Short answer
Prioritize habit formation and clear demonstration of performance benefits when designing and implementing LLM tools for software engineers.
- Field
- Human Factors
- Source
- ACM Transactions on Software Engineering and Methodology (2025)
- Method
- Quantitative research using Partial Least Squares-Structural Equation Modelling (PLS-SEM).
- Sample
- 188 participants
- Evidence
- Strong effect
Software engineers are more likely to adopt Large Language Models (LLMs) driven by ingrained habits and the perceived improvement in their performance, rather than their cultural background. This human factors research insight is drawn from a 2025 study published in ACM Transactions on Software Engineering and Methodology. Using Quantitative research using partial least squares-structural equation modelling (pls-sem). with 188 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Prioritize habit formation and clear demonstration of performance benefits when designing and implementing LLM tools for software engineers.
Habit and Performance Drive LLM Adoption in Software Engineering, Not Culture
Software engineers are more likely to adopt Large Language Models (LLMs) driven by ingrained habits and the perceived improvement in their performance, rather than their cultural background.
ACM Transactions on Software Engineering and Methodology · 2025
Key Findings
- 01Habit is a primary driver for LLM adoption.
- 02Performance expectancy is a primary driver for LLM adoption.
- 03Cultural values did not significantly moderate the adoption process.
Application
Design takeaway
Prioritize habit formation and clear demonstration of performance benefits when designing and implementing LLM tools for software engineers.
How to apply
When introducing new AI tools like LLMs, design training programs that highlight efficiency gains and encourage regular, integrated use within daily tasks to build habits.
Project actions
- 01When researching user adoption, consider how existing routines can be leveraged.
- 02Clearly articulate the benefits of a new design in terms of user efficiency and task completion.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Utilizes a robust theoretical framework (UTAUT2 and Hofstede's dimensions).
- +Employs a statistically sound methodology (PLS-SEM).
Limitations
The study was conducted on software engineers, so the findings might differ for users in other professions or with different levels of technical expertise.
Reliability & validity
The use of PLS-SEM and a structured survey based on established theories enhances the reliability and validity of the findings regarding the relationships between variables.
Think critically
If cultural values don't significantly moderate LLM adoption, what other socio-technical factors might be more influential, and how could they be investigated?
Design Principles
"Technology adoption is primarily influenced by established routines and perceived utility."
Understanding the primary motivators for technology adoption is crucial for effective implementation strategies. This insight suggests that focusing on habit formation and clearly demonstrating performance benefits will be more impactful than tailoring approaches to specific cultural values when introducing LLMs into software development teams.
What This Means for Your Design
People are more likely to start using new AI tools for coding if they already have a habit of using similar tools or if they see that the AI makes their work faster and better. Their culture doesn't seem to matter much for this.
How to use in your project
- 1.Use this research to justify focusing on habit formation and performance benefits in your user research and design choices for technology-based projects.
Add to My Project
Quick Cite
Paragraph starter
This research indicates that the adoption of advanced tools like Large Language Models in professional settings is predominantly driven by the user's existing habits and their perceived impact on performance, rather than by cultural values. Therefore, design strategies should focus on integrating these tools seamlessly into established workflows to foster habit formation and clearly communicate tangible benefits to encourage widespread adoption.
Source
ACM Transactions on Software Engineering and Methodology
Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering
journal · 2025
View sourceQuestions About This Research
- What does the research say about habit and performance drive llm adoption in software engineering, not culture?
- Prioritize habit formation and clear demonstration of performance benefits when designing and implementing LLM tools for software engineers. Evidence: ACM Transactions on Software Engineering and Methodology (2025).
- Why does "Habit and Performance Drive LLM Adoption in Software Engineering, Not Culture" matter for design?
- Understanding the primary motivators for technology adoption is crucial for effective implementation strategies. This insight suggests that focusing on habit formation and clearly demonstrating performance benefits will be more impactful than tailoring approaches to specific cultural values when introducing LLMs into software development teams.
- How can designers apply this research?
- Prioritize habit formation and clear demonstration of performance benefits when designing and implementing LLM tools for software engineers.
- What were the main findings?
- Habit is a primary driver for LLM adoption.. Performance expectancy is a primary driver for LLM adoption.. Cultural values did not significantly moderate the adoption process.
- What research method was used?
- Quantitative research using Partial Least Squares-Structural Equation Modelling (PLS-SEM). with 188 participants.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2025 journal from ACM Transactions on Software Engineering and Methodology.
- What should I do differently in my next project?
- When introducing new AI tools like LLMs, design training programs that highlight efficiency gains and encourage regular, integrated use within daily tasks to build habits.
- What are the limitations?
- The study's findings may not generalize to all technological adoptions or all professional fields, and the specific LLMs and their integration methods were not detailed.