Task, interrupted: understanding the effect of time costs on task interruptions during data entry

Computer-based work often involves looking up information from different sources. Though these interruptions are required to progress with work, switching away from a task can be disruptive: it slows people down, increases errors and it is challenging to remain focused on work. This thesis investigates how interruption management tools can better support people in managing these types of work-required interruptions in the context of data entry work. The first part of the thesis reports two qualitative studies looking at understanding data entry in an office setting. They demonstrate that physical interruptions are postponed until a convenient moment in the task if they are expected to take time, but digital interruptions are addressed immediately as these are presumed to be quick to deal with. The second part of the thesis reports three controlled experiments to test the hypothesis that people manage interruptions by avoiding time costs. Results show that if people are able to learn the expected time costs of digital interruptions, they avoid interruptions with a high time cost. They reduce the number of these interruptions and postpone them until later in the task, and address interruptions with low time costs first. The third part of the thesis reports an online experiment and a field study that evaluate a design intervention showing people the duration of their interruptions. These studies demonstrate that making people aware of the time costs of digital interruptions makes people reflect on what they were doing during an interruption, reduces the duration of interruptions, and makes people faster and more accurate in completing data entry tasks. Taken together, this thesis demonstrates that people manage interruptions based on expected time costs, and that giving people feedback on the time they spend on interruptions can help them manage their interruptions better. It makes a theoretical contribution by showing how people adapt to small changes in time costs by reducing the number and duration of interruptions, and postponing them until later in a task. It makes a practical contribution by showing that giving people feedback on time costs can help them to reduce the duration of interruptions, and improve their focus on the task at hand

The Effect Of Interface Type On Visual Error Checking Behavior

During data entry tasks, small errors can result in catastrophe, for instance adding an extra zero to a drug dose when programming an infusion in a hospital. For this reason understanding users’ error checking behavior is highly important. One aspect that can affect error checking is the interface that a user must interact with to enter data. Often user interaction with interfaces is evaluated based on speed or error rate. In this paper, in addition to this, we also explore how different types of interface can affect a user’s error checking behavior in a multitasking environment. We show that a fast to use and familiar interface discourages users from carrying out thorough visual checking in a number transcription task. We also found that having participants perform an additional secondary task while doing the number entry task made participants less likely to check the inputted numbers for errors

Wording Matters: the Effect of Linguistic Characteristics and Political Ideology on Resharing of COVID-19 Vaccine Tweets

Social media platforms are frequently used to share information and opinions around vaccinations. The more often a message is reshared, the wider the reach of the message and potential influence it may have on shaping people’s opinions to get vaccinated or not. We used a negative binomial regression to investigate whether a message’s linguistic characteristics (degree of concreteness, emotional arousal, and sentiment) and user characteristics (political ideology and number of followers) may influence users’ decisions to reshare tweets related to the COVID-19 vaccine. We analyzed US English-language tweets related to the COVID-19 vaccine between May 2020 and October 2021 (N = 236,054). Tweets with positive and high-arousal words were more often retweeted than negative, low-arousal tweets. Tweets with abstract words were more often retweeted than tweets with concrete words. In addition, while Liberal users were more likely to have tweets with a positive sentiment reshared, Conservative users were more likely to have tweets with a negative sentiment reshared. Our results can inform public health messaging on how to best phrase vaccine information to impact engagement and information resharing, and potentially persuade a wider set of people to get vaccinated

Understanding underlying moral values and language use of COVID-19 vaccine attitudes on twitter.

Public sentiment toward the COVID-19 vaccine as expressed on social media can interfere with communication by public health agencies on the importance of getting vaccinated. We investigated Twitter data to understand differences in sentiment, moral values, and language use between political ideologies on the COVID-19 vaccine. We estimated political ideology, conducted a sentiment analysis, and guided by the tenets of moral foundations theory (MFT), we analyzed 262,267 English language tweets from the United States containing COVID-19 vaccine-related keywords between May 2020 and October 2021. We applied the Moral Foundations Dictionary and used topic modeling and Word2Vec to understand moral values and the context of words central to the discussion of the vaccine debate. A quadratic trend showed that extreme ideologies of both Liberals and Conservatives expressed a higher negative sentiment than Moderates, with Conservatives expressing more negative sentiment than Liberals. Compared to Conservative tweets, we found the expression of Liberal tweets to be rooted in a wider set of moral values, associated with moral foundations of care (getting the vaccine for protection), fairness (having access to the vaccine), liberty (related to the vaccine mandate), and authority (trusting the vaccine mandate imposed by the government). Conservative tweets were found to be associated with harm (around safety of the vaccine) and oppression (around the government mandate). Furthermore, political ideology was associated with the expression of different meanings for the same words, e.g. science and death. Our results inform public health outreach communication strategies to best tailor vaccine information to different groups