PurposeFailures in both followership and leadership become inevitable as mega construction projects are directed and controlled by toxic leaders. Consequently, team member's desire for knowledge hoarding silence is triggered and goal alignment between the leader and team members suddenly fades away to realize success in mega projects. Considering the growing importance of these rarely examined constructs and fragmented literature on toxic leadership (TL), team silence and mega project success (PS) in the global construction industry, the present study aimed to examine the effects of TL and project team member's silence (PTMS) on the success of mega construction projects. Moreover, the mediating influence of PTMS to link TL and mega construction PS has also been explored.Design/methodology/approachDrawing on survey data of 326 project professionals directly associated with mega construction projects worth US$62bn under the China–Pakistan Economic Corridor (CPEC), the conceptual model was tested with covariance-based structural equation modeling (CB-SEM) using Mplus program. Scales were adapted from previous research to measure TL (with its five-dimensions including abusive supervision, authoritarian leadership, self-promotion, narcissism and unpredictability), PS (with its three-dimensions including project management success, project ownership success and project investment success) and project team members' silence. Reflective–formative second order assessments were specifically applied to measure the multi-dimensional nature of TL and PS, respectively.FindingsMplus estimations revealed that TL negatively influences PS, besides forcing a culture of silence among project team members. Interestingly, the relationship between TL and PS is also negatively mediated by the PTMS.Research limitations/implicationsThe present study's findings are derived from data of project professionals (N = 326) to examine success in megaprojects under the CPEC. Hence, these findings may be re-validated through future studies on similar megaprojects (e.g. China's Belt and Road Initiative (BRI) worth US$8tn) that may also be predicated by TL tendencies, silent cultures and high-stakes involved to seize PS.Practical implicationsPolicymakers, construction practitioners and other key stakeholders (e.g. departmental heads/supervisors) can take advantage of this new evidence to better interpret the success paradox in mega projects, and to reduce the spread and long-term damage of TL on team members and eventually create opportunities for PS.Originality/valueThe present study's novelty is manifested within this first empirical evidence on TL that breeds team silence in underperforming mega projects. Notably, present study offers alarming evidence on mega projects that can be easily derailed from success, as they continue to suffer from team silence and TL.

Delay factors are frequent in the construction industry globally, resulting in significant overruns in project cost and time. In context, megaprojects can be more prone to critical delays, hence, demanding a high degree of self-confident leadership. Despite the continuous scholarly attempts to examine mega construction project success, the underlying role of critical delay factors and leadership self-efficacy has been largely overlooked. Hence, to address these rarely examined linkages, the present study empirically explored the effects of critical delay factors (CDFs) on transnational mega construction project (TMCP) success with the moderating influence of leadership self-efficacy (LSE). Based on a study sample (N = 211) extracted from the China–Pakistan Economic Corridor, the hypothesized relationships were tested through partial least squares–structural equation modeling. The study included nine critical delay factors and three subdimensions of TMCP success, derived from previous research. The findings revealed a negative relationship between CDFs and TMCP success, as a 1% increase in CDFs triggered a 28.8% negative change in TMCP success. A positive moderating effect of LSE on the relationship between CDFs and TMCP success was also empirically supported, as 1% increase in LSE resulted in 18.4% positive change in TMCP success. The present study bridges the fragmented literature on critical delay factors in the global construction industry, megaproject success, and project leadership, by providing the first empirical evidence linking these potential relationships. Moreover, the present study also provides an extension to existing studies to identify the role of CDFs and LSE in impacting multi-faceted success (i.e., management success, ownership success, and investment success) in mega construction projects.

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Globally, demands for sustainable strategies in the ICT industry have attracted greater momentum as high-tech projects continue to fail in large numbers. Recent studies have underpinned project resilience as a major factor for overcoming these increasing project failures, delays, or termination. However, the complex behaviors of resilient project leaders, especially in post-failure conditions, have been largely overlooked. To address this critical research gap, the present study identifies the direct relationships between three potential behavioral traits of project leaders (i.e., resilience, self-esteem, and self-efficacy) and examines how they move forward beyond project failures. The present study also explored whether self-esteem mediates project leaders’ resilience and self-efficacy. Drawing on data from 232 project leaders in Pakistan’s high-tech start-ups, the new findings suggest that there are significant positive effects of project leaders’ resilience and self-esteem on their self-efficacy, and that project leaders’ resilience and self-efficacy is significantly mediated by their self-esteem. As the project resilience theory gains traction, the present study findings have pinpointed major steps for meeting project challenges ahead of time, allowing leaders and teams to learn from failures, and also for improving organisations’ ability to implement successful and sustainable high-tech projects especially in emerging economies.

The factors that affect productivity are a major focus in construction. This article proposes a machine learning–based approach to predict task productivity by using a subjective measure (compatibility of personality), together with external and site conditions, and other workers' characteristics. The approach integrates K‐nearest neighbor (KNN), deep neural network (DNN), logistic regression, support vector machine (SVM), and ResNet18 to discover the mapping between input and output variables, alongside rigorous statistical analyses to interpret data. A database including 1977 productivity measures is utilized to train, test, and validate the approach. Results test rules in the masonry industry, which do not seem to have been tested before: Small crews are more productive than large crews; higher compatibility results in higher productivity in easy but not in difficult tasks; the relevance of experience to task productivity may depend on the difficulty of the task.