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Speaker 1 0:02 As an HR practitioner, one of the best ways you can add value to the organization is by being able to identify, predict, understand and potentially influence and improve the performance of employees at work, which ultimately drives the performance of the organization as a whole. So, a major first step is to understand which level of work performance it is that you're interested in identifying, predicting and understanding more about, because there are many different levels of work performance that we can potentially look at from individual level performance. So that is the performance of individuals on the job. And this is usually the one we think about when we speak about job performance as it's also the critical basis of higher levels of work performance, such as team unit or organization level performance. And indeed, without individual job performance, there would be no performance at the team unit or organizational levels. So as a dependent outcome variable or deviate, it's pretty important and even though other common DVDs such as job satisfaction, employee engagement, and employee stress, health and well being are also very important. Without individual level job performance, there would be no job and none of these other variables to even look at so let's focus on understanding individual level job performance. Broadly speaking, individual job performance could be defined as the things actions and behaviors that people actually do to contribute to organizational goals. And we need to identify those actions that are relevant to the organization's goals and that those are not regardless of whether they are in a written job description. So you have to figure out what level of action is considered to be good or proficient performance. And you can see that this is a really broad definition so that what constitutes job performance can vary across different jobs. Or even in the same role across time as the goals of an organization and hence the work that needs to be done by individuals in the organization are also likely to change over time. So it's quite flexible in that sense. And the definition also doesn't describe the level of specificity meaning that the job performance may either be specific or general. And unless an organization has communicated clear and consistent guidelines on what constitutes good performance, it's essentially left up to individual employees to decide for themselves what actions will be best to in order to meet organizational goals, which probably leads to a number of biases in any ratings of job performance, but particularly with general measures of overall performance where they may be more open to individual interpretation. So it's something to consider set out if you're ever designing a measure of job performance and deciding between a general performance measure or one that's more specific and task based. Thirdly, job performance and job performance is also assumed to be multi dimensional, as advancing an organization's goals, typically requires a range of different types of individual actions. So you can see that in that sense, it makes sense that it would be multi dimensional rather than uni dimensional, which is just a single coherent factor that will be the same all the time. And some different models are for example, cabling, colleagues, I fact general factors of job performance, which include job specific technical proficiency, or nonspecific technical proficiency, communication demonstrated effort and initiative, personal discipline, facilitating P and team performance, supervision, Leadership and Management Administration. And around the same time, Bowman and Mater Whitlow proposed a model of performance with two general factors which they labeled core task performance and contextual performance. And task performance refers to both the activities that directly transform raw materials into the goods and services that are an organization's products, and the activities that service that service and maintain this technical core to enable it to function effectively and efficiently. So task performance is more obvious than contextual performance. And it's usually what we see in job descriptions. In contrast, contextual performance is related to behaviors that contribute to organizational effectiveness through the effects on the psychological, social and organizational context of work. So some examples of contextual performance are promoting positive effect in others, resolving conflicts and encouraging interpersonal trust and social cohesiveness, some other dimensions of job performance are organizational citizenship behavior, and this was proposed by Oregon in 1988. Based on management theory, who looked at discretionary individual behaviors that are not necessarily part of the formal job description, so basically, those behaviors that were not directly or explicitly recognized by the formal reward system, and that in aggregate still promotes the effective functioning of the organization. Were the behaviors that would be classes organizational citizen, should behavior and this is also another dimension of job performance. And I guess really, though, this definition is very similar to Bowman and Mater we'd lose contextual performance but the original definition here remains to be continued to be quite pervasive in the literature. Even though more recently Ogun refined his definition to remove the discretionary and not formally rewarded aspects of it. So that essentially is the same as Foreman and motor weakness, contextual performance dimension. The next one that I want to talk to you about was counterproductive work behavior. And that's just another broad factor that was identified in the literature which generally includes any actions or behaviors that detract from organizational goals. Or bring about undesirable consequences for the organization or its stakeholders, and counterproductive work behavior can also include deviant behaviors directed at the organization such as theft or absence, or any deviant behaviors directed at others, such as in the case of gossiping or bullying other employees and more recently, Griffin, Ed owl proposed a model of performance that better distinctions between the context or level of behaviors, so it could be at the individual, team or organizational levels, and performance that reflects three different aspects of task proficiency. adaptability to change, and proactive behaviors.
演讲者 1 0:02 作为一名人力资源从业者,为组织增加价值的最佳方法之一是能够识别、预测、理解并潜在地影响和提高员工的工作绩效,从而最终推动员工的绩效整个组织。因此,重要的第一步是了解您有兴趣识别、预测和了解更多的工作绩效水平,因为我们可以从个人绩效水平来看待许多不同的工作绩效水平。这就是个人在工作中的表现。当我们谈论工作绩效时,这通常是我们考虑的问题,因为它也是更高水平的工作绩效(例如团队单位或组织级别的绩效)的关键基础。事实上,没有个人工作绩效,就不可能有团队单位或组织层面的绩效。因此,作为因变量或偏差,它非常重要,尽管其他常见的 DVD(例如工作满意度、员工敬业度和员工压力)、健康和福祉也非常重要。如果没有个人层面的工作绩效,就不会有工作,甚至没有其他变量可供查看,因此让我们专注于了解个人层面的工作绩效。从广义上讲,个人工作绩效可以定义为人们为实现组织目标而实际采取的行动和行为。我们需要确定那些与组织目标相关的行动,以及那些与组织目标无关的行动,无论它们是否包含在书面职位描述中。所以你必须弄清楚什么水平的行动被认为是良好或熟练的表现。 您可以看到,这是一个非常广泛的定义,因此不同工作的工作绩效的构成因素可能有所不同。或者甚至在一段时间内与组织的目标处于相同的角色,因此组织中的个人需要完成的工作也可能随着时间的推移而改变。所以从这个意义上来说它是相当灵活的。而且该定义也没有描述具体程度,这意味着工作绩效可以是具体的,也可以是一般的。除非组织就什么是良好绩效传达了明确且一致的指导方针,否则基本上要由员工自己决定采取什么行动来实现组织目标,这可能会导致任何方面的许多偏见。工作绩效的评级,特别是总体绩效的一般衡量标准,它们可能更容易接受个人解释。因此,如果您正在设计工作绩效衡量标准并在一般绩效衡量标准或更具体和基于任务的绩效衡量标准之间做出决定,那么这是需要考虑的事情。第三,工作绩效和工作绩效也被认为是多维的,因为推进组织的目标通常需要一系列不同类型的个人行动。所以你可以看到,从这个意义上说,它是多维的而不是单维的,这只是一个始终相同的单一连贯因素。 一些不同的模型例如,布线,同事,事实上工作绩效的一般因素,其中包括工作特定的技术熟练程度,或非特定的技术熟练程度,沟通表现出的努力和主动性,个人纪律,促进P和团队绩效,监督,领导力和管理行政。大约在同一时间,鲍曼和马特·惠特洛提出了一个绩效模型,其中包含两个一般因素,他们将其标记为核心任务绩效和情境绩效。任务绩效是指将原材料直接转化为组织产品的商品和服务的活动,以及服务和维护该技术核心以使其有效且高效地运作的活动。所以任务绩效比情境绩效更明显。这通常是我们在职位描述中看到的。相比之下,情境绩效与通过影响心理、社会和组织工作情境而有助于组织有效性的行为相关。因此,情境绩效的一些例子是促进他人的积极影响、解决冲突、鼓励人际信任和社会凝聚力,工作绩效的其他一些维度是组织公民行为,这是俄勒冈州于 1988 年提出的。自由裁量的个人行为不一定是正式工作描述的一部分,所以基本上,这些行为没有直接或明确地得到正式奖励制度的认可,但总的来说仍然促进组织的有效运作。 如果班级的行为是组织公民,那么就应该有行为,这也是工作绩效的另一个维度。我想,尽管如此,这个定义与鲍曼和马特非常相似,我们会失去上下文性能,但这里的原始定义仍然在文献中继续相当普遍。尽管最近奥贡改进了他的定义,删除了自由裁量权和未正式奖励的方面。因此,这本质上与 Foreman 和运动弱点、情境表现维度相同。我想和大家谈谈的下一个问题是适得其反的工作行为。这只是文献中确定的另一个广泛因素,通常包括任何有损组织目标的行动或行为。或者给组织或其利益相关者带来不良后果,而适得其反的工作行为还可以包括针对组织的异常行为,例如盗窃或缺勤,或任何针对他人的异常行为,例如八卦或欺凌其他员工而最近,Griffin、Ed owl提出了一种绩效模型,可以更好地区分行为的背景或水平,因此可以在个人、团队或组织层面上,绩效反映任务熟练程度的三个不同方面。适应变化和积极主动的行为。

Speaker 1 7:16 This table, table 7.1 from the Edwards and Edwards predictive HR analytics textbook, this some examples of team and individual level measures of performance in the first column. So for example, as team level measures, they've listed employee opinion survey output, Stonebridge customer feedback, customer loyalty, customer reinvestment or repeat business, and staff turnover. And interestingly, these are all outcome level type. Or results based measures rather than behavioral measures of performance, like those that are listed here as examples of the individual level measures. So in the individual level measures, they've got performance appraisal ratings, behavioral ratings, sales, performance figures, checkout scan rates, individual customer feedback, peer feedback or rating codes. Which is a mix of both behavioral as well as outcome type measures. So I just want to note that that's not because the individual level rituals can't be aggregated at the team level, but rather that it's harder to measure behaviors that teams do without this being in some way an aggregate of individual behaviors anyway, so it's entirely appropriate to use team level outcomes such as those listed here as measures of team level performance. And also it's acceptable to use aggregate measures of individual performance as a proxy for team level performance. It's important to also note though, that it may not always be appropriate to judge performance at the individual level, especially when there may be teams involved in the work process or outcomes. So when for example, you need to protect the confidentiality of data that was collected at the individual level, such as in the case of most employee opinion surveys and employee engagement surveys, that it might be better to look at performance at the team level rather than individual level and this table also lists some common predictor variables at each level in this column on the right, so it's important to not only consider how we measure performance, but also what measures we might have or need to collect data on that we can use to then predict performance. So I just want to talk through now some examples of potential research questions you may be interested in testing that relate to the broad topic areas of performance reward and remuneration. The first example research question is fairly is a fairly obvious one that aims to predict interview employee performance as measured by annual performance appraisal ratings. So, this example question is that is can annual performance ratings be predicted by perceived organizational support job satisfaction, job strain and sick leave days in the previous year? And because we're interested in predicting performance whenever it makes sense to and it's possible to it's good practice to use past data to predict some future outcome as I've highlighted in this example question. The second example question is, Can customer loyalty that is
演讲者 1 7:16 该表,表 7.1 来自 Edwards 和 Edwards 预测性人力资源分析教科书,这是第一列中团队和个人层面绩效衡量的一些示例。例如,作为团队层面的衡量标准,他们列出了员工意见调查结果、Stonebridge 客户反馈、客户忠诚度、客户再投资或重复业务以及员工流动率。有趣的是,这些都是结果级别类型。或者基于结果的衡量标准,而不是绩效的行为衡量标准,就像此处列出的个人级别衡量标准的示例一样。因此,在个人层面的衡量标准中,他们有绩效评估评级、行为评级、销售、绩效数据、结账扫描率、个人客户反馈、同行反馈或评级代码。这是行为和结果类型测量的结合。所以我只是想指出,这并不是因为个人层面的仪式不能在团队层面上聚合,而是因为无论如何,如果不以某种方式聚合个人行为,就很难衡量团队所做的行为,所以这是完全适合使用团队级别的结果,例如此处列出的团队级别绩效衡量标准。使用个人绩效的总体衡量标准作为团队绩效的代理也是可以接受的。但还需要注意的是,在个人层面判断绩效可能并不总是合适的,特别是当可能有团队参与工作流程或结果时。 因此,例如,当您需要保护在个人级别收集的数据的机密性时,例如在大多数员工意见调查和员工敬业度调查的情况下,最好在团队级别而不是在团队级别查看绩效。比个人级别,该表还列出了右侧此列中每个级别的一些常见预测变量,因此不仅要考虑我们如何衡量绩效,而且还要考虑我们可能拥有或需要收集哪些数据,这一点很重要然后用于预测性能。因此,我现在只想谈谈您可能有兴趣测试的一些潜在研究问题的示例,这些问题与绩效奖励和薪酬这一广泛的主题领域相关。第一个示例研究问题相当明显,旨在预测通过年度绩效评估评级衡量的面试员工绩效。那么,这个示例问题是,年度绩效评级是否可以通过感知到的组织支持工作满意度、工作压力和上一年的病假天数来预测?因为我们有兴趣在有意义的情况下预测性能,并且正如我在这个示例问题中强调的那样,使用过去的数据来预测未来的结果是一种很好的做法。 第二个示例问题是,客户忠诚度是否可以

Speaker 1 10:34 the likelihood of using product or services again, the predicted by salesperson characteristics such as understanding of customers need salesperson confidence having a recommendation being knowledgeable and salesperson gender. And I wanted to flag this question because because it is the type of question that can be answered using a single data source such as a brief customer feedback survey given to each customer after a sale or transaction has been completed. So in this case, rather than looking at ratings of the behavior itself, we're more interested in an outcome based measure of performance which is in this case, customer loyalty and customer perceptions of how the employee is performing. The third performance example question is similar to the second one but utilizes different data sources to predict customer loyalty. So the question is Can customer loyalty in a financial services firm that is the likelihood of reinvesting be predicted by employee engagement and manager feedback? So in this example, we need to link out outcome measure of customer loyalty. Different sources. In the three reward and remuneration examples here of just based on research hypothesis listed in Decatur and Hofmann's paper, examining the effects of reward satisfaction on employee turnover and performance. And in the first example, the research question is, does the. effect of satisfaction with financial material and psychological rewards on task performance differ significantly between employees? To just this question to get an Hofmann's used an exploratory method called cluster wise regression analysis, which is just an extension of the regression analysis we've done, but it is much higher level and that's something we'll be covering in the course but I wanted to just make a note of that so that you're aware of this different methods that they have used. And what they use the cluster was regression analysis for is to test for whether there is any evidence of clusters or subgroups of different relationships between the IVs and the DV so are there different regression equations that can be supported by the data? And this is a relatively advanced analysis requiring a specialized statistical modeling tool like M plus, which is what to get and Hoffman to use in their analysis. And it's fairly new so despite some of the, I guess, methodological issues in the data and Hoffman's paper, it was still the analytic method of this cluster wise regression is quite innovative. So, the second and third examples are a bit more specific and ask if the effect of satisfaction with financial rewards on task performance is stronger for employees who value financial security. And the third question is, is the effect of satisfaction psychological rewards? On task performance stronger for employees who value recognition although to get an Hoffman's didn't do this in their paper, in that they did the continued the analysis based on the results of their cluster wise regression analysis. You could easily examine a similar research question answer these using testing for a significant interaction effect between in the first or sorry in the second example, between satisfaction with financial rewards and the importance of financial security. So, you test for the interaction effect in effect between those two IVs on task performance and then have a look and see if it is significant, then, is the direction of each effect and the effects together in the way that this hypothesis is stating So, are those who value financial security compared to those who don't really value financial security, having a stronger relationship between satisfaction with financial rewards and their task? performance? And that's what you'd be looking for. And as I said, previously, one of the best ways to look for that is to help is to visualize it. So you know, plot it out and see what that relationship between those two IVs look at for the DV of task performance. And in the third example, you do something similar and just test for the interaction effect between satisfaction with psychological rewards and the importance of recognition and how this interaction effect predicts task performance. And if the interaction effect is significant than again, you would explore that relationship between psychological reward satisfaction and the importance of recognition to see if those for example, who value recognition a lot more satisfied and who are more satisfied with psychological rewards have the highest task performance compared to those who are less satisfied. And then also for employees who don't value record initial, not much. What does the relationship look like for that group? If they have high or low cycle satisfaction with psychological rewards on task performance,
发言者 1 10:34 再次使用产品或服务的可能性、销售人员特征的预测,例如对客户需求的了解、销售人员的信心、有推荐的知识渊博以及销售人员的性别。我想标记这个问题,因为它是可以使用单个数据源来回答的问题类型,例如在销售或交易完成后向每个客户提供的简短客户反馈调查。因此,在这种情况下,我们更感兴趣的是基于结果的绩效衡量标准,而不是查看行为本身的评级,在这种情况下,即客户忠诚度和客户对员工表现的看法。第三个绩效示例问题与第二个类似,但利用不同的数据源来预测客户忠诚度。因此,问题是金融服务公司的客户忠诚度(即再投资的可能性)是否可以通过员工敬业度和经理反馈来预测?因此,在这个例子中,我们需要链接出客户忠诚度的结果衡量标准。不同的来源。这里的三个奖励和薪酬例子只是基于迪凯特和霍夫曼论文中列出的研究假设,考察了奖励满意度对员工流动率和绩效的影响。在第一个例子中,研究问题是,是否。财务物质满意度和心理奖励对任务绩效的影响在员工之间存在显着差异吗? 为了获得霍夫曼的这个问题,使用了一种称为聚类回归分析的探索性方法,这只是我们所做的回归分析的扩展,但它的水平要高得多,这是我们将在课程中介绍的内容,但是我只想记下这一点,以便您了解他们使用的不同方法。他们使用聚类回归分析的目的是测试 IV 和 DV 之间是否存在不同关系的聚类或子组的证据,那么数据是否可以支持不同的回归方程?这是一个相对高级的分析,需要专门的统计建模工具,例如 M plus,这是霍夫曼在分析中使用的工具。它是相当新的,所以尽管我猜数据和霍夫曼的论文中存在一些方法论问题,但这种聚类明智回归的分析方法仍然是相当创新的。因此,第二个和第三个例子更加具体,询问对于重视财务安全的员工来说,财务奖励满意度对任务绩效的影响是否更强。第三个问题是,满足感的作用是心理奖励吗?对于重视认可的员工来说,任务绩效更强,尽管在他们的论文中没有得到霍夫曼的认可,因为他们根据聚类回归分析的结果进行了继续分析。您可以轻松地检查类似的研究问题,使用测试第一个或第二个示例中的抱歉之间的显着交互作用来回答这些问题,即对财务奖励的满意度与财务安全的重要性之间的交互作用。 因此,您测试这两个 IV 之间对任务绩效的交互作用,然后看看它是否显着,然后,每个效果的方向以及效果按照该假设所述的方式组合在一起,与那些并不真正重视财务安全的人相比,那些重视财务安全的人对财务奖励的满意度与他们的任务之间的关系是否更强?表现?这就是您要寻找的。正如我之前所说,寻找帮助的最佳方法之一就是将其可视化。所以你知道,把它画出来,看看这两个 IV 之间的关系对于任务绩效的 DV 有何影响。在第三个例子中,你做了类似的事情,只是测试心理奖励的满意度和认可的重要性之间的交互效应,以及这种交互效应如何预测任务绩效。如果交互效应比以往任何时候都显着,那么您将探索心理奖励满意度与认可重要性之间的关系,看看那些更看重认可的人和那些对心理奖励更满意的人是否拥有最高的任务与不太满意的人相比的表现。对于那些不重视初始记录的员工来说,也没什么价值。该群体的关系如何? 如果他们对任务绩效的心理奖励有高或低的循环满意度,

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