About the Journal

The Journal of Business and Data Analytics (JBDA) is a peer-reviewed, open-access journal devoted to research at the intersection of business and data analytics. It publishes work that develops, applies, or critically examines analytical methods in the service of business decision-making, and that connects the technical advances of data science with the questions that managers, organizations, markets, and policymakers actually face.

Mission

Data has become the medium through which most business decisions are now made, yet the literatures that study this transformation remain divided. Research on analytical methods is often published without attention to the organizational settings in which those methods are used, while research on management and markets often treats analytics as a black box. JBDA exists to bring these strands together. The journal provides a forum for rigorous, reproducible research that shows how data and analytical models change what businesses know, how they decide, and how well they perform, and that is equally credible to methodologists and to the practitioners whose decisions are at stake.

The journal's mission has three parts: to advance the methods of business analytics; to build a cumulative body of empirical evidence on how analytics is adopted and what it achieves; and to translate that knowledge into insight that practitioners, educators, and policymakers can use.

Editorial Philosophy

Five principles guide the journal's editorial judgment.

Rigor with relevance. JBDA expects sound research design, appropriate methods, and honest reporting of results, but rigor alone is not enough. Every article should make clear why its question matters to business practice and what a decision-maker would do differently because of it.

Reproducibility and transparency. Analytical claims are only as credible as the ability of others to check them. The journal encourages authors to share data, code, and model specifications wherever possible, to describe data preparation and modeling choices in full, and to report results, including negative and null findings, without selective emphasis.

Interdisciplinarity. The most useful work in business analytics draws on several fields at once: statistics and machine learning, operations research, economics, accounting and finance, marketing, information systems, organizational behavior, and management strategy. JBDA welcomes research that crosses these boundaries and asks authors to write so that readers from adjacent disciplines can follow the argument.

Responsible analytics. Data-driven decisions carry consequences for customers, employees, markets, and society. The journal gives weight to research that addresses fairness, explainability, privacy, governance, and the limits of algorithmic decision-making, and it expects all authors to reflect on the ethical dimensions of their work.

Practical impact. JBDA values research grounded in real organizational data and real decision problems. Case studies, field experiments, and industry collaborations are welcome alongside methodological and theoretical contributions, provided they meet the same standards of evidence.

Scope

JBDA publishes research across the full range of business analytics and its applications. Areas of interest include, but are not limited to:

Business intelligence and decision support: data warehousing, reporting and dashboards, decision support systems, and the organizational use of analytics.
Predictive and prescriptive analytics: forecasting, optimization, simulation, and recommendation systems applied to business problems.
Machine learning and artificial intelligence in business: supervised and unsupervised learning, deep learning, natural language processing, and generative AI in management, marketing, finance, operations, and human resources, including model validation, generalizability, and deployment.
Big data strategy and infrastructure: data architecture, cloud analytics, data integration, and the strategic management of data as an organizational asset.
Financial and accounting analytics: financial forecasting, credit and risk modeling, fraud detection, audit analytics, and analytics in financial reporting and corporate governance.
Marketing and customer analytics: customer segmentation, churn and lifetime value modeling, pricing, digital marketing attribution, and consumer behavior analysis.
Operations and supply chain analytics: demand planning, inventory and logistics optimization, quality analytics, and resilience of supply networks.
Economic and market analytics: econometric and data-driven analysis of firms, industries, markets, and economic policy, including emerging and developing economies.
Data visualization and communication: visual analytics, storytelling with data, and the presentation of analytical results to decision-makers.
Algorithmic decision-making, ethics, and governance: fairness, accountability, transparency, explainability, privacy, regulation, and data governance frameworks.
Analytics education and capability: curricula, skills, analytics maturity, and the organizational adoption of data-driven practices.

Article Types

The journal publishes the following types of article:

Original research articles – empirical, methodological, or theoretical studies presenting new findings or new analytical approaches.
Review articles – systematic reviews, scoping reviews, and critical syntheses of the literature in an area of business analytics, with a transparent search and appraisal method.
Case studies – detailed accounts of the application of analytics in a specific organization or decision context, with clear lessons for research and practice.
Expert commentaries – invited or proposed perspectives on emerging developments, controversies, or the future direction of the field.

Manuscripts should be written in English and should follow the Author Guidelines. Authors who are unsure whether their work fits the journal's scope are welcome to contact the editorial office with a presubmission inquiry.

Standards of Evidence and Reporting

JBDA holds all articles to the standards appropriate to their design:

Empirical studies should describe data sources, sampling, variable construction, and data cleaning in enough detail for the study to be reproduced, and should report effect sizes, uncertainty, and robustness checks alongside statistical significance.
Machine learning studies should specify the train, validation, and test procedure; the handling of class imbalance, leakage, and hyperparameters; the baselines used for comparison; and the conditions under which reported performance can be expected to generalize.
Reviews should follow an established reporting guideline, such as PRISMA 2020 for systematic reviews or PRISMA-ScR for scoping reviews, and should state how the literature was identified, selected, and assessed.
Authors are encouraged to deposit data and code in a public repository and to cite the deposit in the article. Where data cannot be shared for legal or commercial reasons, authors should explain the restriction and provide what can be shared, such as synthetic data, code, or aggregate statistics.
All manuscripts must include statements on author contributions, funding, conflicts of interest, data availability, ethical approval where human participants or personal data are involved, and the use of generative AI tools.

All submissions undergo double-blind peer review by independent experts. Reviewers assess not only technical soundness but also the clarity of the contribution and its value to research and practice. The journal follows the Core Practices of the Committee on Publication Ethics (COPE).

Readership

JBDA is written for researchers in business analytics, data science, and the management disciplines; for analytics professionals, data scientists, and managers who need evidence on what works; for educators developing analytics curricula; and for policymakers concerned with the economic and social effects of data-driven decision-making. By publishing research that is rigorous in method and clear about its implications, the journal aims to shorten the distance between analytical innovation and better business decisions.

Open Access

JBDA is a fully open-access journal. All articles are freely available immediately upon publication under the Creative Commons Attribution 4.0 International license (CC BY 4.0). Authors retain copyright of their work. There are no subscription charges and no charges to readers; publication costs are covered by an article processing charge, with waivers available under the conditions described in the Publication Fee Policy.

Journal Information

Title: Journal of Business and Data Analytics (JBDA)
Online ISSN: 3068-3661
Publisher: INATGI (Institute of Advanced Technology and Green Innovation LLC), Henrico, Virginia, USA
Editor-in-Chief: Lili Gai, PhD, The University of Texas Permian Basin, USA
Publication frequency: Quarterly (four issues per year)
Access model: Open access under CC BY 4.0
Peer review: Double-blind
Article processing charge: USD 500 (see Publication Fee Policy)
Language: English
Contact: editorial@inatgi.net