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P2 Staying or Leaving? How Job Satisfaction, Embeddedness and Antecedents Predict Turnover Intentions of Software Professionals

留下还是离开?工作满意度、嵌入性及前因如何预测软件专业人员的离职意向
ICSE Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering2026匹配度 0/10 · 该论文研究软件从业者的离职意向,采用组织心理学调查与PLS-SEM方法,完全不涉及程序语言、静态分析、形式化验证、浮点程序分析、贝叶斯程序分析或LLM/Agent与程序分析的交叉,与用户研究方向无关。引用 3 inbox
Miikka Kuutila, Paul Ralph, Huilian Sophie Qiu, Ronnie de Souza Santos, Morakot Choetkiertikul, Amin Milani Fard, Rana Alkadhi, Xavier Devroey, Gregorio Robles, Hideaki Hata, Sebastian Baltes, Hera Arif, Vladimir Kovalenko, Shalini Chakraborty, Eray Tuzun, Gianisa Adisaputri
crossref: 2026-09-12dblp: 2026-09-12arxiv: 2026-09-12

AI 速览

一句话:通过224名软件专业人员的调查与PLS-SEM,发现工作满意度与工作嵌入性负向预测离职意向,组织公平等是关键前因。

软件行业自愿离职普遍,增加招聘与入职成本并导致组织与隐性知识流失。本文研究软件专业人员的工作满意度、工作生活平衡、工作嵌入性及其前因(工作质量、人格特质、对技术与社会技术基础设施的态度、组织公平感知)如何关联离职意向。作者对224名地域多样的软件专业人员进行横断面调查,使用偏最小二乘结构方程模型(PLS-SEM)分析,模型含反映性与形成性构念,检验15个假设并探索间接效应。结果:工作满意度与工作嵌入性显著负向关联离职意向,工作生活平衡无直接效应;工作生活平衡与工作质量是工作满意度的最强前因,组织公平是工作嵌入性的最强预测因子;满意度与嵌入性中介了工作质量、组织公平和工作生活平衡对离职意向的影响。模型解释力高于以往研究,表明心理与组织因素共同影响离职意向,改善公平、工作质量与平衡有助于留任。

问题
软件行业自愿离职常见,本文研究软件专业人员的工作满意度、工作生活平衡、工作嵌入性及其前因与离职意向之间的关系。
方法
对224名地域多样的软件专业人员进行横断面调查,使用偏最小二乘结构方程模型(PLS-SEM)检验基于职业心理学与软件工程文献的15个假设,并探索间接效应。
评估
模型显示工作满意度与工作嵌入性负向预测离职意向,工作生活平衡无直接效应;组织公平、工作质量与工作生活平衡等前因通过满意度与嵌入性中介影响离职意向,且模型解释力高于以往软件开发情境研究。
原文摘要(Abstract)

Context: Voluntary turnover is common in the software industry, increasing recruitment and onboarding costs and the risk of losing organizational and tacit knowledge. Objective: This study investigates how job satisfaction, work-life balance, job embeddedness, and their antecedents, including job quality, personality traits, attitudes toward technical and sociotechnical infrastructure, and perceptions of organizational justice, relate to software professionals’ turnover intentions. Method: We conducted a geographically diverse cross-sectional survey of software professionals (N = 224) and analyzed the data using partial least squares structural equation modeling (PLS-SEM). Our model includes both reflective and formative constructs and tests 15 hypotheses grounded in occupational psychology and software engineering literature. Additionally, indirect effects were explored. Results: Job satisfaction and embeddedness were significantly negatively associated with software professionals’ turnover intentions, while work-life balance showed no direct effect. The strongest antecedents for job satisfaction were work-life balance and job quality, while organizational justice was the strongest predictor of job embeddedness. Exploratory analysis suggests that job satisfaction and job embeddedness mediate the effect of job quality, organizational justice, and work-life balance on turnover intentions. Discussion: The resulting PLS-SEM model has considerably higher explanatory power for key outcome variables than previous work conducted in the software development context, highlighting the importance of both psychological (e.g., job satisfaction, job embeddedness) and organizational (e.g., organizational justice, job quality) factors in understanding turnover intentions of software professionals. Our results imply that improving job satisfaction and job embeddedness is the key to retaining software professionals. In turn, enhancing job quality, supporting work-life balance, and ensuring high organizational justice can improve job satisfaction and embeddedness, indirectly reducing turnover intentions. Our results broadly suggest that retention strategies are best focused on fairness, meaningful work, and work-life balance.

链接与引用

DOI 原文 · arXiv · DBLP

BibTeX
@inproceedings{abs-2512-00869,
  title = {Staying or Leaving? How Job Satisfaction, Embeddedness and Antecedents Predict Turnover Intentions of Software Professionals},
  author = {Miikka Kuutila and Paul Ralph and Huilian Sophie Qiu and Ronnie de Souza Santos and Morakot Choetkiertikul and Amin Milani Fard and Rana Alkadhi and Xavier Devroey and Gregorio Robles and Hideaki Hata and Sebastian Baltes and Hera Arif and Vladimir Kovalenko and Shalini Chakraborty and Eray Tuzun and Gianisa Adisaputri},
  booktitle = {ICSE},
  year = {2026},
  doi = {10.1145/3744916.3773124}
}