范围(计算机科学)
集合(抽象数据类型)
多样性(政治)
心理学
认知
领域(数学)
应用心理学
管理科学
计算机科学
社会学
经济
人类学
数学
神经科学
程序设计语言
纯数学
作者
Charles A. Scherbaum,Adam W. Meade
摘要
Despite its importance, measurement has received less attention in the management sciences than it deserves. Currently, there is an over‐reliance on a narrow set of methods of measuring cognitive, affective, motivational, attitudinal and individual difference constructs that are often of interest in behavioural management research. The authors argue that there is a need to expand the scope of the measurement methods commonly employed by management researchers and that a greater diversity of measurement methods would benefit the field by contributing to theory development and the pursuit of new areas of research. The goals of this review are twofold: (1) to increase awareness among management researchers of the alternative measurement methods that can capture many of the cognitive, affective, motivational, attitudinal and individual difference constructs of interest; (2) to critically evaluate how these methods can and should be used, with a focus on both the strengths and limitations of each method. This review focuses on three classes of measures: physiological and biological measures; experience‐sampling measures; and implicit measures. These measures have had a tremendous impact on the research and theories of other fields such as marketing and economics, despite still being in their infancy. The authors believe that these three classes of measures have the potential to impact the nature and scope of management research and theory as well.
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