# Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering

arXiv: https://arxiv.org/abs/2409.05055 · HTML: https://mlsanity.com/paper/2409.05055/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [LLM Company Policies and Policy Implications in Software Organizations](https://mlsanity.com/paper/2510.06718/) (2025)
- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)
- [From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?](https://mlsanity.com/paper/2412.13846/) (2024)
- [Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study](https://mlsanity.com/paper/2601.16700/) (2026)
- [Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review](https://mlsanity.com/paper/2607.24601/) (2026)

## Abstract (arXiv)

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process exemplifies the socio-technical nature of software development. Although LLMs influence the development process, software development remains fundamentally human-centric, necessitating an investigation of the human factors in this adoption. Thus, with this study we explore the factors influencing the adoption of LLMs in software development, focusing on the role of professionals' cultural values. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2) and Hofstede's cultural dimensions, we hypothesized that cultural values moderate the relationships within the UTAUT2 framework. Using Partial Least Squares-Structural Equation Modelling and data from 188 software engineers, we found that habit and performance expectancy are the primary drivers of LLM adoption, while cultural values do not significantly moderate this process. These findings suggest that, by highlighting how LLMs can boost performance and efficiency, organizations can encourage their use, no matter the cultural differences. Practical steps include offering training programs to demonstrate LLM benefits, creating a supportive environment for regular use, and continuously tracking and sharing performance improvements from using LLMs.