What makes job satisfaction in the information technology industry?

Nimasha Arambepola, Lankeshwara Munasinghe
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引用次数: 1

Abstract

Having a rich human resource is critical for an organization to move towards success. Especially, for business organizations such as technology companies, the human resource is the driving factor of the company's growth which depends on employees' motivation, skills and quality of work. Employees often change their jobs when they are not satisfied with it. Different factors may cause a change in the level of job satisfaction of an employee. For example, the dynamic nature of the Information Technology (IT) industry is an impactful factor that determines the job satisfaction of IT professionals. Foreseeing the employees' job satisfaction makes it easy for a company to take swift actions to improve the job satisfaction of its employees. In this research, we analyzed the effectiveness of machine learning (ML) methods for predicting job satisfaction using employee job profiles. There are job-specific factors in each job domain, and those factors may influence job satisfaction levels. Therefore, this research focused on the following fundamental questions: 1) How do existing ML models perform when predicting job satisfaction of software developers? 2) Can the job satisfaction prediction models be generalized to the other job roles in the IT industry? This study compared the performance of classification models: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) in predicting the level of job satisfaction. Our experiments used two benchmark datasets: Stack Overflow developer survey and IBM HR analytics dataset. The experimental analysis shows that both employee-related factors and company-related factors contribute similarly to predicting job satisfaction. On average, the above ML models predict the job satisfaction of software developers with an accuracy of around 79%.
是什么导致了信息技术行业的工作满意度?
拥有丰富的人力资源是一个组织走向成功的关键。特别是对于像科技公司这样的商业组织来说,人力资源是公司成长的驱动因素,这取决于员工的积极性、技能和工作质量。当员工对工作不满意时,他们经常会换工作。不同的因素可能会导致员工工作满意度的变化。例如,信息技术(IT)行业的动态特性是决定IT专业人员工作满意度的一个影响因素。预见员工的工作满意度使公司更容易采取迅速的行动来提高员工的工作满意度。在这项研究中,我们分析了机器学习(ML)方法在使用员工工作概况预测工作满意度方面的有效性。每个工作领域都有特定于工作的因素,这些因素可能会影响工作满意度。因此,本研究主要关注以下基本问题:1)现有的机器学习模型在预测软件开发人员的工作满意度时表现如何?2)工作满意度预测模型是否可以推广到IT行业的其他工作角色?本研究比较了随机森林(RF)、逻辑回归(LR)、支持向量机(SVM)和神经网络(NN)等分类模型在预测工作满意度水平方面的性能。我们的实验使用了两个基准数据集:Stack Overflow开发人员调查和IBM人力资源分析数据集。实验分析表明,员工相关因素和公司相关因素对工作满意度的预测作用相似。平均而言,上述ML模型预测软件开发人员工作满意度的准确率约为79%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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