Identifying technology trends can be a key success factor for companies to be competitive and take advantage of technological trends before they occur. The companies always work to plan for future products and services. For that, it is important to turn to methods that are used for technology forecasting. These tools help the companies to define potential markets for innovative new products and services. This paper uses text mining techniques along with expert judgment to detect and analyze the near-term technology evolution trends in a Software as a Service (SaaS) case study. The longer-term technology development trend in this case is forecasted by analyzing the gaps between science and technology. This paper contributes to the technology forecasting methodology and will be of interest to those in SaaS technology. Our findings reveal five trends in the technology: 1) virtual networking, 2) the hybrid cloud, 3) modeling methodologies, 4) mobile applications, and 5) web applications. Among the results achieved, we can summarize the interesting ones as follows: it is possible to say that traditional information systems are now evolving into online information systems. On the other hand, the use of a licensing model based on subscriptions triggers the change in perpetual licensing models. The product range that has evolved towards mobile technologies has put pressure on information storage technologies and has led to the search for new methods especially in the development of database systems.

Artificial intelligence (AI) tools are becoming part of how students think, not only what they use. Whether a learner engages with such a tool or delegates to it is a decision about how their own cognitive resources are deployed, which places that decision within the realm of study of human intelligence. Investment accounts of intellectual development hold that ability is built through motivated engagement with cognitively demanding material, yet technology acceptance frameworks emphasize utility judgments and underrepresent the motivational processes that drive such engagement. This study tests an extended Technology Acceptance Model in which self-efficacy shapes perceived usefulness through motivation and enjoyment. Survey data from 185 undergraduate School of Business students at a Saudi university who used generative AI tools during coursework were analyzed using partial least squares structural equation modeling. All nine hypotheses were supported. Self-efficacy strongly predicted both motivation and enjoyment, but motivation was the dominant mediating pathway to perceived usefulness (β = 0.295, p < 0.001), with enjoyment a weaker affective route (β = 0.086, p < 0.05), alongside a significant serial pathway from self-efficacy through motivation to enjoyment (β = 0.113, p < 0.001). The model explained 54.4% of the variance in behavioral intention. The findings indicate that beliefs about one’s own technical competence, rather than appraisal of the tool alone, govern how students commit to intelligent systems, with implications for whether AI use develops or displaces learners’ own intellectual capacity.

The rapid growth of Industrial IoT (IIoT) has led to large volumes of sensor data, increasing the need for scalable and data-driven maintenance solutions. This paper presents a replay-based streaming framework for predictive maintenance that integrates Apache Kafka for data ingestion, Apache Spark Streaming for distributed processing, and an LSTM-based model for fault prediction. The NASA CMAPSS FD001 dataset is used in a controlled streaming simulation to evaluate the feasibility of processing multivariate degradation data under different workload levels. To reduce data leakage, engine units are split before sliding-window sequence construction. Experimental results show that the LSTM model achieves 90.4% accuracy, 89.2% precision, 87.6% recall, and an F1-score of 88.4% on the test set. Under the controlled replay conditions, the observed post-Spark prediction-stage latency remained below 600 ms across the evaluated workload levels. Since the Kafka and Spark timing components and the original micro-batch interval were not retained separately, these measurements should not be interpreted as end-to-end pipeline latency. The results demonstrate the feasibility of integrating stream-processing technologies with deep learning for predictive maintenance, while full end-to-end latency and horizontal scalability remain subjects for future evaluation.

The COVID‐19 pandemic has significantly affected small‐ and medium‐sized enterprises (SMEs) and poses challenges in acquiring financing. Growing green investments require transformative infrastructure and innovative green financing solutions. Blockchain technology is ideal for delivering green environmental information because it provides real‐time, shareable, and transparent data on an immutable ledger that is accessible to network members. Drawing on the technology–organization–environment (TOE) framework and stakeholder theory, this study examines the mechanisms linking blockchain technology adoption to green finance practices, such as the mediating roles of transparency and information security awareness. Furthermore, the moderating effect of digitization on these relationships was investigated. We collected multisourced and time‐lagged survey data from SMEs in three phases. The empirical results show that blockchain technology adoption positively influences green finance, and transparency and information security awareness mediate this relationship. Furthermore, digitization acts as a significant moderator in the relationship between transparency, information security awareness, and green finance. This study extends the existing theories and contributes to the literature by providing empirical evidence for the TOE framework and stakeholder theory, highlighting the significance of integrating transparency, information security awareness, and digitization in the context of blockchain technology adoption and green finance in SMEs. These results are particularly useful for advancing environmental sustainability in emerging markets and offer valuable insights for policymakers and practitioners.