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There are different interactions and physiological patterns in coordination meetings between non-lean and last planner system (LPS)-based project teams. This phenomenon is the result of the top-down workflow control in the LPS that seems to influence group interaction and physiological processes differently compared to non-lean production planning and control approaches that use a bottom-up workflow control. The current LPS management practices focus on developing performance indicators instead of establishing process-oriented initiatives that optimize the temporal patterns of social interaction and physiological processes. Understanding the differences in behavioral and physiological patterns among non-lean and LPS-based project teams can support non-lean project teams in transitioning to LPS-based social and physiological patterns. Incorporating video recordings and physiological sensors such as electrodermal activity (EDA) can capture group interactions and concurrent physiological responses. The multiuser immersive virtual reality-based LPS simulation game (MILPS) is an innovative simulation tool to investigate these patterns in a controlled environment. This study uses MILPS, EDA sensors, and video recordings to investigate the impact of LPS on social interactions and physiological patterns during coordination meetings. A total of 90 participants, forming 30 groups, participated in a two-round experiment consisting of both non-lean and LPS-based project planning and working tasks. The video and EDA data were analyzed to assess the temporal pattern of interactions and associated shared physiological arousals (SPA) in both rounds. The results indicate that more iterative patterns were observed between interaction phases in LPS-based rounds, whereas more sequential patterns were observed in non-lean rounds. A higher level of SPA was found during the negotiation, identification in LPS-based round compared with the non-lean round. The contribution of this study is twofold: (1) the behavioral and physiological patterns during coordination meetings for both non-lean and LPS-based project teams are characterized; and (2) insights are generated into management and training strategies to facilitate LPS implementation. [ABSTRACT FROM AUTHOR]

Over the last 30 years, the last planner system (LPS), as a Lean-driven production planning and control tool, has consistently demonstrated its capability to improve planning reliability and overall performance in construction projects. Recent research provides evidence suggesting that the LPS ability to do good to projects has not reached its full potential yet. In fact, it has been observed that by identifying the missing social mechanisms in its implementation process and managing more effectively the complex socio-technical nature of the LPS, its implementation effectiveness is improved. However, commonly used research approaches, such as the case study method, have been criticized for lacking scientific control, replicability, and external validity. Alternatively, immersive virtual reality (IVR)-based simulation games have the methodological capability to enable the study of the social mechanisms that the LPS engenders when implemented in projects, by providing a highly controlled, reproducible, and ecologically valid experimental environment. However, how to leverage effectively IVR-based gaming technology to study the social mechanisms that the LPS engenders in construction organizations is not well understood. In order to bridge this gap, this paper established the conceptual and technical foundations to set IVR-based experimental environments and developed an IVR prototype encompassing nonlean and LPS-based rounds. The evaluation of the IVR prototype involved both quantitative and qualitative research approaches, focusing on its functionality, user-perceived presence and usability, task performance, and lean expert feedback. The results demonstrated the prototype's capability to create a controlled experimental environment while offering participants a user-friendly, immersive, and realistic LPS simulation experience. The contributions of this study are twofold: (1) the addition of a new method to the research methodology toolbox in construction engineering and management; and (2) the generation of design insights to develop IVR prototypes for research in construction engineering and management, and preliminary evidence for its ecological validity. [ABSTRACT FROM AUTHOR]

Wearable biosensors, in conjunction with machine learning, have been employed to develop less invasive monitoring techniques for assessing stress among construction workers during fieldwork. However, existing techniques face limitations in terms of scalable field application due to their subject and context dependency; it is difficult to apply them to new people in new contexts without additional labeled data collection. Therefore, this study developed a stress detection technique that incorporates domain adaptation, simultaneously learning a classifier and a subject- and context-independent features, in this way advancing generalizability. The proposed technique consistently demonstrated superior accuracy compared with benchmarks in classifying stress levels within a testing data set whose subjects and contexts were different from those of training data sets. Thus, the technique can advance generalizability across subjects and contexts. This finding can help us to reliably detect stress for new people in new contexts without additional labeled data collection, thereby contributing to scalable field application of wearable-based stress monitoring at construction sites. [ABSTRACT FROM AUTHOR]