Organizational Resistance to Technology Diffusion: The Case of IPv6

There are growing concerns about security as the usage of computers in academic settings continues to increase. This research aims to investigate the level of awareness among university students regarding security threats associated with ransomware. This study examines students' behaviour and preventive motivation for ransomware attacks, along with the measures taken to mitigate these security threats. The study model combines the theory of planned behaviour (TPB) and preventive motivation theory (PMT) with additional threat awareness (TA) variables. The research findings indicate a high level of awareness regarding the dangers. TA has a positive influence on other factors, as indicated by the significant t-values (perceived severity (PS)=4.479, perceived vulnerability (PV)=3.251, response efficacy (RE)=14.344, and self-efficacy (SE)=8.034). This research also demonstrates that subjective norm (SN) and affective responses (AR) have a key impact on behavioural intention (BI). Moreover, two of the preventive motivation factors, PS and PV, significantly contribute to BI, while the other two (RE and SE) did not show a significant contribution to BI.

This study examines how dual appraisals of value and risk jointly shape purchase intention in live-streaming commerce, refining the Stimulus–Organism–Response (S-O-R) framework for an understudied market context. Drawing on data from Indonesian live-stream shoppers, we demonstrate that stimuli such as streamer credibility, interactivity, and media richness enhance value perceptions and reduce perceived risk, which in turn increases purchase intention. The findings indicated that perceived value is the dominant pathway to intention, while perceived risk shows a small but positive association in this context. The study contributes by advancing S-O-R with dual appraisals and offers design guidance for stream formats. Practically, the findings provide explicit direction for allocating resources towards media richness and structured interactivity, while simultaneously emphasising the importance of safeguards. These design decisions consistently increase consumers’ purchase intentions and perceived value.

Background of study: Accurate fruit classification is vital for agricultural automation, yet traditional methods are often subjective and inefficient. Convolutional Neural Networks (CNNs) are effective but struggle with global context in fine-grained tasks. Vision Transformers (ViTs), inspired by NLP models, offer global attention mechanisms that may improve classification in complex scenarios.Aims and scope of paper: This study compares the performance of EfficientNet-B0 (a CNN model) and ViT-B/16 (a Transformer model) on a fruit classification task involving five fruit types. The goal is to evaluate their strengths and weaknesses under controlled experimental conditions using a moderately sized dataset.Methods: A dataset of 10,000 fruit images was preprocessed with standard augmentation techniques and split into training and validation sets. Both models were fine-tuned using pretrained weights. Performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices.Result: EfficientNet-B0 achieved higher overall accuracy (94%) than ViT-B/16 (92%). The CNN model performed consistently across all classes, particularly excelling in bananas and strawberries. ViT-B/16 showed superior results for strawberries but struggled with apples. Confusion matrices revealed class-specific strengths and weaknesses.Conclusion: EfficientNet-B0 is better suited for general fruit classification due to its balanced performance, while ViT-B/16 excels in capturing fine-grained visual features. A hybrid approach may leverage both models’ strengths for enhanced performance in real-world applications.