工作满意度指标及其相关因素外文翻译（可编辑）

工作满意度指标及其相关因素外文翻译 外文翻译Job Satisfaction Indicators and Their Correlates Material Source:American Behavioral scientistAuthor: Stanley E. Seashore Conceptions of job satisfaction until very recently have been largely psychological and individualistic in orientation. Empirical studies have been confined to local situations or special populations with interpretive purposes reflecting the values of employed individuals or of their managers. However, if job satisfaction measures are to be useful in monitoring the quality of employment on a societal scale, it will be necessary to enlarge the perspective, to invoke some societal and political values, and to begin to treat job satisfaction in the context of a larger array of associated variablesThe measurement of job satisfaction as a social indicator may have three roles: 1 to represent a valued product of society-a component of the psychological GNP; 2 to provide a monitoring and diagnostic aid for early warning of societal dislocations, policy or program failure, and slowly developing societal changes; and 3 to provide a significant component in the theories and models to be used in the formulation of social policy and programs. Opinions differ on how prominent and how effective job satisfaction measures will be in these three roles. The utility of job satisfaction measures rests on the development of multiple measurement methods that are standardized, suitable for wide use, and capable of detecting population differences and population changes. In addition, the utility rests upon these measures having an agreed conceptual and “real world” reference as well as a known matrix of causal and consequential relationships to other significant variables. Both requirements must be met before convincing proof can be advanced as to the practical utility of job satisfaction measures for anticipating, understanding, and influencing future outcomes of present societal conditionsThese themes provide the structure for this paper. In the next section, we give an overview of the state of the art in the measurement of job satisfaction. The section following that provides an approach to organizing, or modeling, the correlates of job satisfaction. The final section suggests some priorities for further research and development. JOB SATISFACTION INDICATORS This section summarizes considerations that bear upon the choice of approaches and operational methods for measuring job satisfaction. We shall limit the discussion to approaches that rest upon direct inquiry through interview or questionnaire methods to produce data that can be aggregated to provide job satisfaction indicators for variously defined populations. We exclude from discussion: 1 indirect approaches that draw inferences about job satisfaction from presumed causal or consequential phenomena; 2 approaches that are primarily individualistic and diagnostic and, therefore, not usually applicable for generating population indicators; and 3 approaches that have utility primarily for empirical and theoretical discovery rather than for population description purposes. We first review the commonly used forms of primary data, then some commonly used derivative job satisfaction indicators. A scheme is presented to guide the evaluation of these several indicators. These are applied to draw implications for preferred future methods. Throughout the paper, except where noted, we will use the term “job satisfaction” inclusively to refer also to dissatisfaction without intended prejudice whether satisfaction and dissatisfaction are best treated as polar opposites or as two conceptually different variables.PRIMARYDATA By primary data we mean the “raw” responses given by individual respondents to verbal questions or comparable stimuli. There appears to be a fixed roster of basic forms of primary data, even though innumerable variations on these are known. Two kinds of primary data are distinguished: facet-free and facet-specific. Facet-free primary data are obtained when the respondent is asked to indicate his global satisfaction with his job and job environment without specifying in advance the facets to be considered or how they are to be combined. In effect, each respondent provides a net response derived from his own set of facets, weighted or otherwise combined in his own unique fashion, with unstated and unique assumptions not only about the context for evaluation, but also about his own “fit” to the job and its environment, and with the environmental “reality” defined by his own perceptions and cognitions. Normative, cognitive, and unconscious elements in the evaluation are invited. The stimulus questions are usually phrased or nonverbally displayed with an intent to impose the fewest possible constraints upon his perceptual, cognitive, and evaluative processes. Several complementary stimuli may be used to diversify the unavoidable constraints. Facet-specific primary data are obtained when the respondent is asked to represent his satisfaction with respect to some specified facet of his job or job environment. Since the facet specification is never exhaustive or definitive, the difference between a facet-free and a facet-specific inquiry is only one of degree. For example, the query “How satisfied are you with your pay?” elicits a net response that includes consideration of unspecified subfacets amount of pay, certainty of pay, rate of increase, adequacy to need, and so forth, unspecified “reality” last weeks pay, pay after deductions, pay confidently expected next year, and the like, and unknown perceptual, cognitive, and evaluative processes. Nevertheless, facet-specific methods allow the inquirer some control over the range of facets to be included in his data, an added degree of comparability among different respondents, and closer and more confident linkage between the response obtained and the “reality” of the job environment or of the person under investigation. Facet-specific queries, thus, vary in their specificity. In addition, they take the following forms: a direct report of degree of satisfaction with facet satisfaction; b amount or degree of facet provided by job is now; c amount or degree of facet respondent would like to have would like; d amount or degree of facet respondent should be provided should be; e importance of facet to respondent importance. The forms of response exist in great variety, including simple check-list or “yes-no” responses, rank ordering, scalar responses e.g., Likert scales, “faces,” and the like, and the more complex forms such as “self-anchoring” scales. While these alternatives invite useful discussion about their relative reliability, efficiency, simplicity, item utility, and conceptual assumptions, such issues will not be raised here. Each alternative provides primary data permitting aggregation for population comparison or social indicator purposes.DERIVED DATA In the case of primary data that represent the direct or implied expression of job satisfaction, social indicators may be derived by a simple aggregation of primary data for individuals and then an aggregation of individual data for the population. This is often done, for example, with respect to multi-item, facet-free primary data, and with primary data of types a and b above. However, more complex forms of derivative indexes are commonly preferred for various reasons. Procedures for deriving indexes from primary data include: 1 differential weighting of items; 2 clustering of items into factors or dimensions on conceptual or empirical grounds; 3 converting primary data to derived discrepancy scores on theoretical, conceptual, or empirical grounds before aggregation; 4 retaining individual facet item data for differential uses in interpretation or analysis; 5 removing some uncontrolled response variance before aggregation ; and 6 adjusting primary data for known or presumed bias before aggregation. Any of these procedures may be employed singly or in combination with others. The last three procedures are relatively trivial or at least noncontroversial at the present time; the first three are topics of current inquiry and dispute. CORRELATES OF JOB SATISFACTION This section reviews what is known and what should become known with respect to the correlates of job satisfaction. The range of known correlatives is displayed in a way that will aid the assessment of the potential role of job satisfaction as one indicator, among others, of the quality of employment. Some examples of reported empirical correlations will be given for illustrative purposes, but we do not attempt to review and catalog all published reports bearing on the matter, nor to provide evaluation of the various empirical generalizations that have been advanced. We shall ignore for the present the diversity of concept and measurement of job satisfaction treated in the preceding section.SOCIAL INDICATORS AND INTERPRETATIVE MODELS The meaning of any social indicator is found in its assigned role in some conception of how the society “works.” Thus, a measured change in some indicator-infant mortality rate, for example-is uninterpretable apart from some known or assumed dynamic structure of sequential changes that relates the observed change to causes, consequences, and moderating conceptual factors. Ideally, one should have an empirically validated theory, broad in scope, embracing multiple causes and consequences, capable of accommodating additional variables i.e., an open system, and one that treats changes over time i.e., a dynamic theory. Such an interpretive model would permit the evaluation of a change in some social indicator in several useful ways, most importantly in estimating future implications of the observed change and in identifying possible societal actions to forestall or counteract undesirable consequences. With respect to job satisfaction, there does not exist any such comprehensive theoretical model. However, there are micromodels treating limited segments of such a more comprehensive model, and there are known empirical correlations that help to identify classes of variables that must be taken into account and which can guide future work into profitable directions. One example of such a micro-model specifies that more challenging jobs i.e., those with more autonomy, allowing greater use of valued skills, and so on are associated with higher job satisfaction. In a dynamic form with causal specification, it is asserted that “enrichment” with respect to the degree of challenge leads to an increase in job satisfaction. There is ample correlational and experimental evidence that such an association can exist and can be quite strong Lawler, 1969; W. E. Upjohn Institute for Employment Research, 1973: 188-201; but rather little is known about the contextual conditions within which the association holds Hulin and Blood, 1968 and about variables that moderate the strength of the association. The generalization stands as a valid and useful one even though parts of the relevant correlational matrix remain unexplored. Other available micro-models treat job satisfaction in a causal rather than a consequential role. An example is the formulation that occupations that are relatively high in extrinsic job satisfaction will induce relatively high rates of premature death from chronic heart diseases, while occupations relatively high in intrinsic job satisfaction will induce lower death rates. This proposition has been supported in only two correlational tests but with impressively large correlation coefficients House,1972. Two points are illustrated by this example: 1 job satisfaction cannot in all circumstances be treated as a unidimensional construct; and 2 relationships that are trivial and unreliable at the individual level may be highly significant and interpretable when aggregated in this case, aggregated to the occupational level.MACRO-ENVIRONMENTAL FACTORS Although relatively little programmatic inquiry has been made into the role of economic, political, cultural, and similar broad factors as they affect job satisfaction, evidence suggests that this class of variables is indeed relevant to job satisfaction. For example,Hulin and Blood 1968 and also Kendall 1963 found that characteristics of the communities in which workers reside need to be taken into account to understand job satisfaction. Form 1973, comparing auto assembly plant workers in four countries, shows that there are differences in work-related values, motives, and satisfactions associated with degree of industrialization, while other relational regularities appear to be impervious to culture and context. There are speculations, but no adequate evidence, that fluctuations in unemployment rate and general public optimism about future economic conditions impact on job satisfaction.OCCUPATIONAL CHARACTERISTICS That job satisfaction is related to general characteristics of occupations not to be confused with properties of jobs and the occupational structure has been consistently demonstrated from the earliest comparative study of Hoppock 1935 to the more recent studies such as those of Quinn et al. 1973. Numerous studies show significant relationships between job satisfaction and such properties of occupations as status, prestige, power, and control, among others. However, because of defects in study design, not much is known about the degree to which the various occupational characteristics contribute independently to job satisfaction.ORGANIZATIONAL ENVIRONMENT This domain of causal variables is extensively represented in the theoretical and empirical literature. Variables which have shown evidence as significant organizational antecedents to job satisfaction include structural variables such as size and “shape” e.g., Carzo and Yanouzas, 1969, complexity, centralization, and formalization e.g., George and Bishop, 1971; process variables such as prevailing decision-making and conflict management styles, team collaboration and role conflict; and such encompassing variables as “organizational climate”Litwin and Stringer, 1968.THE JOB AND JOB ENVIRONMENT By far the major part of the job satisfaction research has been concerned with the proposition that an individuals job satisfaction is in substantial part a direct product of the objective characteristics of his job and its immediately relevant environment. Many hundreds of reports assert or imply such a proposition and present empirical data bearing upon it. These data are diverse in quality and scope and offer a somewhat bewildering array of correlations and choice of job characteristics for treatment, but they without doubt confirm the general proposition. Smith et al. 1969 report that in a number of replications in different settings, the amount of pay associated with a job correlates positively with degree of job satisfaction. No one is surprised at this, although some are surprised at the rather low magnitude of the correlations-perhaps about .20 for the employed population as a whole.译文工作满意度指标及其相关因素 资料来源: 美国行为科学家 作者:斯坦利?西肖尔,托马斯?D?泰伯 工作满意度概念直到最近一直在方向主要是心理上和个人主义。实证研究也仅限于当地的情况,反映特殊人群的目的,如个人或者雇佣他们的经理的价值。 但是,如果工作满意度的措施是在检测的就业对整个社会有益的规模质量,将有必要扩大视野,引用一些社会和政治价值,并开始对一组相关变量较大的阵列中的工作表示满意。 对工作的满意度作为衡量社会指标可能有三个角色:(1)代表了作为社会的一个组成部分的国民生产总值的心理价值的产品;(2)提供一个对社会混乱,政策或程序故障,慢慢地发展社会变化的早期预警监测和诊断援助;(3)提供一个在理论和模型的重要组成部分将在社会政策和方案的制定中。不同的意见,并就如何突出工作满意度如何有效的措施将在这三个角色。 工作满意度的措施效用取决于多个测量方法,是标准化的发展,适合广泛使用,检测族群的差异和人口变化的能力。此外,在于对这些公用事业有一个商定的概念和“现实世界”的提出,以及已知的因果关系矩阵和相应的其他重要因素的措施。必须满足两个要求令人信服的证据之前,可以以先进的工作满意度措施,预测、了解,当前社会条件影响未来结果的实用价值。 这些主题提供了这个文件的结构。在下一节中,我们给出了一个在工作满意度测量的最先进的概述。本文提供了一个方法来组织或模型,相关的工作表示满意。最后一节建议进一步研究和发展的重点。 工作满意度指标 本节总结注意事项后,方法和工作满意度测量方法的选择承担业务。我们想讨论限制办法,通过面谈或者问卷调查后,直接询问休息方法产生的数据进行汇总,可以提供各种不同的定义群体工作满意度指标。我们排除讨论:(1)制定有关作业或间接从推定因果现象;(2)个人注意和诊断的办法,通常不会产生人口使用指标;(3)与其有经验和理论搜索,还不如以人口的描述工具为目的。 首先回顾初级资料,并衍生一些常用的工作满意度指标的常用形式。提出一项计划,以指导这几个指标的评价。这是使用于未来的首选方法绘制的影响。除非另有说明,我们将使用术语“工作满意度”,是指不打算包容涉及影响到是否满意或者不满意的两个对立概念,或者两个概念不同的变量。主要数据: 根据主要数据,“原始”代表了受访者个人的口头问题的反映或类似的刺激。尽管那些被成为无数量的变化,原始数据似乎是一个固定名册的基本形式。两种主要数据的类型主要区别在于:自由层面和指定层面。 自由层面的数据的获得,应答者描述不事先制定的整体满意度及他们的工作和工作环境来考虑的或者他们是如何进行组合。实际上,每个受访者提供了一个从他的净反应方面获得自己的一套,与未声明的,独特的假设不仅对评估范围内的加权或以其他方式在他自己独特的方式结合起来,而且还对自己的“合适”工作及其环境,以及与环境的“现实”由他自己的看法和认知定义。规范、认知、评价和无意识的元素参加。刺激的问题通常是措辞(或非语言的显示),有强加于他人意图的感知,认知和评价过程尽可能少的限制。几个互补的刺激问题可用于多种不可避免的限制问题。 指定层面的数据的获得,当受访者回答一些指定的工作或工作环境的满意度。由于层面的规格从来没有详尽或明确描述,自由层面和指定层面调查的区别只有一个单位。例如,查询“您工资支付的满意度如何?”引出一个净值反应,其中包括未制定层面以下的审议(薪酬、工资的确定、增长的速度、足够的需求,等等),未指定的“现实”(上周的工资、扣减后的工资、下一年的预计薪酬等等),和未知的知觉、认知和评价过程。然而具体层面方法使得一些范围外的数据进行控制,包括自己的数据,不同受访者的不同程度的回应,更密切、更自信得响应联系和“现实”的工作环境或受访人员。指定层面的查询,改变他们的特殊性。另外,他们还采取以下形式:直接报告和层面(满意度)的满意度;由工作提供的数量和程度(现在);受访者的数量和程度;提供受访者的数量和程度;受访者的重要性。 反映的形式存在于多种多样,包括简单的核对清单或“是否”的反映,在秩序、标准量的反映(例如,李克特量标之类的),以及诸如更复杂的形式“锚”。尽管这些替代邀请了有益的讨论对他们的相对可靠性、高效、简单、实用的项目,和概念的假设,这些问题将不会在此提出。允许每个代替的人员提供社会指标比较或原始汇总数据的目的。衍生数据: 原始数据代表直接或暗示的工作满意度时,表达了社会指标可以通过一个简单的原始数据集合体,然后对个人的一个集合的个人资料。然而,更复杂的形式衍生指标通常是优先考虑各种各样的原因。程序从原始数据产生的指标包括:(1)微分加权的物品;(2)项目进入聚类或纬度因素,在概念或经验主义的理由;(3)主要数据转换得分了理论、概念的差异,还是实证的理由在聚集;(4)保持个人方面为了不同的物品数据用于解释或分析;(5)将在不受控制的响应差异聚集;(6)原始数据调整前已知名或假设的偏见,形成良性循环。这些程序可采用单独或联合别人。最后三个程序在目前级小或至少无可非议的调查和争议。 工作满意度相关因素 这个部分回顾了已知的工作满意度和我们遵守的工作满意度的相关因素。工作满意度相关联的范围将有效得评估工作满意度作为潜在的工作绩效指标,尤其是在员工质量方面。一些经验主义的报告的例子将会作出说明,但是我们不尝试复审和建立目录来说明所有有关的已出版的报告,或者提供各种经验的推论有进步的评估。我们应该忽略目前有关员工满意度的各种概念和测评方法。社会指标和解释模型: 一些社会指标被赋予对社会“有用”的概念。因此,测试一些指标消失率,例如,除了不可识别的指标,一些已知或者承担顺序变化动态结构观察关系变化的原因、结果、调节概念因素。观念上的,应该有经过经验论证的理论,广义的来说,拥有各种原因和结果,调节额外变量的能力(如,开放系统),和调整所花费的时间(如,动力学原理)。解释模型允许社会指标在应用过程中的估计,最重要的是评估未来的变化和社会行为的识别为垄断或者阻止不受欢迎的结果。 关于工作满意度,并不存在任何完整的理论模型。可是,工作满意度将综合性的模型分为几个有限的细致的模型,用已知的经验,帮助识别在工作中必须考虑考虑的变量,并指导今后的工作往有利的方向发展。 这样一个细小的例子(工作具有自主权、能更好的利用自身价值的能力等)说明有挑战性的工作都是伴随着更高的工作满意度。在一个动态的因果形式的准则下,“丰富”的程度直接导致了员工满意度的提高。有足够的研究和实验证据表明这种关系的存在并逐渐变强;但是在不清楚的背景条件下成立协会,关于变量的相关性。即使是一个有效的和有用的相关性矩阵研究也不能解释这一情况。 其他利用这一例子说明员工满意度是一个原因而不是一个举足轻重的结果。例如相对教高的职业外在员工满意度会产生相对较高的来自慢性心脏病的死亡率,然而较高的职业内在满