This study investigates how big data analytics capabilities and managerial AI literacy jointly shape supply‐chain decision agility in multinational firms. Using 511 valid questionnaires from senior executives of high-tech electronics multinationals headquartered in Taiwan, we find that both managerial and technical aspects of big data analytics significantly boost decision agility. All three dimensions of AI literacy likewise show positive effects, with descriptive and diagnostic analytics exerting the most pronounced influence. Interaction tests further demonstrate that higher AI literacy magnifies the positive impact of analytics capabilities on agility. The findings suggest that firms seeking agile supply chains must not only upgrade data-management processes and technology platforms but also cultivate executives’ understanding of AI model logic, bias detection, and results application—enabling rapid sensing, swift decision-making, and timely execution in volatile environments. The study extends the integrated lens of dynamic capabilities and technology acceptance theory and provides a practical organisational-cognitive framework for multinational digital transformation.
Y. Tai, Chutima Ruanguttamanun, Luke H.C. Hsiao· Science Technology & Society· 0 citations
Background: Groundnut farming is affected by several leaf diseases that reduce crop yield. Farmers often rely on visual inspection, which can be inaccurate and time-consuming. Early and precise identification of leaf diseases is essential for effective crop management. This study presents a deep learning-based solution to automate disease detection. Methods: A modified EfficientNetB0 architecture is proposed for classifying five types of groundnut leaf conditions: healthy, leaf spot (early and late), alternaria leaf spot, rust and rosette. The dataset is sourced from the Mendeley database that was collected from Ramchandrapur village in West Bengal, India, under natural lighting. A total of 1,720 images were captured using a DSLR camera. After verification and cleaning, the dataset was split into 1,204 training and 516 testing images. All images were resized, normalized and label-encoded. Data augmentation techniques such as rotation, flipping and zoom were used to improve generalization. Regularization was applied to reduce overfitting. The model was trained for 100 epochs using the RMSprop optimizer and early stopping. Result: The model achieved a test accuracy of 99.22%. Evaluation metrics confirm high performance across all classes. The model outperformed existing methods such as ResNet50 (82.3%), CNN with progressive resizing (96.12%) and LeafNet (97.23%). It also maintained low training and validation loss throughout training. These results highlight the model’s robustness, accuracy and potential for real-time field applications. The approach is lightweight and suitable for mobile-based disease detection tools.
Jie-Shin Lin, Y. Tai, Suh-Chen Hsiao et al.· Legume Research An Internati...· 0 citations