“Data‑Enabled Analytics for Insightful and Responsible Decision‑Making”

Call for Papers – Special Issue: “Data‑Enabled Analytics for Insightful and Responsible Decision‑Making”

Journal: Omega
Publisher: Elsevier
Impact Factor: 7.2 | CiteScore: 14.9
Submission deadline: 31 December 2026

Overview

This special issue explores how data‑enabled analytics—integrating efficiency analysis, causal inference, predictive and prescriptive models, explainable AI, and real‑time analytics—can generate actionable, responsible, and trustworthy decisions in complex environments. It emphasizes methods that move beyond siloed descriptive analytics toward decision‑oriented, integrated frameworks applicable across sectors (operations, healthcare, finance, public policy, sustainability, etc.).

Core Themes

1. Hybrid analytics frameworks

• Studies that integrate multiple methods, for example:

  • DEA (Data Envelopment Analysis) with machine learning, causal inference, or optimization.

  • Simulation‑based optimization with AI‑driven forecasts.

• Research on multi‑method architectures that address conflicting objectives (cost vs. equity, efficiency vs. fairness, speed vs. accuracy).

2. Causal and predictive modeling

• Causal‑plus‑predictive models that combine forecasting accuracy with interpretability and transparency (e.g., instrumental‑variable models, synthetic controls, double‑machine‑learning, structural‑time‑series).

• Applications that show how causal insights translate into actionable managerial or policy levers (e.g., which interventions most reliably change performance).

3. Performance and benchmarking analytics

• Beyond surface metrics: frameworks that uncover systemic inefficiencies, hidden bottlenecks, and improvement levers (e.g., benchmarking, efficiency‑frontier analysis, process‑mining–based diagnostics).

• Empirical studies demonstrating how analytics redesign processes, resource allocation, or service delivery in practice.

4. Fairness, ethics, and trustworthy AI

• Methods for bias detection and mitigation in predictive models (e.g., fairness‑aware machine learning, fairness metrics in DEA‑type benchmarks).

• Case studies on equitable resource allocation, privacy‑conscious analytics, and responsible deployment of AI in high‑stakes settings (healthcare, public services, finance, justice).

5. Game‑theoretic and strategic analytics

• Game‑theoretic models, mechanism design, and equilibrium analysis in data‑rich environments (e.g., competition, cooperation, auctions, two‑sided markets).

• Studies on strategic responses to algorithms and analytics by users, regulators, or rivals (e.g., signaling, moral hazard, principal‑agent settings).

6. Human‑centric and explainable decision support

• Designs that bridge complex models and human decision‑makers, including:

  • Explainable AI (XAI) interfaces.

  • Decision‑support systems that embed causal logic, uncertainty, and trade‑offs in an intuitive way.

• User studies evaluating how analytics tools affect perceived trust, adoption, and decision quality.

7. Real‑time and dynamic analytics

• Dynamic models that update as new data arrives (e.g., online learning, rolling‑forecast + optimization, adaptive control).

• Applications under uncertainty and changing contexts (e.g., crisis‑response, supply‑chain disruption, surge‑demand in healthcare).

8. Empirical validation and impact

• Rigor‑plus‑impact papers that demonstrate measurable organizational or societal outcomes (e.g., cost savings, quality improvements, fairness gains) via analytics.

• Industry‑collaboration studies, field experiments, or large‑scale implementations that show how analytics changes behaviors, policies, or resource‑use patterns.

Expected Contributions

• Methodological rigor with clear theoretical foundations and replication‑ready designs.

• Strong practical relevance, with explicit implications for managers, policymakers, or key stakeholders.

• Validation through empirical data, simulation, or real‑world deployment (e.g., proof‑of‑concept pilots, A/B tests, case studies).

Guest Editors

• Dariush Khezrimotlagh, Pennsylvania State University – Harrisburg, USA

• Joshua Ignatius, Aston Business School, Aston University, UK

Submission Details

• Submission window: Open 10 November 2025; deadline 31 December 2026.

• Submission platform: Omega Editorial Manager at:
  https://www.editorialmanager.com/omega/default.aspx

• When submitting, select the article type “VSI: Data‑Enabled Analytics” to link to the special issue.

• Follow the journal’s Guide for Authors:
  https://www.sciencedirect.com/journal/omega/publish/guide-for-authors

Why This Issue Matters

• The world of analytics is moving from “showing what happened” to “guiding what to do, for whom, and with which ethical trade‑offs.”

• This SI explicitly seeks design‑oriented, multi‑method work that connects advanced analytics (DEA, AI, causal inference, game‑theory) with insightful, transparent, and responsible decision‑making across high‑complexity, high‑stake settings.

ServiceSetu Academics — Premier Platform for Academic Opportunities & Research Collaboration

Visit official website of the publisher