Call for Papers: Advancing project management through data science: methods, applications and impacts Free

Data science is increasingly indispensable to project management, enabling evidence-based planning, execution, monitoring, and control across the project lifecycle (Project Management Institute, 2024; Singh, 2015). Integrating predictive analytics and machine learning allows teams to forecast schedule slippage, cost overruns, and emergent risks, enabling proactive intervention and smarter resource allocation (Bauskar et al., 2024; Singh, 2015). Empirical guidance and studies show that data-driven decisions improve project performance relative to intuition-based approaches (Bauskar et al., 2024). Embedding analytics within PMOs strengthens governance and benefits realization by linking leading indicators to strategic outcomes and continuous learning (Project Management Institute, 2024; Singh, 2015)

This Special Issue seeks to explore and advance the intersection of data science and project management by promoting the integration of data science into practice – showcasing how techniques such as machine learning, predictive analytics, and big data can enhance project planning, execution, monitoring, and control. It fosters methodological innovation by encouraging contributions that develop or adapt data science approaches specifically for project environments, including agile, traditional, and hybrid settings. The issue highlights real-world applications and impacts through empirical studies, case analyses, and industry insights that surface the tangible benefits, challenges, and boundaries of applying data science across domains such as IT, construction, healthcare, and sustainability. It also bridges theory and practice by advancing understanding of how data-driven decision-making transforms project processes, competencies, and success criteria, while encouraging interdisciplinary collaboration by drawing together project management and data science researchers to spur cross-disciplinary dialogue and innovation. Beyond tool adoption or algorithmic detail, the Special Issue aims to articulate how data-enabled decision processes, governance mechanisms, and capabilities reconfigure project value creation.

The intersection of data science and project management is emerging as a critical area of inquiry and practice, yet remains underexplored in mainstream project studies. As organizations increasingly operate in data-rich (Çelik, 2020), technology-driven environments, project professionals are being called upon to make faster, evidence-based decisions using complex and dynamic data sources (Liu et al., 2024; Walker and Lloyd-Walker, 2019). Despite this shift, the project management discipline has only recently begun to engage seriously with the tools, methods, and theoretical implications of data science.

While isolated studies have begun to explore the application of predictive analytics (Almalki, 2025; Hammad et al., 2020), artificial intelligence (Mariani et al., 2023; Wang et al., 2012), and big data (Marnewick and Marnewick, 2024; Monageng et al., 2024; Olsson and Bull-Berg, 2015; Whyte et al., 2016) in projects, there is currently no cohesive body of knowledge that systematically investigates the integration of data science into project practices. Most contributions remain fragmented across disciplines (e.g. information systems, operations research, sociology, and engineering), and there is a lack of frameworks or empirical studies that examine the organizational, managerial, and methodological implications of data-driven project management (Shen et al., 2024). Research in the project community has a crucial role in addressing these knowledge needs to enable project practice to fully benefit from the transformative benefits of data analytics. It is necessary to boost the “technical” perspective that has not received enough attention in project management research. This Special Issue addresses this gap by offering a dedicated scholarly space to understand:

1. How data science reshapes traditional project roles, tools, and processes?

2. What new competencies are required from project professionals?

3. How project outcomes, benefits realization, and governance can be improved through intelligent data use?

4. What precautions should be taken when using data science?

The proposal is timely for several reasons:

1. The post-pandemic shift to digitalization has accelerated the demand for data-driven agility in project environments.

2. The rise of AI-enabled project tools (e.g. project bots, autonomous forecasting, real-time risk dashboards) requires theoretical grounding and critical evaluation.

3. Both academic programs and professional bodies are beginning to revise curricula and standards to include data literacy and analytics in project management.

This SI stimulates critical rethinking of the boundaries of project management as a discipline, enabling it to evolve in response to digital transformation. It also provides novel insights and frameworks that can inform project governance, strategic alignment, performance management, and benefits realization in the age of data.

While previous Special Issues in IJMPB and related journals have addressed themes such as digital transformation, project analytics, or AI in projects, none have explicitly focused on the broader ecosystem of data science as it applies to the full project life cycle.

This proposal is distinct in that:

1. It embraces the full spectrum of data science – from machine learning and natural language processing to data visualization and decision support systems – within the context of project strategy, delivery, and governance.

2. It invites cross-disciplinary contributions from project scholars, data scientists, and practitioners to foster a more integrated and critical understanding of this convergence.

3. It addresses both conceptual and applied dimensions, creating space for theoretical innovation, technical development, and real-world impact.

By framing data science not just as a technical add-on but as a transformative force in project thinking, this Special Issue offers a novel and necessary contribution to advancing the field of project management.

This Special Issue (SI) offers novelty and originality by integrating the full data science ecosystem into project management scholarship and explicitly targeting holistic, discipline-wide transformation, rather than the partial treatments of AI, analytics, or digital tools seen in prior work. It addresses clear empirical gaps and methodological needs. Despite strong interest in analytics, surveys indicate that 82% of data science teams lack formal project methodologies and only about 25% employ explicit data science frameworks, leaving no coherent methodological backbone for managing data-intensive projects. Accordingly, the SI aims to stimulate research on integrated lifecycle models that unite CRISP-DM, agile practices, governance, ethics, and real-time analytics within structured frameworks. The SI also expands the theoretical boundaries of project management by moving beyond narrow domain applications (e.g. construction forecasting or risk) to examine how data science transforms classic constructs such as stakeholder engagement, governance, sustainability, and value creation, while encouraging theorization around data ecosystems, algorithmic project roles, and data-driven decision hierarchies. To bridge disciplines, it fosters transdisciplinary dialogue between project scholars and data scientists and incorporates critical theory to reflect concerns about data justice and ethics. Finally, the SI emphasizes societal and practical value by confronting contemporary challenges – recognizing that improved sustainability and outcomes require robust data-driven governance in sectors like disaster recovery, public health, and infrastructure, even as it acknowledges risks such as misuse, bias, widening inequalities (the “data divide”), and an over reliance on easily documented facts (“what's not in the data does not exist”). By advancing well-considered, data-inclusive project standards, the SI supports the UN Sustainable Development Goals, including industry innovation (SDG 9) and institutional transparency (SDG 16).

The proposed Special Issue addresses a critical gap in project management literature by integrating data science not merely as a technical tool but as a transformative force with strategic, methodological, and societal implications. Despite increasing interest in AI, predictive analytics, and big data, current studies remain fragmented, lacking a cohesive framework that connects data science to core project processes such as governance, decision-making, and benefits realization. Recent global events have underscored the urgency for real-time, data-enabled project management, yet many organizations still struggle to harness data effectively. This SI offers a platform to explore the impacts of data science on project roles, ethics, performance, and long-term value. It also responds to pressing societal challenges and aligns directly with several UN Sustainable Development Goals, including SDG 9 (Industry, Innovation and Infrastructure), SDG 16 (Peace, Justice and Strong Institutions), SDG 4 (Quality Education), and SDG 17 (Partnerships for the Goals). By encouraging cross-disciplinary engagement and emphasizing the human and ethical dimensions of data use, this SI aims to shift the boundaries of project management thinking and practice in a data-intensive world.

The themes map the ways data science intersects with project management, e.g. linking decision-making, methodology, governance, competence, tools, and impact. The themes reflect practical needs, theoretical gaps, and ethical concerns, while deliberately remaining broad to encourage interdisciplinary approaches from information systems, operations research, sociology, and engineering. Together the themes support inquiry into both micro-level changes (team skills, dashboards, automation) and macro-level shifts (ecosystems, benefits realization, SDG-aligned impact), enabling contributors to advance frameworks, empirical evidence, and practice guidance that directly address contemporary challenges in data-rich project environments.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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 visualization tools in project management.

6. Data ecosystems and infrastructure in large-scale or complex projects: Studies addressing the architecture, integration, and governance of data sources across distributed project networks.

7. Cross-disciplinary applications of data science in projects: Interdisciplinary work linking project management with fields such as information systems, operations research, sociology, and engineering.

8. 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.

9. Data-enabled project evaluation, benefits realization, and impact assessment: Using data science to better measure and predict the value, outcomes, and sustainability of projects, including alignment with SDGs.

10. 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.

11. 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.

1. Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/ijmpb.

2. 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”.

3. Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

4. Scope reminder: This SI does not consider papers whose primary contribution is algorithmic novelty, code, or model derivations; emphasis should be on managerial/theoretical insight and empirical relevance.

5. Methodological guidance: We encourage empirically grounded studies (e.g. multi-case studies, surveys, longitudinal fieldwork, secondary/archival analyses, panel data, realist evaluation, QCA, action/design-science producing managerial artefacts). Mixed-methods and conceptual/theory-building papers are welcome. Submissions should emphasize managerial insights; model development or code-centric contributions are out of scope.

6. Key deadlines

   • Opening date for abstract submissions: 1 April 2026

   • Closing date for abstract submission: 31 July 2026

   • Notification of abstract acceptance for full paper submission: 31 August 2026

   • Closing date for manuscript submission: 31 January 2027

7. Authors wishing to submit papers should submit an extended abstract (1,500 words). The submitted document must cover four components of the research:

   • Relevance of the problem (a description of the real-world phenomenon and the need for research),

   • Theoretical underpinning of the research

   • Methodology (a clear description of the research design steps and a description of the data), and

   • Expected theoretical/practical contributions to the discipline of project studies.

8. Authors should submit extended abstracts to Prof Carl Marnewick (cmarnewick@uj.ac.za). Please use the exact title of the call and the journal in the subject line of the e-mail. Guest editors will review the proposals and contact authors with their recommendations. If the proposal is accepted, author(s) must submit the full paper before the deadline for submission. Selected authors will be invited to attend a Paper Development Workshop before the submission deadline. While submitting an extended abstract and attending the PDW is highly recommended, it is not a prerequisite for paper submission.

Generally, for the SI to be completed, we expect approximately two years. However, once a paper will be accepted for publication, it will be made available online before entering the Special Collection.