The focus of this study is to investigate the major factors influencing entrepreneurial intent and behavior, by addressing the role of entrepreneurial education, business opportunity recognition, and the need for achievement. The research data were collected online using a self-administered questionnaire among 148 Moroccan women having completed an entrepreneurship training program. The data analysis using a PLS-SEM provided evidence of the positive impact of entrepreneurial education on entrepreneurial self-efficacy and PBC. Likewise, the findings supported the direct impact of subjective norms and entrepreneurial self-efficacy on entrepreneurial attitude. Furthermore, entrepreneurial attitude, business opportunity recognition, and the need for achievement were identified as predictors of entrepreneurial intent, leading to enhanced entrepreneurial behavior. These results highlight a number of useful practical recommendations for public policy-makers on how to stimulate and encourage entrepreneurship initiatives among women.

This data article focuses on the relationship between consumer satisfaction, brand attitude, brand preference, and purchase intentions. The data was collected from dairy products consumers within the Laayoune-Sakia El Hamra region in Morocco. The research data are collected via an on a self-administered online questionnaire, from a sample of 195 Moroccan consumers of dairy products (Sakia brand). The data were analysed using a structural equation modeling method under the Partial Least Squares approach (PLS-SEM). Data analysis was performed using SmartPLS 3 software.

Artificial intelligence is becoming a defining force in public governance, yet many institutions still struggle to adopt it in ethical, sustainable, and scalable ways. This article reports on a systematic literature review in line with PRISMA 2020 guidelines, covering 67 peer-reviewed studies published between 2014 and 2024. The review shows that AI can help public institutions work faster and more transparently, but it also reveals several common problems. Many organizations still face fragmented data, weak connections between systems, limited digital tools, a lack of staff skills, and ethical risks such as bias and privacy concerns. To address these problems, the study introduces the AI Integration Capability Model, a framework based on the Technology Acceptance Model, Digital-Era Governance, and Dynamic Capabilities theory. The model highlights four institutional pillars: data access and interoperability, digital infrastructure and redesigned processes, workforce skills and learning capacity, and leadership and management reform. Its relevance was tested through a three-round Delphi study with 15 senior experts from Moroccan public institutions, who agreed on the feasibility and urgency of all four pillars. The findings offer policymakers practical guidance for AI adoption and outline a roadmap for aligning innovation with institutional readiness and public trust.