Integrating AI design tools into traditional design workflows: A study on collaborative tool usage willingness based on the Push-Pull-Mooring framework

In the context of rapid AI development, designers are increasingly integrating AI tools into traditional workflows, forming a collaborative rather than substitutive mode of tool usage. Grounded in the Push-Pull-Mooring (PPM) framework, this study investigates the key factors influencing designers' willingness for collaborative tool usage-defined as the intention to integrate AI tools while retaining traditional ones. Based on survey data from 404 professional designers analyzed through PLS-SEM, the results reveal that operational complexity and tool limitations intensify tool burnout, which in turn reduces collaborative willingness. Among pull factors, tool complementarity significantly enhances willingness, whereas perceived humanization-contrary to common assumptions-exerts a negative effect, indicating that excessive anthropomorphic features may undermine designers' sense of creative autonomy. Mooring factors such as tool trust, social influence, habit, and integration adaptability strongly promote collaboration between AI and traditional tools. These findings highlight the dual psychological burden of emotional fatigue and autonomy concerns in human-AI cooperation. The study extends the PPM framework beyond directional switching to explain multi-tool co-use behaviors and provides actionable insights for the design of AI-assisted tools that respect designers' creative agency while optimizing human-machine collaboration.

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Publication Details

Journal
Acta Psychologica
Published
2026-01-12
DOI
https://doi.org/10.1016/j.actpsy.2025.106084
Primary Topic
Design Education and Practice
Type
article
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article

Integrating AI design tools into traditional design workflows: A study on collaborative tool usage willingness based on the Push-Pull-Mooring framework

Zichuan Liu, Mangting He, Hong Zou
Acta Psychologica
Design Education and Practice
article

Integrating AI design tools into traditional design workflows: A study on collaborative tool usage willingness based on the Push-Pull-Mooring framework

Zichuan Liu, Mangting He, Hong Zou
article en

Abstract

In the context of rapid AI development, designers are increasingly integrating AI tools into traditional workflows, forming a collaborative rather than substitutive mode of tool usage. Grounded in the Push-Pull-Mooring (PPM) framework, this study investigates the key factors influencing designers' willingness for collaborative tool usage-defined as the intention to integrate AI tools while retaining traditional ones. Based on survey data from 404 professional designers analyzed through PLS-SEM, the results reveal that operational complexity and tool limitations intensify tool burnout, which in turn reduces collaborative willingness. Among pull factors, tool complementarity significantly enhances willingness, whereas perceived humanization-contrary to common assumptions-exerts a negative effect, indicating that excessive anthropomorphic features may undermine designers' sense of creative autonomy. Mooring factors such as tool trust, social influence, habit, and integration adaptability strongly promote collaboration between AI and traditional tools. These findings highlight the dual psychological burden of emotional fatigue and autonomy concerns in human-AI cooperation. The study extends the PPM framework beyond directional switching to explain multi-tool co-use behaviors and provides actionable insights for the design of AI-assisted tools that respect designers' creative agency while optimizing human-machine collaboration.

Acta PsychologicaVol. 263
Guangdong Polytechnic Normal University (CN), Zhengzhou Business University (CN)
Partnerships for the goals
Openalex Percentile: Top 90%
Design Education and Practice
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Integrating AI design tools into traditional design workflows: A study on collaborative tool usage willingness based on the Push-Pull-Mooring framework — Zichuan Liu, Mangting He, et al. · Acta Psychologica (2026) | TGRS Research Map | TGRS