Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

The future of work is rapidly changing, with remote and hybrid settings blurring the boundaries between professional and personal life. To understand how work rhythms vary across different work settings, we conducted a month-long study of 65 software developers, collecting anonymized computer activity data as well as daily ratings for perceived stress, productivity, and work setting. In addition to confirming the double-peak pattern of activity at 10:00 am and 2:00 pm observed in prior research, we observed a significant third peak around 9:00 pm. This third peak was associated with higher perceived productivity during remote days but increased stress during onsite and hybrid days, highlighting a nuanced interplay between work demands and work settings. Additionally, we found strong correlations between computer activity, productivity, and stress, including an inverted U-shaped relationship where productivity peaked at around six hours of computer activity before declining on more active days. These findings provide new insights into evolving work rhythms and highlight the impact of different work settings on productivity and stress.

Line managers form the first level of management in organizations, and must make complex decisions, while maintaining relationships with those impacted by their decisions. Amidst growing interest in technology-supported decision-making at work, their needs remain understudied. Further, most existing design knowledge for supporting social decision-making comes from domains where decision-makers are more socially detached from those they decide for. We conducted iterative design research with line managers within a technology organization, investigating decision-making practices, and opportunities for technological support. Through formative research, development of a decision-representation tool—DISCERN—and user enactments, we identify their communication and analysis needs that lack adequate support. We found they preferred tools for externalizing reasoning rather than tools that replace interpersonal interactions, and they wanted tools to support a range of intuitive and calculative decision-making. We discuss how design of social decision-making supports, especially in the workplace, can more explicitly support highly interactional social decision-making.

Email continues to serve as a central medium for managing collaborations. While unstructured email messaging is lightweight and conducive to coordination, it is easy to overlook commitments and requests for collaborations that are embedded in the text of free-flowing communications. Twenty-one years ago, Bellotti et al. proposed TaskMaster with the goal of redesigning the email interface to have explicit task management capabilities. Recently, AI-based task recognition and reminder services have been introduced in major email systems as one approach to managing asynchronous collaborations. While these services have been provided to millions of people around the world, there is little understanding of how people interact with and benefit from them. We explore knowledge workers' experiences with Microsoft's Viva Daily Briefing Email to better understand how AI-powered reminders can support asynchronous collaborations. Through semi-structured interviews and surveys, we shed light on how AI-powered reminders are incorporated into workflows to support asynchronous collaborations. We identify what knowledge workers prefer AI-powered reminders to remind them about and how they would like to interact with these reminders. Using mixed methods and a self-assessment methodology, we investigate the relationship between information workers' work styles and the perceived value of the Viva Daily Briefing Email to identify users who are more likely to benefit from AI-powered reminders for asynchronous collaborations. We conclude by discussing the experiences and futures of AI-powered reminders for collaborative tasks and asynchronous collaborations.

Work-nonwork balance is an important aspect of workplace well-being with associations to improved physical and mental health, job performance, and quality of life. However, realizing work-nonwork balance goals is challenging due to competing demands and limited resources within organizational and interpersonal contexts. These challenges are compounded by technologies that blur the boundaries of work and nonwork in the always-on work cultures. At an individual level, such challenges can be subsided through the effective application of self-regulation techniques, such as implementation intentions and mental contrasting (IIMC). Further supporting these techniques through reflection on personal data, we implement the idea of data-driven IIMC into a self-tracking and behavior planning system and evaluate it in a three-week between-participant study with 43 information workers who used our system for improving work-nonwork balance. We find evidence that reflection on personal data improves awareness of behavior plan compliance and rescheduling, which are important in realizing work-nonwork balance goals. We also observe the value of micro-reflection, reflection on limited data of the very recent past, for IIMC. Our findings highlight opportunities for automation in data collection and sense-making and for further exploring the role of data-driven IIMC as boundary negotiating artifacts in support of work-nonwork balance goals.

Information workers often struggle to balance their time for a variety of activities like focused work, communication, and caring. This study analyzes the impact of a commercially available computer-assisted time protection intervention that automatically and preemptively schedules calendar time for self-determined activities. We analyzed the behaviors and self-reports of workers in two naturalistic studies. First, we studied 27 workers who were already using Computer-Assisted Protected Time (CAP time) and found that they mainly used it for focused work. Second, we analyzed the effect of CAP time as a randomized intervention on 89 workers who never had CAP time and found that those with it self-reported an increase in performance, job resources, and immersion. In both studies, workers with CAP time exhibited a rearrangement of activities leading to an overall reduction in work activity. This study highlights new opportunities for intelligent time-management interventions and the importance of protected time at work.

With the heightened digitization of the workplace, alongside the rise of remote and hybrid work prompted by the pandemic, there is growing corporate interest in using passive sensing technologies for workplace wellbeing. Existing research on these technologies often focus on understanding or improving interactions between an individual user and the technology. Workplace settings can, however, introduce a range of complexities that challenge the potential impact and in-practice desirability of wellbeing sensing technologies. Today, there is an inadequate empirical understanding of how everyday workers---including those who are impacted by, and impact the deployment of workplace technologies--envision its broader socio-ecological impacts. In this study, we conduct storyboard-driven interviews with 33 participants across three stakeholder groups: organizational governors, AI builders, and worker data subjects. Overall, our findings surface how workers envisioned wellbeing sensing technologies may lead to cascading impacts on their broader organizational culture, interpersonal relationships with colleagues, and individual day-to-day lives. Participants anticipated harms arising from ambiguity and misalignment around scaled notions of "worker wellbeing,'' underlying technical limitations to workplace-situated sensing, and assumptions regarding how social structures and relationships may shape the impacts and use of these technologies. Based on our findings, we discuss implications for designing worker-centered data-driven wellbeing technologies.

Task management tools allow people to record, track, and manage task-related information across their work and personal contexts. As work contexts have shifted amid the COVID-19 pandemic, it has become important to understand how these tools are continuing or failing to support peoples’ work-related and personal needs. In this article, we examine and probe practices for managing task-related information across the work–life boundary. We report findings from an online survey deployed to 150 information workers during Summer 2019 (i.e., pre-pandemic) and 70 information workers at the same organization during Summer 2020 (i.e., mid-pandemic). Across both survey cohorts, we characterize these cross-boundary task management practices, exploring the central role that physical and digital tools play in managing task-related information that arises at inopportune times. We conclude with a discussion of the opportunities and challenges for future productivity tools that aid people in managing task-related information across their personal and work contexts.

With the growing prevalence of affective computing applications, Automatic Emotion Recognition (AER) technologies have garnered attention in both research and industry settings. Initially limited to speech-based applications, AER technologies now include analysis of facial landmarks to provide predicted probabilities of a common subset of emotions (e.g., anger, happiness) for faces observed in an image or video frame. In this paper, we study the relationship between AER outputs and self-reports of affect employed by prior work, in the context of information work at a technology company. We compare the continuous observed emotion output from an AER tool to discrete reported affect obtained via a one-day combined tool-use and diary study (N = 15). We provide empirical evidence showing that these signals do not completely align, and find that using additional workplace context only improves alignment up to 58.6%. These results suggest affect must be studied in the context it is being expressed, and observed emotion signal should not replace internal reported affect for affective computing applications.

Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people’s productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking).

People often capture photos or notes from their phones to integrate later into a document. But current mobile capture tools can make this hard, with the captured information ending up fragmented and decontextualized. This paper explores how to help document authors capture, contextualize, and use document-related information. A survey of 66 information workers reveals that document-focused information capture differs from other types of mobile information capture, and that while people capture a broad range of information types while mobile, most document-related capture comes in the form of photos, notes, and bookmarks. Based on this survey we built Scraps, which consists of two parts: 1) a mobile app that makes it easy for people to capture and add context to information from their phone, and 2) a Word sidebar that helps them later link that information to a document on their desktop. In a field study with 11 information workers, we find that Scraps streamlined the process of capturing and using document-related information, and enabled people to focus on writing over integrating captured information.

Information workers perform jobs that demand constant multitasking, leading to context switches, productivity loss, stress, and unhappiness. Systems that can mediate task transitions and breaks have the potential to keep people both productive and happy. We explore a crucial initial step for this goal: finding opportune moments to recommend transitions and breaks without disrupting people during focused states. Using affect, workstation activity, and task data from a three-week field study (N=25), we build models to predict whether a person should continue their task, transition to a new task, or take a break. The R-squared values of our models are as high as 0.7, with only 15% error cases. We ask users to evaluate the timing of recommendations provided by a recommender that relies on these models. Our study shows that users find our transition and break recommendations to be well-timed, rating them as 86% and 77% accurate, respectively. We conclude with a discussion of the implications for intelligent systems that seek to guide task transitions and manage interruptions at work.

The unique limitations of mobile environments make content creation and editing difficult. Microtasking—breaking down complex tasks into subtasks—requires shorter attention spans and quick interactions, making it suitable for mobile usage scenarios. Writing is an ideal process for mobile microtasking because of its many subgoals, but little is known about how writers can use this decomposition through the evolution of a document. In this paper we present findings from a controlled, week long study to characterize how writers use mobile microtasks while authoring a document. We found that writers created microtasks for editing and inserting information that generally required minimal writing. These tasks were especially well suited for mobile devices with writers completing tasks on commutes or while waiting for meetings. Writers who microtasked found it easy to interact with their document and complete tasks, writing and editing their document more overall compared to writers who instead edited their document directly on their phone.

As AI technology advances, it offers promising opportunities to improve educational outcomes when integrated with an overall learning experience. We investigate forward-looking interactive reading experiences that leverage both automatic question generation and analysis of attention signals, such as gaze tracking, to improve short- and long-term learning outcomes. We aim to expand the known pedagogical benefits of adjunct questions to more general reading scenarios, by investigating the benefits of adjunct questions generated after participants attend to passages in an article, based on their gaze behavior. We also compare the effectiveness of manually-written questions with those produced by Automatic Question Generation (AQG). We further investigate gaze and reading patterns indicative of low vs. high learning in both short- and long-term scenarios (one-week followup). We show AQG-generated adjunct questions have promise as a way to scale to a wide variety of reading material where the cost of manually curating questions may be prohibitive.

Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human-AI interaction design principles.

Microtasks enable people with limited time and context to contribute to a larger task. In this paper we explore casual microtasking, where microtasks are embedded into other primary activities so that they are available to be completed when convenient. We present a casual microtasking experience that inserts writing microtasks from an existing microwriting tool into the user's Facebook feed. From a two-week deployment of the system with nine people, we observe that casual microtasking enabled participants to get things done during their breaks, and that they tended to do so only after first engaging with Facebook's social content. Participants were most likely to complete the writing microtasks during periods of the day associated with low focus, and would occasionally use them as a springboard to open the original document in Word. These findings suggest casual microtasking can help people leverage spare micromoments to achieve meaningful micro-goals, and even encourage them to return to work.

In-car intelligent assistants offer the opportunity to help drivers productively use previously unclaimed time during their commute. However, engaging in secondary tasks can reduce attention on driving and thus may affect road safety. Any interface used while driving, even if speech-based, cannot consider non-driving tasks in isolation of driving---alerts for safer driving and timing of the non-driving tasks are crucial to maintaining safety. In this work, we explore experiences with a speech-based assistant that attempts to help drivers safely complete complex productivity tasks. Via a controlled simulator study, we look at how level of support and road context alerts from the assistant influence a driver's ability to drive safely while writing a document or creating slides via speech. Our results suggest ways to support speech-based productivity interactions and how speech-based road context alerts may influence driver behavior.

This paper investigates how to sketch NLP-powered user experiences. Sketching is a cornerstone of design innovation. When sketching, designers rapidly experiment with a number of abstract ideas using simple, tangible instruments such as drawings and paper prototypes. Sketching NLP-powered experiences, however, presents challenges, i.e. How to visualize abstract language interaction? How to ideate a broad range of technically feasible intelligent functionalities? As a first step towards understanding these challenges, we present a first-person account of our sketching process when designing intelligent writing assistance. We detail the challenges we encountered and emergent solutions, such as a new format of wireframe for sketching language interactions and a new wizard-of-oz-based NLP rapid prototyping method. Drawing on these findings, we discuss the importance of abstraction in sketching and other implications.

There has been considerable research on how software can enhance programmers' productivity within their workspace. In this paper, we instead explore how software might help programmers make productive use of their time while away from their workspace. We interviewed 10 software engineers and surveyed 78 others and found that while programmers often do work while mobile, their existing mobile work practices are primarily exploratory (e.g., capturing thoughts or performing online research). In contrast, they want to be doing work that is more grounded in their existing code (e.g., code review or bug triage). Based on these findings, we introduce Mercury, a system that guides programmers in making progress on-the-go with auto-generated microtasks derived from their source code's current state. A study of Mercury with 20 programmers revealed that they could make meaningful progress with Mercury while mobile with little effort or attention. Our findings suggest an opportunity exists to support the continuation of programming tasks across devices and help programmers resume coding upon returning to their workspace.

As vehicles of the future take on more of the driving responsibility and the role of the driver transitions into more of a monitoring capacity, the traditional notions of interruption and attention management needs to be reconsidered for automated vehicles. We argue that the transfer of control between the automated vehicle and the human driver can be considered as an interruption handling process, and that this process goes through a series of ten explicit stages. Each stage has its own characteristics and implications for practice and future research. Therefore, in this paper we identify for each stage what is known from theory, together with important implications for safety, design, and future research, especially for human-machine interaction. More generally, the framework makes explicit that it is not appropriate to think of transfer of control as a single event or even small set of events. The framework also highlights that it might not be realistic to expect human drivers to immediately respond correctly to a system initiated request to transfer control, given that humans interleave their attention between non-driving and driving tasks, and given that a transition constitutes of multiple stages. These nuances are accounted for in the framework.

Information workers are experiencing ever-increasing online distractions in the workplace, and software to block distractions is becoming more popular. We conducted an exploratory field study with 32 information workers in their workplace using software to block online distractions for one week. We discovered that with online distractions blocked, participants assessed their focus and productivity to be significantly higher. Those who benefited most were those who reported being less in control of their work, associated with personality traits of lower Conscientiousness and Lack of Perseverence. Unexpectedly, those reporting higher control of work experienced a cost of higher workload with online distractions blocked. Those who reported the greatest increase in focus with distractions blocked were those who were more susceptible to social media distractions. Without distractions, people with higher control of work worked longer stretches without physical breaks, with consequently higher stress. We present design recommendations to promote focus for our observed coping behaviors.

Research has shown that productivity is mediated by an individual's ability to detach from their work at the end of the day and reattach with it when they return the next day. In this paper we explore the extent to which structured dialogues, focused on individuals' work-related tasks or emotions, can help them with the detachment and reattachment processes. Our inquiry is driven with SwitchBot, a conversational bot which engages with workers at the start and end of their work day. After preliminarily validating the design of a detachment and reattachment dialogue frame-work with 108 crowdworkers, we study SwitchBot's use in-situ for 14 days with 34 information workers. We find that workers send fewer e-mails after work hours and spend a larger percentage of their first hour at work using productivity applications than they normally would when using SwitchBot. Further, we find that productivity gains were better sustained when conversations focused on work-related emotions. Our results suggest that conversational bots can be effective tools for aiding workplace detachment and reattachment and help people make successful use of their time on and off the job.

Mobile devices offer people the opportunity to get useful tasks done during time previously thought to be unusable. Because mobile devices have small screens and are often used in divided attention scenarios, people are limited to using them for short, simple tasks; complex tasks like editing a document present significant challenges in this environment. In this paper we demonstrate how a complex task requiring focused attention can be adapted to the fragmented way people work while mobile by decomposing the task into smaller, simpler microtasks. We introduce Play Write, a microproductivity tool that allows people to edit Word documents from their phones via such microtasks. When participants used Play Write while simultaneously watching a video, we found that they strongly preferred its microtask-based editing approach to the traditional editing experience offered by Mobile Word. Play Write made participants feel more productive and less stressed, and they completed more edits with it. Our findings suggest microproductivity tools like Play Write can help people be productive in divided attention scenarios.

While having a step-by-step breakdown for a task-an action plan-helps people complete tasks, prior work has shown that people prefer not to make action plans for their own tasks. Getting planning support from others could be beneficial, but it is limited by how much domain knowledge people have about the task and how available they are. Our goal is to incorporate the benefits of having action plans in the complex domain of writing, while mitigating the time and effort costs of creating plans. To mitigate these costs, we introduce a vocabulary-a finite set of functions pertaining to writing tasks-as a cognitive scaffold that enables people with necessary context (e.g. collaborators) to generate action plans for others. We develop this vocabulary by analyzing 264 comments, and compare plans created using it with those created without any aid, in an online study with 768 comments (N=145) and a lab study with 96 comments (N=8). We show that using a vocabulary reduces planning time and effort and improves plan quality compared to unstructured planning, and opens the door for automation and task sharing for complex tasks.

Semi-autonomous vehicles occasionally require control to be handed over to the driver in situations where the vehicle is unable to operate safely. Currently, such handover requests require the driver to take control almost instantaneously. We investigate how auditory pre-alerts that occur well before the handover request impact the success of the handover in a dual task scenario. In a study with a driving simulator, drivers perform tasks on their phone while the car is in an autonomous mode. They receive a repeated burst audio pre-alert or an increasing pulse audio pre-alert preceding the standard warning for immediate handover. Results show that pre-alerts caused people to look more at the road before the handover occurred, and to disengage from the secondary task earlier, compared to when there was no pre-alert. This resulted in safer handover situations. Increasing pulse pre-alerts show particular promise due to their communication of urgency. Our detailed analysis informs the design and evaluation of alerts in safety-critical systems with automation.

Crowd work is typically limited to simple, context-free tasks because they are easy to describe and understand. In contrast, complex tasks require communication between the requester and workers to achieve mutual understanding, which can be more work than it is worth. This paper explores the notion of structured communication: using structured microtasks to support communication in the domain of complex writing. Our studies compare a variety of communication mechanisms with respect to the costs to the requester in providing information and the value of that information to workers while performing the task. We find that different mechanisms are effective at different stages of writing. For early drafts, asking the requester to state the biggest problem in the current write-up is valuable and low cost, while later it is more useful for the worker if the requester highlights the text that needs to be improved. These findings can be used to enable richer, more interactive crowd work than what currently seems possible. We incorporate the findings in a workflow for crowdsourcing written content using appropriately timed mechanisms for communicating with the crowd.

Organizing personal information by folders or tags has proved to be effective for finding, remembering, and understanding information. However, past studies have shown that the cost of organization can be too high for some users to be worth the effort. Mixed‐initiative approaches attempt to reduce the burden of manual organization by automatically identifying and suggesting organizational units such as folders to users. However, little is known about how such mixed‐initiative approaches influence users' organizational experiences. In this paper, we explore a mixed‐initiative approach that suggests high‐level organizational units to users to facilitate e‐mail organization. In 2 in‐situ experiments with 34 knowledge workers, we study how our mixed‐initiative approach influenced users' experience with organization. We show that our approach made it easier to create organizational units without negatively affecting recall of those units, and led to the creation of units that otherwise would have not been created. Our findings suggest ways computers and people can most effectively work together to organize information.

Microtasks are small units of work designed to be completed individually, eventually contributing to a larger goal. Although microtasks can be performed in isolation, in practice people often complete a chain of microtasks within a single session. Through a series of crowd-based studies, we look at how various microtasks can be chained together to improve efficiency and minimize mental demand, focusing on the writing domain. We find that participants completed low-complexity microtasks faster when they were preceded by the same type of microtask, whereas they found high-complexity microtasks less mentally demanding when pre-ceded by microtasks on the same content. Furthermore, participants were faster at starting high-complexity microtasks after completing lower-complexity microtasks, but completion time and quality were not affected. These findings provide insight into how microtasks can be ordered to optimize transitions from one microtask to another.

In HCI research, attention has focused on understanding external influences on workplace multitasking. We explore instead how multitasking might be influenced by individual factors: personality, stress, and sleep. Forty information workers' online activity was tracked over two work weeks. The median duration of online screen focus was 40 seconds. The personality trait of Neuroticism was associated with shorter online focus duration and Impulsivity-Urgency was associated with longer online focus duration. Stress and sleep duration showed trends to be inversely associated with online focus. Shorter focus duration was associated with lower assessed productivity at day's end. Factor analysis revealed a factor of lack of control which significantly predicts multitasking. Our results suggest that there could be a trait for distractibility where some individuals are susceptible to online attention shifting in the workplace. Our results have implications for information systems (e.g. educational systems, game design) where attention focus is key.

While email provides numerous benefits in the workplace, it is unclear how patterns of email use might affect key workplace indicators of productivity and stress. We investigate how three email use patterns: duration, interruption habit, and batching, relate to perceived workplace productivity and stress. We tracked email usage with computer logging, biosensors and daily surveys for 40 information workers in their in situ workplace environments for 12 workdays. We found that the longer daily time spent on email, the lower was perceived productivity and the higher the measured stress. People who primarily check email through self-interruptions report higher productivity with longer email duration compared to those who rely on notifications. Batching email is associated with higher rated productivity with longer email duration, but despite widespread claims, we found no evidence that batching email leads to lower stress. We discuss the implications of our results for improving organizational email practices.

This paper presents the MicroWriter, a system that decomposes the task of writing into three types of microtasks to produce a single report: 1) generating ideas, 2) labeling ideas to organize them, and 3) writing paragraphs given a few related ideas. Because each microtask can be completed individually with limited awareness of what has been already done and what others are doing, this decomposition can change the experience of collaborative writing. Prior work has used microtasking to support collaborative writing with unaffiliated crowd workers. To instead study its impact on collaboration among writers with context and investment in the writing project, we asked six groups of co-workers (or 19 people in total) to use the MicroWriter in a synchronous, collocated setting to write a report about a shared work goal. Our observations suggest ways that recent advances in microtasking and crowd work can be used to support collaborative writing within preexisting groups.

Despite a long history and a large volume of affective research, measuring affective states is still a non-trivial task that is complicated by numerous conceptual and methodological decisions that the researcher has to make. We suggest that inconsistent results reported in some areas of research can be partially explained by the choice of measurements that capture different manifestations of affective phenomena, or focus on different elements of affective processes. In the present study we examine one of such topics - a relationship between stress and individual's work role. In a 2-week, multi-method in situ study we collected affective information from 40 subjects. All participants provided continuous physiological (cardiovascular) data for the entire duration of the study, submitted multiple daily self-reports of momentary affect, and filled out a onetime assessment of the global perceived stress. We found that individuals' job role (specifically, decision-making workload) was not related to the cumulative measures of momentary affect, but was negatively correlated with the overall level of perceived stress. We further found that this negative relationship was partially mediated by individuals' coping behaviors. Our results emphasize the important difference between fleeting and global (appraised) affective states, and remind about intervening variables that can significantly modify affective processes. We suggest directions for future research and discuss practical applications for stress management.

A large, seemingly overwhelming task can sometimes be transformed into a set of smaller, more manageable microtasks that can each be accomplished independently. For example, it may be hard to subjectively rank a large set of photographs, but easy to sort them in spare moments by making many pairwise comparisons. In crowdsourcing systems, microtasking enables unskilled workers with limited commitment to work together to complete tasks they would not be able to do individually. We explore the costs and benefits of decomposing macrotasks into microtasks for three task categories: arithmetic, sorting, and transcription. We find that breaking these tasks into microtasks results in longer overall task completion times, but higher quality outcomes and a better experience that may be more resilient to interruptions. These results suggest that microtasks can help people complete high quality work in interruption-driven environments.

Language barrier is the primary challenge for effective cross-lingual conversations. Spoken language translation (SLT) is perceived as a cost-effective alternative to less affordable human interpreters, but little research has studied how people interact with such technology. Using a prototype translator application, we performed a formative evaluation to elicit how people interact with the technology and adapt their conversation style. We conducted two sets of studies with a total of 23 pairs (46 participants). Participants worked on storytelling tasks to simulate natural conversations with 3 different interface settings. Our findings show that collocutors naturally adapt their style of speech production and comprehension to compensate for inadequacies in SLT. We conclude the paper with design guidelines that emerged from the analysis.

Striking up a good conversation with new acquaintances is often a difficult problem. In this paper we report on the perceptions of wearable device users who were given real-time personalized topic suggestions during a conversation with a person they just met. Suggestions were generated using a ranking recommendation algorithm, and were delivered via Google Glasses. We conducted a study with 38 pairs of strangers, who received such suggestions while conversing for the first time. Participants found the suggestions to be helpful, but only at the right moments, and for certain types of speakers. Our results contribute to the understanding of how communication interventions influence people's experience and behaviors, and enhance interpersonal interactions. Our study also presents design implications for applications on wearable devices to facilitate conversations between strangers.

A common assumption in studies of interruptions is that one is focused in an activity and then distracted by other stimuli. We take the reverse perspective and examine whether one might first be in an attentional state that makes one susceptible to communications typically associated with distraction. We explore the confluence of multitasking and workplace communications from three temporal perspectives -- prior to an interaction, when tasks and communications are interleaved, and at the end of the day. Using logging techniques and experience sampling, we observed 32 employees in situ for five days. We found that certain attentional states lead people to be more susceptible to particular types of interaction. Rote work is followed by more Facebook or face-to-face interaction. Focused and aroused states are followed by more email. The more time in email and face-fo-face interaction, and the more total screen switches, the less productive people feel at the day's end. We present the notion of emotional homeostasis along with new directions for multitasking research.

While distractions using digital media have received attention in HCI, understanding engagement in workplace activities has been little explored. We logged digital activity and continually probed perspectives of 32 information workers for five days in situ to understand how attentional states change with context. We present a framework of how engagement and challenge in work relate to focus, boredom, and rote work. Overall, we find more focused attention than boredom in the workplace. Focus peaks mid-afternoon while boredom is highest in early afternoon. People are happiest doing rote work and most stressed doing focused work. On Mondays people are most bored but also most focused. Online activities are associated with different attentional states, showing different patterns at beginning and end of day, and before and after a mid-day break. Our study shows how rhythms of attentional states are associated with context and time, even in a dynamic workplace environment.

What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.

With proliferation of mobile devices that provide ubiquitous access to information, the question arises of how distracting processing information in social settings can be, especially during face-to-face conversations. However, relevant information presented at opportune moments may help enhance conversation quality. In this paper, we study how much information users can consume during a conversation and what information delivery mode, via audio or visual aids, helps them effectively conceal the fact that they are receiving information. We observe that users can internalize more information while still disguising this fact the best when information is delivered visually in batches (multiple pieces of information at a time) and perform better on both dimensions if information is delivered while they are not speaking. Interestingly, participants qualitatively did not prefer this mode as being the easiest to use, preferring modes that displayed one piece of information at a time.

We contrast the Chameleon Lens, which uses 3D movement of a mobile device held in the nonpreferred hand to support panning and zooming, with the Pinch-Flick-Drag metaphor of directly manipulating the view using multi-touch gestures. Lens-like approaches have significant potential because they can support navigation-selection, navigation-annotation, and other such compound tasks by off-loading navigation to the nonpreferred hand while the preferred hand annotates, marks a location, or draws a path on the screen. Our experimental results show that the Chameleon Lens is significantly slower than Pinch-Flick-Drag for the navigation subtask in isolation. But our studies also reveal that for navigation between a few known targets the lens performs significantly faster, that differences between the Chameleon Lens and Pinch-Flick-Drag rapidly diminish as users gain experience, and that in the context of a compound navigation-annotation task, the lens performs as well as Pinch-Flick-Drag despite its deficit for the navigation subtask itself.

Despite the common use of mobile computing devices to communicate and access information, the effects of peripheral computing tasks on people's attention is not well understood. Studies that have identified consequences of multitasking in diverse domains have largely focused on influences on productivity. We have yet to understand perceptions and preferences regarding the use of computing devices for potentially extraneous tasks in settings such as presentations at seminars and colloquia. We explore costs and attitudes about the use of computing devices by people attending presentations. We find that audience members who use devices believe that they are missing content being presented and are concerned about social costs. Other attendees report being less offended by multitasking around them than the device users may realize.

Conversing on cell phones while driving is a risky, yet commonplace activity. State legislatures in the U.S. have enacted rules that limit hand-held phone conversations while driving but that allow for hands-free conversations. However, studies have demonstrated that the cognitive load of conversation is a significant source of distraction that increases the likelihood of accidents. We explore in a controlled study with a driving simulator the effectiveness of proactive alerting and mediation of communications during phone conversations while driving. We study the use of auditory messages indicating upcoming critical road conditions and placing calls on hold. We found that such actions reduce driving errors and that alerts sharing details about situations were more effective than general alerts. Drivers found such a system valuable in most situations for maintaining driving safety. These results provide evidence that context-sensitive mediation systems could play a valuable role in focusing drivers' attention on the road during phone conversations.

Conversing on cell phones while driving an automobile is a common practice. We examine the interference of the cognitive load of conversational dialog with driving tasks, with the goal of identifying better and worse times for conversations during driving. We present results from a controlled study involving 18 users using a driving simulator. The driving complexity and conversation type were manipulated in the study, and performance was measured for factors related to both the primary driving task and secondary conversation task. Results showed significant interactions between the primary and secondary tasks, where certain combinations of complexity and conversations were found especially detrimental to driving. We present the studies and analyses and relate the findings to prior work on multiple resource models of cognition. We discuss how the results can frame thinking about policies and technologies aimed at enhancing driving safety.

The impact of interruptions on workflow and productivity has been extensively studied in the PC domain, but while fragmented user attention is recognized as an inherent aspect of mobile phone usage, little formal evidence exists of its effect on mobile productivity. Using a survey and a screenshot-based diary study we investigated the types of barriers people face when performing tasks on their mobile phones, the ways they follow up with such suspended tasks, and how frustrating the experience of task disruption is for mobile users. From 386 situated samples provided by 12 iPhone and 12 Pocket PC users, we distill a classification of barriers to the completion of mobile tasks. Our data suggest that moving to a PC to complete a phone task is common, yet not inherently problematic, depending on the task. Finally, we relate our findings to prior design guidelines for desktop workflow, and discuss how the guidelines can be extended to mitigate disruptions to mobile taskflow.

Desktop notifications are designed to provide awareness of information while a user is attending to a primary task. Unfortunately the awareness can come with the price of disruption to the focal task. We review results of a field study on the use and perceived value of email notifications in the workplace. We recorded users' interactions with software applications for two weeks and studied how notifications or their forced absence influenced users' quest for awareness of new email arrival, as well as the impact of notifications on their overall task focus. Results showed that users view notifications as a mechanism to provide passive awareness rather than a trigger to switch tasks. Turing off notifications cause some users to self interrupt more to explicitly monitor email arrival, while others appear to be able to better focus on their tasks. Users acknowledge notifications as disruptive, yet opt for them because of their perceived value in providing awareness.

Although there has been significant research into gender regarding educational and workplace practices, there has been little investigation of gender differences pertaining to problem solving with programming tools and environments. As a result, there is little evidence as to what role gender plays in programming tools---and what little evidence there is has involved mainly novice and end-user programmers in academic studies. This paper therefore investigates how widespread such phenomena are in industrial programming situations, considering three disparate programming populations involving almost 3000 people and three different programming platforms in industry. To accomplish this, we analyzed four industry "legacy" studies from a gender perspective, triangulating results against each other and against a new fifth study, also in industry. We investigated gender differences in software feature usage and in tinkering/exploring software features. Furthermore, we examined how such differences tied to confidence. Our results showed significant gender differences in all three factors---across all populations and platforms.

A notification represents the proactive delivery of information to a user and reduces the need to visually scan or repeatedly check an external information source. At the same time, notifications often interrupt user tasks at inopportune moments, decreasing productivity and increasing frustration. Controlled studies have shown that linking notification delivery to the perceptual structure of a user's tasks can reduce these interruption costs. However, in these studies, the scheduling was always performed manually, and it was not clear whether it would be possible for a system to mimic similar techniques. This article contributes the design and implementation of a novel system called Oasis that aligns notification scheduling with the perceptual structure of user tasks. We describe the architecture of the system, how it detects task structure on the fly without explicit knowledge of the task itself, and how it layers flexible notification scheduling policies on top of this detection mechanism. The system also includes an offline tool for creating customized statistical models for detecting task structure. The value of our system is that it intelligently schedules notifications, enabling the reductions in interruption costs shown within prior controlled studies to now be realized by users in everyday desktop computing tasks. It also provides a test bed for experimenting with how notification management policies and other system functionalities can be linked to task structure.

We present a novel system for notification management and report results from two studies testing its performance and impact. The system uses statistical models to realize defer-to-breakpoint policies for managing notifications. The first study tested how well the models detect three types of breakpoints within novel task sequences. Results show that the models detect breakpoints reasonably well, but struggle to differentiate their type. Our second study explored effects of managing notifications with our system on users and their tasks. Results showed that scheduling notifications at breakpoints reduces frustration and reaction time relative to delivering them immediately. We also found that the relevance of notification content determines the type of breakpoint at which it should be delivered. The core concept of scheduling notifications at breakpoints fits well with how users prefer notifications to be managed. This indicates that users would likely adopt the use of notification management systems in practice.

Notifications can have reduced interruption cost if delivered at moments of lower mental workload during task execution. Cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during execution of goal-directed tasks, focusing on regions between adjacent chunks within the tasks, that is, the subtask boundaries. In a controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured using an eye tracking system. The workload data was extracted from the pupil data, precisely aligned to the corresponding task models, and analyzed. Our principal findings include (i) workload changes throughout the execution of goal-directed tasks; (ii) workload exhibits transient decreases at subtask boundaries relative to the preceding subtasks; (iii) the amount of decrease tends to be greater at boundaries corresponding to the completion of larger chunks of the task; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.

The ability to detect and differentiate breakpoints during task execution is critical for enabling defer-to-breakpoint policies within interruption management. In this work, we examine the feasibility of building statistical models that can detect and differentiate three granularities (types) of perceptually meaningful breakpoints during task execution, without having to recognize the underlying tasks. We collected ecological samples of task execution data, and asked observers to review the interaction in the collected videos and identify any perceived breakpoints and their type. Statistical methods were applied to learn models that map features of the interaction to each type of breakpoint. Results showed that the models were able to detect and differentiate breakpoints with reasonably high accuracy across tasks. Among many uses, our resulting models can enable interruption management systems to better realize defer-to-breakpoint policies for interactive, free-form tasks.

We report on a field study of the multitasking behavior of computer users focused on the suspension and resumption of tasks. Data was collected with a tool that logged users' interactions with software applications and their associated windows, as well as incoming instant messaging and email alerts. We describe methods, summarize results, and discuss design guidelines suggested by the findings.

Pupillary response is a valid indicator of mental workload and is being increasingly leveraged to identify lower cost moments for interruption, evaluate complex interfaces, and develop further understanding of psychological processes. Existing tools are not sufficient for analyzing this type of data, as it typically needs to be analyzed in relation to the corresponding task’s execution. To address this emerging need, we have developed a new interactive analysis tool, TAPRAV. The primary components of the tool include; (i) a visualization of pupillary response aligned to the corresponding model of task execution, useful for exploring relationships between these two data sources; (ii) an interactive overview + detail metaphor, enabling rapid inspection of details while maintaining global context; (iii) synchronized playback of the video of the user’s screen interaction, providing awareness of the state of the task; and (iv) interaction supporting discovery driven analysis. Results from a user study showed that users are able to efficiently interact with the tool to analyze relationships between pupillary response and task execution. The primary contribution of our tool is that it demonstrates an effective visualization and interaction design for rapidly exploring pupillary response in relation to models of task execution, thereby reducing the analysis effort.

A challenge in building interruption reasoning systems is to compute an accurate cost of interruption (COI). Prior work has used interface events and other cues to predict COI, but ignore characteristics related to the structure of a task. This work investigates how well characteristics of task structure can predict COI, as objectively measured by resumption lag. In an experiment, users were interrupted during task execution at various boundaries to collect a large sample of resumption lag values. Statistical methods were employed to create a parsimonious model that uses characteristics of task structure to predict COI. A subsequent experiment with different tasks showed that the model can predict COI with reasonably high accuracy. Our model can be expediently applied to many goal-directed tasks, allowing systems to make more effective decisions about when to interrupt.

To contribute to systems that reason about human attention, our work empirically demonstrates how a user's mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.