The present study used smart phones to collect contextualized data on professionals' daily working activities; our purpose was to trace professionals' work engagement and socio-emotional activities. We used two tools, the Contextual Activity Sampling System (CASS-Q) and ContextLogger for collecting, in parallel, complementary self-report and location-sensor data. This allowed us to compare the types of data and their richness of information. The methods and instruments developed enabled one to trace various aspects of the mobile multi-locational workers' positive and negative self-reported affects in work contexts, as well as their activities and experiences of challenge and competence. The secondary working contexts (e.g., seminars, meetings, customer's office), especially, included interactions with others leading to both high positive and negative affects. The results also indicate that the participants' self-reported locations corresponded closely with the actual location documented by ContextLogger. Our results suggest possibilities for developing an algorithm that uses location information to automatically recognize certain activity contexts. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Muukkonen, H., Hakkarainen, K., Li, S., & Vartiainen, M. (2014). Tracking mobile workers’ daily activities with the contextual activity sampling system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8518 LNCS, pp. 289–300). Springer Verlag. https://doi.org/10.1007/978-3-319-07626-3_27
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