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Team effectiveness is often predicated by a group’s ability to communicate. However, the effect of stress response on communication success, particularly nonverbal engagement, and how this might affect team performance, is not clear; a “phenomenon” this study sought to explore.

This was an observational study in a cohort of professional rugby players. Participants gave presentations to their peers on two separate occasions during a “live-in” camp designed to have psychologically stressful elements. Presentations were video recorded and audience engagement was measured. Testosterone and cortisol were used as biomarkers of stress response, with a high testosterone–cortisol ratio considered positive. A team training session followed the presentations and participants were rated for training quality.

A small decline in testosterone was observed each day after waking. Conversely, cortisol rose after waking, with the rise being the highest on the first day. A decline in testosterone–cortisol ratio was also seen each day after waking; the decline was greatest on the first day. Presentation duration and audience engagement was greatest for the second presentation; when the testosterone-cortisol ratio decline and the cortisol increase after waking was smaller. Training quality was also better that day. Pooled data revealed a moderate inverse relationship and weak positive relationships for audience engagement with post-meeting cortisol and post-meeting testosterone–cortisol ratio, respectively. Training quality was related to testosterone and testosterone–cortisol ratio, but inversely related to cortisol.

This study suggests that in stressful conditions, as suggested by an awakening hormone response, communication and team performance could become compromised with reduced ability to engage with others.

This paper aims to present an alternative practical framework to classify the skill and knowledge of the individual trainees by comparing it with the expert in an organization. This framework gives the benefit to the organization in order to know the ability level of the personnel and to be able to provide the personnel development method both in academic learning and workplace learning.

This research develops the framework based on relevant methodologies. Competency-Based Development is applied to investigate the knowledge and skill of the specific task. Knowledge Engineering is used to capture the experiences and construct knowledge model from relevance parties. Capability Maturity Model is then adapted to develop the capability and maturity level of the personnel. It can then be used to cluster the knowledge and skill. Finally, the Substation Maintenance Department of Provincial Electricity Authority (PEA), Thailand, is selected as a case study to test the proposed framework.

The results have shown that the proposed framework can be utilized to identify the capability level of the individual personnel. Furthermore, the appropriate maturity development of the employees in each level can also be identified. This proposed framework provides better results when comparing to the current PEA competency model, as the criteria in this framework are systematically derived from experts rather than relying solely on the proficiency level. Although, this framework was tested with the switchgear maintenance task, the results and its systematic approach have indicated that it can also be used to develop the capability maturity model for other fields of work.

The main originality of this research is the proposed competency analysis table, which integrates human resource development with knowledge management, risks management and management information system. Rather than performing these tasks separately for continuous quality improvement, organization can practically plan and perform the quality improvement-related tasks spontaneously. Moreover, the application of the capability maturity model to classify knowledge and skill of the maintenance tasks into maturity level is another academic value presented in this paper. The proposed framework gives the benefit to organization to classify the capability of the personnel. This is potentially beneficial to the human resource development personnel than traditional methods in the sense that it provides the information on how to develop the specific skill of the employees.

Proper identification of the key motivating factors (or key drivers) is needed to ensure successful adaption and implementation of the lean concept for construction projects. However, there lacks a study investigating the complex interrelationships existing among the key drivers contributing to Sustainable and Successful Lean Construction (SSLC) implementation for such projects. To address this shortcoming, this study aims to uncover the main critical key drivers towards the implementation of SSLC for the very first time by capturing the complexity of this vexing problem.

In this study, a new hybrid framework is developed through the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Social Network Analysis (SNA). The novel developed framework is called the DSNA approach.

Considering the case of Malaysian construction projects, the developed DSNA gives the following major outcomes: (1) Most important critical key drivers are seen to be optimization, continuous improvement, and, improve company culture, and (2) For SSLC adoption, the critical drivers impacting other key drivers are seen to be “improve teamwork”, “reduce leadership conflict”, and “improve company culture”, thereby demanding more attention.

The outcomes of this study give insight for decisions and policymakers in the construction industry regarding critical key drivers and their complex interrelationships towards the further adoption of SSLC, promoting the sustainability paradigm within the respective sector.

This paper not only presents a list of critical drivers and the corresponding association among them towards SSLC adoption, but also proposes DSNA as a novel approach for uncovering the complex interrelationship existing in an intricate problem, improving the intricate process of decision-making.