调查文化价值观在软件工程采用大型语言模型中的作用

Stefano Lambiase, Gemma Catolino, Fabio Palomba, Filomena Ferrucci, Daniel Russo
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引用次数: 0

摘要

作为一种社会技术活动,软件开发涉及人与技术的紧密联系。将大型语言模型(LLM)整合到这一过程中,体现了软件开发的社会技术性质。虽然 LLM 会影响开发过程,但软件开发从根本上说仍然是以人为中心的,因此有必要对采用 LLM 过程中的人为因素进行研究。因此,我们在本研究中探讨了影响软件开发中采用 LLM 的因素,重点关注专业人员文化价值观的作用。在技术接受与使用统一理论(UTAUT2)和霍夫斯泰德文化维度的指导下,我们假设文化价值观对UTAUT2框架内的关系具有调节作用。通过使用偏最小二乘法-结构方程模型和来自 188 名软件工程师的数据,我们发现习惯和绩效预期是采用 LLM 的主要驱动因素,而文化价值观对这一过程的调节作用并不明显。这些研究结果表明,通过强调 LLM 可以如何提高性能和效率,企业可以鼓励员工使用 LLM,无论文化差异如何。实际步骤包括提供培训计划以展示 LLM 的益处,为定期使用 LLM 创造支持性环境,以及持续跟踪和分享使用 LLM 所带来的绩效改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering
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.
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