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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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相关实验视频

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在基于技术的中小企业中,使用机器学习对员工忠诚度进行评估.

Yong Shi1, Yuan Wang2, Hongkun Zuo3

  • 1School of Computer Science, Huainan Normal University, Huainan City, Anhui, China. shiyong@hnnu.edu.cn.

Scientific reports
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

在以技术为基础的中小企业 (TSME) 中,员工忠诚度至关重要. 机器学习模型有效地预测忠诚度,帮助人才管理和创新型中小企业的可持续增长.

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科学领域:

  • 人力资源管理 人力资源管理
  • 创新研究 研究 创新研究
  • 数据科学数据科学数据科学

背景情况:

  • 员工忠诚对于技术型中小企业 (TSME) 的可持续增长至关重要.
  • 跨越小型企业是创新的关键驱动力,具有显著的活力和潜力.
  • 高员工忠诚度对于这些创新企业的成功和发展至关重要.

研究的目的:

  • 用历史评估数据分析影响中国中小企业员工忠诚度的因素.
  • 调查这些因素与员工忠诚度之间的关系.
  • 开发一个客观和适用的智能评估模型,用于TSMEs的员工忠诚度.

主要方法:

  • 利用来自中国中小企业的历史员工评估数据.
  • 应用了几种机器学习模型和算法用于忠诚度预测.
  • 采用决策分析来支持跨越型中小企业内的人才评估.

主要成果:

  • 证明了使用机器学习来预测员工忠诚度的可行性.
  • 确定了影响员工忠诚度的关键因素,在TSMEs的背景下.
  • 为智能人才评估系统提供了基础.

结论:

  • 机器学习提供了一种可行的方法来预测和管理中小企业的员工忠诚度.
  • 开发的模型支持跨越型中小企业识别,激励,吸引,培养和保留科学和技术人才.
  • 这项研究有助于促进跨越型中小企业的可持续和高质量的管理实践.