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Earned value management systems (EVMS), also called integrated project and program management systems, have been greatly examined in the literature, which has typically focused on their technical aspects rather than social. This study aims to hypothesize that improving both the technical maturity of EVMS and the social environment elements of EVMS applications together will significantly impact project performance outcomes. For the first time, empirical evidence supports a strong relationship between EVMS maturity and environment.

Data was collected from 35 projects through four workshops, attended by 31 industry practitioners with an average of 19 years of EVMS experience. These experts, representing 23 organizations, provided over 2,800 data points on sociotechnical integration and performance outcomes, covering projects totaling $21.8 billion. Statistical analyses were performed to derive findings on the impact of technical maturity and social environment on project success.

The results show statistically significant differences in cost growth, compliance, meeting project objectives and business drivers and customer satisfaction, between projects with high EVMS maturity and environment and projects with poor EVMS maturity and environment. Moreover, the technical and social dimensions were found to be significantly correlated.

Key contributions include a novel and tested performance-driven framework to support integrated project management using EVMS. The adoption of this detailed assessment framework by government and industry is driving a paradigm shift in project management of some of the largest and most complex projects in the U.S.; specifically transitioning from a project assessment based upon a binary approach for EVMS technical maturity (i.e. compliant/noncompliant to standards) to a wide-ranging scale (i.e. 0–1,000) across two dimensions.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

Climate hazards in the form of cyclones are projected to become more intense under the pressures of future climate change. These changes represent a growing hazard to low lying coastal cities like Beira, Mozambique. In 2019, Beira experienced the devastating impact of Cyclone Idai. One of the many impacts resulting from this Cyclone was disrupted drinking water access. This investigation explores the distribution of Cyclone Idai’s impact on drinking water access via an environmental justice lens, exploring how preexisting water access characteristics may have predisposed households to the impacts of Cyclone Idai in Beria.

Relying on household survey data collected in Beira, the investigation applied a decision tree algorithm to investigate how drinking water disruption was distributed across the household survey sample using these preexisting vulnerabilities.

The investigation found that households that mainly relied upon piped water sources and experienced inconsistent access to water in the year prior to Cyclone Idai were more likely to experience disrupted drinking water access immediately after Cyclone Idai. The results indicate that residents in formal areas of Beira, largely reliant upon piped water supply, experienced higher rates of disrupted drinking water access following Cyclone Idai.

These findings question a commonly held assumption that informal areas are more vulnerable to climate hazards, like cyclones, than formal areas of a city. The findings support the inclusion of informal settlements in the design of climate change adaptation strategies.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Crowdfunding is an increasingly popular channel for project fundraising for entrepreneurial ventures. Such efforts require fundraisers to develop and manage a crowdfunding campaign over a period of time and several stages. Thus, the authors aim to identify the stages fundraisers go through in their crowdfunding campaign process and how their engagement evolves throughout this process.

Following a multiple case study research design analysing six successful campaigns, the current study suggests a taxonomy of stages the fundraisers go through in their crowdfunding campaign management process while identifying the types of engagement displayed and their relative intensity at each of these stages.

The study proposes a five-stage process framework (pre-launch, launch, mid-campaign, conclusion and post-campaign), accompanied by a series of propositions outlining the relative intensity of different types of engagement throughout this process. The authors show that engagement levels appear with high intensity at pre-launch, and to a lesser degree also at the post-launch stage while showing low intensity at the stages in between them. More specifically, cognitive and behavioural engagement are most prominent at the pre- and post-launch stages. Emotional engagement is highest during the launch, mid-launch and conclusion stages. And social engagement maintains moderate levels of intensity throughout the process.

This study focuses on the campaign process using engagement theory, thus identifying the differing engagement patterns throughout the dynamic crowdfunding campaign management process, not just in one part.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

Productivity increase is correlated with profitability, sustainability and competitiveness of the construction firms. Recent studies reveal that the primary causes of productivity decline are poor usage of scientific and technological advances, ineffective supervision strategies and poor apprenticeship facilities/opportunities. Accordingly, the purpose of this study was to evaluate how well construction supervisors can utilise fundamental science and technological concepts/ideas to increase the efficiency and productivity of construction activities.

A new strategic layout was designed with the use of potential training guide tools. Based on the designed layout, a new supervisory training programme was developed, and 62 construction supervisors were selected, trained and evaluated in line with six parts of competencies and the relevant learning domains. An assessment guide with different levels of descriptions and criteria was developed through literature analysis and expert interviews. The research tools were verified using comprehensive approaches.

The overall mean values of supervisors’ performance scores indicate proficient-level grades in the competency characteristics related to taking measurements, generating drawings/designs using manual techniques and computer-aided tools, involving Bill of Quantities (BOQ) preparations and preparing training plans/materials for improving the competencies of labourers on estimation, measurements and understanding drawings. Their proficiency was notably lower in the use of information and communication technology application tools in construction tasks compared to others. The findings point to a modern generalised guideline that establishes the ranges of supervisory attributes associated with science and technology-related applications.

The study outcomes produce conceptualised projections to restructure and revalue the job functions of various working categories by adding new definitions within the specified scope. This may result in constructive benefits to upgrading the current functions associated with urbanisation, sustainability and society. The implementation of the study’s findings/conclusions will have a significant impact on present and future practices in other developing nations and developing industries, even if they are directly applicable to the Sri Lankan construction industry.

Up to certain limits/stages, the study fills not only the knowledge gap in the field of creating protocols and application techniques connected to lifelong learning and skill enhancement/upgrading but also the existing gaps in work attributes and roles of construction supervisors associated with the utilisation of fundamental science and technological concepts/ideas towards reinforcing sustainable and productive site operations.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

Studies show that building information modelling (BIM) technology can improve construction productivity regarding the design, construction and maintenance of a project life cycle in the 21st century. Revit has been identified as a frequently used tool for delivering BIM in the built environment. Studies about BIM technology via Revit are scarce in training middle-level workforce higher education institutions. Thus, this study aims to investigate the relevance of BIM technology and offer measures to promote digitalisation in Nigeria’s built environment polytechnic undergraduates via Revit.

Given the unexplored nature of training the middle-level workforce in Nigeria, 37 semi-structured virtual interviews were conducted across Nigeria, and saturation was achieved. The participants were knowledgeable about construction-related BIM. The researchers used a thematic analysis for the collected data and honed them with secondary sources.

Improved visualisation of design, effective and efficient work productivity, automatic design and quantification, improved database management and collaboration and data storage in the centrally coordinated model, among others, emerged as BIM’s benefits. BIM technology via Revit is challenging, especially in Nigeria’s polytechnic education curriculum. The 24 perceived issues were grouped into government/regulatory agencies-related, polytechnic management-related and polytechnic undergraduate students-related hindrances in Nigeria’s built environment.

This study is limited to BIM implications for Nigeria’s built environment polytechnic undergraduates.

This study contributes to the literature paucity in attempting to uncover perceived issues hindering the implementation of BIM technology via Revit in training Nigeria’s built environment polytechnic undergraduates via a qualitative approach.

This paper aims to present insights on the governance of sustainability transitions in higher education in Africa. The authors interrogate the research literatures on the governance of socio-technical transitions in water, electricity, transport and waste management, and identify barriers and enabling factors that enhance transformative practices in universities.

The analytical framework proposed in this paper combines the elements of governance network theory (GNT) and transition topology. The framework of this study is grounded in an actor-centric approach using GNT to understand networks conducive to sustainability transitions. Events and governance networks were mapped on a transition topology to visualise organisational and institutional changes over time. The study engaged students, management, academic and administrative staff in building a community of practice towards sustainability. This research is based on qualitative content analysis grounded in interview data, focus group discussions, workshops, webinars and secondary data analysis.

The findings show that the university has consolidated a sustainability vision and targets, but several factors prevent the community from achieving these targets, including hierarchical decision-making processes, a multitude of disjointed committees and fragmentation in the campus community.

This research adds to an emerging body of literature in the field of sustainability in higher education with two contributions. Firstly, the study presents a novel perspective(s) on the governance of sustainability transitions by combining the literatures on governance and sustainability transitions using a new methodological approach of transition topology to show organisational and institutional changes. Secondly, the study presents new empirical evidence for improving the governance of sustainability transitions in a diverse and highly unequal African university community in the process of (de)colonisation of knowledge and governance.