With the growing complexity of AI models, and as well as the requirement for transparency and trust, integrating XAI into IoT applications is gaining greater momentum. Most traditional AI models are regarded as "black boxes," in which understanding decision-making processes presents a challenge in crucial domains such as healthcare, smart cities, and industrial automation. This paper looks at various methodologies of XAI that can be used to increase interpretability and offer better insight into the AI models of IoT environments. This research follows a taxonomy-based approach in the classification of ante-hoc and post-hoc XAI techniques, with the main focus on model-agnostic methods such as tree Explainer SHAP and LIME. Various real-world use cases are analysed for the effectiveness of XAI in enhancing trust, reducing biases, and improving the accuracy of decision-making in IoT systems. Challenges associated with the trade-off between model complexity and interpretability while maintaining efficiency are discussed. The results indicated that XAI techniques could significantly enhance the transparency and trust of IoT applications on account of clear and interpretable explanations of AI-driven decisions. These findings have been considered most effective in domains where ethical compliance and accountability are considered paramount. The study also points to the contribution of XAI in optimizing energy efficiency and improving data security in IoT frameworks. The integration of XAI into IoT systems bridges the gap in transparency and opens ways for scalable, trustworthy, and ethical AI applications. Of course, scalability challenges have to be overcome, and further security mechanisms have to be developed in future research to realize the full potential of XAI in IoT domains.

Background: This research aims to comprehensively evaluate the influence of firm capabilities, resources, and Artificial Intelligence (AI) on supply chain financing within the Jordanian context. It also analyzes the mediating role of blockchain technology and trust in these relationships. Methods: A conceptual model was utilized to empirically examine the suggested relationships. Data were gathered from a sample of 349 Jordanian start-ups focusing on AI and blockchain technologies via a five-point Likert scale questionnaire. Partial Least Square regression-based Structural Equation Modeling (PLS-SEM) facilitated by SmartPLS4 was used to perform the analysis. Results: The findings reveal that firm capabilities/resources and AI positively impact supply chain financing. Blockchain technology and trust serve as mediators, enhancing the effects of these factors on supply chain financing. Conclusions: The study highlights the role of innovative technologies in improving financial information security and collaboration among supply chain partners. It emphasizes how firm capabilities, resources, and emerging technologies such as AI and blockchain influence supply chain financing as they foster trust and security in financial transactions, offering valuable insights for decision-makers in the supply chain domain.

Biometrics, such as fingerprint technology, is a novel technology that verifies an individual's identity based on their distinct physical or behavioural features. Despite the technology's increasing prevalence in the public and private sectors due to its potential benefits, its adoption by end users is proceeding slowly. This study proposes a framework that explores the influence of various dimensions of perceived benefits and risks on banking customers’ intentions to use fingerprint ATMs. Empirical data was collected from banking customers of Jordanian banks that have implemented fingerprint technology at their ATMs. The proposed framework was evaluated with potential adopters of the target technology. The findings suggest that personal innovativeness positively influences banking customers’ perceived value of fingerprint ATMs. Perceived benefits, including time convenience, cognitive effort saving, increased security and perceived enjoyment, also positively influence the perceived value of fingerprint ATMs. Perceived risks, except for physical risk, have negative effects on perceived value. In addition, perceived value is recognised as a key enabler of fingerprint ATM adoption intention. The findings of this study provide valuable insights that can help Jordanian banks promote and encourage their customers to use fingerprint ATMs.