Investigating Writing Professionals'Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes

2026-02-09

AI Summary

This study investigates how professional writers navigate the interplay between competition and collaboration when interacting with generative AI, balancing short-term efficiency against long-term career development. Grounded in relational orientation theory from social psychology, the research draws on cross-sectional survey data from 403 writing professionals and employs quantitative analysis to reveal that a high competitive orientation supports skill maintenance and relational restructuring, whereas a high collaborative orientation enhances task redesign and job satisfaction but may lead to skill atrophy. Crucially, a “dual-high” pattern—characterized by simultaneously high levels of both orientations—effectively reconciles the tension between immediate productivity and sustained capability development, significantly amplifying positive work outcomes. These findings offer theoretical grounding and practical insights for establishing new paradigms of human–AI collaboration.

Technology Category

• Humans and AI: Teamwork, Team formation
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Generative AI
• professional writers
• rivalry
• collaboration
• work practices

Innovation

• generative AI
• rivalry
• collaboration
• work practices
• skill maintenance