Robust Protocols for Peer Review
Publication Guidelines
The Machine Leadership Journal anchors all publications against its peer review process, emphasizing originality, breadth of insight, and quality research.
01.
Submit Abstract
Proposals should be scholarly, insightful, comprehensive, and aligned with the emerging discipline of Machine Leadership. Submissions that include data analysis will require peer review to confirm reproducibility.
Content: 500 Words
Citation: APA Format
02.
Proposal Approval
Submissions authors that receive approval will be notified by The Machine Leadership Journal and a final proposal will be authorized by the IRB board to confirm the breadth, depth, and quality of the paper and citations.
Content: Full Proposal
Signed Ethics Statement
03.
Committee Review
Draft submissions are reviewed by the IRB Board to ensure the paper maintains authenticity, adequate citation (where applicable), robust insights, and alignment with the emerging field of Machine Leadership.
Content: Proposal data is subject to peer review
04.
Publication
Once the scholarly paper has been approved by the IRB Board, the research shall be included in The Machine Leadership Journal publication and indexed to Google Scholar and other academic databases.
Full Publication and DOI
Indexed globally
Global Editorial Board
The Machine Leadership Journal is led by an editorial board that consists of diverse backgrounds in AI, executive leadership, professors of practice, and luminaries. Their collective experience spans every geographic region, sector, industry, and AI use case. This allows our Editorial Board to utilize a truly global approach to supporting research findings that advance the field of Machine Leadership.
Our Editors are Listed Below (Alphabetical Order)
Research Associates
The Machine Leadership Journal relies on a robust peer review process to maintain high quality standards for thought leadership articles and DOI publications. The research team is responsible for managing the peer review process with the Editorial Board including the abstract and proposal review, committee decisions, citation and generative AI evaluation, and DOI assignment.
Our Editors are Listed Below (Alphabetical Order)
Call for Papers
“How do you lead an AI tool that is better at human tasks than you are?”
The Machine Leadership Journal is dedicated to advancing the emerging field of AI leadership. We are focused on identifying, developing, and sharing the best practices for AI strategy and adoption. This includes understanding the unique traits and cognitive processes that predict AI leadership success, the methods for integrating machines into the workforce, the process for safely introducing AI to children, and domain specific AI leadership practices that optimize use cases across industries. For our inaugural edition, we are seeking research studies and thought leadership articles that focus on five themes. Each area addresses a specific aspect of AI leadership, while providing a foundation for additional lines of inquiry. Though our Editorial Board maintains a robust peer review process, we welcome author(s) from a variety of professional and academic backgrounds, and AI experience.
The Machine Leadership Journal is seeking contributions for the emerging field of AI leadership. Details regarding topic areas and submission requirements are listed below.
Focus Area 1: AI Talent Assessments
Machines are a new species that requires leaders to have a broad set of skills. In the age of AI, leaders must be able to: 1) lead machines; 2) lead engineers that lead machines; and 3) lead hybrid organizations that adopt AI. Our Editorial Board is interested in research studies and/or thought leadership that highlights the unique competencies that are required to lead machines and AI teams. Topics may include but are not limited to: AI leadership traits, AI cognitive processes, talent assessment methods for AI leaders, and job role and readiness predictors for AI technical domains. Other focus areas could include unique AI leadership factors that impact key industries. The Board is interested in highlighting the similarities and differences between existing theory and emerging trends.
Focus Area 2: The AI Playground
The safe adoption of AI with children is a critical priority for society. This includes AI skills at each child development stage such as preschool, elementary, and adolescence. Our Editorial Board is interested in research studies and/or thought leadership that highlights methods, tools, or best practices for introducing AI to children. This may include but is not limited to: AI child development plans; child data privacy and cyber protection policies; toolkits for parents; and safe adoption practices. The Board is interested in methods for balancing child development between the physical and digital environments, ensuring optimal outcomes in both.
Focus Area 3: AI Strategy and Adoption
The ability to maximize AI within an organization or society are important practices. Our Board is interested in research studies and/or thought leadership that analyzes the process for effective adoption of AI. This may include but is not limited to: AI strategy development; managing the balance between Machine Autonomy and Trust; building AI competencies within the workforce; handling ethical AI dilemmas; data governance and privacy policies; keeping up with the pace of AI innovation; and leading change programs that introduce AI tools, platforms, and models. The Board is interested in approaches that balance productivity and automation with human centric design. Specifically, how companies can find the optimal approach to introducing AI?
Focus Area 4: AI Leadership for Specific Domains
AI leadership practices are strongly influenced by the industry and use case. Our Board is interested in research studies and/or thought leadership that highlights best practices for leading machines, engineers, and AI teams within specific contexts such as technical domains or industry use cases. This may include but is not limited to: AI leadership practices for the C-Suite; computer vision; robotics and autonomous vehicles; big data and MLOps; LLMs and Transformers; AI agents and Prompt Engineering; data governance and privacy; cyber security; and ethical AI. The Board is interested in AI leadership practices that impact the technical domain and specific industries. This includes AI leadership methods within technology, healthcare, telecom, energy, among others.
Focus Area 5: The Future of AI
The Future of AI is one where machines will be fully integrated into human society. Our Board is interested in research studies and/or thought leadership that forecasts potential pathways for innovation across all AI technical domains and use cases. This may include but is not limited to: AI enhancements to existing models such as LLMs and Transformers; new AI tools that will be introduced; scenarios where AI becomes further integrated into human life such as IoT and wearables; and new use cases impacting specific industries such as healthcare, finance, energy, among others. The Board is interested in visionary thinking that anchors projections based on historical adoption patterns, the current pace of AI innovation, and the potential pathways for society to adopt new AI tools.
Research Guidelines
Submission Process
Machine Leadership utilizes a four step process for publication in our journal. Researchers may submit individual inquiries or respond directly to Call for Papers. The first step is the abstract proposal. ORC IDs should be included in the submission, alongside relevant academic citations (where applicable), a summary of the research team's background, details regarding existing copyrights, and any affiliations. The abstract is reviewed for compliance by the Editorial Board and then a formal proposal is requested. During this stage, the research team is required to submit a complete overview of the paper. This includes the research method, data analysis techniques, tools and frameworks, ethical guidelines, potential covariants and excluded variables, and additional lines of inquiry. Machine Leadership will review the proposal to ensure the proposed findings from the paper can be reproduced, that the content conforms to ethical research principles, and the intended scope is relevant to the topic of Machine Leadership. The Committee reviews final proposals for breadth, depth, and quality. This includes determining whether peer review is needed and to what extent. Once the paper is submitted for final approval, the Editorial Board assigns a DOI and indexes the paper in an upcoming periodical of The Machine Leadership Journal.
On average, submissions take 4-6 months for Editorial review and publication.
Access and DOI Maintenance
Machine Leadership provides the journal for our members. Students receive subsidized access as part of their studies. Authors have the option to make their publications Open Access and assign the copyright to the public domain. Editorial review, DOI assignment and maintenance, and marketing are completed by the company. Our goal is to advance the field of Machine Leadership, AI Strategy and Adoption through scholarly research and innovative collaboration. Our rigorous methodology allows students, professors, and corporate practitioners to understand the latest trends impacting their domains. That said, our Editorial standards remain rigorous and in line with similar technical and business journals. We aim to publish on a quarterly basis. However, we may provide more frequent research due to emerging trends or benchmarking studies.
Since Machine Leadership is an emerging field, we recognize that many voices, luminaries, and thought leaders can positively contribute to its advancement. Therefore, we do not place restrictions on the affiliations or research team experience in order to submit abstracts. Each proposal is reviewed on its merits for novelty, industry relevance, and reproducibility of data findings.
Copyright and related IP
The Machine Leadership Journal takes seriously copyright protections and academic citation. Researchers are required to properly cite preexisting frameworks, methods, tools, and models using APA format. Failure to do so will result in the publication being removed and a correction issued. In situations where a published article is suspected of copyright infringement or plagiarism, we encourage researchers to contact us at: research@machineleadership.com. Researchers that publish with our journal are required to sign an IP policy, DOI maintenance, ethical research guidelines, and publication process.
Use of Data
Machine Leadership complies with relevant data privacy regulations including GDPR, CCPA, The Texas Data Privacy Security Act, among others. Please review our privacy policy for more details. In situations where PII data is impacted, our Editorial Board will review the study and ensure the paper confirms to our data privacy guidelines and all relevant industry regulations. At times, Machine Leadership will utilize study results for benchmarking purposes. Any data that is used will be in aggregate form and shared with study participants in advance.
Research Grants and Sponsorship
Machine Leadership provides research grants and sponsorship for proposals that advance scholarly research within our topic area. To be eligible to apply, researchers must be enrolled as members and meet our ethical guidelines. Given the volume of requests, the Journal does not publicly advertise research grants. The Editorial Board establishes a unique submission process for each award.
For more information, please contact us at: research@machineleadership.com