This study examines how artificial intelligence (AI) generates operational value in data centers through the Task–Technology Fit (TTF) framework. Using survey data from data center professionals, the study applies covariance-based structural equation modeling (CB-SEM) to test relationships among task characteristics, technology characteristics, managerial competence, organizational support, TTF, AI adoption, and operational efficiency. The findings indicate that task, technology, and managerial factors strengthen TTF, which serves as the key mechanism driving AI adoption. Organizational support also facilitates adoption by creating conditions that enable effective technology integration. AI adoption, in turn, enhances operational efficiency. The study contributes to AI adoption research by showing that AI value depends not only on technological capability but also on alignment with task demands, managerial expertise, and supportive organizational contexts. It offers practical insights for improving AI implementation in complex data center environments.
As artificial intelligence (AI) continues to shape commercial and governmental innovation, understanding the potential of large language models (LLMs) to improve software testing has become increasingly critical. This paper explores the application of LLMs for generating test cases-an essential yet resource-intensive activity in software quality assurance. Using theoretical lenses of the Design Science Research Evaluation (DSRE) framework, Measurement Theory, and Cognitive Load Theory, the authors systematically design and evaluate procedures for test case generation and execution to benchmark AI-generated test cases against those produced by human testers and traditional pairwise analysis. They develop two custom-built applications seeded with intentional defects and execute test cases generated through each method. This research contributes to the evolving discourse on AI-driven software engineering, offering insights into when LLMs can autonomously generate test cases, when human expertise is indispensable, and when hybrid approaches yield the greatest value.
The COVID-19 pandemic accelerated the adoption of wearable health technologies (WHTs), reshaping patterns of use and motivations. This study applies Social Cognitive Theory and Digital Divide frameworks to compare pre-and post-pandemic determinants of WHT adoption using survey-weighted logistic regressions and subgroup analyses by race and gender. Findings show that while personal agency and social context remain relevant, structural factors such as smart device ownership, insurance coverage, and digital health integration became more decisive after the pandemic. The influence of education declined, and income-based disparities widened, signaling a shift from skill-based divides to access-based divides. Demographic trends remained stable, although health-risk salience increased and equity patterns diverged. The results suggest that motivation and confidence alone are insufficient to influence WHT adoption. Material access and digital infrastructure now play a central role. Implications are discussed for temporally sensitive IS theory and inclusive technology design and policy.
As artificial intelligence (AI) continues to shape commercial and governmental innovation, understanding the potential of large language models (LLMs) to improve software testing has become increasingly critical. This paper explores the application of LLMs for generating test cases—an essential yet resource-intensive activity in software quality assurance. Using theoretical lenses of the Design Science Research Evaluation (DSRE) framework, Measurement Theory, and Cognitive Load Theory, the authors systematically design and evaluate procedures for test case generation and execution to benchmark AI-generated test cases against those produced by human testers and traditional pairwise analysis. They develop two custom-built applications seeded with intentional defects and execute test cases generated through each method. This research contributes to the evolving discourse on AI-driven software engineering, offering insights into when LLMs can autonomously generate test cases, when human expertise is indispensable, and when hybrid approaches yield the greatest value.
In the Web 3.0 era, blockchain emerges as a disruptive technology offering robust solutions for industries aiming to optimize and sustain their supply chains. With features such as decentralization, transparency, traceability, and immutability, blockchain ensures data security, prevents corruption or unauthorized access, and enables seamless interoperability. This study identifies and analyzes 10 key strategies for blockchain adoption in supply chains through a rigorous PRISMA-based literature review. Using the m-TISM methodology, a strategic framework is developed to define interrelationships among strategies, followed by a MICMAC analysis to classify them based on driving power and dependence. Findings highlight three pivotal strategies: identifying complex processes suited for blockchain, spreading awareness among supply chain partners, and fostering training and capacity building. These strategies drive effective adoption across industries. Further, a path analysis suggests three implementation pathways, offering industries a structured roadmap for smooth blockchain integration.
