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.

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.

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.

Deep neural network-based English handwriting recognition has revolutionised the security or verification system of today; nevertheless, there are certain risks that are inevitable. This paper examines the status of the present technologies in the handwriting recognition systems, and more particularly, by the various deep neural network architectures. It also evaluated common cybersecurity risks such as data poisoning, model inversion, and adversarial attacks, which can be devastating to such systems, as well as common privacy and ethical issues. The potential regulatory compliance and mitigation measures that can be taken to avert these risks and hurdles are also addressed in detail, with requisite emphasis being made on the future outlook of a more secure handwriting recognition system.

The Industrial Era 4.0 has seen industries start shifting towards implementing Decision Support System (DSS) in the manufacturing sector. Technological advancements have made it possible for the development of DSS to be based on Artificial Intelligence (AI) using past data generated by industry, especially in the furniture manufacturing industry. The furniture manufacturing industry is now faced with the challenge of Extreme Programming (XP) model complexity that hinders production and inventory management. The manufacturing industry finds it difficult to comprehend which industries to produce based on the current market trends. This research, therefore, seeks to comprehend how an AI-based DSS system can learn furniture model production trends. Based on such problems, this research can potentially assist in designing an AI-based DSS employing the Autoregressive Integrated Moving Average (ARIMA) model from the XP system development paradigm. This research is segmented into five phases, i.e., problem identification, decision model design, data collection and processing, system development and integration, and implementation. The delivery of this research is a list of best-selling furniture fads from market analysis generated through DSS. These findings are useful in the development of DSS, especially in AI to make predictions of furniture model trends.

The study explores the link between digital leadership and cloud intelligence in the context of ethical artificial intelligence (EAI) in relation to three telecom operators in Jordan: Orange, Zain, and Umniah. A total of 424 e-questionnaires were also sent to managers (senior and junior) and staff. The results were processed using SmartPLS4 in PLS-SEM. These results demonstrate that we can develop improved cloud technology solutions to enhance our ethical AI capabilities. This ethical AI facilitates the mediating process in the link between digital leadership and business innovation. Findings lead telecom companies to be much more responsible and ethical in their responsiveness and trust-building with the help of AI-induced cloud intelligence. Finally, the results will summarize theoretical and empirical evidence about responsibility dimensions in AI innovation and data-intensive telecom companies. Along with other important variables such as innovation and integrity, the study emphasizes that telecom managers in today’s digital leadership era are expected to think ahead to ensure that both technological advancement and corporate social responsibility not only develop but genuinely prosper.