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Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

The construction industry is complex, human-intensive and driven by monetary values. Hence, disputes are widespread. Initial conflicts among parties may develop into a disastrous dispute that costs the project success and good relationships and affects stakeholders' expectations. There has been a focus on causes of construction-related disputes, and studies over the past three decades have attempted to identify a more comprehensive list of reasons for disputes. Some of these studies' limitations were geographical, project delivery methods and project types. The purpose of this study is to identify the most recent and conclusive list of causes of disputes based on current literature by undertaking a systematic literature review (SLR).

Considering the large number of studies that focused on causes of disputes, this study aims to develop a comprehensive list of causes, using a SLR, as it ensures that all previous articles in multiple databases are reviewed to produce a comprehensive outcome. A six-stage SLR was followed from background study to analysis and reporting.

Not surprisingly, the number of publications has increased over time, most from the Middle East region. The interconnected nature of the causes was widely emphasised. The SLR has produced eight common core causes of disputes. They are: poor contractual arrangements, employer-initiated scope changes, unforeseen site changes, poor contract understanding and administration, contractor’s quality of works, the inability of the contractor to achieve time targets, non- or delayed payments and poor quality of design. The majority of previous authors realised that disputes could be avoided by parties’ involvement during the early stages, avoiding being opportunistic and acting collaboratively.

Even though numerous studies have been carried out to identify the causes of disputes in the construction industry, none did a SLR. This study aggregates all the previous studies that focused on construction-related disputes systematically. Categorising causes based on the party primarily responsible help various stakeholders by providing a distinct list of factors to avoid that contribute to disputes.

This study aims to examine the interaction between different types of supply chain integration (SCI) and competitive strategies on firm performance.

The research method is quantitative. The study develops a structured model and a set of hypotheses based on the related theories. Empirical data is collected in a cross-sectional manner using an online survey to test the model and its hypotheses. The survey comprised standard scales to measure study variables. The scales had high reliability and validity and were commonly used in the literature. The study was conducted on Iranian firms working in steel and auto parts industries. Based on official reports, a total of 305 active companies were found in these two industries. Due to the small population size, all companies in the two industries were contacted for participation. Overall, 84 firms completed the survey. To test the research hypotheses, the authors used hierarchical regression analyses using SPSS 27 software.

Results showed that internal integration improves financial performance when firms follow a cost leadership strategy. Differentiation strategy and process integration interact positively and improve operational performance. Type of industry had no significant effect. The results add to the SCI literature by showing that a match between competitive strategy and integration type can influence both operational and financial performance. In total, firms should focus on the appropriate dimension of SCI based on their competitive strategy to improve the performance of the firm.

The impact of competitive strategy as a moderator on the SCI-performance relationship has not been widely addressed in the literature. In one study, Huo et al. (2014) investigated this relationship using data from 604 manufacturing companies in China. The results provided support for financial performance but not for operational performance. However, the influence of competitive strategies on the SCI performance may vary in different countries and contexts with dissimilar environmental, social and cultural characteristics. As several supply chain scholars have argued, understanding contextual conditions that influence the effect of best practices such as SCI on performance are critical