Generative AI is rapidly reshaping young people’s digital experiences, from providing emotional support to introducing new dimensions of risks. Yet, existing safety frameworks are not equipped to handle the unique risks posed by GenAI. To investigate how youth safety is being addressed in this new landscape, we conducted a systematic review of (N=30) GenAI-youth studies from 2014-2025. We found that GenAI-youth-related research was primarily led by AI experts with minimal involvement from youth development experts or young people themselves. Safety was typically framed as a technical system feature, optimized through filters, benchmarks, or guardrails, rather than a relational, contextual, and developmentally grounded concern. We call on the HCI community to re-evaluate its approach to participation in AI. We must move beyond reactive, system-driven GenAI approaches to youth safety towards a more holistic, proactive model where multistakeholder inclusion is a core aspect throughout the AI-lifecycle, leading to safer and equitable systems.

Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, the AI agent produces a response. However, the interaction dynamics are more complex within group settings. How should an agent behave in these settings? We report on two experiments aimed at uncovering users' experiences of an AI agent's participation within a group, in the context of group ideation (brainstorming). In the first study, participants benefited from and preferred having the AI agent in the group, but participants disliked when the agent seemed to dominate the conversation and they desired various controls over its interactive behaviors. In the second study, we created functional controls over the agent's behavior, operable by group members, to validate their utility and probe for additional requirements. Integrating our findings across both studies, we developed a taxonomy of controls for when, what, and where a conversational AI agent in a group should respond, who can control its behavior, and how those controls are specified and implemented. Our taxonomy is intended to aid AI creators to think through important considerations in the design of mixed-initiative conversational agents.

Explainability of AI systems is critical for users to take informed actions. Understanding who opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups—people with and without AI background—perceive different types of AI explanations. Quantitatively, we share user perceptions along five dimensions. Qualitatively, we describe how AI background can influence interpretations, elucidating the differences through lenses of appropriation and cognitive heuristics. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design. Carrying critical implications for the field of XAI, our findings showcase how AI generated explanations can have negative consequences despite best intentions and how that could lead to harmful manipulation of trust. We propose design interventions to mitigate them.

Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective and safe use. We present six principles for the design of generative AI applications that address unique characteristics of generative AI UX and offer new interpretations and extensions of known issues in the design of AI applications. Each principle is coupled with a set of design strategies for implementing that principle via UX capabilities or through the design process. The principles and strategies were developed through an iterative process involving literature review, feedback from design practitioners, validation against real-world generative AI applications, and incorporation into the design process of two generative AI applications. We anticipate the principles to usefully inform the design of generative AI applications by driving actionable design recommendations.

When a group makes a decision, it necessitates the understanding and amalgamation of information from different group members. This process becomes particularly intricate in cross-boundary teams, which consist of individuals from diverse organizational backgrounds, each bringing in unique informational tools and representation modalities. People share information generated from their personal tools, and the variance in representation of such information makes it challenging to form cohesive group decisions. We conducted workshop studies with 11 knowledge workers to understand current practices of tool adaptation and negotiation in such teams. The results indicate a reluctance to adopt new tools due to perceived violations of social acceptance, often leading to negative judgments of those suggesting new tools. Consequently, participants in cross-boundary teams gravitated towards their preferred tools, complicating the aggregation of inputs and impeding cohesive decision-making. To address these challenges, we developed a platform facilitating sensemaking and decision-making without necessitating compromises on tool preferences. In our mixed-method within-subject experiments, this approach enabled faster, more informed decision-making with reduced mental load and increased engagement through enhanced social interaction and acknowledgment of diverse contributions.

When people need help from their supervisors or peers, they often have to manage up to get things done. However, unlike managing subordinates (managing down), managing people of equal or higher status (managing up) are not obligated to help. These requests often involve collaborative tasks between requesters and performers. Through interviews, we found that these collaborative tasks require coordination work that is not materialized in existing management tools. We also found that requesters are willing to take on this coordination work to see their requests fulfilled. To address this issue, we propose a system called TaskLight , which allows requesters to handle coordination work themselves. For example, requesters can collect useful context and information for their performers. We conducted two deployment studies and found that TaskLight leads to better outcomes because requesters are able to assist performers more effectively. Our findings demonstrate a new way to reduce the social burdens of managing up and improve collaboration.

This work aims to explore how human assessments and AI predictions can be combined to identify misinformation on social media. To do so, we design a personalized AI which iteratively takes as training data a single user’s assessment of content and predicts how the same user would assess other content. We conduct a user study in which participants interact with a personalized AI that learns their assessments of a feed of tweets, shows its predictions of whether a user would find other tweets (in)accurate, and evolves according to the user feedback. We study how users perceive such an AI, and whether the AI predictions influence users’ judgment. We find that this influence does exist and it grows larger over time, but it is reduced when users provide reasoning for their assessment. We draw from our empirical observations to identify design implications and directions for future work.

Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used within development tools, these systems typically treat each model invocation independently from all previous invocations, and only a specific limited functionality is exposed within the user interface. This approach to user interaction misses an opportunity for users to more deeply engage with the model by having the context of their previous interactions, as well as the context of their code, inform the model’s responses. We developed a prototype system – the Programmer’s Assistant – in order to explore the utility of conversational interactions grounded in code, as well as software engineers’ receptiveness to the idea of conversing with, rather than invoking, a code-fluent LLM. Through an evaluation with 42 participants with varied levels of programming experience, we found that our system was capable of conducting extended, multi-turn discussions, and that it enabled additional knowledge and capabilities beyond code generation to emerge from the LLM. Despite skeptical initial expectations for conversational programming assistance, participants were impressed by the breadth of the assistant’s capabilities, the quality of its responses, and its potential for improving their productivity. Our work demonstrates the unique potential of conversational interactions with LLMs for co-creative processes like software development.

HCI engages with data science through many topics and themes. Researchers have addressed biased dataset problems, arguing that bad data can cause innocent software to produce bad outcomes. But what if our software is not so innocent? What if the human decisions that shape our data-processing software, inadvertently contribute their own sources of bias? And what if our data-work technology causes us to forget those decisions and operations? Based in feminisms and critical computing, we analyze forgetting practices in data work practices. We describe diverse beneficial and harmful motivations for forgetting. We contribute: (1) a taxonomy of data silences in data work, which we use to analyze how data workers forget, erase, and unknow aspects of data; (2) a detailed analysis of forgetting practices in machine learning; and (3) an analytic vocabulary for future work in remembering, forgetting, and erasing in HCI and the data sciences.

What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. Using scenario-based design and question-driven XAI design approaches, we explore users’ explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion. We conducted 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users’ explainability needs. Drawing from prior work, we also propose 4 types of XAI features for GenAI for code and gathered additional design ideas from participants. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.

Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone.

Conversational interfaces require two types of curation: data curation by data science workers and content curation by domain experts. Recent years have seen the possibilities for content curators to instruct conversational machines in the customer service domain (i.e., Machine Teaching). The activities of curating specialized data are time-consuming. These activities have a learning curve for the domain expert, and they rely on collaborators beyond the domain experts, including product owners, technology expert curators, management, marketing, and communication employees. However, recent research has looked at making this task easier for domain experts with a lack of knowledge in the Machine Learning system, and few papers have investigated the work practices and collaborations involved in this role. This paper aims to fill this gap, presenting and unveiling practices extracted from eleven semi-structured interviews and four design workshops with experts in Banking, Technical support, Humans Resources, Telecommunications, and Automotive sectors. First, we investigate the articulation work of the content curators and tech curators in training conversational machines. Second, we inspect the curatorial and collaboration strategies they use, which are not afforded by current conversational platforms. Third, we draw the design implications and possibilities to support individual and collaboration curating practices. We reflect on how those practices rely on self and collaboration with others for curation, trust, and data tracking and ownership.

Geographically distributed teams often face challenges in coordination and collaboration, lowering their productivity. Understanding the relationship between team dispersion and productivity is critical for supporting such teams. Extensive prior research has studied these relations in lab settings or using qualitative measures. This paper extends prior work by contributing an empirical case study in a real-world organization, using quantitative measures. We studied 117 new research project teams from the same discipline within an industrial research lab for 6 months. During this time, all teams shared one goal: submitting research papers to the same target conference. We analyzed these teams' dispersion-related characteristics as well as team productivity. Interestingly, we found little statistical evidence that geographic and time differences relate to team productivity. However, organizational and functional distances are predictive of the productivity of the dispersed teams we studied. We discuss the open research questions these findings revealed and their implications for future research.

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants’ satisfaction with their computational notebook.

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST’s effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.

Ground-truth labeling is an important activity in machine learning. Many studies have examined how crowdworkers apply labels to records in machine learning datasets. However, there have been few studies that have examined the work of domain experts when their knowledge and expertise are needed to apply labels.

Labeling data is an important step in the supervised machine learning lifecycle. It is a laborious human activity comprised of repeated decision making: the human labeler decides which of several potential labels to apply to each example. Prior work has shown that providing AI assistance can improve the accuracy of binary decision tasks. However, the role of AI assistance in more complex data-labeling scenarios with a larger set of labels has not yet been explored. We designed an AI labeling assistant that uses a semi-supervised learning algorithm to predict the most probable labels for each example. We leverage these predictions to provide assistance in two ways: (i) providing a label recommendation and (ii) reducing the labeler’s decision space by focusing their attention on only the most probable labels. We conducted a user study (n=54) to evaluate an AI-assisted interface for data labeling in this context. Our results highlight that the AI assistance improves both labeler accuracy and speed, especially when the labeler finds the correct label in the reduced label space. We discuss findings related to the presentation of AI assistance and design implications for intelligent labeling interfaces.

Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming language to another. The artifacts produced in this way may contain imperfections, such as compilation or logical errors. We examine the extent to which software engineers would tolerate such imperfections and explore ways to aid the detection and correction of those errors. Using a design scenario approach, we interviewed 11 software engineers to understand their reactions to the use of an NMT model in the context of application modernization, focusing on the task of translating source code from one language to another. Our three-stage scenario sparked discussions about the utility and desirability of working with an imperfect AI system, how acceptance of that system’s outputs would be established, and future opportunities for generative AI in application modernization. Our study highlights how UI features such as confidence highlighting and alternate translations help software engineers work with and better understand generative NMT models.

Human labeling of training data is often a time-consuming, expensive part of machine learning. In this paper, we study "batch labeling", an AI-assisted UX paradigm, that aids data labelers by allowing a single labeling action to apply to multiple records. We ran a large scale study on Mechanical Turk with 156 participants to investigate labeler-AI-batching system interaction. We investigate the efficacy of the system when compared to a single-item labeling interface (i.e., labeling one record at-a-time), and evaluate the impact of batch labeling on accuracy and time. We further investigate the impact of AI algorithm quality and its effects on the labelers' overreliance, as well as potential mechanisms for mitigating it. Our work offers implications for the design of batch labeling systems and for work practices focusing on labeler-AI-batching system interaction.

The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators. In this paper, we report on a study including analyses of both interviews with AI developers and artifacts they produced for communication. Using the analytic lens of shared mental models, we report on the types of communication gaps that AI developers face, how AI developers communicate across disciplinary and organizational boundaries, and how they simultaneously manage issues regarding trust and expectations.

We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies - qualitative interviews, a controlled experiment, and a card-sorting task - to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool.

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither do they trust the outputs. In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process. We find that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system.

Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.

With the rise of big data, there has been an increasing need for practitioners in this space and an increasing opportunity for researchers to understand their workflows and design new tools to improve it. Data science is often described as data-driven, comprising unambiguous data and proceeding through regularized steps of analysis. However, this view focuses more on abstract processes, pipelines, and workflows, and less on how data science workers engage with the data. In this paper, we build on the work of other CSCW and HCI researchers in describing the ways that scientists, scholars, engineers, and others work with their data, through analyses of interviews with 21 data science professionals. We set five approaches to data along a dimension of interventions: Data as given; as captured; as curated; as designed; and as created. Data science workers develop an intuitive sense of their data and processes, and actively shape their data. We propose new ways to apply these interventions analytically, to make sense of the complex activities around data practices.

In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations often face challenges. In this paper, we aim to decipher this collaboration complexity through a semi-structured interview study with 22 interviewees from teams of bio-medical scientists collaborating with data scientists. In the analysis, we adopt the Olsons' four-dimensions framework proposed in Distance Matters to code interview transcripts. Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process. In contrast to prior works' general account of building a high level of common ground, the breakdowns of content common ground together with the strengthen of process common ground in this process is more beneficial for scientific discovery. We discuss why that is and what the design suggestions are, and conclude the paper with future directions and limitations.

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.

Many conversational agents (CAs) are developed to answer users' questions in a specialized domain. In everyday use of CAs, user experience may extend beyond satisfying information needs to the enjoyment of conversations with CAs, some of which represent playful interactions. By studying a field deployment of a Human Resource chatbot, we report on users' interest areas in conversational interactions to inform the development of CAs. Through the lens of statistical modeling, we also highlight rich signals in conversational interactions for inferring user satisfaction with the instrumental usage and playful interactions with the agent. These signals can be utilized to develop agents that adapt functionality and interaction styles. By contrasting these signals, we shed light on the varying functions of conversational interactions. We discuss design implications for CAs, and directions for developing adaptive agents based on users' conversational behaviors.

As technology and data access continue to evolve, research ethics in the areas of Human-Computer Interaction and social computing are becoming increasingly complex. Despite increasing interest among researchers, there is still a lack of consistent community norms around ethical gray areas. One charge of the SIGCHI ethics committee is to help develop these norms by facilitating open conversations with different stakeholders. This panel will be an opportunity to develop a collective understanding of diverse perspectives on ethics, and to gather input from the GROUP research community around the ethical challenges we face as researchers who study social and collaborative computing systems and those who use these systems.

When employees participate in organizational crowdfunding, they seek partial funding from their existing social networks. Among proposers of projects, teams with larger social networks tend to be more successful in reaching their funding goals. However, little is known about the consequences of participation on employees' social networks, during and after the crowdfunding campaign. In a study of activity logs and social networks from a very large scale organizational crowdfunding campaign, we found that people in different crowdfunding roles experienced different degrees of growth in their social networks, during and after the crowdfunding campaign, as compared with baseline nonparticipants. These findings contribute to previous work on the strongly social nature of crowdfunding. Organizations can use these results to increase the density of their internal social networks. Employees can use these results to strategize their participation in workplace social networks and in organizational innovation.

StackExchange is a network of Question & Answer (Q&A) sites that support collaborative knowledge exchange on a variety of topics. Prior research found a significant imbalance between those who contribute content to Q&A sites (predominantly people from Western countries) and those who passively use the site (the so-called "lurkers"). One possible explanation for such participation differences between countries could be a mismatch between culturally related preferences of some users and the values ingrained in the design of the site. To examine this hypothesis, we conducted a value-sensitive analysis of the design of the StackExchange site Stack Overflow and contrasted our findings with those of participants from societies with varying cultural backgrounds using a series of focus groups and interviews. Our results reveal tensions between collectivist values, such as the openness for social interactions, and the performance-oriented, individualist values embedded in Stack Overflow's design and community guidelines. This finding confirms that socio-technical sites like Stack Overflow reflect the inherent values of their designers, knowledge that can be leveraged to foster participation equity.

Enterprise social networking (ESN) platforms have been implemented by organizations to support employees' collaboration. However, there has been a reported lack of user participation on these platforms. This study focuses on interventions that can be used to overcome barriers of usage. A case study was conducted at IBM to investigate the effect of several interventions to positively influence ESN acceptance within IBM's Human Resources (HR) function. In this study we combined two rounds of interview data with activity log data. We interviewed the initiators of interventions, and we interviewed the employees targeted by those interventions. The activity logs allowed us to contrast actual usage of the ESN by employees from HR vs. other organizational functions. Compared to other ESN studies this study explicitly identifies factors driving or inhibiting ESN use, and shows how these factors can be addressed by interventions to significantly increase ESN use. Our results can inform and enable other organizations interested in the design of interventions to increase the use of ESN.

Request management occurs at an intersection of CSCW and personal information management (i.e., to-do management), with particular emphasis on social relationships and organizational accountability. We explore diverse work practices and representations for person-to-person requests in organizations, detailing the scatter of communications channels, difficulty of aggregation and prioritization, differences due to context, and the sometimes extraordinary effort and passion that knowledge workers expend on this part of their work, as well as the compassion with which they view their colleagues' efforts. We close with a proposal for the study of socially implicated objects in physical and/or virtual organizations, and implications for the design of services to support request management.

Behavioral statescan be transferred to others, leading people to behave in ways similar to those around them. Can this phenomenon of behavioral contagion be seen in the workplace? Using employees' organizational social media data and their workplace hierarchical network structure, we studied contagion across a large multinational corporation, focusing on an important workplace behavior - employee engagement. We measured employees' engagement based on their word choice in organizational social media, and we applied a longitudinal statistical technique which controls for homophily, employees' traits and prior expressions of engagement. We found that engagement and disengagement spread from one employee to another with direct peers exerting the strongest influence. While engagement-spread was more powerful laterally among people at the same organizational level, disengagement-spread followed the vertical managerial chain. Further, we found that disengaged co-workers exerted a stronger influence on employee's future engagement compared to the engaged co-workers. Our results suggest the need for organizations to sense and address workplace disengagement promptly. Moreover, our findings offer opportunities for using workplace interventions to promote engagement and mitigate disengagement.

Personal agent software is now in daily use in personal devices and in some organizational settings. While many advocate an agent sociality design paradigm that incorporates human-like features and social dialogues, it is unclear whether this is a good match for professionals who seek productivity instead of leisurely use. We conducted a 17-day field study of a prototype of a personal AI agent that helps employees find work-related information. Using log data, surveys, and interviews, we found individual differences in the preference for humanized social interactions (social-agent orientation), which led to different user needs and requirements for agent design. We also explored the effect of agent proactive interactions and found that they carried the risk of interruption, especially for users who were generally averse to interruptions at work. Further, we found that user differences in social-agent orientation and aversion to agent proactive interactions can be inferred from behavioral signals. Our results inform research into social agent design, proactive agent interaction, and personalization of AI agents.

Cross-cultural network ties have been shown to improve decision-making, creativity, conflict-resolution and use of collaborative technologies. Nevertheless, cultural barriers are difficult to overcome. We used data from an internal Social Networking System (SNS) in a large global company to see if membership in the same company might reduce the effect of cultural homophily. We found no effect. However, when we focused on members of 87 distributed workgroups, we found that the effect of cultural differences actually reversed, indicating greater cultural diversity in online friendship ties than observed in the company at large. We discuss alternative explanations for this finding and the implications for work environments in global companies.

Social ties have been hypothesized to help people to gain support in achieving collaborative goals. We test this hypothesis in a study of organizational crowdfunding (or 'crowdfunding behind the firewall'). 201 projects were proposed for peer-crowdfunding in a large international corporation. The crowdfunding website allowed people to join a project as Co-Proposers. We analyzed the funding success of 114 projects as a function of the number of (Co-)Proposers. Projects that had more co-proposers were more likely to reach their funding targets. Using data from an organizational social-networking service, we show how employees' social ties were associated with these success patterns. Our results have implications for theories of collaboration in social networks, and the design of crowdfunding websites.

No abstract available.

Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.

Employee engagement is a reflection of an employee's experience of work. Previous research has analyzed each employee's experience in terms of individual factors. We provide the first report of the influence on engagement of peers (who report to the same manager) and friends (who share social ties in an internal social network), using linear regression to model employee engagement in a sample of more than 44,000 employees. We show that an employee's engagement is associated with the engagement of her/his peers, friends, and manager. Our results contribute to analyses of social factors at work, and argue for revisions to existing theories of employee engagement.

Mobile technologies offer the potential for enhanced healthcare, especially by supporting self-management of chronic care. For these technologies to impact chronic care, they need to work for older adults, because the majority of people with chronic conditions are older. A major challenge remains: integrating the appropriate use of such technologies into the lives of older adults. We investigated how older adults would accept mobile technologies by interviewing two groups of older adults (technology adopters and non-adopters who aged 60+) about their experiences and perspectives to mobile technologies. Our preliminary results indicate that there is an additional phase, the intention to learn, and three relating factors, self-efficacy, conversion readiness, and peer support, that significantly influence the acceptance of mobile technologies among the participants, but are not represented in the existing models. With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies. Future work should investigate the validity of the proposed model by testing our findings against younger people.

Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.

In online communities, satisfied members are essential to community success, since they are more likely to contribute and consume content, engage with other members, and feel committed to the community. However, it is difficult for community leaders to know, on an on-going basis, whether members are satisfied. In this paper, we explore the relationship between member satisfaction and language use within content posted in workplace online communities. We hope to find patterns of language use that are associated with satisfied members. We employ linguistic analysis based on LIWC, and a survey to directly measure member satisfaction in 142 workplace communities. We contribute a better understanding of how members interact in effective workplace communities, and show that linguistic analysis could be a useful part of future methods to automatically assess community member satisfaction.

Enterprise crowdfunding offers a series of opportunities for voluntary or unplanned collaborations within organizations. In an enterprise crowdfunding experiment, we study the influence of interpersonal attributes-in-common on collaborations. Using ideas from Homophily Theory and Social Identity Theory, we analyze attributes-in-common in terms of multiple identity facets: of geography, of formal corporate structure, and of working groups/teams. We combine quantitative and self-report data to show how each identity facet has an influence on the likelihood of voluntary collaborations, and we show their "superadditive" combination. We propose new questions for theory, and we consider how our results can lead to new features and technologies to enhance voluntary collaborations in organizations.

Whether in private or professional life, individuals frequently adapt the technology around them and work with what they have at hand to accomplish a certain task. In this one-day workshop, we will discuss how this form of technology appropriation is used to satisfy communication needs. Thereby, we specifically focus on technology that was not intended to facilitate communication, but which led to appropriation driven by individuals' communication needs. Our aim is to identify "unexpected" communication needs, to better address these in the design of interactive systems. We focus on a variety of different contexts, ranging from not restricted contexts to environments that are characterized by strict regulations (e.g., production lines with 24/7 shift production cycles). Consequently, this workshop aims at better understanding how users adapt technology to match their individual communication purposes and how these appropriation practices interrelate with and support organizational cooperation.

Online communities are increasingly being deployed in enterprises to increase productivity and share expertise. Community leaders are critical for fostering successful communities, but existing technologies rarely support leaders directly, both because of a lack of clear data about leader needs, and because existing tools are member- rather than leader-centric. We present the evidence-based design and evaluation of a novel tool for community leaders, Community Insights (CI). CI provides actionable analytics that help community leaders foster healthy communities, providing value to both members and the organization. We describe empirical and system contributions derived from a long-term deployment of CI to leaders of 470 communities over 10 months. Empirical contributions include new data showing: (a) which metrics are most useful for leaders to assess community health, (b) the need for and how to design actionable metrics, (c) the need for and how to design contextualized analytics to support sensemaking about community data. These findings motivate a novel community system that provides leaders with useful, actionable and contextualized analytics.

We describe a first experiment in enterprise crowdfunding - i.e., employees allocating money for employee-initiated proposals at an Intranet site, including a trial of this system with 511 employees in IBM Research. Major outcomes include: employee proposals that addressed diverse individual and organizational needs; high participation rates; extensive inter-departmental collaboration, including the discovery of large numbers of previously unknown collaborators; and the development of goals and motivations based on collective concerns at multiple levels of project groups, communities of practice, and the organization as a whole. We recommend further, comparative research into crowd-funding and other forms of employee-initiated innovations.

Online communities are important in enterprises, helping workers to build skills and collaborate. Despite their unique and critical role fostering successful communities, community leaders have little direct support in existing technologies. We introduce CommunityCompare, an interactive visual analytic system to enable leaders to make sense of their community's activity with comparisons. Composed of a parallel coordinates plot, various control widgets, and a preview of example posts from communities, the system supports comparisons with hundreds of related communities on multiple metrics and the ability to learn by example. We motivate and inform the system design with formative interviews of community leaders. From additional interviews, a field deployment, and surveys of leaders, we show how the system enabled leaders to assess community performance in the context of other comparable communities, learn about community dynamics through data exploration, and identify examples of top performing communities from which to learn. We conclude by discussing how our system and design lessons generalize.

Questions of human values often arise in HCI research and practice. Such questions can be difficult to address well, and a principled approach can clarify issues of both theory and practice. One such approach is Value Sensitive Design (VSD), an established theory and method for addressing issues of values in a systematic and principled fashion in the design of information technology. In this essay, we suggest however that the theory and at times the presentation of VSD overclaims in a number of key respects, with the result of inhibiting its more widespread adoption and appropriation. We address these issues by suggesting four topics for next steps in the evolution of VSD: (1) tempering VSD's position on universal values; (2) contextualizing existing and future lists of values that are presented as heuristics for consideration; (3) strengthening the voice of the participants in publications describing VSD investigations; and (4) making clearer the voice of the researchers. We propose new or altered approaches for VSD that address these issues of theory, voice, and reportage.

In this paper, we propose to improve time management in the enterprise by providing users interactive visualizations of how they are spending their time. Through an interview study (n=21) in a multi-national corporation, we were able to determine the data available for visualizations and the value of a number of general visualizations of employees' calendar data. We develop implications for design in improving personal time management.

Tragic events struck northern Japan in March-April 2011. This note presents a case study of rapid distributed brainstorming for disaster response, involving 275 contributors from 23 countries within a three-day period, conducted among the staff in a multinational company. Factors that appear to have contributed to the success of this brainstorming include: Social media that could be easily appropriated; and employee familiarity with large-scale brainstorming. The formation of this "flash-community" joins other CHI reports to point toward a new genre of rapid large-scale responses to disasters through social media.

There is a growing body of research into the adoption and use of social software in enterprises. However, less is known about how groups, such as communities, use and appropriate these technologies, and the implications for community structures. In a study of 188 very active online enterprise communities, we found systematic differences in size, demographics and participation, aligned with differences in community types. Different types of communities differed in their appropriation of social software tools to create and use shared resources, and build relationships. We propose implications for design of community support features, services for potential community members, and organizations looking to derive value from online groups.

We examine patterns of participation by employees who are members of multiple online communities in an enterprise communities service. Our analysis focuses on statistical patterns of contributing vs. "lurking". The majority of contributors (in one or more communities) were also lurkers (in one or more other communities). These results argue against hypotheses derived from common theories of participation and lurking. We propose that contributing and lurking are partially dependent on a trait (a person's overall engagement), modified by the individual's disposition toward a particular topic, work task or social group. Contributions include critique of theory, an analytic framework, and implications for design of community services.

There is a growth in the popularity of social file sharing systems. This paper describes the design of Cattail, a social file sharing system for the enterprise. Through a 'Recent Events' stream, Cattail supports social navigation and exploratory search by inferring relevant social connections rather than purely relying on user-specified contacts. Social navigation is further supported through pivot browsing from a consolidated history of user actions on an individual's files. Through usage log analysis over an 8-month period, we found that Cattail's novel network inference and social navigation features enabled a net gain of clickthroughs. Interviews with users revealed that this led to increased discovery of relevant people and content. We conclude with a discussion of several possible enhancements to the system. Findings from our research provide a strong foundation for the design of social file sharing systems for enterprise settings.

In this brief personal essay, I describe some of the ways that feminism has influenced my life as a researcher and practitioner in HCI and CSCW – in the creation of work to be read by others, in the critical reading of works that were created by others, and in the planning and framing of practical work in enterprise workplaces. I discuss three variations of “Who” questions that feminism helps us to ask in HCI: The “who” of the identity of the user; the “who” of the identity of organizational actors; and the “who” of the practitioner or researcher.

How do people use an enterprise file-sharing service? We describe patterns of usage in a social file-sharing service that was deployed in a large multinational enterprise. Factor analyses revealed four factors: Upload & Publicize (regarding one's own files); Annotate & Watch (add information to files and maintain awareness); Discover & Tell (find files uploaded by other users, and communicate to additional users about those files); and Refind (re-use one's own files). We explore the attributes of users who score highly on each of these factors, and we propose implications for design to encourage innovation in usage.

Most knowledge repositories focus on the role of knowledge-creators. In this paper, by contrast, we examined the work of Lurkers in an enterprise file-sharing service, and we compared their lurking behaviors to the lurking behaviors of users who uploaded files (Uploaders), and users who contributed metadata about files (Contributors). For comparability, we restricted our analyses to the consuming behaviors that are common to the three roles (Uploaders, Contributors, and Lurkers). Independent principal components analysis showed highly similar seven-factor solutions of lurking activities across all three roles, although the relative emphases of those factors varied across roles. Uploaders tended to view and download more groups of files, showed less emphasis on searching for files, and tended to work directly with the file-sharing application, unmediated by external applications. Contributors showed the opposite pattern: more emphasis on searching and responding to recommendations from other users, often via a form of remote access. Lurkers' lurking behaviors were less intense, and showed little difference in emphases among the lurker factors. We use these results, and the published research literature, to motivate a research agenda for lurkers in social media.

This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.

Presented at the 2009 ACM SIGCHI international conference on supporting group work, May 10–13 2009, Sanibel Island, Florida

Developers frequently add annotations to source code to help them remember pertinent information and mark locations of interest for future investigation. Finding and refinding these notes is a form of navigation that is integral to software maintenance. Although there is some tool support in modern development environments for authoring and navigating these comments, we have observed that these annotations often fail to remind and are sometimes difficult to find by the programmer. To address these shortcomings, we have designed a new approach for software navigation called tags for software engineering activities (TagSEA). TagSEA combines the notion of waypointing (a mechanism for marking locations in spatial navigation) with social tagging to support programmers in defining semantically rich annotations to source code comments. The tool provides support for creating, editing, navigating, and managing these annotations. We present the results from two empirical studies, where we observed and then analyzed how professional programmers used source code annotations to support their development activities over 24 months. Our findings indicate that the addition of semantic information to annotations can improve their value. We also provide suggestions on how annotation tools in general may be improved.

Social networking sites support a variety of shared content types such as photos, videos, or music. More structured or form-based social content types are not mainstream but we have started seeing sites evolve that support them. This paper describes the design and use of structured lists in an enterprise social networking system. As a major feature of our shared lists, we introduced the ability to reuse someone else's list. We report the results on the use and reuse of shared lists based on three months of usage data from 285 users and interviews with 9 users. Our findings suggest that despite the structured nature of lists, our users socialize more around lists than photos, and use lists as a medium for self-representation.

Social tagging systems provide users with the opportunity to employ tags in a communicative manner. To explore the use of tags for communication in these systems, we report results from 33 user interviews and employ the concept of social roles to describe audience-oriented tagging, including roles of community-seeker, community-builder, evangelist, publisher, and team-leader. These roles contribute to our understanding of the motivations and rationales behind social tagging in an international company, and suggest new features and services to support social software in the enterprise.

This paper investigates using social tags for the purpose of making personalized content recommendations. Our tag-based recommender creates a personalized bookmark recommendation model for each user based on current and general interest tags, defined by different time intervals.

The introduction of a social networking site inside of a large enterprise enables a new method of communication between colleagues, encouraging both personal and professional sharing inside the protected walls of a company intranet. Our analysis of user behavior and interviews presents the case that professionals use internal social networking to build stronger bonds with their weak ties and to reach out to employees they do not know. Their motivations in doing this include connecting on a personal level with coworkers, advancing their career with the company, and campaigning for their projects.

User profiles on today's social networking sites support only a small set of predefined questions. We report on an alternative way for users to richly describe themselves, by entering not only responses, but their own questions as well. Data from 10 months of usage shows that users of a social networking site created thousands of diverse questions and reused existing questions from other users. Our findings suggest that those with highly diverse user profiles have a higher number of friends.

Knowledge workers perform many different activities daily. Each activity defines a distinct work context with different information needs. In this paper we leverage users' activity representations, stored in an activity management system, to automatically recommend resources to support knowledge workers in their current activity. We developed a collaborative activity predictor to both predict the current work activity and measure a resource's relevance to a specific activity. Relevant resources are then displayed in a contextual side bar on the desktop. We describe the system, our new activity-centric search algorithm, and experimental results based on the data from 50 real users.

Traditional social networking sites allow users to enter responses to a set of predefined fields when populating their personal profiles. In the system discussed in this work, freeform 'About You' entries allow users to craft their own questions / topics. We found that this kind of flexibility often leads to low content contributions and infrequent updates. The 'About You' recommender system described in this paper differs from many recommender systems in that it recommends content for users to create, rather than consume. We present empirical data from an experiment with 2,000 users of a social networking site during a one month period. Our findings suggest that users who receive recommendations create more entries and update them more over time. Further, using articulated social network information for recommendations performed better than content-based matching.

Tagclouds are visual presentations of a set of words, typically a set of "tags" selected by some rationale, in which attributes of the text such as size, weight, or color are used to represent features, such as frequency, of the associated terms. This note describes two studies to evaluate the effectiveness of differently constructed tagclouds for the various tasks they can be used to support, including searching, browsing, impression formation and recognition. Based on these studies, we propose a paradigm for evaluating tagclouds and ultimately guidelines for tagcloud construction.

Activity-centric collaboration environments help knowledge workers to manage the context of their shared work activities by providing a representation for an activity and its resources. Activity management systems provide more structure and organization than email to execute the shared activity but, as the number of shared activities increases, it becomes more and more difficult for users to focus on important activities that need their attention. This paper describes a personalized activity prioriti-zation approach implemented on top of the Lotus Connections Activities management system. Our prototype implementation allows each user to view activities ordered by her/his predicted priorities. The predictions are made using a ranking Support Vector Machine model trained with the user’s past interactions with the activities system. We describe the prioritization interface and the results of an offline experiment based on data from 13 users over 6-months. Our results show that our feature set derived from shared activity structures can significantly increase prediction accuracy compared to a recency baseline.

We describe the Dogear Game, which works with an enterprise social bookmarking system. The game is designed to accomplish individual, collaborative, and organization goals. Individual players receive entertainment and learn about their colleagues' bookmarks. The player's colleagues receive recommendations of websites and documents of potential interest to them. And the organization benefits from a richer knowledge-base of bookmarks as recommendations are accepted. The Dogear Game builds on von Ahn's "serious games," useful in motivating and distributing game-like entertaining "work" to a large group of game players. This note presents the design and implementation of a working prototype and some initial user feedback.

We compare four tagging-based enterprise services, which respectively stored bookmarks to webpages and documents, to people, to blog entries, and to hierarchically-structured activity records. Analysis of user data and tag data showed relatively small overlaps in tags used. Conventional normalization strategies produced only modest improvement. These results suggest difficulties in combining exploratory searches across multiple social-tagging services. We recommend strategies for cross-service tag integration at the points of tag storage and tag search, rather than at the conventional point of tag entry. We close with a research agenda around this strategy. Categories and Subject Descriptors H.5.3. Group and organizational interfaces/Collaborative computing & CSCW.

We have developed a new approach for software navigation called TagSEA (Tagging of Software Engineering Activities). TagSEA combines the notion of "waypointing" with "social tagging" to support programmers in defining navigational structures over a software system. In this paper we present the results from a case study series, conducted with professional programmers, that demonstrates how this tool supports navigation and under what circumstances. We conclude with insights into user-definable navigational structures, and how they can support software maintenance more effectively.

Employee directories play a valuable role in helping people find others to collaborate with, solve a problem, or provide needed expertise. Serving this role successfully requires accurate and up-to-date user profiles, yet few users take the time to maintain them. In this paper, we present a system that enables users to tag other users with key words that are displayed on their profiles. We discuss how people-tagging is a form of social bookmarking that enables people to organize their contacts into groups, annotate them with terms supporting future recall, and search for people by topic area. In addition, we show that people-tagging has a valuable side benefit: it enables the community to collectively maintain each others' interest and expertise profiles. Our user studies suggest that people tag other people as a form of contact management and that the tags they have been given are accurate descriptions of their interests and expertise. Moreover, none of the people interviewed reported offensive or inappropriate tags. Based on our results, we believe that peopletagging will become an important tool for relationship management in an organization.

As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naive Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.

We explored the use of computer screen plus audio recording as a methodological approach for collecting empirical data on how teams use their computers to coordinate work. Screen recording allowed unobtrusive collecting of a rich record of actual computer work activity in its natural work setting. The embedded nature of screen recording on laptops made it easy to follow the user's mobility among various work sites. However, the invasiveness of seeing all of the user's interactions with and through the computer raised privacy concerns that made it difficult to find people to agree to participate in this type of detailed study. We discuss measures needed to develop trust with the researchers to enable access to this rich, empirical data of computer usage in the field.

This paper describes a new collaboration technology that is based on the support of lightweight, informally structured, opportunistic activities featuring heterogeneous threads of shared items with dynamic membership. We introduce our design concepts, and we provide a detailed analysis of user behavior during a five month field study. We present the patterns of media use that we observed, using a variety of analytical methods including thread clustering and analysis. Major findings include four patterns of media use: communicating, exchanging mixed objects, coordinating, (e.g., of status reports), and semi-archival filing. We observed differential use of various media including highly variable use of chats and surprisingly informal uses of files. We discuss the implications for the design of mixed media collaborative tools to support the work activities of small to medium sized work teams.

Chat Spaces are rich persistent chats that provide light-weight shared workspaces for small to medium-scale group activities. Chat Spaces can accommodate brief, informal interactions (similar to Instant Messaging), and can also support longer-term complex threaded conversations including large numbers of people and shared resources. Our design maps a hierarchical thread representation onto a time-ordered two-column user interface. This mapping allows a user to follow the global dynamics of the entire thread in the chronological column on the left while being able to participate in a selected topical branch in a second column on the right. We also present a dynamic thread map that provides an overview of the entire conversation and supports quick navigation of topical branches in the thread.

This paper describes a new collaboration technology that is carefully poised between informal, ad hoc, easy-to-initiate collaborative tools, vs. more formal, structured, and high-overhead collaborative applications. Our approach focuses on the support of lightweight, informally structured, opportunistic activities featuring heterogeneous threads of shared objects with dynamic membership. We introduce our design concepts, and we provide a detailed first look at data from the first 100 days of usage by 20 researchers and 13 interns, who both confirmed our hypotheses and surprised us by reinventing the technology in several ways.

We describe a popular kind of large, topic-centered, transient discussion, which we term a flash forum. These occur in settings ranging from web-based bulletin boards to corporate intranets, and they display a conversational style distinct from Usenet and other online discussion. Notably, authorship is more diffuse, and threads are less deep and distinct. To help orient users and guide them to areas of interest within flash forums, we designed ForumReader, a tool combining data visualization with automatic topic extraction. We describe lessons learned from deployment to thousands of users in a real world setting. We also report a laboratory experiment to investigate how interface components affect behavior, comprehension, and information retrieval. The ForumReader interface is well-liked by users, and our results suggest it can lead to new navigation patterns. We also find that, while both visualization and text analytics are helpful individually, combining them may be counterproductive.

This brief essay considers the three papers of the special issue of Interacting with Computers by Picard and colleagues, from several perspectives. First, I question two aspects of the work: the Computers Are Social Actors (CASA) approach, and the use of psychophysiological measurements of emotion without a stated theory of emotion. Despite these criticisms, the contributions of Picard and colleagues are valuable and powerfully challenging. I suggest three convergent ways to pursue this important research program.

We describe a new collaborative technology that is mid-way between the informality of email and the formality of shared workspaces. Email and other ad hoc collaboration systems are typically lightweight and flexible, but build up an unmanageable clutter of copied objects. At the other extreme, shared workspaces provide formal, structured collaboration, but are too heavyweight for users to set up. To bridge this gap between the ad hoc and formal, this paper introduces the notion of "object-centric sharing", where users collaborate in a lightweight manner but aggregate and organize different types of shared artifacts into semi-structured activities with dynamic membership, hierarchical object relationships, as well as real-time and asynchronous collaboration. We present a working prototype implemented with a replicated peer-to-peer architecture, which we describe in detail, and demonstrate its performance in synchronous and asynchronous modes.

We provide the first study of instant messaging (IM) based on large samples of users' self reports. Previous studies have relied on ethnographic methods or analysis of server logs. Our self-report approach has its own strengths (large-sample; focus on attitudes, beliefs, and value attributions), as well as weaknesses (self-selection by respondents). We describe the introduction of Lotus Sametime™, an IM product, into three business organizations. Across the three organizations, we found substantially similar patterns in savings (reduced use of other communications channels), attitudes, and social networks. In one organization, we made a detailed study of the maturation of IM over a 24-month period, showing early and stable savings accompanied by much more gradual developments in chat behaviors, control of visibility and awareness, social networks, and attitudes. We conclude with a methodological self-critique, and an outline of an Instant Messaging Maturity Model.

This paper provides a description of designers' work practices in a software company. We describe a participatory analysis of the diversity of working relations and roles of designers of IBM's Lotus software products. Designers are an example of a minority discipline - that is, a discipline whose members are often isolated in their work teams among coworkers with different training, backgrounds, and career paths. We explore differences between the practices of designers of Lotus software products and the published reports of design practices in group settings

CARD (Collaborative Analysis of Requirements and Design) is an influential technique for participatory design and participatory analysis that is in use on three continents. This paper reviews three case studies that document the development of a layered CARD approach, which distinguishes among the following: (1) observable, formal components, (2) skill and craft, and (3) interpretative description. The layered approach simplifies the CARD materials, and moves the deliberately informal technique toward a more principled analysis.

We investigate the phenomenon of adversarial collaboration, through field studies of a legal firm. Adversarial collaboration requires that people with opposing goals (adversaries) come to agreement, usually producing a shared product that reflects the interests of the adversarial parties. Adversarial collaboration is characterized by secrecy, advocacy and discovery. To support this activity, software should provide flexible, selective sharing of awareness and access. These requirements contrast with conventional shared resource and awareness systems, which tend to assume cooperative collaboration, characterized by open processes and static membership lists. We illustrate these ideas in a redesign of our PeopleFlow research prototype.

No abstract available.

Article Free Access Share on Toward an HCI research and practice agenda based on human needs and social responsibility Authors: Michael J. Muller US WEST Advanced Technologies, 4001 Discovery Drive, Boulder, CO US WEST Advanced Technologies, 4001 Discovery Drive, Boulder, COView Profile , Cathleen Wharton US WEST Advanced Technologies, 4001 Discovery Drive, Boulder, CO US WEST Advanced Technologies, 4001 Discovery Drive, Boulder, COView Profile , William J. McIver US WEST Advanced Technologies, 4001 Discovery Drive, Boulder, CO US WEST Advanced Technologies, 4001 Discovery Drive, Boulder, COView Profile , Lila Laux US WEST Communications, 1801 California Street, Denver, CO US WEST Communications, 1801 California Street, Denver, COView Profile Authors Info & Claims CHI '97: Proceedings of the ACM SIGCHI Conference on Human factors in computing systemsMarch 1997 Pages 155–161https://doi.org/10.1145/258549.258640Published:27 March 1997Publication History 25citation943DownloadsMetricsTotal Citations25Total Downloads943Last 12 Months57Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

PICTIVE is a participatory design technique for increasing the direct and effective involvement of users and other stakeholders in the design of software. This paper reviews a year of the use of PICTVE on products and research prototypes at Bellcore. What we have learned is illustrated through five brief case studies. The paper concludes with a summary of our current PICTIVE practice, expressed as three developing, interrelated models: an object model, a process model, and a participation model.

Five groups of developers with experience in the design of character-based user interfaces were taught graphical user interface design through a short workshop with a focus on practical design exercises using low-tech tools derived from the PICTIVE method. Several usability problems were found in the designs by applying the heuristic evaluation method, and feedback on these problems constituted a way to make the otherwise abstract usability principles concrete for the designers at the workshop. Based on these usability problems and on observations of the design process, we conclude that object-oriented interactions are especially hard to design and that the developers were influenced by the graphical interfaces of personal computers with which they had interacted as regular users.

It is generally accepted that it is important to involve the end users of a Graphical User Interface (GUI) in all stages of its design and development. However, traditional GUI development tools typically do not support collaborative design. TelePICTIVE is an experimental software prototype designed to allow computer-naive users to collaborate with experts at possibly remote locations in designing GUIs.

This paper describes PICTIVE, an experimental participatory design technique that is intended to enhance user participation in the design process. PICTIVE combines low-tech objects with high(er)-tech video recording. The low-tech objects — i.e., non-computer representations of system functionality — are intended to insure that all participants have equal opportunity to contribute their ideas. The video recording makes record-keeping easy, reduces social distance during the design session, and may give rise to informal video “design documents.” The session proeeedsbyakind ofbrainstorming, with the low-tech objects used to express each participant’s ideas to the others. This paper describes our initial experiences with the PICTIVE technique, informal analyses about why the teehnique works, and several Bellcore projeets and products to which it has been applied.

Article Free Access Share on Participatory design in Britain and North America: responses to the “Scandinavian Challenge” Authors: Michael J. Muller Bellcore, 444 Hoes Lane, Piscataway NJ Bellcore, 444 Hoes Lane, Piscataway NJView Profile , Jeanette L. Blomberg Xerox PARC Xerox PARCView Profile , Kathleen A. Carter Rank Xerox Ltd. Rank Xerox Ltd.View Profile , Elizabeth A. Dykstra Pacific Bell Pacific BellView Profile , Kim Halskov Madsen City University of NY City University of NYView Profile , Joan Greenbaum City University of NY City University of NYView Profile Authors Info & Claims CHI '91: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsApril 1991 Pages 389–392https://doi.org/10.1145/108844.108962Published:01 March 1991Publication History 21citation1,340DownloadsMetricsTotal Citations21Total Downloads1,340Last 12 Months28Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The multifunctional cursor (MC) is a technique for representing multiple operations in direct manipulation user interfaces. Icons for each of several simultaneously-available operations are overlaid into the cursor image. The MC improves user interface practice by removing syntactic inconsistencies, by reducing cognitive load, and by providing support for repeated operations.

The author describes an architecture and a protocol to solve a number of problems that may arise in a distributed computing setting, including incompatible hardware and software environments, incompatible user interfaces, and customization requirements. The focus is on providing an enabling environment for flexible user interface specification that can provide both common functionality and, if needed, local customization. The overall architecture of the solution is described, and examples of the protocol for specifying user interface functionality are presented.>