The development of research, innovation, and entrepreneurship (RIE) competencies has been positioned as a strategic priority within Saudi Arabia’s Vision 2030; however, a persistent discrepancy between awareness and active engagement remains insufficiently characterised. In this study, the levels of RIE awareness, perceptions, and experiential participation among university students in Saudi Arabia, with particular reference to the Eastern region, were systematically examined, and their statistical associations with competency development were evaluated. A cross-sectional survey design was employed, in which data were collected from 301 students during April–May 2025 using a validated 24-item, five-point Likert-scale instrument encompassing five constructs: RIE awareness, influencing factors, perceptions and attitudes, educational experiences, and sustainability orientation. High internal consistency was demonstrated (Cronbach’s α = 0.89–0.93), and construct validity was assessed through exploratory factor analysis (EFA). Descriptive statistics indicated that RIE awareness was moderately high (M = 3.54, SD = 1.00), whereas a pronounced participation gap was observed: although 56.6% of respondents reported involvement in research activities, substantially lower engagement was recorded in innovation and entrepreneurship initiatives (24.9%) and start-up activities (19.2%). Perceived importance of RIE for future career development was high (M = 4.13), yet awareness of entrepreneurial mindset constructs remained comparatively limited (M = 3.15). Significant positive correlations were identified among the principal constructs (Spearman’s ρ = 0.666–0.902, p < 0.001), although potential inflation effects attributable to shared measurement items were noted and critically considered. Ordinal logistic regression analysis revealed that participation in research projects and exposure to structured educational experiences constituted the most robust predictors of RIE competency development, surpassing attitudinal variables in explanatory power. These findings suggest that favourable perceptions alone are insufficient to foster competency acquisition in the absence of sustained experiential engagement. It is therefore implied that higher education institutions should prioritise the integration of practice-oriented RIE programmes, strengthen mentorship quality, and enhance transparency in resource accessibility, with policy interventions oriented towards capability development rather than motivational reinforcement. The study provides an empirically grounded baseline for assessing RIE competencies in emerging higher education contexts and offers a transferable measurement framework applicable to Gulf and comparable innovation-driven economies.

The advent of artificial intelligence chatbots such as ChatGPT has revolutionized the field of education by offering convenient information accessibility, although accompanied by worrying concerns about the cultivation of critical thinking skills. Nevertheless, there is a lack of extensive research regarding the extent to which learners can develop critical thinking skills in a certain discipline through the utilization of ChatGPT. This research aims to evaluate the capability of ChatGPT to demonstrate critical thinking in its responses, particularly in the domain of cybersecurity. Its objective is to conduct a complete assessment of the analytical capacities of ChatGPT, considering its growing integration into educational settings. In this connection, ChatGPT was presented with a series of inquiries with increasing levels of complexity within the intricate realm of cybersecurity. The responses were subjected to analysis using Lee’s Model of Thinking Levels, which involved categorizing them into “recall”, “rationalization”, or “reflectivity”. The findings suggested that ChatGPT exhibited a prominent level of critical thinking skills, especially in the authentic contexts.

Social media provides a rich source of consumer-generated data that can support product innovation decisions. However, firms still face difficulties in transforming unstructured online discussions into measurable insights that are linked to market success. This study proposes a data-driven social media framework for product innovation by integrating text mining, Principal Component Analysis (PCA), and Principal Component Regression (PCR). The framework identifies dominant consumer attribute discussions, reduces correlated attributes into interpretable latent components, and tests their relationship with standardized sales indicators. The study uses two competitive smartphone products, Apple iPhone 8 and Samsung Galaxy Note 7, as empirical cases. Text mining results show that consumers discussed visual, ecosystem, connectivity, battery, charging, camera, and platform-related attributes across both products. PCA results indicate that a small number of principal components can explain most of the variance in consumer discussions. For the iPhone 8, the first two components explain 79% of the total variance, while for the Galaxy Note 7, they explain 81%. The regression results show strong links between selected social media-derived components and market success. For the iPhone 8, Principal Component (PC) 1 has the strongest relationship with standardized sales, with an R² of 0.956. For the Galaxy Note 7, PC7 shows the strongest relationship, with an R² of 0.889, while other components show both positive and negative relationships. These findings show that social media discussions can provide early signals of consumer needs, product risks, and market response. The proposed framework offers a systematic tool for attribute prioritization, launch monitoring, and evidence-based product development.