Projects today unfold in data‑rich, technology‑mediated contexts, yet the central managerial challenge is not technical implementation—it is translating data into better decisions, governance, and value. This Special Issue foregrounds the managerial implications of data science for project, programme and portfolio management: how leaders plan, steer, assure, and realise benefits in environments saturated with real‑time information and algorithmic insights.
We seek contributions that illuminate how data‑enabled practices reshape core management processes across the lifecycle—planning and prioritisation, monitoring and control, risk and opportunity management, stakeholder engagement, and benefits realisation—without dwelling on algorithmic detail. Of particular interest are studies that show how data improves decision quality, cadence, and accountability; how governance mechanisms (roles, rights, controls) adapt to data‑driven work; and how competencies for managers and teams evolve in agile, traditional, and hybrid settings.
The timing is critical. AI‑enabled dashboards, autonomous forecasting, and project “bots” are diffusing rapidly, and professional standards and curricula are moving to embed data literacy. Yet organisations still struggle to integrate these capabilities into managerial routines that are ethical, explainable, and strategically aligned. We therefore invite research that surfaces success factors and limits: adoption conditions, change‑management levers, safeguards for bias and privacy, and mechanisms for transparency and accountability in decision‑making.
Distinct from prior collections on AI or digital transformation, this Special Issue positions data science as a discipline‑wide managerial transformation rather than a technical add‑on. We welcome conceptual frameworks, theory‑building, and empirically grounded cases that produce actionable artifacts for managers—decision frameworks, governance templates, capability and maturity models, performance and benefits metrics, and implementation roadmaps. Cross‑sector studies (e.g., IT, construction, healthcare, sustainability) that demonstrate tangible managerial outcomes are especially encouraged, as are works connecting project practices to organisational strategy and to broader societal value, including SDG‑aligned impact.
Submissions should prioritise managerial insight, theoretical contribution, and practice guidance over technical expositions of algorithms or code. By consolidating a fragmented discourse and bridging project studies with data‑informed management practice, the Special Issue aims to equip leaders with the concepts, capabilities, and governance mechanisms required to steer projects more effectively in the age of data.
List of Topic Areas
Data-driven decision-making in project environments: Exploring how data science enhances or transforms decision processes across the project lifecycle, including planning, monitoring, and risk management emphasizing chances, challenges and risk.
Integration of data science methodologies into project management practices: Studies applying or adapting methods such as machine learning, data mining, or predictive analytics to project-specific contexts.
Data science and project governance, ethics, and accountability: Examining the implications of algorithmic decision-making, data privacy, and ethical use of data in managing projects.
The evolving role of the project manager in data-rich contexts: Investigating new competencies, skills, and leadership styles required for managing data-intensive or AI-augmented projects. Discussing the relationship between project managers and data science experts in project management.
Real-time data, dashboards and analytics for project monitoring and control: Contributions on the design and limits, use and evaluation of real-time performance tracking and visualisation tools in project management.
Data ecosystems and infrastructure in large-scale or complex projects: Studies addressing the architecture, integration, and governance of data sources across distributed project networks.
Cross-disciplinary applications of data science in projects: Interdisciplinary work linking project management with fields such as operations research, information systems, computational social science, or engineering
AI, big data and automation in project scheduling, forecasting, and resource allocation: Empirical or conceptual work on how intelligent systems are used to optimize project efficiency and adaptability.
Data-enabled project evaluation, benefits realisation, and impact assessment: Using data science to better measure and predict the value, outcomes, and sustainability of projects, including alignment with SDGs.
Theoretical and conceptual contributions linking data science and project studies: Frameworks and models that critically examine the intersection of data science and project management as evolving disciplines.
AI-empowered stakeholder engagement in large-scale or complex projects: Leveraging AI to enhance stakeholder analysis, communication, and collaboration in complex projects, optimizing engagement strategies and improving project outcomes through predictive insights and automated interaction tools.
Submissions Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/ijmpb
Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/ijmpb
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Journal Information: Scopus Journal Q2, H-Index 54
Key Deadlines
Opening date for manuscripts submissions: 01/04/2026
Closing date for manuscripts submission: 31/01/2027
Closing date for abstract submission: 31/07/2026
Email for submissions: cmarnewick@uj.ac.za
For more details refer here
