Decision-makers in the architecture, engineering, and construction (AEC) industry lack knowledge about the implementation of digitalisation to generate value. We applied a scenario planning method developed by Schoemaker and Mavaddat to provide decision-makers with information for using digital data and technolo-gies to create value for customers. We aim to theoretically understand how the scenario planning process helps AEC decision-makers to make sense of the fu-ture. Our findings show that boundary spanners are needed for steering the dis-cussions among industry actors toward shared knowledge about the technologi-cal, social, economic and political changes needed at the industry level to opti-mise the benefits of digitalisation. Our findings also show that boundary spanners apply scenario figures as boundary objects to cross knowledge boundaries. Based on our findings, we theoretically conceptualise scenario planning as a boundary-spanning activity that enables AEC decision-makers from different fields to share tacit knowledge and to cross knowledge boundaries. The practical implication is that scenario planning provides a method for AEC decision-makers to make sense of the changes needed to realise the preferred future for the industry.

BackgroundExcessive stress has a negative impact on many aspects of life for both individuals and societies, from studying and working to health and well-being. Each individual has their unique level of stress-proneness, and positive or negative outcomes of stress may be affected by it. Technology-aided interventions have potential efficacy in the self-management of stress. However, current Web-based or mobile stress management solutions may not reach the individuals that would need them the most, that is, stress-sensitive people.ObjectiveThe aim of this study was to examine how personality is associated with stress among Finnish university students and their interest to use apps that help in managing stress.MethodsWe used 2 structured online questionnaires (combined, n=1001) that were advertised in the University of Helsinki’s mailing lists. The first questionnaire (n=635) was used to investigate intercorrelations between the Big Five personality variables (neuroticism, extraversion, openness, agreeableness, and conscientiousness) and other stress-related background variables. The second questionnaire (n=366) was used to study intercorrelations between the above-mentioned study variables and interest in using stress management apps.ResultsThe quantitative findings of the first questionnaire showed that higher levels of extraversion, agreeableness, and conscientiousness were associated with lower self-reported stress. Neuroticism, in turn, was found to be strongly associated with rumination, anxiety, and depression. The findings of the second questionnaire indicated that individuals characterized by the Big Five personality traits of neuroticism and agreeableness were particularly interested to use stress management apps (r=.27, P<.001 and r=.11, P=.032, respectively). Moreover, the binary logistic regression analysis revealed that when a person’s neuroticism is one SD above average (ie, it is higher than among 84% of people), the person has roughly 2 times higher odds of being interested in using a stress management app. Respectively, when a person’s agreeableness is one SD above average, the person has almost 1.4 times higher odds of being interested in using a stress management app.ConclusionsOur results indicated that personality traits may have an influence on the adoption interest of stress management apps. Individuals with high neuroticism are, according to our results, adaptive in the sense that they are interested in using stress management apps that may benefit them. On the contrary, low agreeableness may lead to lower interest to use the mobile stress management apps. The practical implication is that future mobile stress interventions should meaningfully be adjusted to improve user engagement and support health even among less-motivated users, for instance, to successfully engage individuals with low agreeableness.

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Stress has become an important health problem, but existing stress detectors are inconvenient in long-term real-life use because users either have to wear dedicated devices or expend notable interaction efforts in system adaptation to specifics of each person. Adaptation is necessary because individuals significantly differ in their perception of stress and stress responses, but typical adaptation employs supervised learning methods and hence requires fairly large sets of labelled data (i.e. information on whether each reporting period was stressful or not) from every user. To address these problems, we propose a novel unsupervised stress detector, based on using a smartphone as the only device and using discrete hidden Markov models (HMM) with maximum posterior marginal (MPM) decisions for analysis of phone data. Our detector requires neither additional hardware nor data labelling and hence is truly unobtrusive and suitable for lifelong use. Its accuracy was evaluated using two real-life datasets: in the first case, adaptation was based on very short (a few days) phone interaction histories of each individual, and in the second case-on longer histories. In these tests, the proposed HMM-MPM achieved 59 and 70% accuracies, respectively, which is comparable with results of fully supervised methods, reported by other works.