Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes
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Abstract (arXiv)
This study investigates how professional writers' complex relationship with GenAI shapes their work practices and outcomes. Through a cross-sectional survey with writing professionals (n=403) in diverse roles, we show that collaboration and rivalry orientation are associated with differences in work practices and outcomes. Rivalry is primarily associated with relational crafting and skill maintenance. Collaboration is primarily associated with task crafting, productivity, and satisfaction, at the cost of long-term skill deterioration. Combination of the orientations (high rivalry and high collaboration) reconciles these differences, while boosting the association with the outcomes. Our findings argue for a balanced approach where high levels of rivalry and collaboration are essential to shape work practices and generate outcomes aimed at the long-term success of the job. We present key design implications on how to increase friction (rivalry) and reduce over-reliance (collaboration) to achieve a more balanced relationship with GenAI.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 135 references that are in the map. First posted on arXiv 9 Feb 2026.
Builds on
21 of 135 references.
References · 21
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
- 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 165
- 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 137
- 2020Participation is not a Design Fix for Machine Learningcited by 110
- 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
- 2020Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithmscited by 35
- 2023The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualizationcited by 23
- 2024"If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishmentcited by 19
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
- 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Modelscited by 17
- 2022"It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Toolscited by 14
9 more
- 2024From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practicecited by 14
- 2025Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyondcited by 13
- 2023Overcoming Algorithm Aversion: A Comparison between Process and Outcome Controlcited by 12
- 2025Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Makingcited by 9
- 2024Development in times of hype: How freelancers explore Generative AI?cited by 8
- 2025Making the Write Connections: Linking Writing Support Tools with Writer's Needscited by 7
- 2025Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMscited by 5
- 2025AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professionscited by 3
- 2025Beyond Replacement or Augmentation: How Creative Workers Reconfigure Division of Labor with Generative AIcited by 1
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Citing papers per year: 2026: 2.
2 papers.