This research intends to evaluate the effect on the performance of small and medium enterprises (SMEs) of technological developments, including digital marketing, and to determine the variables that affect the relationship. Because these businesses are essential to driving local economies, boosting local economic growth, and creating job opportunities, this is a very important question, and figuring out how to best help them succeed through digital marketing is critical. The data were generated from a sample of 190 marketing companies, and each of the managers of these companies was surveyed for the information needed. This research focuses on the impact of SMEs’ performance through digital marketing strategies, including online advertising, social media marketing, search engine optimization (SEO), and customer engagement through digital channels. A further analysis was performed looking at the mediating effect of digital transformation on the relationship between digital marketing and firm performance. The theoretical lens in the current study is the Technology Acceptance Model (TAM), which argues that technology use depends on perceived use and perceived usefulness. The results revealed that digital marketing is essential for SME effectiveness, as a driver of digital transformation, leading to stronger economic results and an enlarged market presence. Engagement with customers on digital channels to understand their behavior and preferences helps organizations create better experiences and products, and sell smarter as well. This offers application insights to the practitioners and proves the empirical data telling the SMEs how to plan the adoption of digital marketing. It also offers policy recommendations to authorities on how to promote digital innovation and provide financial and technical assistance to SMEs in this vital area.

Using adaptive learning technologies, personalized feedback, and interactive AI tools, this study investigates how these tools affect student engagement and what the mediating role of individuals’ digital literacy is at the same time. The study will target 500 students from different faculties such as science, engineering, humanities, and social sciences. With the changing trends in educational technology, it is important to know if these tools allow students to interact with learning materials. Through this study, we explore how adaptive learning technologies, which adapt content to students’ progress, are influenced by student motivation and participation during the learning process using AI tools that provide real-time feedback and interaction. Also, digital literacy is presented as a moderating factor that may either accelerate or impede the effectiveness of these tools. These findings demonstrate that more adaptive learning technologies, which have organized feedback, and interactive AI tools help improve student engagement. Additionally, students with higher levels of digital literacy are more involved with digital tools. This research recognizes that teachers should incorporate these technologies into their courses in such a manner as it synergizes with student’s digital capabilities to reap the benefits of technology on students’ engagement and learning outcomes.

Type of the article: Research ArticleAbstractInvestments such as technological investments in useful marketing intelligence are an important organizational challenge because companies raise the pace of their digital transformation activities. The research is based on the Resource-Based View and Dynamic Capabilities Theory and aims to investigate how the data on important organizational and technological enablers’ data-driven culture, technological readiness, top management support, and marketing analytics maturity influence Business Intelligence (BI) capability in marketing functions. The primary data were gathered between February and May 2025 with the help of a structured online survey among the marketing managers, BI specialists, analytics professionals, and IT decision-makers, working in Amman, Irbid, Zarqa, and Aqaba, Jordan. Based on a purposive sampling technique, 602 valid responses were interpreted with the help of Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that Marketing Analytics Maturity has the most significant impact on BI capability (β = 0.367, p = 0.001), then Data Culture (β = 0.321, p = 0.001) and Technological Readiness (β = 0.287, p = 0.01). The positive, relatively weak effect (β = 0.224, p < 0.05) is demonstrated by Technology Readiness. The structural model shows significant explanatory power and explains 78.1 percent of the variance on BI capability. Such results indicate that building BI capabilities among emerging market companies is not as much about acquiring technology, but rather about integrating managerial support and analytics maturity, which emphasizes the importance of readiness in an organization in changing digital investments into the value of marketing intelligence.

The rising demand for online learning and the high academic reliance on artificial intelligence (AI) have led to the need for the development of an automated system that detects and mitigates instances of student cheating, especially in large settings, to protect the academic process. The dramatic shift towards online learning, especially after COVID-19, has urged the development of such systems to ensure exam integrity. This research aims to develop an advanced Neural Network-CNN framework to detect and predict cheating in online and large-scale lab assessments. The proposed framework aimed to detect cheating through specific measures and patterns depending on students’ actions or responses, such as eye blinking, attention, lip movement, and time-automated data series. This research employed various data inputs through a complex neural network architecture during assessments, and the framework uses a multi-modal approach that analyzes all the interactions of the students and their patterns. The results show that the AI framework reduces up to 41.2% of cheating among n students and can predict cheating up to 35.3% among 100 students in one exam.