使用基于机器学习的二进制分类器来预测组织成员对协作软件的用户满意度
1Management Information System, Chungbuk National University, Cheongju, South Korea.
PeerJ. Computer science
|August 7, 2023
概括
预测员工对协作软件的满意度对于数字化转型至关重要. 这项研究使用机器学习在实施之前预测用户满意度,识别关键影响因素.
科学领域:
- 信息系统信息系统信息系统
- 人与计算机的交互
- 数据科学数据科学数据科学
背景情况:
- 企业采用协作软件进行数字化转型,但低用户满意度可能会阻碍收益.
- 现有的研究重点是实施后的满意度,在预测方法中留下了一个空白.
- 这项研究解决了在实施前预测用户满意度的需求.
研究的目的:
- 开发和验证基于机器学习的预测方法,以确定员工对协作软件的满意度.
- 在软件实施之前确定影响用户满意度的关键因素.
主要方法:
- 利用来自韩国国家信息社会机构的国家公共数据.
- 应用机器学习,特别是二进制分类器,在对预测变量进行分离后.
- 使用特征重要性得分和预测准确度指标验证了预测模型.
主要成果:
- 确定了10个关键因素,可以预测机构指导,ICT环境,公司文化和人口统计数据的用户满意度.
- 纯粹的贝叶斯 (NB) 分类器获得了最高的准确性 (0.780),其次是后勤回归 (LR) (0.767).
- 其他评估的模型包括XGBoost,SVM,KNN和决策树,准确率各不相同.
结论:
- 该研究提供了预测协作软件用户满意度的重要指标.
- 企业可以利用这些发现来评估当前的协作状态,并制定软件采用战略.
- 介绍了一种新的,经过验证的机器学习方法来预测用户满意度.
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