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Remote work presents a challenge to workers’ creativity, especially during the COVID-19 pandemic and the stay-at-home requirements. Individual differences in creativity, considered through the lens of distributional models, and their stability across different conditions are unknown. We assess the between-person variability in common metrics of creativity, despite sharing similar experiences of virtual reality and mindfulness. The paper also assesses the stability of an individual’s creativity over time. We measured the creativity of 20 remote-workers daily, during a 9-week study. Creativity was measured with respect to divergent thinking and convergent thinking. Distributional models show significant individual differences in variability of creativity. Stability analyses also revealed that individuals’ creativity is relatively unstable over time— both within and across conditions. Although one measure of divergent creativity was relatively stable, the other was not. We suggest more research should assess the extent of variability in creativity relative to individual differences and under different conditions. 

The concept of using automated vehicles as mobile workspaces is now emerging. Consequently, the in- vehicle environment of automated vehicles must be redesigned to support user interactions in performing work-related tasks. During the design phase, interaction designers often use personas to understand target user groups. Personas are representations of prototypical users and are constructed from user surveys and interview data. Although data-driven, large samples of user data are typically assessed qualitatively and may result in personas that are not representative of target user groups. To create representative personas, this paper demonstrates a data analytics approach to persona development for future mobile workspaces using data from the occupational information network (O*NET). O*NET consists of data on 968 occupations, each defined by 277 features. The data were reduced using dimensionality reduction and 7 personas were identified using cluster analysis. Finally, the important features of each persona were identified using logistic regression. 

One advantage of highly automated vehicles is drivers can use commute time for non-driving tasks, such as work-related tasks. The potential for an auto-mobile office—a space where drivers work in automated vehicles—is a complex yet underexplored idea. This paper begins to define a design space of the auto- mobile office in SAE Level 3 automated vehicles by integrating the affinity diagram (AD) with a computational representation of the abstraction hierarchy (AH). The AD uses a bottom-up approach where researchers starting with individual findings aggregate and abstract those into higher-level concepts. The AH uses a top-down approach where researchers start with first principles to identify means-ends links between system goals and concrete forms of the system. Using the programming language R, the means-ends links of AH can be explored statistically. This computational approach to the AH provides a systematic means to define the design space of the auto-mobile office. 

What is the future of work and wellbeing? How did the COVID-19 crisis affect this future? What can, and what should, researchers and practitioners in the field of human-computer interaction do, as they develop interfaces for work and wellbeing? These are the questions that we explore in a weekly online series of conversations with HCI experts. In this paper we share some of the insights from our first nine conversations of the series. 

Data classification is central to human factors research, and manual data classification is tedious and error prone. Supervised learning enables analysts to train an algorithm by manually classifying a few cases and then have that algorithm classify many cases. However, algorithms often fail to leverage human insight. To address this, we augment supervised learning with unsupervised learning and data visualization. Unsupervised learning highlights potential classification errors, explains the underlying classification, and identifies additional cases that merit manual classification. We illustrate this using the Occupational Information Network database to classify occupations as having tasks that might be performed in an automated vehicle.