Aims & Scope

Aims and Scope

Journal of Machine Intelligence for Sustainable Industry Practices (JMISIP) is a peer-reviewed, open access journal dedicated to advancing the responsible application of machine learning and artificial intelligence toward sustainable outcomes in industry, business, and management. The journal's central aim is to close the gap between theoretical research and real-world implementation by prioritizing studies that demonstrate measurable, verifiable impact within actual organizations.

Aims

JMISIP aims to:

  • Advance scholarly and practical understanding of how machine intelligence can drive sustainable transformation across industries and organizations
  • Provide a rigorous, credible publishing venue for research that has been implemented and evaluated in real enterprise settings, not only proposed theoretically
  • Bridge the persistent gap between academic research and practitioner application in the fields of AI, sustainability, and management
  • Support evidence-based decision-making for businesses, policymakers, and researchers navigating the intersection of technology and sustainability
  • Encourage transparent reporting of outcomes, including studies that report partial success, mixed results, or implementation challenges, recognizing that honest evaluation strengthens the field more than success-only reporting

Scope

The journal welcomes original research articles, case studies, systematic reviews, and applied studies within (but not limited to) the following areas:

  • Artificial intelligence and machine learning applications in sustainable operations, manufacturing, and supply chain management
  • Sustainable enterprise and business strategy informed by data-driven and intelligent systems
  • Environmental, social, and governance (ESG) performance measurement and improvement through AI/ML methods
  • Circular economy and resource efficiency enabled by intelligent automation and predictive analytics
  • Responsible AI governance, ethics, and regulatory considerations in industrial and organizational contexts
  • Energy systems and industrial sustainability supported by machine intelligence
  • Digital transformation of management practices with a sustainability lens
  • Human-AI collaboration in sustainability-focused decision-making
  • Case studies and applied research reporting outcomes from real organizational deployments, including implementation challenges and lessons learned

What Sets JMISIP Apart

Unlike journals that treat practical relevance as a secondary consideration, JMISIP structurally prioritizes applied, outcome-based research. Authors submitting empirical or case-study work are encouraged (and, for select article types, required) to report on:

  1. The real organization, industry, or context where the work was implemented
  2. Concrete outcomes observed, measured against clearly stated objectives
  3. Limitations, unexpected challenges, or negative results encountered during implementation

This approach is designed to make JMISIP a trusted reference point for practitioners and policymakers seeking research that has already been tested against real-world conditions — not just theorized.

Interdisciplinary Scope

JMISIP is intentionally interdisciplinary, welcoming contributions from computer science, data science, business and management studies, environmental science, engineering, and public policy, provided the work meaningfully engages with the intersection of machine intelligence, sustainability, and industry practice.