Hawthorne Effect[霍索恩效应]
(Mayo)

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摘要,论坛,最佳做法,专家提示和信息来源。

投稿人: Eric Goh See Khai

158 项 • 791.451 次访问


总结

Western Electric Hawthorne Work, Chicago什么是霍索恩效应? 释义

霍索恩实验最初的研究是探讨一系列控制条件(薪水、车间照明度、湿度、休息间隔,等)对员工工作表现的影响。 研究中意外发现,各种试验处理对生产效率都有促进作用,甚至当控制条件回归初始状态时,促进作用仍然存在。 这一现象发生在每一名受试验者身上,对于受试验者整体而言,促进作用的结论亦为真。
很显然,实验假设的各项条件并非是唯一的或决定性的生产效率影响因素。 对此,Elton Mayo以及他的助手们所做的解释是,受试者对于新的实验处理会产生正向反应,即由于环境改变(试验者的出现)而改变行为。所以绩效的提高,并非由实验操控造成。 这种效果就是我们所称的“霍索恩效应”(Hawthorne Effect)。
 

因而这些实验是在所有生产力模型在无形的属性必须析因例如人类行为的第一个征兆之中。


为了更好地理解霍索恩效应,有必要掌握两个概念: Yerkes-Dockson法则边际效用递减法则(the Law of Diminishing Marginal Utility) 尽管刺激因素确实能够将生产效率提高到一定程度,但是任何刺激因素(如薪水)都不是非常有效的,因为它总有效用饱和的那一刻。 所以,不能够完全指望霍索恩效应提高生产效率,此外,还应该辅以其他技术性办法,如工作再设计、工作扩大,以及培育学习型组织,等等。
 

相关效应。

  • 毕马龙效应(Pygmalion Effect。 指在有目的的情境中,个人对自己(或别人对自己)所预期者,常在自己以后行为结果中应验。例如,学生甲成绩比学生乙好,仅仅是因为老师对学生甲的预期如此。
  • 安慰剂效应(Placebo Effect)。 最初是在医学研究中发现的现象,在对病人进行某一种药物处理后,病人就报告病情有所好转,但这种药并不能医治病人的病,原因仅仅是由于病人的自我暗示,相信这种药能够发挥作用。 此效应可通过双盲法实验进行观测、研究。

霍索恩实验。 历史

霍索恩效应的起源于1924年至1933年间的一系列实验研究,其后,从1927年到1932年,Elton Mayo教授持续多年对霍索恩实验结果进行研究、分析。 霍索恩一词源于用于实验的工厂,它是美国西部电气公司坐落在芝加哥的一间工厂的名称。 实验最开始研究的是工作条件与生产效率之间的关系,包括外部环境影响条件(如照明强度、湿度)以及心理影响因素(如休息间隔、团队压力、工作时间、管理者的领导力)。


霍索恩效应的演算。 公式

霍索恩效应没有具体的定量的计算公式可资利用,因为不同的企业、不同的行业的工作条件可能相差甚远。 然而,Yorkes-Dockson法则却可以超越所有的行业,它包含有最大化生产力的最佳动机。 动机不足或者过足都有可能使得生产力降低。 所以, y = - ax2 + bx + c (y = 生产力, x = 工作环境属性)。


霍索恩实验的运用。 应用

  • 工厂环境。 例如: 装配车间。
  • 设计性/创造性行业。 例如: 绘图员。
  • 教育/服务部门。 例如: 护士。

霍索恩效应的步骤。 流程

  1. 辨认影响生产效率的工作环境属性。 例如 x1,x2 ...xnetc
  2. 根据柏拉图分析(Pareto Analysis),对环境属性进行排列,选择关键性影响属性。 例如: x1, x2, x3 (比方3是最重要的属性)。
  3. 为各属性配置权重(比方w1,w2,w3)。 定义模型, y = - ax2 + bx + c(y = 生产力, x = 加权后的最终输入值)。
  4. 对加权最终输入值建模 x = w1*x1 + w2*x2 + w3*x3
  5. 输入公式: y = - ax2 + bx + c

霍索恩实验的优势。 优点

  • 能够清楚地发现员工关心的事项。
  • 如果模型建设适当、准确地话,它所给出的解决生产力的办法具有长期的、可持续的特点。
  • 对员工工作条件进行持续性衡量评估,有助于管理者指定长期的战略决策。

霍索恩效应得局限。 缺点

  • 一些内在的工作环境属性难以辨识,如组织动力。
  • 生产力模型的参数选择abc比较主管,取决于管理人员的个人认识。
  • 关键性的工作环境属性是动态的,模型需要不断调整反映现实情况。
  • 从总体上来看,生产力模型的准确度与管理人员的个人判断力、敏锐性紧密相关。

霍索恩效应的假设。 条件

  • 重要的工作环境属性能够被大量捕获。
  • 没有暗藏的或隐晦的信息。

参考书: Bailey - Human Performance Engineering


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