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The research problem in this study is how a client (as a project owner) should organise early stakeholder involvement and integration in the front-end phase of a project. This study aims to create normative managerial statements as propositions from the client's perspective and to combine them into a set of activities enabling efficient organisation in the front-end phase of a hospital construction project.

Action design research (ADR) was carried out in a large hospital construction project where the first author acted as an “involved researcher” and the other authors acted as “outside researchers”.

The authors created seven normative managerial propositions that were verified by the case project stakeholders and developed a managerial framework describing the client's essential stakeholder involvement and integration activities in the front-end phase of a hospital construction project based on these propositions. The authors have also depicted the subphases of the front-end phase: value definition phase in the client permanent organisation, value proposition phase in the client Programme Management Office (PMO) and finally development phase in the alliance organisation ending on the final investment decision.

The collaborative contract delivery model enables the early involvement and integration of stakeholders. It has been somewhat surprising to note the extent to which collaborative contracts change the client role in the project front-end. The results offer practical activities for how clients can manage front-end activities in collaborative contracts.

The case project offered a platform to analyse how the collaborative contract delivery model changes the emphasis of activities in the front-end of a project. One of the key benefits of collaborative contracts is that development, design and delivery occur partially in parallel, thereby enabling contributions from production to be included in the design and development. The benefit of having a real-life case under study provides the possibility to triangulate and analyse rich data, however limited by the qualitative case method.

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.

Contingency studies within logistics and supply chain management have shown a need for longitudinal studies on fit. The purpose of this paper is to investigate the logistics strategy from a process of establishing fit perspective.

A large Swedish building contractor's logistics strategy process was analysed using a longitudinal single-case study for a period of 11 years (2008–2019).

The case study reveals three main constraints to logistics strategy implementation: a dominant purchasing organisation, a lack of incentives and diverging top-management priorities. This suggests that logistics strategy fit is not a conscious choice determined by contextual factors.

Establishing fit is a continuous cycle of regaining fit between the logistics context and logistics strategy components. Fit can be achieved by a change to the logistics context or to logistics strategy components.

Logistics managers may need to opt for satisfactory fit in view of the costs incurred by changing strategy versus the benefits to be gained from a higher degree of fit.

This paper adopts a longitudinal case design to study the fit between the logistics context and strategy, adding to the body of knowledge on organisational design and strategy in logistics and supply chain management.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

This study aims to examine the impact of housing construction on single-family housing values and the implications for urban development.

To achieve this objective, the author used the difference-in-difference methodology to examine the effect of multifamily and single-family housing construction on surrounding single-family homes in Stockholm, Sweden. The author analysed data from approximately 480 housing construction projects between 2009 and 2014 and 17,000 single-family detached house transactions between 2005 and 2018.

The research found that multifamily construction projects did not affect the value of surrounding single-family homes, while single-family home construction had a negative impact. The author attributes this result to single-family housing projects typically located in areas with initially positive externalities, while multifamily housing projects are often located on the edge of areas with negative externalities before construction.

The research is limited by its focus on a specific geographic area and time frame, and future research could expand the scope to include other cities and regions and different periods. Additionally, further research could examine the impact of housing construction on other economic factors beyond housing values.

The research has practical implications for urban planners and policymakers. They should consider the potential negative impact of new single-family home construction on existing single-family housing areas while balancing the need for new housing in urban areas. By carefully evaluating construction locations, policymakers can create more sustainable, livable and equitable urban environments that benefit all members of society.

This research paper contributes to the field of housing economics by examining the impact of housing construction on single-family housing values in the context of urban development and climate change mitigation. Using a difference-in-difference methodology, the study provides evidence of the price effect of multifamily and single-family housing construction on surrounding single-family homes, which has important policy implications for urban planners and policymakers. By identifying the negative impact of single-family home construction on surrounding areas and highlighting the need for careful evaluation of construction locations, the research provides valuable insights for creating sustainable, livable and equitable urban environments that benefit all members of society.

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.

There is some evidence to suggest that the historical challenge associated with recruiting and retaining Black and Brown Science, Technology, Engineering and Math (STEM) collegians is tied to early their teaching and learning experiences in Mathematics. This paper describes an National Science Foundation (NSF) funded project (NSF #2151043) whose goal is to attract, prepare and retain math teachers of color in high need school districts ensure that those teachers remain in the field long enough to make a meaningful impact on the minds and hearts of BIPOC students who are often, extrinsically, and intrinsically, discouraged from pursuing careers in STEM professions.

This mixed-methods study, which began in the summer of 2023, seeks to recruit, prepare, support and retain nineteen (19) Black and Brown math teachers for two (2) high need urban school districts. The expectancy value theory will be used to explain the performance, persistence, and choices of the teachers, while grounded theory will be utilized to understand the impact of the intensive mentorship and wellness coaching that applied over the first year of their preservice preparation and subsequent in-service years.

Measures of project efficacy won’t begin until 2025 and as such there are no findings or implications to draw from for the study at this time.

The intention of this paper is to augment the body of knowledge on recruiting and retaining Black and Brown math teachers for urban schools where the need for quality STEM teachers is critical.

Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.

The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.

Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.

From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).

Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.

This study aims to examine the relationship between financial inclusion, CO₂ emissions and financial sustainability across 17 African countries.

Data were sourced from the World Development Indicators for the period 2004-2021. The study performs the principal component analysis, panel fixed effects model and quantile regression estimations to investigate the relationship between financial inclusion, CO₂ emissions and financial sustainability.

The study finds that an increase in automated teller machine (ATM) penetration rate, savings and credits increases CO₂ emissions. Findings also reveal that financial sustainability reduces financial inclusion, with significant negative effects on the conditional mean of CO₂ emissions and the conditional distribution of CO₂ emissions across quantiles.

This study is beneficial for policymakers, particularly in the age of digitalization and drive for low-carbon emissions, to develop green credits for energy players and investors to take up renewable and green energy projects characterized by high levels of carbon storage and carbon capture. Further, the banking sector’s credits and liquid assets should be used to finance alternative banking energy-related equipment and services, such as solar photovoltaic wireless ATMs, and fewer bank branches.