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Studies on the influence of traffic noise on children's health are usually very comprehensive and collect data on a large number of measured variables in comparatively large samples. In the NORAH Study, for example, almost 700 variables have been considered including more than 100 variables related to traffic noise. With a theory-based approach, the statistical evaluation of that data focused on a limited number of variables to be included in the regression models as predictors, mediators, moderators, or confounders. In contrast, machine learning (ML) methods are able to consider the complete scope of variables in an analysis. Random forest models are one type of ML methods for dealing with possible multicollinearity of predictors or nonlinear relationships. Although these methods can offer advantages, they have hardly been used in relation to traffic noise and children's health. In the EU project EqualLife, random forest models are computed in order to obtain information on the significance of individual exposomes (e.g., traffic noise) for children's health. In the present paper, we compare the results of a regression model and a random forest model using the NORAH Study as an example. Possible advantages and disadvantages of the methods are discussed.

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Purpose: Productivity in the construction trade has fallen significantly behind other sectors of the economy. Digital assistance systems can help to improve productivity, however they are rarely used in most smaller construction companies. To change this, it is the scope of this paper to investigate acceptance factors of digital assistance systems within small construction companies. Design/methodology: Construction workers of five smaller German construction companies participated in a survey (N = 74). We conducted a generic technology acceptance model (GTAM) that combines two strands of research: attitude-behaviour research and the technology acceptance model. We analysed acceptance factors using structural equation modeling. Findings: Applying GTAM we show the importance of factors that lead to the formation of acceptance among construction workers from small building companies. We found strong relationships between general attitudes and beliefs. General attitudes can be described as the first acceptance barrier in the adoption process. Perceived usefulness was the only significant belief for prediction of behavioral attitude in our model. Practical implication: The GTAM is parsimonious enough to be used in smaller pilot and industrial projects in the construction sector. Construction companies would probably be more likely to achieve acceptance of digital assistance systems in construction if acceptance-enhancing activities started with general attitudes and later considered the beliefs during implementation.Originality/ value: Our empirical study aims to fill the gap that little is known so far about which factors promote or inhibit individual acceptance of digital assistance systems in the construction trade.

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