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To investigate how providers of product-service bundles design and manage their contracts with upstream suppliers to incentivise incremental innovation for the benefit of their downstream customers, who contract the provider based on performance.

An embedded multiple-case study was conducted to examine elements of a European jet fighter’s manufacturing and after-sales supply chain. The embedded cases concern provider contracts with first-tier suppliers of product and service offerings. Data collection involved 21 semi-structured interviews, documents and other secondary data sources. Data analysis was informed by agency theory to assess the effectiveness of contract design and management in delivering incremental innovation and to identify related contracting strategies.

We identify four strategies for fostering incremental innovation in contracts between providers and their first-tier suppliers. These include two contract design strategies, i.e. reducing goal incongruence and addressing information asymmetry; and two contract management strategies, i.e. reducing outcome uncertainty and promoting inter-firm integration between providers and sub-suppliers.

The research offers managerial guidelines regarding how providers can design and manage their tier-one supplier contracts to achieve incremental innovation. These include encouraging early supplier involvement, using focussed KPIs in contracts, and managing product and service-offering suppliers differently.

The research shows the contingent effect during contract design and management of a sub-supplier’s product vs. service offering, which, in turn, impacts incremental innovation. We also find that using focussed key performance indicators in sub-supplier contracts can be effective in improving product and service quality.

While stress, anxiety and depression rank as the second leading cause of work-related ill health in the UK construction sector, there exists a scarcity of empirical studies explicitly focused on investigating the sources of occupational stress among construction workers and professionals at both the construction project and supply chain levels. This study seeks to identify and investigate the primary stressors (sources of stress) in UK construction projects and to propose effective strategies for preventing or reducing stress in this context.

The study adopted a qualitative multi-methods research approach, comprising the use of a comprehensive literature review, case study interviews and a focus group. It utilised an integrated deductive-inductive approach theory building using NVivo software. In total, 19 in-depth interviews were conducted as part of the case-study with a well-rounded sample of construction professionals and trade supervisors, followed by a focus group with 12 policy influencers and sector stakeholders to evaluate the quality and transferability of the findings of the study.

The results reveal seven main stressors and 35 influencing factors within these 7 areas of stress in a UK construction project, with “workflow interruptions” emerging as the predominant stressor. In addition, the results of the focus-group, which was conducted with a sample of 12 prominent industry experts and policy influencers, indicate that the findings of the case study are transferrable and could be applicable to other construction projects and contexts. It is, therefore, recommended that these potential stressors be addressed by the project team as early as possible in construction projects. Additionally, the study sheds empirical light on the limitations of the critical path method and identifies “inclusive and collaborative planning” as a proactive strategy for stress prevention and/or reduction in construction projects.

The findings of this study are mainly based on the perspectives of construction professionals at managerial and supervisory levels. It is, therefore, suggested that future studies are designed to focus on capturing the experiences and opinions of construction workers/operatives on the site.

The findings from this study have the potential to assist decision-makers in the prevention of stress within construction projects, ultimately enhancing workforce performance. It is suggested that the findings could be adapted for use as Construction Supply Chain Management Standards to improve occupational stress management and productivity in construction projects. The study also provides decision-makers and practitioners with a conceptual framework that includes a list of effective strategies for stress prevention or reduction at both project and organisational levels. It also contributes to practice by offering novel ideas for incorporating occupational stress and mental health considerations into production planning and control processes in construction.

To the best of the authors’ knowledge, this is the first, or one of the very few studies, to explore the concept of occupational stress in construction at the project and supply chain levels. It is also the first study to reveal “workflow” as a predominant stressor in construction projects. It is, therefore, suggested that both academic and industry efforts should focus on finding innovative ways to enhance workflow and collaboration in construction projects, to improve the productivity, health and well-being of their workforce and supply chain. Further, it is suggested that policymakers should consider the potential for incorporating “workflow” into the HSE's Management Standards for stress prevention and management.

This paper provides a thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality. By methodically reviewing and synthesizing the body of research conducted in this period, it underscores key contributions and breakthroughs in the application of AI within construction management (AICM). Additionally, the study aims to shed light on emerging trends and forecast future directions for technological innovation in the construction management sector.

Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this research conducts a bibliometric analysis of 176 relevant publications from the past five years. The analysis focuses on the adoption of AICM across three critical areas: construction equipment management, improvement of construction safety and construction cost optimization. Additionally, the study systematically identifies and examines 14 emerging themes within this domain, ensuring a comprehensive exploration aligned with PRISMA guidelines.

This manuscript summarizes recent research from the past five years in three key areas: construction equipment management, construction safety management and construction cost management within the realm of AICM. It identifies key gaps and outlines future research directions, including enhancing AI-driven equipment integration, developing sophisticated AI-based safety systems and optimizing cost management with advanced data analytics. These findings and directions are essential for steering the field toward greater digital innovation and sustainability.

This research provides a detailed analysis of the literature within the AICM domain, thoughtfully compiling significant findings and highlighting the importance of addressing user needs. The insights and recommendations shared aim to be beneficial for both academic researchers and industry professionals, contributing to the ongoing development of AICM as it moves toward a future characterized by digital innovation and sustainability.

Following the COVID-19 pandemic, most higher education institutes shifted to online learning as the sole alternative to continuing education while mitigating the risks imposed by the pandemic. This has raised several concerns regarding students’ learning experience, satisfaction and academic achievement, particularly in countries where students have restrained technological resources (i.e. developing nations). The current research aims to investigate the key factors influencing students’ attitudes, satisfaction and academic achievement among university students in an emerging market context (i.e. Morocco). The moderating effect of students’ motivation to study online was also scrutinized.

The authors propose an integrated conceptual framework that combines the technology acceptance model (TAM) with the outcomes of prior literature related to online learning. Based on data collected from 850 Moroccan university students, the authors empirically tested the conceptual model using a partial least squares (PLS) estimation.

First, attitude toward online learning and satisfaction positively impact university students’ academic achievement; at the same time, attitude positively impacts students’ satisfaction with online learning. Second, students’ satisfaction and attitude toward online learning were found to be mainly influenced by instructor performance, ease of use of the online learning platform, information quality, interactivity and perceived usefulness (PU). Finally, student motivation acts as a moderator, e.g. students with higher motivation to learn online are more likely to develop a favorable attitude toward online learning and can, therefore, accomplish better academic performance.

The current study makes a considerable contribution to the literature by contributing to the on-going debate about the potentials and challenges of online learning, particularly in an emerging country where education remains a considerable challenge. The study findings can help higher education institutes gauge the quality of online education programs and design efficient strategies to develop high-quality online learning for students. Our findings have implications not only for educational institutions and instructors in developing markets but also for the vendors of online course delivery software.

This study aims to extend the investigation on leader communication by assessing the usage of motivating language (ML) by leaders in Waqf institutions in Malaysia.

Data analysis was carried out using structural equation modelling via the partial least squares. The probability sampling technique was deemed more suitable for this study as the available data was definable for constructing the sampling frame.

Management effectiveness was shown to have a significant effect on direction-giving and meaning-making language (MML), but not on empathetic language (EL). The findings demonstrate that increasing use of directive and MML leads to greater management performance; however, increased use of EL has no such effect.

The findings should not be taken as a comprehensive solution for improving the management effectiveness of all Waqf institutions. As the study only focused on the aspect of leader communication in Waqf institutions, the findings cannot be generalized to other contexts. Additionally, this study had only examined religious-based non-profit organizations (NPOs) with affiliations to a religious body, mission statements that incorporate religious values, financial support from religious sources and governance structure and employee selection based on religious processes. Hence, the findings cannot be used as a reference in the context of non-religious NPOs.

This paper contributes to the theoretical enhancement of existing literature about leader communication towards improving institutional effectiveness. The current study has empirically tested the model through the integration of the ML theory. Thus, the leader’s choice of language improves employee motivation and ultimately institutional productivity and effectiveness.

There is a glaring gap in empirical studies on the relationship between ML usage by leaders and management effectiveness specifically in the context of Malaysian organizations. Based on rigorous searches using the Scopus and Web of Sciences databases, it was found that past studies investigating the said relationship had focused more on Western countries. This is a crucial gap that must be addressed to gain a deeper understanding of the effect of ML on management effectiveness, especially in the Malaysian setting.

This proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.

An EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO₂ in indoor spaces is highly reduced.

The MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.

The findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.