Author Guidelines

The title should be concise, informative, and accurately represent the research content. It should clearly reflect the principal technology, research approach, or innovation being investigated. Avoid unnecessary words, unexplained abbreviations, and overly general expressions. The title must use Times New Roman, centered, bold, 16-point font.

Author names must be accompanied by institutional affiliations, valid email addresses, and ORCID identifiers. One author must be designated as the corresponding author. Academic titles and professional positions should not be included. Author identification details must be provided separately from the anonymized manuscript to maintain the confidentiality required for double-blind peer review.

The Abstract must contain 200–250 words, written in one paragraph using Times New Roman 10-point italic font. It should follow the Background, Objective, Method, Results, and Conclusion (BOMRC) structure and clearly explain the research novelty, methodological approach, principal findings, and scientific contributions. The Abstract must reflect the relevance of the study to AJRI's focus and scope. Citations, unexplained abbreviations, and unnecessary technical details should be avoided.

Authors must provide exactly five (5) relevant keywords that represent the principal research concepts, methodologies, technologies, or applications. Keywords should support article discoverability and indexing and demonstrate relevance to AJRI's research areas, such as Blockchain, Artificial Intelligence, Risk Analytics, Supply Chain Management, AI Governance, Cybersecurity, Cloud Computing, Big Data, and Financial Technology, as appropriate to the manuscript.

The Introduction should explain the research background, problem statement, relevant previous studies, research gap, novelty, and objectives. Authors must demonstrate the scientific significance of the research and its relevance to one or more of AJRI's five focus areas. The Introduction should highlight existing challenges involving blockchain and AI integration, data-driven risk and supply chain management, sustainable AI governance, cybersecurity, cloud computing, or fintech innovation. Where applicable, authors should also explain the research contribution to relevant United Nations Sustainable Development Goals (SDGs), particularly SDG 8, SDG 9, SDG 12, and SDG 13.

This section should critically examine relevant theories, conceptual frameworks, technological approaches, and previous research. Authors must identify limitations in existing studies, establish the theoretical or technological foundation of the research, and clearly describe the research gap. Studies should prioritize relevant recent literature concerning blockchain-enabled AI, data analytics, supply chain optimization, sustainable technologies, AI governance, digital infrastructure security, cloud computing, and fintech. Conceptual frameworks or research hypotheses should be developed where appropriate.

The methodology must clearly explain the research design, data sources, sampling procedures, research instruments, data collection, and analytical techniques. Computational and experimental studies should describe datasets, preprocessing procedures, implementation environments, model development, evaluation metrics, validation methods, and baseline comparisons. Research involving organizations or financial institutions should explain the study population, sampling strategy, measurement instruments, and statistical or qualitative methods. The methodology must provide sufficient detail to ensure transparency, reproducibility, and scientific reliability.

This section must systematically present and critically discuss the research findings using appropriate evidence, including tables, figures, statistical analyses, experimental evaluations, or qualitative findings. Authors should interpret the results, compare them with previous studies, and explain their theoretical and practical contributions. Relevant evaluation indicators may include AI prediction accuracy, blockchain transaction performance, supply chain forecasting effectiveness, sustainability metrics, cybersecurity detection performance, cloud scalability, or fintech system efficiency. The discussion must clearly establish the study's novelty and contributions to technological innovation.

This section must explain how the research findings can be applied by managers, business practitioners, technology developers, organizations, financial institutions, or policymakers. Recommendations should be practical, actionable, and directly supported by the findings. Depending on the research topic, implications may address the adoption of blockchain-enabled AI for operational transparency, data-driven supply chain risk management, responsible AI governance, cybersecurity readiness, sustainable cloud infrastructure, or fintech innovation. Authors should explain the potential organizational benefits, implementation considerations, and decision-making improvements resulting from the research.

The Conclusion must summarize the principal research findings, scientific contributions, practical implications, study limitations, and opportunities for future research. It should clearly address the research objectives and explain the significance of the findings for technological development or business innovation. Future research recommendations may include further experimental validation, algorithmic improvements, broader organizational implementation, or sustainable technology applications. The Conclusion must be written in coherent paragraph form without bullet points and should not repeat the Abstract.

All manuscripts must include the following declarations:

References must follow the numerical IEEE citation style and be arranged according to their order of appearance in the manuscript. Authors should prioritize reputable peer-reviewed journal publications from the last five years, while relevant foundational studies may also be included. All references must be cited in the manuscript, and Digital Object Identifiers (DOIs) should be provided where available. The reference list should demonstrate sufficient coverage of recent scientific developments relevant to AJRI's research areas. Authors are encouraged to use Mendeley or Zotero to maintain citation consistency and accuracy.

Explores the integration of blockchain and artificial intelligence to enhance security, transparency, and efficiency in business processes, emphasizing decentralized applications and data-driven decision-making.

Investigates the use of data analytics and machine learning in identifying and mitigating risks, while optimizing supply chain logistics, demand forecasting, and operational efficiency.

Examines the ethical, sustainable application of AI in business, emphasizing frameworks for governance, environmental sustainability, and reducing carbon footprints in operations.

Focuses on strategies to secure digital infrastructures during transitions to cloud computing and global digital operations, ensuring data integrity and multi-cloud scalability.

Investigates the role of big data analytics in financial technology (fintech) advancements, highlighting innovations in peer-to-peer lending and automated business finance processes.