This study examines the evolving landscape of Business Intelligence (BI) research and its impact on organizational decision-making through a comprehensive bibliometric analysis. Analyzing 2442 BI-related articles from the Scopus database published between 2014 and 2024, we identified key trends, influential authors, leading institutions, and significant research gaps. Despite substantial literature, our findings reveal a declining trend in BI publications and significant under-exploration in integrating BI with competitive intelligence, decision-making processes, data mining, and human resources management. The study highlights geographical disparities in research output, with the United States and China leading in publications and citations. Our analysis underscores the need for more interdisciplinary research approaches and increased collaboration among researchers and institutions to address these gaps. This study provides valuable insights for researchers and practitioners, offering direction for future research to enhance the application of BI in organizational decision-making and strategic development.

The study aims at identifying the role of competitive intelligence in improving company performance through organizational learning in start-ups, Relied on a descriptive-analytical approach with the use of a questionnaire to collect data, which was distributed to a random sample of 255 Start-ups in Algeria. The structural equation modelling was also used through the Smart pls 4 program to test the study's hypotheses.The study concluded that there is a weak indirect role between competitive intelligence and the performance through organizational learning expressed in a correlation coefficient estimated at 23.1%, while the direct role was greater with a correlation coefficient of 61.6%.This is due to the fact that the mediator variable does not play its active role in strengthening the relationship between competitive intelligence and the start-ups performance despite this impact, start-ups in Algeria does not effectively carry out research to obtain available opportunities in the market.

For small and medium-sized enterprises (SMEs) seeking to adhere to sustainable standards and gain a sustained competitive edge, green marketing and cleaner production are increasingly imperative. Green marketing has grown a lot in popularity in the present market, which makes it possible to rebrand and repackage existing products. This study looks at how green marketing in Algerian industrial SMEs might help achieve sustainability through cleaner production. Data analysis was performed using Smart PLS 4 softwareV.4.1.0.8 and structural equation modeling. With a mediation effect of 47.4%—higher than the direct impact of cleaner production on sustainability—the study’s conclusion is that green marketing strongly mediates the relationship between cleaner production and sustainability. This suggests that by combining green marketing with cleaner production methods, SMEs can reap significant profits. In accordance with their financial and environmental goals, SMEs can effectively incorporate cleaner production methods and green marketing with the help of the research’s practical recommendations.

The study aims to analyze the impact of artificial intelligence components—such as machine learning, software engineering, and data analytics—on business intelligence elements including decision-making, human resources, and information systems, with a focus on startups that drive economic growth. The research gap centers on the lack of applied studies examining the relationship between artificial intelligence and business intelligence within the Algerian context, particularly regarding the integrated effect of these advanced technologies on institutional performance in startups. To address this gap, the study employed a quantitative methodology by designing a questionnaire distributed to a random sample of 345 startups in Algeria, with data analyzed using Smart PLS 4 software. The results revealed statistically significant positive effects of artificial intelligence components on business intelligence elements in Algerian startups, with varying effects from weak to moderate. Additionally, a strong and positive overall impact of artificial intelligence on business intelligence was confirmed with a value of 0.863, indicating a significant and robust relationship and the importance of AI in enhancing business intelligence performance in startups. The study recommends increasing investments in technical staff training, improving data infrastructure and information systems, and adopting comprehensive digital transformation strategies in startups. It also emphasizes the importance of future research focusing on the long-term impact of AI technologies across multiple sectors of the national economy to ensure sustainable development and foster innovation in the modern business environment.