Assessing an Immersive Virtual Reality–Based Simulation Game Capability to Study the Social Mechanisms Enabled by the Last Planner System in Projects
Bibliographic record
Abstract
In recent years, research has found that the effectiveness of the Last Planner System (LPS) can be significantly enhanced by identifying and effectively managing the missing social mechanisms that LPS introduced within project teams. To investigate these mechanisms, we have developed an experimental tool called the multiuser immersive virtual reality–based LPS simulation game (MILPS). Prior research shows its feasibility in creating an experimental environment to investigate LPS-specific social mechanisms by assessing usability, perceived presence, and task performance. However, how similar the social mechanisms obtained from MILPS are to the real world (i.e., ecological validity) has yet to be studied, reducing its potential in future studies. To address this gap, this paper assessed the ecological validity of MILPS by comparing findings from the virtual world with empirical findings from the real world. We employed MILPS to conduct a two-round experiment consisting of both non-Lean and LPS-based project planning, and working tasks to study the key social mechanisms associated with LPS. Participants’ behavioral (i.e., communications), cognitive, and affective responses (i.e., shared understanding and stress levels) were measured with video recordings, physiological sensors, and questionnaires during and after these tasks. Results indicate that MILPS captured the key social mechanisms associated with LPS (e.g., improved shared understanding, communication patterns, and reduced stress levels), which established the ecological validity of using MILPS for studying the LPS-associated social mechanisms. The contributions of this study are twofold: (1) providing insights into designing and conducting group IVR experiments that collect behaviors and psychological and physiological data and (2) sharing an innovative and ecologically valid experimental tool that can be used to study the LPS within the Lean community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".