About the Journal

Artificial Intelligence, Machine Learning, & Robotics in Business

Potential Areas of Research:

  1. Business Intelligence and Strategy: Leveraging AI and ML for strategic decision-making, competitive analysis, and business model innovation.
  2. Transportation and Smart Mobility: Exploring the role of AI and robotics in revolutionizing transportation systems, improving traffic management, and enhancing safety and sustainability.
  3. Engineering and Automated Systems: The application of AI in engineering solutions, automation of processes, and the development of intelligent systems.
  4. Hospitality and Service Industry: Utilizing AI to enhance customer experience, service personalization, and operational efficiency in the hospitality sector.
  5. Community Development and Social Impact: AI-driven strategies for community engagement, urban planning, and addressing societal challenges.
  6. Climate Change and Environmental Sustainability: Investigating AI's role in monitoring, mitigating, and adapting to climate change impacts.
  7. Logistics and Supply Chain Forecasting: Application of AI and ML in predicting logistics trends, optimizing supply chains, and enhancing inventory management.
  8. Experiential Learning and Education Technologies: Leveraging AI for personalized learning experiences, educational advancements, and skill development.
  9. Future Workforces and Remote Work: Exploring AI's impact on workforce dynamics, remote working models, and the gig economy.
  10. Entrepreneurship and Startup Ecosystems: AI's role in fostering innovation, supporting startups, and driving entrepreneurial ventures.
  11. Talent Management and HR Analytics: Use of AI in talent acquisition, workforce optimization, and human resource management.
  12. Ethical, Legal, and Societal Implications: Addressing ethical considerations, regulatory challenges, and societal impacts of AI in business.

The journal encourages a broad range of submissions, including original research, review articles, case studies, and reports on practical implementations, aiming to bridge the gap between theory and practice. It seeks to foster a collaborative and inclusive community of scholars, practitioners, and industry leaders who are driving innovation in the realm of AI, machine learning, and robotics in business contexts.


Open Access Policy: Artificial Intelligence, Machine Learning, & Robotics in Business provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. There are no publication charges, and all content is freely available without charge to the user or their institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author for non-commercial purposes. Nonetheless, reproduction, posting, transmission or other distribution or use of the article or any material therein requires credit to the original publication source with a link to both the article and the license. This open access policy is in accordance with the Budapest Open Access Initiative's (BOAI) definition of open access.

Copyright to Your Publication

As described in the author agreement, authors retain copyright to their publications. As an open access journal, we disseminate all content under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. 

Self-Archiving Policy: This journal permits and encourages authors to post items submitted to the journal on personal websites or institutional repositories both prior to and after publication, while providing bibliographic details that credit, if applicable, its publication in this journal.

Preservation Policy: Content published in Artificial Intelligence, Machine Learning, & Robotics in Business will be preserved by the George A. Smathers Libraries at the University of Florida. The Libraries are committed to long-term digital preservation of all materials in UF-supported collaborative projects. Redundant digital archives, adherence to proven standards, and rigorous quality control methods protect digital objects. The UF Digital Collections provide a comprehensive approach to digital preservation, including technical supports, reference services for both online and offline archived files, and support services by providing training and consultation for digitization standards for long-term digital preservation. Content is also preserved in the PKP Preservation Network (PKP PN) and CLOCKSS.

Content will be preserved indefinitely, unless a specific request for removal of a specific item is directed to the journal managers. If you believe that your copyrighted material has been deposited into this journal without consent, please contact the administrators at RACHELJUICHIFU@UFL.EDU.

Plagiarism Statement: Artificial Intelligence, Machine Learning, & Robotics in Business does not accept articles containing material plagiarized from other publications or authors. For the purposes of this policy, plagiarism is defined as copying of or reliance on work — including text, images and data — by others or yourself without proper attribution. Please be aware that you can plagiarize yourself; you must provide proper attribution in all cases where your previously published material or previously used data or images are included in your manuscript.

Plagiarism detected prior to publication will cause rejection of your manuscript. Plagiarism detected after publication will cause the published article to be amended to state that it contains plagiarized material; in extreme cases of plagiarism, the publication will be removed at the Editors’ discretion, and the reason for removal stated on the journal's website.

Artificial Intelligence, Machine Learning, & Robotics in Business does not consider the following situations to be plagiarism when proper attribution is made:

  • Translations into English of a previously published paper not in English;
  • Publication of all or part of a revised thesis or dissertation;
  • Publication of a paper previously made public as a conference presentation, white paper, technical report, or preprint

Artificial Intelligence, Machine Learning, & Robotics in Business follows workflows developed by the Committee on Publication Ethics (COPE) to deal with cases of plagiarism.

Use of Third-Party Copyrighted Materials: When submitting your manuscript, please be mindful of copyright laws in the United States and (if outside the U.S.) your home country. Artificial Intelligence, Machine Learning, & Robotics in Business respects the intellectual property of scholars, students, and publishers, and we ask that you secure appropriate permissions or evaluate whether your incorporation of images, figures, charts, quotations, and other materials falls within the scope of fair use/fair dealing.

If you are incorporating published materials that you have previously authored, be aware that in many cases your publisher may now own the copyright and you may need to seek permission to reprint your own work.

The University of Florida George A. Smathers Libraries provides resources on copyright and fair use, with an emphasis on U.S. Copyright Law: https://guides.uflib.ufl.edu/copyright

Correction, Retraction, and Removal of Articles

Correction. Despite the best of efforts, errors occur and their timely and effective remedy are considered the mark of responsible authors and editors.  Artificial Intelligence, Machine Learning, & Robotics in Business will publish a correction if the scholarly record is seriously affected (e.g., if accuracy/intended meaning, scientific reproducibility, author reputation, or journal reputation is judged to be compromised). Corrections that do not affect the contribution in a material way or significantly alter the reader's understanding of the contribution, such as misspellings or grammatical errors, will not be published. When a correction is published, it will link to and from the work. The correction will be added to the original work so that readers will receive the original work and the correction. All corrections will be as concise as possible.

Retraction. Artificial Intelligence, Machine Learning, & Robotics in Business reserves the right to retract items, with a retraction defined as a public disavowal, not an erasure or removal. Retractions will occur if the editors and editorial board finds that the main conclusion of the work is undermined or if subsequent information about the work comes to light of which the authors or the editors were not aware at the time of publication. Infringements of professional ethical codes, such as multiple submission, inaccurate claims of authorship, plagiarism, fraudulent use of data will also result in retraction of the work.

Removal. Some circumstances may necessitate removal of a work from Artificial Intelligence, Machine Learning, & Robotics in Business. This will occur when the article is judged by the editors and editorial board to be defamatory, if it infringes on legal rights, or if there is a reasonable expectation that it will be subject to a court order. The bibliographic information about the work will be retained online, but the work will no longer be available through Artificial Intelligence, Machine Learning, & Robotics in Business. A note will be added to indicate that the item was removed for legal reasons.

Data-Sharing Policy: Authors of research papers submitted for publication in Artificial Intelligence, Machine Learning, & Robotics in Business are encouraged to make the data underlying their articles available online whenever possible. For the purposes of this policy, the term "data" is understood broadly and refers to both quantitative and qualitative research outputs, spanning observations and analysis of social settings (producing numbers, texts, images, multimedia or other content) to numbers attained through instrumental and other raw data gathering efforts, quantitative analysis, text mining, or citation analysis, as well as protocols, methods, and code used to generate any specific finding reported in the paper. The Artificial Intelligence, Machine Learning, & Robotics in Business editorial board prefers that the data be submitted as supplemental files accompanying the article or be archived in a secure repository that provides a persistent identifier, assures long-term access, and provides sufficient documentation and metadata to support re-use by other investigators. Acceptable solutions include institutional repositories; repositories specifically focused on data curation, or domain specific repositories. If there is no relevant public repository available, and the data cannot easily be included in a supplement, authors should describe how the data are being curated and made available or, in the case where they cannot be made available (e.g., IRB restrictions), why that is so. In any case, a citation to the dataset should be made in the article itself in accordance with the data citation principles of the FORCE11 "Joint Declaration of Data Citation Principles", including an ORCID for the researcher(s) associated with the data. Finally, we recommend that whenever possible authors explicitly define the terms of re-use by assigning a license to their data, choosing, for instance, among Creative Commons or Open Data Commons licenses.

The Artificial Intelligence, Machine Learning, & Robotics in Business data policy does not require data publication and citation at this time due to still-emergent standards for data peer review; the lack of sufficiently robust and distributed infrastructure to support the variety of disciplinary research occurring in our field; uncertainty whether Artificial Intelligence, Machine Learning, & Robotics in Business should provide a third mode of data publication in the form of “data papers” or “data descriptors”; and insufficient preparation and notification to Artificial Intelligence, Machine Learning, & Robotics in Business contributors to ensure datasets are properly curated with the aim of publication. Authors unable to share their data must provide a written explanation of this circumstance in their cover letter at the time of submission.

Name Change Policy

The LibraryPress@UF is committed to supporting requests for author name changes and/or pronoun changes, with as few barriers as possible. Name changes and/or pronoun changes are available to authors upon request, with no legal documentation required. Upon receiving a name change request, the LibraryPress@UF will update all metadata, published content, and associated records under our control to reflect the requested name change. The LibraryPress@UF will not issue a notice of correction for the name change or notify co-authors or editors.

Authors who wish to update or change their name should contact Kat Nguyen, Publications Editorial Coordinator, at knguyen1@ufl.edu. Requests will be treated with respect and confidentiality and addressed as quickly as possible.

 Copyright Notice (click-through notice upon submission)

By submitting to Artificial Intelligence, Machine Learning, & Robotics in Business, the author(s) agree to the terms of the Author Agreement. All authors retain copyrights associated with their article contributions and agree to make such contributions available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license upon publication.

Author Agreement:

Florida OJ Author Agreement (University of Florida)

This agreement takes effect upon acceptance of the Submission entitled ______________ ("Submission") for publication in Artificial Intelligence, Machine Learning, & Robotics in Business.

  • I hereby grant to the University of Florida (“the University”) the non-exclusive right to retain, reproduce and distribute the Submission in whole or in part, in print and electronic format and in any medium. This agreement does not represent a transfer of copyright to the University.
  • The University may make and keep multiple copies of the work for purposes of security, backup, preservation and access; and may migrate the work to any medium or format for the purpose of preservation and access.
  • I represent and warrant to the University that the work is my original work and that I have the authority as sole author or I have the authority on behalf of my co-authors to grant the rights contained in this agreement. I also represent that the work does not, to the best of my knowledge, infringe or violate any rights of others.
  • I further represent and warrant that I have obtained all necessary rights to permit the University to reproduce and distribute the work, including any third-party material. Alternatively, I represent that my use of any third-party material is allowed because the material is not in copyright or I have performed a fair use analysis and reasonably believe my use is permitted. Any content owned by a third party is clearly identified and acknowledged within the work.
  • I grant these same rights to Artificial Intelligence, Machine Learning, & Robotics in Business. Additionally, I grant the right to both the University and Artificial Intelligence, Machine Learning, & Robotics in Business to enter into agreements with third-party entities and the rights necessary to host, print, index and abstract the Submission.

Open Access and Self-Archiving

Artificial Intelligence, Machine Learning, & Robotics in Business follows an open-access publishing model, meaning that all articles will be publicly accessible on the Internet immediately upon publication. I understand that I may share the submitted manuscript (preprint) of the Submission on the Internet at any point before or after publication, with a citation and link to the final version of record to be added as soon as the issue is available. I may disseminate the final peer-reviewed version at any point after publication.

Creative Commons License

Artificial Intelligence, Machine Learning, & Robotics in Business applies a Creative Commons [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)] to encourage sharing and reuse of content and to maximize the impact of published research. By publishing in Artificial Intelligence, Machine Learning, & Robotics in Business, I agree that the terms of this license will be applied to the Submission. Smathers Libraries (copyright@uflib.ufl.edu) may be able to offer additional information.

By granting this license, I acknowledge that I have read and agreed to the terms of this agreement.

Conflict/Competing of Interest Statement 

Conflict of interest exists when a participant in the peer review and publication process as an author, reviewer, or editor has ties to activities that could inappropriately influence their judgment about the validity of submissions. Therefore, Artificial Intelligence, Machine Learning, and Robotics in Business requires all authors and reviewers to declare any conflicts of interest that may be inherent in their submissions. For example, financial relationships with industry through employment, consultancies, stock ownership, honoraria, expert testimony, either directly or through immediate family, are usually considered to be conflicts of interest. However, conflicts can occur for other reasons, such as personal relationships, academic competition, and intellectual passion. Public trust in the peer review process and the credibility of published articles depend in part on how well conflict of interest is handled during writing, peer review, and editorial decision making. Bias can often be identified and mitigated by careful attention to the methods and conclusions of the work. Financial relationships and their effects are less easily detected than other conflicts of interest. Participants in peer review and publication should disclose their competing interests, and the information should be made available so that others can judge their potential effects for themselves. 

  • Authors: Upon submission, authors are responsible for recognizing and disclosing financial and other conflicts of interest that might bias their work, or lack thereof. They should acknowledge in the manuscript all financial support for the work and other financial or personal connections to the work. They should also provide a list of potential reviewers for which there is a likely conflict, so editors are able to avoid inappropriate review requests.  
  • Reviewers: External peer reviewers should disclose to editors any conflicts of interest that could bias their opinions of the submission, and they should disqualify themselves from reviewing specific manuscripts if they believe it appropriate. Additionally, reviewers are forbidden from using knowledge of the work, before its publication, to further their own interests.