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In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

The literature lacks a single and universally accepted definition of major and megaprojects: usually, these projects are described as projects with a budget above $1 billion and a high level of innovation, complexity, and uniqueness both in terms of physical infrastructure and stakeholder network. Moreover, they often provide fewer benefits than what were originally expected and are affected by delays and cost overruns. Despite this techno-economic magnitude, it is still extremely hard to gather lessons learned from these projects in a systematic way. The purpose of this paper is to present an innovative methodology based on benchmarking to investigate good and bad practices and learn from a portfolio of unique megaprojects.

The methodology combines quantitative and qualitative cross-comparison of case studies and statistical analysis into an iterative process.

Indeed, benchmarking offers significant potential to identify good and bad practices and improve the performance of project selection, planning, and delivery.

The methodology is exemplified in this paper using the case of Nuclear Decommissioning Projects and Programmes (NDPs).

Indeed, due to their characteristics, NDPs can be addressed as megaprojects, and are a relevant example for the application of the methodology presented here that collects and investigates the characteristics that mostly impact the performance of (mega)projects, through a continuous learning process.

The results of a research project dealing with knowledge management in project environments and the capability to transfer knowledge across projects teams are presented. A key distinction is made between generic project knowledge (kernel knowledge) and specific project knowledge (ephemeral knowledge). For each type of knowledge, knowledge management benchmarks are described and discussed. The empirical data used in this paper was collected from companies of various sizes operating in the manufacturing, construction and service sectors.

The purpose of this paper is to develop an integrated quality management model that identifies problems, suggests solutions, develops a framework for implementation and helps to evaluate dynamically healthcare service performance.

This study used the logical framework analysis (LFA) to improve the performance of healthcare service processes. LFA has three major steps – problems identification, solution derivation, and formation of a planning matrix for implementation. LFA has been applied in a case‐study environment to three acute healthcare services (Operating Room utilisation, Accident and Emergency, and Intensive Care) in order to demonstrate its effectiveness.

The paper finds that LFA is an effective method of quality management of hospital‐based healthcare services.

This study shows LFA application in three service processes in one hospital. This very limited population sample needs to be extended.

The proposed model can be implemented in hospital‐based healthcare services in order to improve performance. It may also be applied to other services.

Quality improvement in healthcare services is a complex and multi‐dimensional task. Although various quality management tools are routinely deployed for identifying quality issues in healthcare delivery, they are not without flaws. There is an absence of an integrated approach, which can identify and analyse issues, provide solutions to resolve those issues, develop a project management framework to implement those solutions. This study introduces an integrated and uniform quality management tool for healthcare services.

The purpose of the paper was to conduct an empirical investigation to explore the impact of project management maturity models (PMMMs) on improving project performance.

The investigation used a cross-case analysis involving over 90 individuals in seven organisations.

The findings of the empirical investigation indicate that PMMMs demonstrate very high levels of variability in individual's assessment of project management maturity. Furthermore, at higher levels of maturity, the type of performance improvement adopted following their application is related to the type of PMMM used in the assessment. The paradox of the unreliability of PMMMs and their widespread acceptance is resolved by calling upon the “wisdom of crowds” phenomenon which has implications for the use of maturity model assessments in other arena.

The investigation does have the usual issues associated with case research, but the steps that have been taken in the cross-case construction and analysis have improved the overall robustness and extendibility of the findings.

The tendency for PMMMs to shape improvements based on their own inherent structure needs further understanding.

The use of empirical methods to investigate the link between project maturity models and extant changes in project management performance is highly novel and the findings that result from this have added resonance.

The purpose of this paper is to question why current thinking towards project complexity ignores the role of objects in achieving social order and transformation. An alternative, but complementary, approach to address project complexities, drawing upon actor‐network theory (ANT), is offered to redress this concern.

Current thinking towards project complexity is briefly reviewed in the first section to illustrate the reasons why nonhumans are downplayed. An historical case study, the Skye road bridge project, is mobilized to explain, and develop, an ANT perspective on project complexities, and responses to such complexities.

ANT develops accounts of project complexity by highlighting the role of nonhumans in influencing how practitioners register, respond and stabilize project complexities. Front‐end planning and stakeholder analysis is shown to be only one narrow element of four moments through which actors apprehend and stabilize project complexities.

The empirical case study is developed to suggest some significant ways in which ANT could contribute, and complement, extant theories of project complexity. Alternative approaches to socio‐materiality are noted and may yield other important insights.

The paper positions ANT to offer a novel theory of project complexity. It is intended to be primarily of use to project management researchers, and theoretically informed practitioners, who are interested in developing fresh insights into notions of project complexities (unintended consequences, emergence and unpredictability).