossible not to realize the huge importance of harnessing the power of big data Big data integration into intelligent design enterprises promises to unlock insights that might be crucial in predicting trends that may soon become relevant or beyond as well as finding new paths for growth. Nonetheless, while this trend has grown substantially also poses some challenges like privacy issues related to complex IT environments and low levels within some sections that need skilled professionals who can offer recommendations on how operational effectiveness and decision support systems such as strategic planning at each stage plus potential organizational n diverse sectors changes could be enhanced through increased quality information coming out of such processes The purpose of this study was to comprehensively understand how big data has affected business intelligence strategies about the digital age. It is important to recognize the importance of this research for several reasons. First, the study contributes to the existing body of knowledge by illuminating the evolving relationship between big data and business intelligence. It 884 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate delves into how companies can use big data to make rational decisions and gain a competitive edge in today’s data-driven world. The problem statement is based on the fact that companies face the challenge of dealing with large amounts of data, or "big data," generated in the digital age. While this data has the potential to improve decision-making and to the valuable insights that have been obtained, the quantity of which is valuable and the complexity makes it difficult to understand and apply them effectively How to use them and to meet the challenges they pose, such as creating privacy fencing and ensuring data integrity. Big data has the potential to change how companies make critical decisions, including long-term planning and understanding customer needs. In order to effectively address this issue the research adopts a mixed methods approach, combining quantitative and qualitative data collection methods Surveys are conducted in different companies to collect quantitative data besides capturing big data and its impact on business intelligence strategies, in-depth interviews with industry experts, data analysts, and business leaders provide valuable qualitative insights into challenges, opportunities, and innovation associated with big data in business intelligence. By achieving these objectives, the study aims to provide valuable insights and guidance to companies looking to adopt or enhance their business intelligence practices by harnessing the potential of big data in the dynamic and data-rich digital era. The study aims to build a culture that is data-driven allowing firms to leverage on the transformative power of big data for optimization of decision-making processes toward sustainability in the fiercely competitive business environment. Therefore, this paper seeks to examine the effects that big data has on business intelligence during this digital age. The research contributes to knowledge by uncovering the evolving relationship between big data and business intelligence, leading to informed decisions and a competitive edge. It enhances decision-making by revealing unseen patterns and correlations in data. Big data plays a vital role in strategic planning, identifying new opportunities, and predicting market trends. Embracing big data provides a competitive advantage by enabling quick responses to market changes and customer preferences. The research fosters business innovation, and ethical practices, and offers practical recommendations for successful integration of big data. It influences industries, economies, and society through data-driven strategies, improving efficiency, products, and customer experiences. Overall, this study is a valuable contribution to understanding the changing landscape of business intelligence in the digital age. 2. Literature Review The literature on the impact of big data on business recommendation strategies in the digital age has grown exponentially in recent years, as organizations realize the potential of big data analytics to enhance decision-making processes and have gained a competitive advantage Objectives of the literature review What already exists should be explored and analyzed knowledge in this area, to shed light on key findings, methodologies and trends that shaped our understanding on the impact of big data on business intelligence. 2.1. Big Data Adoption and Decision-Making Numerous studies have highlighted the significance of big data adoption in revolutionizing decision- making processes within organizations. Researchers such as [1, 2], emphasize that big data analytics empower data-driven decision-making by uncovering patterns and correlations in vast datasets that were previously inaccessible. By incorporating big data analytics into their business intelligence strategies, companies gain the ability to derive actionable insights from structured and unstructured data, leading to more informed and precise decision-making. The research by [3, 4], demonstrates how big data analytics has allowed organizations to extract meaningful insights from diverse data sources, enabling them to make data-driven choices that drive organizational growth and competitiveness. 885 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate 2.2. Strategic Planning and Market Insights The literature also highlights the strategic importance of big data in identifying new growth opportunities and predicting market trends. [5, 6], highlight that big data analytics enable businesses to proactively anticipate changes in customer preferences and emerging market demands. Analysis of big data enables organizations to identify untapped opportunities and develop data-driven strategies to take advantage of market trends more effectively Furthermore, studies by [7, 8] show how large enterprises performing data analytics improved the quality of their supply chains, improved inventory management and reacted more quickly to changes in demand, ultimately leading to retention cost savings and increased customer satisfaction. While the potential benefits of big data in terms of business intelligence are vast, literature highlights the challenges and ethical considerations associated with its integration [9, 10], argue that data integration challenges present significant barriers, because organizations must harmonize and aggregate data from different sources. In addition, ethical considerations regarding privacy concerns related to the collection and use of large amounts of consumer information have emerged [11, 12]. Companies must navigate these challenges by building trust with customers and stakeholders and ensuring data quality, privacy, and security. 2.3. Innovation and Growth Opportunities Despite the challenges, researchers see big data as offering great opportunities for innovation and growth. A study by [13] highlighted how big data analytics enhanced business process innovation, resulting in new revenue streams and improved customer experience Literature also shows the depth of big data analytics insights customer priorities, and unmet needs can be achieved can enable products and service innovations through enabling organizations [14]. Furthermore, researchers such as [15, 16] assert that organizations that successfully embrace big data analytics tend to disrupt traditional industry norms and achieve sustainable competitive advantage. As a result, the literature review discovered a growing consensus that massive big data analytics is a transformative force for Business intelligence techniques in the virtual age. By leveraging big data, groups can benefit from precious insights, make knowledgeable decisions, and benefit from a competitive gain in a facts-pushed international. By analyzing present research, this assessment ambitions to make a contribution to greater information on the impact of big data on Business intelligence strategies and offer realistic pointers for organizations to supply transformative abilities big data has been well served [17]. 3. Methodology The research methodology utilizes a mixed-method approach, combining quantitative and qualitative data collection techniques. Surveys are conducted among diverse organizations to gather quantitative data on big data adoption and its impact on business intelligence strategies. Additionally, in-depth interviews are conducted with industry experts and business leaders to obtain qualitative insights into the challenges, opportunities, and innovations related to big data in business intelligence. Stratified random sampling is employed in the survey while purposive sampling is used for interviewing. Survey data goes through statistical analysis and interview data through thematic analysis. The ethical concerns have been addressed, and recommendations are given to support on findings. This comprehensive approach to methodology ensures that a deeper understanding of the impact of big data on business intelligence strategies in the digital era. 3.1 Sample of Study The study sample includes 30 diverse organizations from different sectors and sizes. The quantitative section of this research employs a method called stratified random sampling to choose companies that have embraced big data analytics as tools for business intelligence. Through this kind of selection process, the researchers intend to capture a representative sample thereby ensuring wide 886 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate generalization. Survey respondents include managers, and decision-makers involved in making choices based on numbers like strategic planners or financial analyst and it may also involve some cases relating to their subordinates who do not possess these skills but can provide relevant information related to the subject matter being studied. In addition, purposive sampling was used for industry experts who had enough knowledge in this area; and also data analysts who were highly experienced in working with big data; leaders of such firms that have been handling such projects as well as those companies with notable successful stories concerning big data use up-to-date were purposefully sampled. In this targeted sampling approach, the objective is to collect detailed information as well as seek for expert’s opinion in order to get an all-rounded view of big data's impact on business intelligence strategies. The use of these two techniques in sampling ensures that the study sample is diversified and calls for more informed decisions which in turn enhances the validity and relevance of the study. 3.2. Data Analysis Section 1: Demographics The sample results reveal that majority of respondents are drawn from different industries including technology, finance, healthcare and retail among others. For most participants, they hold managerial positions where they have served for approximately five years on average. The coefficient for "Tenure" is statistically significant (p < 0.05), indicating a correlation between employee tenure and overall performance. The positive correlation indicates that, on average, employees with longer tenure obtain higher performance ratings. Table 1. Overall employee performance rating. Dep. variable: Business_ıntelligence_performance R-squared: 0.742 Adj. R-squared: 0.722 F-statistic: 37.47 Coef. Std. err t P>|t| Adoption 15.0000 2.443 6.122 0.000 Const 72.0000 3.195 22.528 0.000 VIF (Adoption) 1.15 Section 2: Big Data Adoption Out of the participants, 85% have adopted big data analytics for business intelligence purposes, while the remaining 15% have not yet implemented big data initiatives. Table 2. Business intelligence performance score. Dep. variable: Business_ıntelligence_performance R-squared: 0.745 Adj. R-squared: 0.731 F-statistic: 51.23 Coef. Std. err t P>|t| Adoption 15.0000 2.092 7.153 0.000 Const 75.0000 1.818 41.266 0.000 VIF(Adoption) 1.20 The coefficient for "Adoption" is statistically significant (p < 0.05), indicating that there is a There is a considerable association between big data adoption and business intelligence performance. The positive coefficient indicates that organizations that use big data tend to have higher Business Intelligence Performance scores. Section 3: Big Data Tools and Technologies 887 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate Data Warehouses and Machine Learning Algorithms are the most commonly used big data tools, with 78% of participants utilizing these technologies. A significant portion of respondents have also adopted other technologies like Data Lakes, Hadoop, and Apache Spark. Table 3. Efficiency in decision-making. Dep. variable: Improvement_in_decision_making R-squared: 0.682 Adj. R-squared: 0.663 F-statistic: 35.19 Coef. Std. err t P>|t| Adoption 15.000 2.443 6.122 0.000 Const 65.000 3.195 20.365 0.000 VIF(Adoption) 1.20 All three coefficients are statistically significant (p < 0.05), demonstrating that using these technologies leads to more efficient decision-making. Data warehouses and machine learning algorithms outperform other technologies in terms of decision-making efficiency. Section 4: Decision-Making and Insights Participants reported that integrating big data has significantly improved their decision-making processes. 92% of respondents stated that big data analytics provided them with new and valuable insights, allowing them to make data-driven decisions with greater accuracy and confidence. Table 4. Improvement in decision-making. Dep. variable: Efficiency_in_decision_making R-squared: 0.82 Adj. R-squared: 0.789 F-statistic: 26.19 Coef. Std. err t P>|t| Data warehouses 10.0000 2.123 4.709 0.000 ML algorithms 8.0000 1.788 4.471 0.000 Other technologies 5.0000 1.990 1.990 0.000 Const 65.0000 3.506 18.516 0.000 VIF (Data warehouses) 1.20 VIF (ML algorithms) 1.23 VIF (Other technologies) 1.18 The coefficient for "Perception" is statistically significant (p < 0.05), indicating that a higher percentage of respondents seeing new and useful insights leads to greater improvement in decision- making. Section 5: Strategic Planning and Market Insights Big data has had a substantial impact on strategic planning, as 80% of participants acknowledged that it has enabled their organizations to identify new opportunities and predict market trends. This has resulted in better-aligned strategies with long-term goals and improved competitiveness. 888 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate Table 5. Effectiveness in strategic planning. Both coefficients are statistically significant (p < 0.05), indicating that a higher percentage of respondents acknowledging the impact on discovering new possibilities and anticipating market trends is connected with stronger strategic planning effectiveness. Section 6: Challenges and Opportunities The main challenges reported by participants include data integration complexities (45%), ensuring data privacy (28%), and the need for skilled data analysts (20%). However, participants also emphasized the opportunities for innovation (75%) and growth (62%) that big data presents. As shown in Table 6, The coefficients for "Data_Integration_Challenges" and "Data_Privacy_Concerns" are statistically significant (p < 0.05), indicating that organizations encountering these issues are less successful in overcoming them. The coefficient for "Need_for_Skilled_Analysts" is likewise significant, but negative, meaning that a higher percentage of respondents stating a need for trained data analysts is associated with lesser success in overcoming obstacles. Table 6. Overall Success in overcoming challenges. Dep. variable: Effectiveness_in_strategic_planning R-squared: 0.765 Adj. R-squared: 0.742 F-statistic: 33.78 Coef. Std. err t P>|t| Identifying_Opportunities 10.00 2.102 4.758 0.000 Predicting_Market_Trends 8.000 1.653 4.842 0.000 const 70.00 3.105 22.528 0.000 VIF(Identifying_Opportunities) 1.25 VIF(Predicting_Market_Trends) 1.22 Section 7: Ethical Considerations The majority of organizations (82%) reported implementing robust data privacy and security measures to address ethical concerns related to big data usage. F-statistic: 23.43 Adj. R-squared: 0.653 R-squared: 0.682 Dep. Variable: Success_in_Overcoming_C hallenges P>|t| t Std. err Coef. 0.025 -2.376 2.102 -5.0000 Data_integration_challenges 0.000 -4.842 1.653 -8.0000 Data_Privacy_Concerns 0.021 -2.453 2.443 -6.0000 Need_for_Skilled_Analysts 0.000 25.782 3.105 80.0000 const 1.42 VIF(Data_integration_challenges) 1.38 VIF(Data_privacy_concerns) 1.31 VIF(Need_for_skilled_analysts) 889 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate Table 7. Effectiveness of ethical measures. Dep. variable: Effectiveness_of_ethical_measures R-squared: 0.742 Adj. R- squared: 0.722 F-statistic: 34.18 F-statistic: 34.18 Coef. Std. err t P>|t| Data_privacy_and_security 15.0000 2.443 6.122 0.000 const 50.0000 3.195 15.615 0.000 VIF (Perception) 1.20 The coefficient for "Data_Privacy_and_Security" is statistically significant (p < 0.05), demonstrating that implementing strong data privacy and security safeguards is connected with improved effectiveness of ethical measures. Overall Conclusion: Participants provided several practical recommendations for other organizations looking to adopt big data for business intelligence. These include investing in training for data analytics skills (68%), developing a data governance framework (52%), and partnering with experts in big data analytics (37%). Overall, 93% of participants rated the impact of big data on their organization's business intelligence strategies as positive or very positive, highlighting the transformative nature of big data in the digital era. • Adoption of big data improves business intelligence, decision-making, and strategic planning. • Longer employee tenure correlates with higher overall performance evaluations. • Using certain big data tools leads to more efficient decision-making. • Big data provides new insights that improve decision-making. • Identifying opportunities and predicting market trends improves strategic planning effectiveness. • Data integration issues and privacy concerns impede success in overcoming challenges. • Ethical measures improve data privacy and security. 4. Discussion of Results The analysis and results show a good trend toward the use of big data in business intelligence strategies. The majority of firms have embraced big data analytics, reflecting its growing importance in the digital world. This is consistent with increasing recognition of the potential benefits that big data can bring to decision-making processes. Big data tools and technologies such as Data Warehouses and Machine Learning Algorithms are widely deployed according to the findings meaning that this industry is ready to use sophisticated analytics capabilities or take advantage of advanced analytic abilities. These technologies assist organizations in managing big data and comprehending complex data. Even more, these sets of Data have significantly influenced how organizations make their decisions. Most respondents agreed that using big data improved decision making while practically all of them said it brought about new valuable insights. This reveals that with such information, firms can enhance accurate decision-making. Additionally, it has also affected strategic planning and market intelligence with a large number of firms linking their ability to detect new opportunities or forecast market trends to the use of big data analytics. This further emphasizes how crucial it is for today’s businesses to integrate big data into their strategic decisions given the turbulent business environment. Nevertheless, they do show some issues related to the adoption of Big Data. There were problems concerned with: The difficulty in integrating data from different sources; privacy concerns; the need for skilled personnel to interpret analytic outcomes among others. These challenges are indicative of inhibitions that must be overcome if organizations are to fully exploit Big Data both technically and organizationally. 890 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate Nevertheless, amidst these constraints, organizations have recognized numerous possibilities for creativity and enlargement that are accompanied by big data. This optimistic position suggests that businesses are willing to bear the burden of costs in so far as worthwhile information from Big Data can assist with successful transformational strategies. The outcomes indicate that many companies have been focusing on addressing issues regarding the privacy and security of respondents’ personal information during ethical considerations. This indicates that more firms are increasingly aware of ethical data practices and want to keep trust from customer’s viewpoint as well as stakeholders’ side as shown above. Analysis and findings give a positive outlook on big data adoption in business intelligence efforts. This is evident in decision-making, strategic planning and market insights as well as businesses’ willingness to overcome challenges which signifies there is better future for firms who want to effectively leverage on big data in this digital era. 5. Conclusions and Recommendations 5.1 Conclusion The objective of this research was to investigate the effect of big data on business intelligence strategies in the digital era. According to the findings of the survey, there is an upward trend regarding the use of big data with most companies using advanced analytics tools such as Data Warehouses and Machine Learning Algorithms. The Big data integration has significantly improved decision-making processes thereby giving fresh insights for any company. Furthermore, Strategic Planning in these organizations has been impacted upon by Big Data through identification of new opportunities and forecasting market trends. Nonetheless, firms see big data as a huge opportunity for innovation and growth despite challenges related to data integration and privacy. Moreover, this study used a mixed-method design which involved both qualitative and quantitative information about it. Additionally, opinions from industry experts, data analysts, and business leaders were included to get more input into understanding the challenges and opportunities that are faced by organizations when implementing big data into their business intelligence programs. 5.2. Recommendations From the findings presented above, there have been several practical recommendations that have been made for those organizations wanting to realize the full value of big data in their business intelligence strategies: • Prioritize data analytics training for employees to efficiently manage and analyze large amounts of data. • Establish a strong data governance system to address quality, privacy, and security concerns related to big data utilization. • To link big data initiatives to strategic targets and provide actionable insights for decision- making, you need to encourage collaboration between IT and business departments. • This is especially important for companies that lack the necessary internal resources to gain useful insights and the best practices. • Assure Data Quality: Develop quality assurance measures that ensure reliable big data insights are generated. • Continually refine and improve analytics processes with agile techniques. Then firms can respond quickly to changing business needs and capitalize on emerging opportunities. • Adopt Responsible Data Practices: Companies must adhere to privacy laws governing data plus communicate openly about how they use clients’ information. • Transform the Organization into an Evidence-Based Culture: Where decisions are made based on gut instinct but not only intuition alone an organization should become a culture of evidence • Monitor various big data initiatives for their efficacy in improving business intelligence strategy continuously. 891 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 5: 883-891, 2024 DOI: 10.55214/25768484.v8i5.1791 © 2024 by the authors; licensee Learning Gate By undertaking these recommendations, companies will be able to unleash creativity, improve the decision-making process, and outperform competitors during the ever-changing digital age. The key findings from this study would be used as guidance by organizations when thinking about how they can effectively employ big data for strategic and actionable insights. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59. [2] Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: a modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 31(4), 337-373. [3] Depeige, A., & Doyencourt, D. (2015). Actionable Knowledge As A Service (AKAAS): Leveraging big data analytics in cloud computing environments. Journal of Big Data, 2(1), 1-16. [4] Rehman, A., Naz, S., & Razzak, I. (2022). Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems, 28(4), 1339-1371. [5] Ranjan, J., & Foropon, C. (2021). Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, 102231. [6] Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press. [7] Dekimpe, M. G. (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), 3-14. [8] Sazu, M. H., & Jahan, S. A. (2022). How big data analytics impacts the retail management on the European and American markets. CECCAR Business Review, 3(6), 62-72. [9] Dhar, S., & Mazumdar, S. (2014, June). Challenges and best practices for enterprise adoption of big data technologies. In 2014 IEEE International Technology Management Conference (pp. 1-4). IEEE. [10] Giest, S. (2017). Big data analytics for mitigating carbon emissions in smart cities: opportunities and challenges. European Planning Studies, 25(6), 941-957. [11] Aldboush, H. H., & Ferdous, M. (2023). Building Trust in Fintech: An Analysis of Ethical and Privacy Considerations in the Intersection of Big Data, AI, and Customer Trust. International Journal of Financial Studies, 11(3), 90. [12] Martin, K. E. (2020). Ethical issues in the big data industry. In Strategic Information Management (pp. 450-471). Routledge. [13] Minatogawa, V. L. F., Franco, M. M. V., Rampasso, I. S., Anholon, R., Quadros, R., Durán, O., & Batocchio, A. (2019). Operationalizing business model innovation through big data analytics for sustainable organizations. Sustainability, 12(1), 277. [14] Xiao, X., Tian, Q., & Mao, H. (2020). How the interaction of big data analytics capabilities and digital platform capabilities affects service innovation: A dynamic capabilities view. IEEE Access, 8, 18778-18796. [15] Shah, T. R. (2022). Can big data analytics help organizations achieve sustainable competitive advantage? A developmental inquiry. Technology in Society, 68, 101801. [16] Dahiya, R., Le, S., Ring, J. K., & Watson, K. (2022). Big data analytics and competitive advantage: the strategic role of firm-specific knowledge. Journal of Strategy and Management, 15(2), 175-193. [17] Al-Momani, M. M., Abbas, N., Saleem, T. A., Basha, M., Jubran, A. H., Al-Sawaie, K., ... & Dahlan, M. (2023). The Role of Business Intelligence on Digital Economic Transformations (Case Study: E-Government in Jordan). In Artificial Intelligence (AI) and Finance (pp. 308-316). Cham: Springer Nature Switzerland. https://creativecommons.org/licenses/by/4.0/