Improving Work-Nonwork Balance with Data-Driven Implementation Intention with Mental ContrastingWork-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.2024YSYasaman S. Sefidgar et al.Session 3b: Work, Non-Work, and Social TechnologiesCSCW
DISCERN: Designing Decision Support Interfaces to Investigate the Complexities of Workplace Social Decision-Making With Line ManagersLine 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.2024PKPranav Khadpe et al.Carnegie Mellon UniversityAI-Assisted Decision-Making & AutomationContext-Aware ComputingCHI
Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational StakeholdersWith 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.2023AKAnna Kawakami et al.Workplace ICSCW
Large Scale Analysis of Multitasking Behavior During Remote MeetingsVirtual 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).2021HCHancheng Cao et al.Stanford UniversityRemote Work Tools & ExperienceNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at WorkInformation 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.2020HKHarmanpreet Kaur et al.University of MichiganNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
Sketching NLP: A Case Study of Exploring the Right Things To Design with Language IntelligenceThis 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.2019QYQian Yang et al.Carnegie Mellon UniversityHuman-LLM CollaborationAI-Assisted Creative WritingCHI
Casual Microtasking: Embedding Microtasks in FacebookMicrotasks 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.2019NHNathan Hahn et al.Carnegie Mellon UniversityCrowdsourcing Task Design & Quality ControlOpen-Source Collaboration & Code ReviewNotification & Interruption ManagementCHI
Guidelines for Human-AI InteractionAdvances 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.2019SASaleema Amershi et al.MicrosoftVoice User Interface (VUI) DesignAI-Assisted Decision-Making & AutomationAlgorithmic Fairness & BiasCHI
Effects of Individual Differences in Blocking Workplace DistractionsInformation 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.2018GMGloria Mark et al.University of California, IrvineNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
Creating Better Action Plans for Writing Tasks via Vocabulary-Based PlanningWhile 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.2018HKHarmanpreet Kaur et al.Language and LinguisticsCSCW
Multitasking with Play Write, a Mobile Microproductivity Writing ToolMobile 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.2018SIShamsi T. Iqbal et al.Intelligent Voice Assistants (Alexa, Siri, etc.)Human-LLM CollaborationKnowledge Worker Tools & WorkflowsUIST
Supporting Workplace Detachment and Reattachment with Conversational IntelligenceResearch 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.2018AWAlex C Williams et al.University of WaterlooConversational ChatbotsNotification & Interruption ManagementCHI
Automotive User Interfaces: Expert DiscussionAutomation is making significant advances in vehicles, with adaptive cruise control and lane keeping assistance being prominent technologies we encounter on the road today. How should we design user interactions for vehicles with automation? Panelists will lead the audience in discussions about (a) how to design interactions for driving-related and non-driving-related activities; (b) how the designs are affected by the availability of different types of vehicle automation, and how their effectiveness can be tested, (c) how we can approach the designs from the perspective of vehicle occupants, as well as from the perspective of other traffic participants, and (d) how to guide not only practice but also theory development about human-machine interaction for automated vehicles.2018SBSusanne Boll et al.University of OldenburgAutomated Driving Interface & Takeover DesignAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesCHI