An assessment of survey research in POM: from constructs to theory
评估了1990-1995年间四本顶级期刊中25篇基于调查的生产与运营管理研究，识别了理想调查研究的属性，并指出未来需关注多条目构念、内容效度和预测试，以推动理论发展。
Abstract The use of field‐based empirical methodologies in the production and operations management (POM) area has been steadily increasing over the past several years. One of the most prominent among these is the survey research methodology which has often been used to capture data from business organizations. However, to effectively contribute to theory development in the POM field, this methodology must be carefully implemented. Poorly designed and executed survey research is of little or no value. This paper attempts to provide a normative perspective on ‘good survey research practices’. In doing so, it attempts to bridge the gap between survey research and theory development. The paper liberally draws from work in other social science disciplines like psychology, marketing, organizational behavior, and other related fields that are more mature in the use of surveys, and applies it to the POM area. A set of ideal survey research attributes are identified, and existing POM survey work in four prestigious academic journals is evaluated with respect to these attributes. Results of assessing 25 survey‐based articles published in these journals between 1990 and 1995 indicate that both exploratory as well as explanatory survey research is being conducted, with current emphasis being more on explanatory kind of research. In order to move the field forward, greater attention will need to be focused on employing multi‐item constructs, assessing them for content validity, and purifying them through field‐based pretesting. More sophisticated POM studies in the future would have to aim for testing theory with reliable and valid scales that are relatively free of measurement related errors. It is hoped that this work will act as a catalyst in compressing the learning curve with respect to survey research practices in POM, and also accelerate the use of greater methodological rigor in future empirical studies in the field.