Workers at a pro-environmental charity in the United Kingdom were evaluated 19 months before the organization moved its headquarters to another town, and then evaluated again 1 and 4 weeks after the move. Travel habit (behavioral automaticity) weakened immediately after the move, and this was equal for those who changed travel mode during the relocation and those who did not. In those who changed mode, habit strength for the old mode did not disappear abruptly but rather decayed over the post-move period, whereas habit strength for the new mode grew concurrently. This demonstrates that even when overt behavior changes during a transition event, the underlying behavioral automaticity does not disappear immediately. Rather, there is a period during which habit for the new behavior becomes established and habit for the old behavior decays. Practical implications of these findings are discussed.

One of the most discussed issues in the design community is the performance gap. In this research, we investigate for the first time whether part of the gap might be caused by the modelling literacy of design teams. A total of 108 building modellers were asked to comment on the importance of obtaining and using accurate values for 21 common modelling input variables, from U-values to occupancy schedules when using dynamic simulation to estimate annual energy demand. The questioning was based on a real building for which high-resolution energy, occupancy and temperature data were recorded. A sensitivity analysis was then conducted using a model of the building (based on the measured data) by perturbing one parameter in each simulation. The effect of each perturbation on the annual energy consumption given by the model was found and a ranked list generated. The order of this list was then compared to that given by the modellers for the same changes in the parameters. A correlation analysis indicated little correlation between which variables were thought to be important by the modellers and which proved to be objectively important. k-means cluster analysis identified subgroups of modellers and showed that 25% of the people tested were making judgements that appeared worse than a person responding at random. Follow-up checks showed that higher level qualifications, or having many years of experience in modelling, did not improve the accuracy of people’s predictions. In addition, there was no correlation between modellers, with many ranking some parameters as important that others thought irrelevant. Using a three-part definition of literacy, it is concluded that this sample of modellers, and by implication the population of building modellers, cannot be considered modelling literate. This indicates a new cause of the performance gap. The results suggest a need and an opportunity for both industry and universities to increase their efforts with respect to building physics education, and if this is done, a part of the performance gap could be rapidly closed. Practical application: In any commercial simulation, the modeller will have to decide which parameters must be included and which might be ignored due to lack of time and/or data, and how much any approximations might perturb the results. In this paper, the judgment of 108 modellers was compared against each other. The results show that the internal mental models of thermal modellers disagree with one another, and disagree with the results of a validated thermal model. The lessons learnt will be of great utility to modellers, and those educating the next generation of modellers.