Although interruptions are daily occurring events for most working people, little research has been done on the impacts of interruptions on workers and their performance. This study examines the e Vects of interruptions on task performance and its regulation, as well as on workers’ psychological and psychophysiological state. Two parallel experiments were carried out in the Netherlands and in Russia, using a common conceptual framework and overlapping designs. Employees with relevant work experience carried out realistic text editing tasks in a simulated o Yce environment, while the frequency and complexity of interruptions were experimentally manipulated. It was hypothesized that interruptions: (i) would cause a deterioration of performance; (ii) evoke strategies to partially compensate for this deterioration; (iii) aVect subjects’ emotions and well-being negatively; and (iv) raise the level of eVort and activation. It was also hypothesized that greater frequency and complexity of interruptions would enhance the expected e Vects. The hypotheses are only partially cone rmed. The results show that, contrary to what was expected, interruptions cause people to perform the main task faster while maintaining the level of quality. Participants develop strategies enabling them to deal e Vectively with the interruptions, while actually over-compensating the potential performance decline. Interruptions do have a negative impact on emotion and well-being, and lead to an increase of e Vort expenditure, although not to an increase in activation. Thus the improved performance is achieved at the expense of higher psychological costs. Greater complexity evoked more favourable responses among the Dutch participants and more unfavourable ones among the Russian participants. These diVerences are interpreted in terms of the participants’ professional background. The research demonstrates that the e Vects of interruptions reach beyond the execution of additional tasks and the change of work strategies. Interruptions appear to have an after-e Vect, ine uencing the workers’ subsequent readiness to perform. Detailed analysis of the activity in the interruption interval, focusing on

Temporal factors in mental work: effects on interrupted activities

Many of today's mobile products and services engage their users proactively via push notifications. However, such notifications are not always delivered at the right moment, therefore not meeting products' and users' expectations. To address this challenge, we aim at developing an intelligent mobile system that automatically infers moments in which users are open to engage with suggested content. To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire. In this study, however, we analyze whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In addition, the study app logged a wide range of data related to their phone use. Based on 120 Million phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that before delivering a notification predicts whether a participant will click on the notification and subsequently engage with the offered content. When compared to a naive baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user's past behavior, predictions improve 5-fold over the baseline. Based on these findings, we discuss the implications for building an intelligent service that identifies opportune moments for proactive user engagement, while, at the same time, reduces the number of undesirable interruptions.

Beyond Interruptibility: Predicting Opportune Moments to Engage Mobile Phone Users

Smartphone usage is widespread in college classrooms, but there is a lack of measurement studies. We conducted a 14-week measurement study in the wild with 84 first-year college students in Korea. We developed a data collection and processing tool for usage logging, mobility tracking, class evaluation, and class attendance detection. Using this dataset, we quantify students' smartphone usage patterns in the classrooms, ranging from simple use duration and frequency to temporal rhythms and interaction patterns. Furthermore, we identify the key predictors of students’ in-class smartphone use and their semester grades. Our results reveal that students use their phones for more than 25% of effective class duration, and phone distractions occur every 3–4 min for over a minute in duration. The key predictors of in-class smartphone use are daily usage habits and class characteristics, and in-class phone usage is negatively correlated with student grades.

Understanding smartphone usage in college classrooms: A long-term measurement study

This exploratory paper addresses the novelty effect in large display field deployments by combining findings from both the existing body of knowledge and our own research. We found that the novelty effect is prevalently present on two occasions: (a) immediately after a new system is deployed in a new environment, and (b) in reoccurring situations, when changes are made to an existing system. Both instances share similarities such as a system’s higher usage during a particular time frame. However, we also observed that their individual reasons to occur are multifaceted. The present work’s main contribution is twofold. Firstly, the paper outlines related literature regarding the novelty effect, particularly in CSCW and HCI. Secondly, the paper illustrates the effect’s complex nature and suggests explicit means that should be considered in related research endeavors.

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The Novelty Effect in Large Display Deployments - Experiences and Lessons-Learned for Evaluating Prototypes.

Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.

A Survey of Attention Management Systems in Ubiquitous Computing Environments

In today's advancing ubiquitous computing age, with its ever-increasing amount of information from various applications and services available for consumption, the management of people's attention has become very important. In particular, the high volume of notifications on mobile devices has become a major cause of interruption of users. There has been much research aimed at detecting the opportune moment to present such information to users with in a way that lowers the cognitive load or frustration. However, evaluation of such systems in the real-world production environment with real users and notifications, and evaluation on user's engagement to the presented notification beyond simple responsiveness have not been adequately studied. To the best of our knowledge, this study is the first to investigate user interruptibility and engagement using a real-world large-scale mobile application and real-world notifications consisting of actual news content. We equipped the Yahoo! JAPAN Android app, one of the most popular applications on the national market, with our mobile-sensing and machine-learning-based interruptibility estimation logic. We conducted a large-scale in-the-wild user study with more than 680,000 users for three weeks. The results show that in most cases delaying the notification delivery until an interruptible moment is detected is beneficial to users and results in significant reduction of user response time (49.7%) compared to delivering the notifications immediately. We also observed a higher number of notifications opened in our system as well as constant improvement in user engagement levels throughout the entire study period.

Attention and engagement-awareness in the wild: A large-scale study with adaptive notifications

An information processing apparatus according to the present application includes an acquisition unit and a generation unit. The acquisition unit acquires, when a user performs one of specific behaviors respectively having a plurality of types, information on a behavior of the user performed on a terminal device and information on context associated with the user. The generation unit executes machine learning by using, as feature amounts, information on each of the specific behaviors and the information on the context, to generate a learning model of the behavior of the user performed on the terminal device.

Information processing apparatus, information processing system, information processing method, and computer-readable recording medium

PROBLEM TO BE SOLVED: To make it possible to easily acquire a user's evaluation on a content.SOLUTION: A display program according to the present application causes a computer to execute a display procedure and an acquisition procedure. The display procedure displays a content, and a plurality of options by which predetermined processing on the content is executed according to selection made by a user who uses a terminal device, the plurality of options including options indicating the user's evaluation on the content. The acquisition procedure acquires information on the user's selection according to the user's operation to the plurality of options.SELECTED DRAWING: Figure 2

Display program, display method, terminal device, information processing apparatus, information processing method, and information processing program

Information processing device, information processing system, information processing method, and information processing program

PROBLEM TO BE SOLVED: To properly estimate a kind of a user's occupation.SOLUTION: An estimating apparatus according to the present application has an acquiring unit and an estimating unit. The acquiring unit acquires action information including a user's positional information. The estimating unit estimates a kind of a user's occupation based on the user's action information acquired by the acquiring unit.SELECTED DRAWING: Figure 3

Masaya Taji

Papers

Attention and engagement-awareness in the wild: A large-scale study with adaptive notifications

Information processing apparatus, information processing system, information processing method, and computer-readable recording medium

Display program, display method, terminal device, information processing apparatus, information processing method, and information processing program

Information processing device, information processing system, information processing method, and information processing program

Estimating apparatus, estimating method, and estimating program