Pith. sign in

REVIEW 2 major objections 7 minor 1 cited by

AI Hasn't Fixed Teamwork, But It Shifted Collaborative Culture: A Longitudinal Study in a Project-Based Software Development Organization (2023-2025)

T0 review · 2 major / 7 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Over two years, AI became a personal booster, not a team coordinator, leaving collaboration problems unresolved while shifting team culture around efficiency and transparency.

desk verdict A rare two-wave qualitative study that contributes a plausible culture-shift finding, but the Wave 2 reminder protocol is a contrast prime in disguise, not the mitigation the paper claims. read the letter →

arxiv 2509.10956 v1 pith:O5RFSXYE submitted 2025-09-13 cs.HC cs.CYcs.SE

Qing Xiao , Xinlan Emily Hu , Mark E. Whiting , Arvind Karunakaran , Hong Shen , Hancheng Cao This is my paper · ORCID
classification cs.HCcs.CYcs.SE
keywords AIandteamworklongitudinalinterviewscollaborativeculturesociotechnicalimaginariesdomesticationtheorygenerativeatworkdistributedsoftwareteamshuman-AIcollaboration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that AI's impact on teamwork in a distributed, project-based software organization was twofold: it largely failed to fix the collaboration problems participants hoped it would solve, and it reshaped the culture of teamwork in ways that persisted. The authors interviewed the same people in early 2023, when ChatGPT had just appeared, and again in 2025, after generative AI became common. In 2023, participants envisioned AI as an intelligent coordinator that would track progress, flag disengagement, and ease communication frictions. By 2025, AI was used mostly as an individual assistant for coding, writing, and documentation; accountability and communication breakdowns remained. What changed was cultural: efficiency became a norm, transparent and responsible AI use became a mark of professionalism, and AI became a taken-for-granted part of collaboration.

What carries the argument

The central machinery is the longitudinal qualitative interview design paired across two waves: each participant's 2023 and 2025 transcripts are analyzed as linked pairs to trace continuity and change in expectations, practices, and norms. The analysis is organized by two conceptual lenses—sociotechnical imaginaries (collectively held visions of desirable technological futures) and domestication theory (how technologies are appropriated, incorporated, and normalized into daily routines). These lenses let the authors treat the difference between imagined and actual AI use not as noise but as a meaningful trajectory: ambitious group-level hopes were domesticated into individual productivity ha

What would settle it

Gather digital-trace data (chat logs, issue-tracker timelines, commit histories, meeting records) from a comparable distributed software organization across 2023–2025 and measure whether coordination failures—silent disengagement, late handoffs, duplicated work, communication fragmentation—actually remained constant or declined while AI adoption grew. If coordination failures show improvement, the claim that AI left core teamwork problems unfixed would be refuted.

Watch

Extended reading notes

Core claim

The paper reports a two-wave longitudinal interview study with 15 members of a remote-first, project-based software development organization in early 2023 and 10 of the same people in 2025. In 2023, participants dreamed of AI as an ambient coordinator—flagging underperformance, tracking milestones, sensing communication breakdowns, and mediating relational friction. By 2025, they were using AI mainly to accelerate individual tasks like coding, writing, and documentation, while the collaboration problems of accountability and communication persisted. The authors argue that AI's main team-level effect was cultural rather than functional: efficiency became an expected norm, transparency and res

Load-bearing premise

The study's central trajectory—hopes for a collaborative coordinator giving way to individual productivity plus cultural normalization—rests entirely on what participants said retrospectively in interviews, since no behavioral or observational data were collected to verify actual changes in teamwork.

Editorial extensions

If this is right

  • Individual productivity gains from AI do not automatically translate into better teamwork; without deliberate design for group awareness, the same coordination failures persist.
  • Team culture is a site where AI exerts measurable influence even when tools are used individually: efficiency expectations rise, transparency becomes a professional virtue, and AI use becomes normalized.
  • Future workplace AI should be designed for proactive sensemaking—flagging slow progress, shifts in tone, or disengagement before crisis—rather than reactive note-taking or summarization.
  • Teams need mechanisms to make cultural shifts visible and negotiable, so that implicit new norms around speed and AI use do not silently create misalignment or mistrust.
  • The 2023 imaginaries of AI as coordinator and mediator remain design opportunities; the study suggests they were displaced but not invalidated by current tool affordances.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the paper leaves implicit: a 'productivity trap' may form at team level—if AI-fueled speed becomes the baseline, those who cannot or will not use AI could be penalized, creating a new form of inequality inside teams.
  • A testable extension the authors do not explore: agentic AI systems that proactively monitor, summarize, and nudge team activity might reactivate the 2023 coordinator imaginaries and actually reduce coordination failures, or they might reproduce the same individual-speed-at-team-cost pattern at higher velocity.
  • Methodologically, the paper's reminder-based interview design is itself an intervention; a future study could vary whether and how prior statements are invoked to estimate how much of the reported 'hope-to-disappointment' trajectory is co-constructed by the recall process.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 7 minor

Summary. This paper reports a two-wave qualitative longitudinal interview study (2023 and 2025) of 15 members (10 re-interviewed) of a distributed, project-based software development organization. In early 2023, participants imagined AI as an intelligent coordinator that could improve accountability and communication. By 2025, participants described AI as mainly an individual productivity tool, with core teamwork problems persisting, while collaborative culture had shifted: efficiency became expected, transparent and responsible AI use became markers of professionalism, and AI was normalized in teamwork. The paper frames this as evidence that AI has not fixed teamwork but has reshaped collaborative culture, and offers design implications for future teamwork AI.

Significance. If the central claim holds, this is a valuable longitudinal empirical contribution to CSCW/HCI and the broader AI-and-work literature: it challenges both techno-optimist and techno-pessimist narratives by showing that AI's team-level effect can be cultural normalization rather than coordination repair. The paper's strengths include a rare two-wave panel design at a strategically relevant inflection point (2023–2025), explicit use of sociotechnical imaginaries and domestication theory, and a candid limitations section that acknowledges the absence of behavioral data and the possibility of co-constructed narratives. The study is also helpfully explicit about its scope: 15 self-selected participants from one organization. The authors do not overclaim statistical generalizability. However, the novelty of the culture-shift claim rests heavily on the design of the Wave 2 protocol, and that design has a critical flaw discussed below.

major comments (2)
  1. [Section 3.3] The stated mitigation for prompt artifacts is logically inverted. The paper says: 'To mitigate the risk that this contrast was simply a prompt artifact, we reminded participants of their 2023 statements before inviting comparison.' Reminding participants of their earlier optimistic statements immediately before asking them to evaluate the present is a textbook contrast prime; it can inflate perceived change and disappointment, not reduce it. The limitation in Section 7 acknowledges that narratives 'might reflect... interpretive frames that we could have co-constructed,' but that does not repair the mischaracterization of the reminder as a mitigation. This is load-bearing because the central culture-shift claim depends on the before/after contrast. Please reframe the procedure as a limitation, and either add triangulating behavioral evidence (e.g., logs, outputs) or soften the causal clai
  2. [Section 3.3 and Section 5.3] The Wave 2 protocol explicitly introduced topics that were not part of the Wave 1 protocol: participants were asked about 'any shifts in team culture, such as norms around efficiency, or accountability' (Section 3.3). Phase 1 (Section 3.2) asked about frictions, workarounds, and forward-looking imaginaries—not about existing cultural norms. The 2025 themes in Section 5.3 (efficiency as a norm, transparency as professionalism, AI as expected) may therefore be an artifact of newly introduced questions rather than independently observed longitudinal change. The reminder of 2023 statements does not remedy this asymmetry. To support the longitudinal claim, the paper should either provide evidence that these norm-related themes were absent from comparable 2023 data (e.g., a systematic comparison of both transcripts for each participant), or reframe the finding as an emergent, retrospectively c
minor comments (7)
  1. [Section 3.2] Typo: 'perfceptions' should be 'perceptions'.
  2. [Section 4.2.3] Typo: 'commutation' should be 'communication' in 'AI for enhancing commutation and maintaining team relationships'.
  3. [Section 2.1] Typo: 'doption' should be 'adoption' in 'Most AI doption research'.
  4. [Section 5.3.3] The sentence 'To short, our follow-up interviews in section 5 confirmed...' should read 'In short, our follow-up interviews in Section 5 confirmed...'.
  5. [Section 6.4] Typo: 'highligting' should be 'highlighting' in design implication 1.
  6. [Section 7] Typo: 'unobstrusive' should be 'unobtrusive'.
  7. [ACM Reference Format] The reference format line contains 'In.ACM' with a stray period; please fix to 'In Proceedings of the ACM Conference...'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: qualitative longitudinal findings are self-contained; self-citations are background only.

full rationale

This is a qualitative longitudinal interview study with no numerical fitting, prediction, or formal derivation chain. The central claims—persistent collaboration challenges and a cultural shift toward efficiency norms, transparency as professionalism, and AI normalization—are empirical summaries of participants' accounts across two waves, not implications derived from prior work or from the study's own inputs by construction. Self-citations (e.g., Cao et al. [17][18], Hu et al. [49], Whiting et al. [119][120], Xiao et al. [123][124]) appear only in background and related-work sections and are not load-bearing premises for the findings. No uniqueness theorem or ansatz is imported from the authors' prior work. The Phase 2 protocol reminder (Section 3.3) is a possible validity threat, and the paper explicitly acknowledges in Section 7 that the narratives 'might reflect... interpretive frames that we could have co-constructed with them during the very process of interviewing.' That is a methodological reflexivity concern, not circularity in the derivation sense. No equation, definition, or fitted parameter makes the outcome equivalent to the input.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No numeric parameters are fitted; this is a qualitative study, and interpretive themes are the analysis output rather than free parameters. The central claim rests on three domain assumptions about self-report validity, site representativeness, and the reliability of the thematic analysis.

assumptions (3)
  • domain assumption Participants' self-reports are a valid proxy for actual collaboration practices and cultural norms.
    Sections 3.3 and 7 note the analysis relies on interviews rather than behavioral logs. The central findings depend on this premise.
  • domain assumption One distributed, project-based software development organization populated by early AI adopters can serve as a strategic venue for broader insights into AI and teamwork.
    Section 3.1 argues the site is well suited; Section 7 narrows transferability. If the site is unrepresentative, the cultural-shift finding may not generalize.
  • domain assumption Reflexive thematic analysis, as conducted by the authors, reliably captures continuity and change across the two waves.
    Sections 3.2 and 3.3 describe the analysis, but no codebook, intercoder checks, or audit trail are provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AI Hasn't Fixed Teamwork, But It Shifted Collaborative Culture: A Longitudinal Study in a Project-Based Software Development Organization (2023-2025)." pith.science (2026). https://pith.science/paper/O5RFSXYE

@misc{pith2026250910956,
  author       = {Pith},
  title        = {Pith review of: AI Hasn't Fixed Teamwork, But It Shifted Collaborative Culture: A Longitudinal Study in a Project-Based Software Development Organization (2023-2025)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O5RFSXYE}},
  note         = {Machine review of arXiv:2509.10956}
}
read the original abstract

When AI entered the workplace, many believed it could reshape teamwork as profoundly as it boosted individual productivity. Would AI finally ease the longstanding challenges of team collaboration? Our findings suggested a more complicated reality. We conducted a longitudinal two-wave interview study (2023-2025) with members (N=15) of a project-based software development organization to examine the expectations and use of AI in teamwork. In early 2023, just after the release of ChatGPT, participants envisioned AI as an intelligent coordinator that could align projects, track progress, and ease interpersonal frictions. By 2025, however, AI was used mainly to accelerate individual tasks such as coding, writing, and documentation, leaving persistent collaboration issues of performance accountability and fragile communication unresolved. Yet AI reshaped collaborative culture: efficiency became a norm, transparency and responsible use became markers of professionalism, and AI was increasingly accepted as part of teamwork.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Who Will Become the Next Senior? How Generative AI Erodes the Development Pathway in Software Engineering

    cs.CY 2026-07 conditional novelty 6.0 of 10

    Generative AI is absorbing the entry-level work that used to train junior software engineers, and the classroom/workplace dynamics that could correct this are structurally blocked.

Reference graph

Works this paper leans on

138 extracted references · 2 linked inside Pith · cited by 1 Pith paper

  1. [1]

    Cevat Giray Aksoy, Jose Maria Barrero, Nicholas Bloom, Steven J Davis, Mathias Dolls, and Pablo Zarate. 2025. The global persistence of work from home.Proceedings of the National Academy of Sciences122, 27 (2025), e2509892122

    2025

  2. [2]

    Jordana Alexandrea. 2025. How many companies use AI in 2025? Key statistics and industry trends. Hostinger tutorial. https://www. hostinger.com/tutorials/how-many-companies-use-ai?

    2025

  3. [3]

    Andrews, J

    Robert W. Andrews, J. Mason Lilly, Divya Srivastava, and Karen M. Feigh. 2023. The Role of Shared Mental Models in Human–AI Teams: A Theoretical Review.Theoretical Issues in Ergonomics Science24, 2 (2023), 129–175

    2023

  4. [4]

    2012.Working with groupware: understanding and evaluating collaboration technology

    JH Erik Andriessen. 2012.Working with groupware: understanding and evaluating collaboration technology. Springer Science & Business Media

    2012

  5. [5]

    Col- laborating

    Callen Anthony, Beth A Bechky, and Anne-Laure Fayard. 2023. “Col- laborating” with AI: Taking a system view to explore the future of work.Organization Science34, 5 (2023), 1672–1694

    2023

  6. [6]

    Anne Archambault and Jonathan Grudin. 2012. A longitudinal study of facebook, linkedin, & twitter use. InProceedings of the SIGCHI conference on human factors in computing systems. 2741–2750

    2012

  7. [7]

    Marianne Aubin Le Quéré, Hope Schroeder, Casey Randazzo, Jie Gao, Ziv Epstein, Simon Tangi Perrault, David Mimno, Louise Barkhuus, and Hanlin Li. 2024. LLMs as research tools: Applications and evaluations in HCI data work. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–7

    2024

  8. [8]

    Kregg Aytes. 1995. Comparing Collaborative Drawing Tools and Whiteboards: An Analysis of the Group Process.Computer Supported Cooperative Work (CSCW)4 (1995), 51–71

    1995

Show all 138 references
  1. [9]

    Baecker (Ed.)

    Ronald M. Baecker (Ed.). 1993.Readings in Groupware and Computer- Supported Cooperative Work: Assisting Human-Human Collaboration. Elsevier, San Mateo, CA

    1993

  2. [10]

    Sanjay Baitha, Vijayakumar Soorya, Osho Kothari, Shinu M Rajagopal, and Niharika Panda. 2024. Streamlining Software Development: a comprehensive study on CI/CD automation. In2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 1299–1305

    2024

  3. [11]

    2021.Why working from home will stick

    Jose Maria Barrero, Nicholas Bloom, and Steven J Davis. 2021.Why working from home will stick. Technical Report. National Bureau of Economic Research

    2021

  4. [12]

    Pernille Bjørn, Juliane Busboom, Melanie Duckert, Susanne Bød- ker, Irina Shklovski, Eve Hoggan, Kellie Dunn, Qianqian Mu, Louise Barkhuus, and Nina Boulus-Rødje. 2024. Achieving Symmetry in Syn- chronous Interaction in Hybrid Work is Impossible.ACM Transactions on Computer-Hu...

    2024

  5. [13]

    Allie Blaising, Yasmine Kotturi, Chinmay Kulkarni, and Laura Dabbish

  6. [14]

    Dawid Booyse and Caren Brenda Scheepers. 2024. Barriers to adopting automated organisational decision-making through the use of artificial intelligence.Management Research Review47, 1 (2024), 64–85

    2024

  7. [15]

    Michelle Brachman, Amina El-Ashry, Casey Dugan, and Werner Geyer

  8. [16]

    Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis.Qualitative Research in Sport, Exercise and Health 11, 4 (2019), 589–597

    2019

  9. [17]

    Hancheng Cao, Chia-Jung Lee, Shamsi Iqbal, Mary Czerwinski, Priscilla NY Wong, Sean Rintel, Brent Hecht, Jaime Teevan, and Longqi Yang. 2021. Large scale analysis of multitasking behavior during re- mote meetings. InProceedings of the 2021 CHI conference on human factors in co...

    2021

  10. [18]

    Hancheng Cao, Vivian Yang, Victor Chen, Yu Jin Lee, Lydia Stone, N’godjigui Junior Diarrassouba, Mark E Whiting, and Michael S Bern- stein. 2021. My team will go on: Differentiating high and low viability teams through team interaction.Proceedings of the ACM on Human- Computer...

    2021

  11. [19]

    Catherine Courage, Jhilmil Jain, and Stephanie Rosenbaum. 2009. Best practices in longitudinal research. InCHI’09 Extended Abstracts on Human Factors in Computing Systems. 4791–4794

    2009

  12. [20]

    W Alec Cram, Martin Wiener, Monideepa Tarafdar, and Alexander Benlian. 2022. Examining the impact of algorithmic control on Uber drivers’ technostress.Journal of management information systems39, 2 (2022), 426–453

    2022

  13. [21]

    Laura Dabbish, Colleen Stuart, Jason Tsay, and Jim Herbsleb. 2012. Social Coding in GitHub: Transparency and Collaboration in an Open Software Repository. InProceedings of the ACM 2012 Conference on Computer Supported Cooperative Work. ACM, Seattle, WA, USA, 1277– 1286

    2012

  14. [22]

    Vedant Das Swain, Lan Gao, Abhirup Mondal, Gregory D Abowd, and Munmun De Choudhury. 2024. Sensible and sensitive AI for worker wellbeing: factors that inform adoption and resistance for information workers. InProceedings of the 2024 CHI Conference on Human Factors in Computin...

    2024

  15. [23]

    Vedant Das Swain, Lan Gao, William A Wood, Srikruthi C Matli, Gre- gory D Abowd, and Munmun De Choudhury. 2023. Algorithmic power or punishment: Information worker perspectives on passive sensing enabled ai phenotyping of performance and wellbeing. InProceedings of the 2023 CH...

    2023

  16. [24]

    Fred D Davis et al . 1989. Technology acceptance model: TAM.Al- Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption205, 219 (1989), 5

    1989

  17. [25]

    2025.The cybernetic teammate: A field experiment on generative AI reshaping teamwork and expertise

    Fabrizio Dell’Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stewart Taub, et al . 2025.The cybernetic teammate: A field experiment on generative AI reshaping teamwork and expertise. Technical Report. Nati...

    2025

  18. [26]

    Wesley Hanwen Deng, Nur Yildirim, Monica Chang, Motahhare Es- lami, Kenneth Holstein, and Michael Madaio. 2023. Investigating practices and opportunities for cross-functional collaboration around AI fairness in industry practice. InProceedings of the 2023 ACM Con- ference on F...

    2023

  19. [27]

    Marco Deseriis. 2023. Reducing the burden of decision in digital democ- racy applications: A comparative analysis of six decision-making soft- ware.Science, Technology, & Human Values48, 2 (2023), 401–427

    2023

  20. [28]

    Wen Duan, Christopher Flathmann, Nathan McNeese, Matthew J Scalia, Ruihao Zhang, Jamie Gorman, Guo Freeman, Shiwen Zhou, Allyson Ivy Hauptman, and Xiaoyun Yin. 2025. Trusting Autonomous Teammates in Human-AI Teams-A Literature Review. InProceedings of the 2025 CHI Conference o...

    2025

  21. [29]

    Wen Duan, Shiwen Zhou, Matthew J Scalia, Guo Freeman, Jamie Gor- man, Michael Tolston, Nathan J McNeese, and Gregory Funke. 2025. Understanding the processes of trust and distrust contagion in Human– AI Teams: A qualitative approach.Computers in Human Behavior165 (2025), 108560

    2025

  22. [30]

    Wen Duan, Shiwen Zhou, Matthew J Scalia, Xiaoyun Yin, Nan Weng, Ruihao Zhang, Guo Freeman, Nathan McNeese, Jamie Gorman, and Michael Tolston. 2024. Understanding the evolvement of trust over time within Human-AI teams.Proceedings of the ACM on Human- Computer Interaction8, CSC...

    2024

  23. [31]

    Clarence Ellis and Jacques Wainer. 1994. A conceptual model of group- ware. InProceedings of the 1994 ACM conference on Computer supported cooperative work. 79–88

    1994

  24. [32]

    J Alberto Espinosa, Sandra A Slaughter, Robert E Kraut, and James D Herbsleb. 2007. Team knowledge and coordination in geographically distributed software development.Journal of management information systems24, 1 (2007), 135–169

    2007

  25. [33]

    Émilie Fabre, Katie Seaborn, Shuta Koiwai, Mizuki Watanabe, and Paul Riesch. 2025. More-than-Human Storytelling: Designing Longitudinal Narrative Engagements with Generative AI. InProceedings of the Ex- tended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–10

    2025

  26. [34]

    Malcolm Fairbrother. 2014. Two multilevel modeling techniques for analyzing comparative longitudinal survey datasets.Political Science Research and Methods2, 1 (2014), 119–140

    2014

  27. [35]

    Raymond Fok, Mingyuan Zhong, Anne Spencer Ross, James Fogarty, and Jacob O Wobbrock. 2022. A large-scale longitudinal analysis of missing label accessibility failures in android apps. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–16

    2022

  28. [36]

    Tim Fountaine, Brian McCarthy, and Tamim Saleh. 2019. Building the AI-powered organization.Harvard business review97, 4 (2019), 62–73

    2019

  29. [37]

    Victor Garro-Abarca, Pedro Palos-Sanchez, and Mariano Aguayo- Camacho. 2021. Virtual teams in times of pandemic: Factors that influence performance.Frontiers in psychology12 (2021), 624637

    2021

  30. [38]

    Shixian Geng, Remi Inayoshi, Chi-Lan Yang, Zefan Sramek, Yuya Umeda, Chiaki Kasahara, Arissa J Sato, Simo Hosio, and Koji Yatani

  31. [39]

    Ian Gorton and Sanjeev Motwani. 1996. Issues in co-operative software engineering using globally distributed teams.Information and software Conference’17, July 2017, Washington, DC, USA Qing Xiao, Xinlan Emily Hu, Mark E. Whiting, Arvind Karunakaran, Hong Shen, and Hancheng Ca...

    1996

  32. [40]

    Jens Emil Sloth Grønbæk, Juan Sánchez Esquivel, Germán Leiva, Ed- uardo Velloso, Hans Gellersen, and Ken Pfeuffer. 2024. Blended white- board: Physicality and reconfigurability in remote mixed reality col- laboration. InProceedings of the 2024 CHI Conference on Human Factors i...

    2024

  33. [41]

    Jonathan Grudin. 1994. Groupware and social dynamics: Eight chal- lenges for developers.Commun. ACM37, 1 (1994), 92–105

    1994

  34. [42]

    Carl Gutwin, Reagan Penner, and Kevin Schneider. 2004. Group aware- ness in distributed software development. InProceedings of the 2004 ACM conference on Computer supported cooperative work. 72–81

    2004

  35. [43]

    Leslie Haddon. 2011. Domestication Analysis, Objects of Study, and the Centrality of Technologies in Everyday Life.Canadian Journal of Communication36, 2 (2011), 311–324

    2011

  36. [44]

    Harris, Diego Gómez-Zará, Leslie A

    Alexa M. Harris, Diego Gómez-Zará, Leslie A. DeChurch, and Noshir S. Contractor. 2019. Joining Together Online: The Trajectory of CSCW Scholarship on Group Formation.Proceedings of the ACM on Human- Computer Interaction3, CSCW (2019), 1–27

    2019

  37. [45]

    Joseph C Hermanowicz. 2013. The longitudinal qualitative interview. Qualitative sociology36, 2 (2013), 189–208

    2013

  38. [46]

    Thomas Herrmann and Sabine Pfeiffer. 2023. Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence.Ai & Society38, 4 (2023), 1523–1542

    2023

  39. [47]

    Pamela J Hinds and Diane E Bailey. 2003. Out of sight, out of sync: Understanding conflict in distributed teams.Organization science14, 6 (2003), 615–632

    2003

  40. [48]

    2024.Generative AI and the Nature of Work

    Manuel Hoffmann, Sam Boysel, Frank Nagle, Sida Peng, and Kevin Xu. 2024.Generative AI and the Nature of Work. Technical Report. CESifo Working Paper

    2024

  41. [49]

    Distance Matters

    Xinlan Emily Hu, Rebecca Hinds, Melissa Valentine, and Michael S Bernstein. 2022. A" Distance Matters" Paradox: Facilitating Intra-Team Collaboration Can Harm Inter-Team Collaboration.Proceedings of the ACM on Human-Computer Interaction6, CSCW1 (2022), 1–36

    2022

  42. [50]

    Super App

    Ying Huang and Weishan Miao. 2021. Re-domesticating Social Media When It Becomes Disruptive: Evidence from China’s “Super App” WeChat.Mobile Media & Communication9, 2 (2021), 177–194

    2021

  43. [51]

    Shamsi T Iqbal and Eric Horvitz. 2007. Disruption and recovery of computing tasks: field study, analysis, and directions. InProceedings of the SIGCHI conference on Human factors in computing systems. 677–686

    2007

  44. [52]

    Jhilmil Jain, Stephanie Rosenbaum, and Catherine Courage. 2010. Best practices in longitudinal research. InCHI’10 Extended Abstracts on Human Factors in Computing Systems. 3167–3170

    2010

  45. [53]

    Sheila Jasanoff and Sang-Hyun Kim. 2009. Containing the Atom: Sociotechnical Imaginaries and Nuclear Power in the United States and South Korea.Minerva47, 2 (2009), 119–146

    2009

  46. [54]

    Jinglu Jiang, Tanya Giannelia, and Ann-Frances Cameron. 2025. Demonstration of Generative AI Assisted Inductive Coding: Exploring Sociotechnical Imaginaries for Qualitative Research.Communications of the Association for Information Systems57, 1 (2025), 27

    2025

  47. [55]

    Harang Ju and Sinan Aral. 2025. Collaborating with ai agents: Field ex- periments on teamwork, productivity, and performance.arXiv preprint arXiv:2503.18238(2025)

    2025

  48. [56]

    Beata Jungselius and Alexandra Weilenmann. 2025. Tracing Change in Social Media Use: A Qualitative Longitudinal Study. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM, 1–14

    2025

  49. [57]

    Esther Y Kang and Sarah E Fox. 2022. Stories from the frontline: recu- perating essential worker accounts of AI integration. InProceedings of the 2022 ACM Designing Interactive Systems Conference. 58–70

    2022

  50. [58]

    Evangelos Karapanos, Jhilmil Jain, and Marc Hassenzahl. 2012. Theo- ries, methods and case studies of longitudinal HCI research. InCHI’12 Extended Abstracts on Human Factors in Computing Systems. 2727– 2730

    2012

  51. [59]

    Vernon Keenan. 2023. Meet the Future of Work: Salesforce’s Slack GPT and Accenture Partnership to Reinvent CRM.SalesforceDevops.net (2023). https://salesforcedevops.net/index.php/2023/05/04/meet-the- future-of-work-salesforces-slack-gpt-and-accenture-partnership- to-reinvent-c...

    2023

  52. [60]

    Katherine C Kellogg, Melissa A Valentine, and Angele Christin. 2020. Algorithms at work: The new contested terrain of control.Academy of management annals14, 1 (2020), 366–410

    2020

  53. [61]

    Eunhye Kim, Kiroong Choe, Minju Yoo, Sadat Shams Chowdhury, and Jinwook Seo. 2025. Beyond Tools: Understanding How Heavy Users Integrate LLMs into Everyday Tasks and Decision-Making.arXiv preprint arXiv:2502.15395(2025)

    2025 arXiv

  54. [62]

    Taemie Kim, Agnes Chang, Lindsey Holland, and Alex (Sandy) Pent- land. 2008. Meeting mediator: enhancing group collaboration with sociometric feedback. InCHI ’08 Extended Abstracts on Human Fac- tors in Computing Systems(Florence, Italy)(CHI EA ’08). Associ- ation for Computin...

    2008

  55. [63]

    Charlotte Kobiella, Teodora Mitrevska, Albrecht Schmidt, and Fiona Draxler. 2025. When Efficiency Meets Fulfillment: Understanding Long-Term LLM Integration in Knowledge Work. InProceedings of the 4th Annual Symposium on Human-Computer Interaction for Work. 1–15

    2025

  56. [64]

    Joanne Leong, John Tang, Edward Cutrell, Sasa Junuzovic, Gre- gory Paul Baribault, and Kori Inkpen. 2024. Dittos: Personalized, embodied agents that participate in meetings when you are unavail- able.Proceedings of the ACM on Human-Computer Interaction8, CSCW2 (2024), 1–28

    2024

  57. [65]

    Seth C Lewis, Andrea L Guzman, Thomas R Schmidt, and Bibo Lin

  58. [66]

    Jie Li, Hancheng Cao, Laura Lin, Youyang Hou, Ruihao Zhu, and Ab- dallah El Ali. 2024. User experience design professionals’ perceptions of generative artificial intelligence. InProceedings of the 2024 CHI conference on human factors in computing systems. 1–18

    2024

  59. [67]

    Lu Liu, Harm Van Essen, and Berry Eggen. 2024. Let Information Flow Into Awareness: A Design Space for Human-Like Experiences to Promote Informal Communication for Hybrid Work. InProceed- ings of the 2024 ACM Designing Interactive Systems Conference, Anna Vallgårda, Li Jönsson...

    2024

  60. [68]

    Xingyu Bruce Liu, Shitao Fang, Weiyan Shi, Chien-Sheng Wu, Takeo Igarashi, and Xiang ’Anthony’ Chen. 2025. Proactive Conversational Agents with Inner Thoughts. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing M...

    2025

  61. [69]

    Generative AI and its disruptive challenge to journalism: An institutional analysis.Communication and Change1, 1 (2025), 9

    2025

  62. [70]

    Natasha Lomas. [n. d.]. This startup wants to help teams get more out of virtual meetings.TechCrunch([n. d.]). https://techcrunch.com/ 2022/06/16/online-meeting-productivity-tldv-seed/

    2022

  63. [71]

    Jiaojiao Ma, Pengcheng Wang, Benqian Li, Tian Wang, Xiang Shan Pang, and Dake Wang. 2025. Exploring user adoption of ChatGPT: A technology acceptance model perspective.International Journal of Human–Computer Interaction41, 2 (2025), 1431–1445

    2025

  64. [72]

    Thomas W Malone and Kevin Crowston. 1994. The interdisciplinary study of coordination.ACM Computing Surveys (CSUR)26, 1 (1994), 87–119

    1994

  65. [73]

    Sonia Livingstone. 1992. The Meaning of Domestic Technologies. In Consuming Technologies: Media and Information in Domestic Spaces, Roger Silverstone and Eric Hirsch (Eds.). Routledge, London, 113–130

    1992

  66. [74]

    Kristina McElheran, J Frank Li, Erik Brynjolfsson, Zachary Kroff, Emin Dinlersoz, Lucia Foster, and Nikolas Zolas. 2024. AI adoption in Amer- ica: Who, what, and where.Journal of Economics & Management Strategy33, 2 (2024), 375–415

    2024

  67. [75]

    Julie McLeod. 2003. Why we interview now–reflexivity and perspec- tive in a longitudinal study.International Journal of Social Research Methodology6, 3 (2003), 201–211

    2003

  68. [76]

    Meyer and Thomas Fritz

    André N. Meyer and Thomas Fritz. 2025. Better Balancing Focused Work and Collaboration in Hybrid Teams by Cultivating the Sharing of Work Schedules.Proceedings of the ACM on Human–Computer Interaction9, 2 (2025), 1–28

    2025

  69. [77]

    Nikola Marangunić and Andrina Granić. 2015. Technology acceptance model: a literature review from 1986 to 2013.Universal access in the information society14, 1 (2015), 81–95

    2015

  70. [78]

    Alexander G Minton, Howe Yuan Zhu, Hsiang-Ting Chen, Yu-Kai Wang, Zhuoli Zhuang, Gina Notaro, Raquel Galvan, James Allen, Matthias D Ziegler, and Chin-Teng Lin. 2025. A Longitudinal Study on the Effects of Circadian Fatigue on Sound Source Identification and Localization using...

    2025

  71. [79]

    Katelyn Morrison, Shamsi T Iqbal, and Eric Horvitz. 2024. AI-Powered Reminders for Collaborative Tasks: Experiences and Futures.Proceed- ings of the ACM on Human-Computer Interaction8, CSCW1 (2024), 1–20

    2024

  72. [80]

    Moveworks

    Inc. Moveworks. [n. d.]. Moveworks Unveils AI Agent Marketplace to Enable AI Agent Discovery and Deployment in Minutes.Business Wire ([n. d.]). https://www.businesswire.com/news/home/20250415636274/ en/Moveworks-Unveils-AI-Agent-Marketplace-to-Enable-AI- Agent-Discovery-and-De...

  73. [81]

    Danny Miller and Peter H. Friesen. 1982. The longitudinal analysis of organizations: A methodological perspective.Management Science28, 9 (1982), 1013–1034

    1982

  74. [82]

    Olson and Judith S

    Gary M. Olson and Judith S. Olson. 2009. Groupware and Computer- Supported Cooperative Work. InHuman-Computer Interaction. CRC AI Hasn’t Fixed Teamwork, But It Shifted Collaborative Culture Conference’17, July 2017, Washington, DC, USA Press, 235–248

    2009

  75. [83]

    OpenAI. [n. d.]. Notion and OpenAI bring intelligence to every work- flow.OpenAI Blog([n. d.]). http://openai.com/index/notion/

  76. [84]

    Solip Park, Perttu Hämäläinen, and Annakaisa Kultima. 2024. Comic- making to Study Game-making: Using Comics in Qualitative Longitu- dinal Research on Game Development. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–11

    2024

  77. [85]

    Ojelanki K Ngwenyama and Kalle J Lyytinen. 1997. Groupware envi- ronments as action constitutive resources: A social action framework for analyzing groupware technologies.Computer Supported Coopera- tive Work (CSCW)6, 1 (1997), 71–93

    1997

  78. [86]

    Robert E Ployhart and Robert J Vandenberg. 2010. Longitudinal re- search: The theory, design, and analysis of change.Journal of man- agement36, 1 (2010), 94–120

    2010

  79. [87]

    Thorsten Prante, Carsten Magerkurth, and Norbert Streitz. 2002. Devel- oping CSCW Tools for Idea Finding: Empirical Results and Implications for Design. InProceedings of the 2002 ACM Conference on Computer Supported Cooperative Work. ACM, New Orleans, LA, 106–115

    2002

  80. [88]

    Rob Procter, Peter Tolmie, and Mark Rouncefield. 2023. Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare. ACM Transactions on Computer-Human Interaction30, 2 (2023), 1–34

    2023

  81. [89]

    Samuli Pekkola. 2003. Designed for Unanticipated Use: Common Artefacts as Design Principle for CSCW Applications. InProceedings of the 2003 ACM International Conference on Supporting Group Work. ACM, Sanibel Island, FL, 359–368

    2003

  82. [90]

    Bogdana Rakova, Jingying Yang, Henriette Cramer, and Rumman Chowdhury. 2021. Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices.Pro- ceedings of the ACM on Human-Computer Interaction5, CSCW1 (2021), 1–23

    2021

  83. [91]

    2012.HCI theory: classical, modern, and contemporary

    Yvonne Rogers. 2012.HCI theory: classical, modern, and contemporary. Vol. 14. Morgan & Claypool Publishers

    2012

  84. [92]

    O’Connor

    Sharon Ryan and Rory V. O’Connor. 2013. Acquiring and Sharing Tacit Knowledge in Software Development Teams: An Empirical Study. Information and Software Technology55, 9 (2013), 1614–1624

    2013

  85. [93]

    Christian Rahe and Walid Maalej. 2025. How Do Programming Stu- dents Use Generative AI?Proceedings of the ACM on Software Engi- neering2, FSE (2025), 978–1000

    2025

  86. [94]

    Saonee Sarker, Manju Ahuja, Suprateek Sarker, and Sarah Kirkeby

  87. [95]

    Beth Schultz. [n. d.]. Otter.ai Has a Meeting Assistant for Your Team. No Jitter([n. d.]). https://www.nojitter.com/ai-voice/otter-ai-has-a- meeting-assistant-for-your-team

  88. [96]

    Yijia Shao, Humishka Zope, Yucheng Jiang, Jiaxin Pei, David Nguyen, Erik Brynjolfsson, and Diyi Yang. 2025. Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the US Workforce.arXiv preprint arXiv:2506.06576(2025)

    2025

  89. [97]

    Nader Salari, Mahan Beiromvand, Amin Hosseinian-Far, Javad Habibi, Fateme Babajani, and Masoud Mohammadi. 2025. Impacts of genera- tive artificial intelligence on the future of labor market: A systematic review.Computers in Human Behavior Reports(2025), 100652

    2025

  90. [98]

    Fangchen Song, Ashish Agarwal, and Wen Wen. 2024. The impact of generative AI on collaborative open-source software development: Evidence from GitHub Copilot.arXiv preprint arXiv:2410.02091(2024)

    2024 arXiv

  91. [99]

    Jaeyoon Song, Zahra Ashktorab, and Thomas W Malone. 2025. Toged- ule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability.arXiv preprint arXiv:2505.01000 (2025)

    2025

  92. [100]

    Stanford Institute for Human-Centered Artificial Intelligence and Google DeepMind. 2025. AI for Organizations Grand Challenge. https: //hai.stanford.edu/industry/ai-for-organizations-grand-challenge. Re- trieved September 2, 2025

    2025

  93. [101]

    Charles Steinfield, Chyng-Yang Jang, and Ben Pfaff. 1999. Supporting virtual team collaboration: the TeamSCOPE system. InProceedings of the 1999 ACM International Conference on Supporting Group Work. 81–90

    1999

  94. [102]

    Kwonsang Sohn and Ohbyung Kwon. 2020. Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products.Telematics and Informatics47 (2020), 101324

    2020

  95. [103]

    Diane Strode, Torgeir Dingsøyr, and Yngve Lindsjorn. 2022. A team- work effectiveness model for agile software development.Empirical Software Engineering27, 2 (2022), 56

    2022

  96. [104]

    2007.Human-machine reconfigurations: Plans and situated actions

    Lucille Alice Suchman. 2007.Human-machine reconfigurations: Plans and situated actions. Cambridge university press

    2007

  97. [105]

    SuperAGI. 2025. AI in Team Communication: Trends, Tools, and Techniques for Enhanced Collaboration in 2025. SuperAGI blog. https://superagi.com/ai-in-team-communication-trends-tools- and-techniques-for-enhanced-collaboration-in-2025/ 34 min Read

    2025

  98. [106]

    Jörg Sydow, Lars Lindkvist, and Robert DeFillippi. 2004. Project-based organizations, embeddedness and repositories of knowledge. , 1475– 1489 pages

    2004

  99. [107]

    Daniel Stokols, Kara L Hall, Brandie K Taylor, and Richard P Moser

  100. [108]

    Feichin Ted Tschang and Esteve Almirall. 2021. Artificial intelligence as augmenting automation: Implications for employment.Academy of Management Perspectives35, 4 (2021), 642–659

    2021

  101. [109]

    Victoria Uren and John S Edwards. 2023. Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management68 (2023), 102588

    2023

  102. [110]

    2025.Flash Teams: Leading the Future of AI-Enhanced, On-Demand Work

    Melissa Valentine and Michael Bernstein. 2025.Flash Teams: Leading the Future of AI-Enhanced, On-Demand Work. MIT Press

    2025

  103. [111]

    Steven Vallas and Juliet B Schor. 2020. What do platforms do? Un- derstanding the gig economy.Annual review of sociology46, 1 (2020), 273–294

    2020

  104. [112]

    Helena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael S Bernstein, and Ranjay Krishna. 2023. Explanations can reduce overreliance on ai systems during decision- making.Proceedings of the ACM on Human-Computer Interaction7, CSCW1 (2023), 1–38

    2023

  105. [113]

    Jason Tsay, Laura Dabbish, and James Herbsleb. 2014. Let’s Talk About It: Evaluating Contributions Through Discussion in GitHub. InProceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, Hong Kong, China, 144– 154

    2014

  106. [114]

    Wallace, Saba Oji, and Craig Anslow

    James R. Wallace, Saba Oji, and Craig Anslow. 2017. Technologies, Methods, and Values: Changes in Empirical Research at CSCW 1990– 2015.Proceedings of the ACM on Human-Computer Interaction1, CSCW (2017), 1–18

    2017

  107. [115]

    Olson, Jingwen Zhang, Trung Nguyen, and Gary M

    Dakuo Wang, Judith S. Olson, Jingwen Zhang, Trung Nguyen, and Gary M. Olson. 2015. DocuViz: Visualizing Collaborative Writing. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, Seoul, Republic of Korea, 1865–1874

    2015

  108. [116]

    Ruotong Wang, Lin Qiu, Justin Cranshaw, and Amy X. Zhang. 2024. Meeting Bridges: Designing Information Artifacts that Bridge from Synchronous Meetings to Asynchronous Collaboration.Proceedings of the ACM on Human–Computer Interaction8, CSCW1 (2024), 1–29

    2024

  109. [117]

    Weili Wang, John Downey, and Fan Yang. 2023. AI Anxiety? Com- paring the Sociotechnical Imaginaries of Artificial Intelligence in UK, Chinese and Indian Newspapers.Global Media and China(2023), 20594364231196547

    2023

  110. [118]

    Justin D Weisz, Shraddha Vijay Kumar, Michael Muller, Karen-Ellen Browne, Arielle Goldberg, Katrin Ellice Heintze, and Shagun Bajpai

  111. [119]

    Misha Vaughan and Catherine Courage. 2007. SIG: capturing lon- gitudinal usability: what really affects user performance over time?. InCHI’07 extended abstracts on Human factors in computing systems. 2149–2152

    2007

  112. [120]

    Mark E Whiting, Irena Gao, Michelle Xing, N’godjigui Junior Diarras- souba, Tonya Nguyen, and Michael S Bernstein. 2020. Parallel worlds: Repeated initializations of the same team to improve team viability. proceedings of the ACM on Human-Computer Interaction4, CSCW1 (2020), 1–22

    2020

  113. [121]

    Microsoft Work Trend Index (WorkLab). 2025. 2025: The Year the Frontier Firm Is Born. Microsoft Work Trend Index (Work- Lab). https://www.microsoft.com/en-us/worklab/work-trend-index/ 2025-the-year-the-frontier-firm-is-born Annual report via Microsoft WorkLab

    2025

  114. [122]

    Stefan Wuchty, Benjamin F Jones, and Brian Uzzi. 2007. The increasing dominance of teams in production of knowledge.Science316, 5827 (2007), 1036–1039

    2007

  115. [123]

    It Might be Technically Impressive, But It’s Practically Useless to us

    Qing Xiao, Xianzhe Fan, Felix Marvin Simon, Bingbing Zhang, and Motahhare Eslami. 2025. " It Might be Technically Impressive, But It’s Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News...

    2025

  116. [124]

    Qing Xiao, Yuhang Zheng, Xianzhe Fan, Bingbing Zhang, and Zhicong Lu. 2025. Let’s Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordi- nated Algorithmic Actions. InProceedings of the 2025 CHI Conference on Human ...

    2025

  117. [125]

    InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

    Examining the use and impact of an ai code assistant on de- veloper productivity and experience in the enterprise. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–13

  118. [126]

    Mark E Whiting, Allie Blaising, Chloe Barreau, Laura Fiuza, Nik Marda, Melissa Valentine, and Michael S Bernstein. 2019. Did it have to end this way? Understanding the consistency of team fracture.Proceedings of the ACM on Human-Computer Interaction3, CSCW (2019), 1–23

    2019

  119. [127]

    Yen-Ting Yeh, Nikhita Joshi, and Daniel Vogel. 2024. The Effects of Up- date Interval and Reveal Method on Writer Comfort in Synchronized Shared-Editors. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu, HI, USA, 1–13

    2024

  120. [128]

    Rui Zhang, Pedro Mestre, Carlos Serodio, Marilia Prada, and Wenjuan Gao. 2020. Web-based teamwork: Distributed software development course under covid-19. In2020 15th International Conference on Com- puter Science & Education (ICCSE). IEEE, 45–50

    2020

  121. [129]

    Zheng Zhang, Weirui Peng, Xinyue Chen, Luke Cao, and Toby Jia-Jun Li. 2025. LADICA: a large shared display interface for generative AI cognitive assistance in co-located team collaboration. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–22

    2025

  122. [130]

    Bu Zhong, Yunya Song, Charles Feng, Jingyuan Shi, Yuner Zhu, Lola Xie, Wanhui April Zhou, et al. 2025. AI Imaginaries Shape Technologi- cal Identity and Digital Futures.Computers in Human Behavior(2025), 108682

    2025

  123. [131]

    Ruican Zhong, Donghoon Shin, Rosemary Meza, Predrag Klasnja, Lucas Colusso, and Gary Hsieh. 2024. AI-assisted Causal Pathway Diagram for Human-centered Design. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1–19

    2024

  124. [132]

    Ni Xu, Kung-Jeng Wang, and Chen-Yang Lin. 2022. Technology ac- ceptance model for lawyer robots with AI: A quantitative survey. International Journal of Social Robotics14, 4 (2022), 1043–1055. Conference’17, July 2017, Washington, DC, USA Qing Xiao, Xinlan Emily Hu, Mark E. Wh...

    2022

  125. [133]

    Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri, Shilpi Sinha, Jef- frey Weston, Connor Joyce, Neha Shah, Kevin Sherman, Brent Hecht, et al. 2022. The effects of remote work on collaboration among infor- mation workers.Nature human behaviour6, 1 (2022), 43–54

    2022

  126. [2008]

    The science of team science: overview of the field and introduc- tion to the supplement.American journal of preventive medicine35, 2 (2008), S77–S89

    2008

  127. [2011]

    The role of communication and trust in global virtual teams: A social network perspective.Journal of management information systems28, 1 (2011), 273–310

    2011

  128. [2021]

    Making it work, or not: A longitudinal study of career trajectories among online freelancers.Proceedings of the ACM on Human-Computer Interaction4, CSCW3 (2021), 1–29

    2021

  129. [2024]

    InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems

    How knowledge workers use and want to use LLMs in an enterprise context. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–8

  130. [2025]

    InPro- ceedings of the 2025 CHI Conference on Human Factors in Computing Systems

    Beyond the Dialogue: Multi-chatbot Group Motivational In- terviewing for Premenstrual Syndrome (PMS) Management. InPro- ceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–18

    2025

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.