مسائل مربوط به اندازه گیری در تحقیقات تجربی: بهبود اقدامات استراتژی عملیات و تکنولوژی تولید پیشرفته

Our objective in this paper is to examine measures used in survey-based research in operations management. Specifically, we examine measures commonly used to assess operations strategy and advanced manufacturing technology in survey-based research. Examining two measures provides the ability to examine problems common to many of the measures used in operations management research. In addition, the two measures are addressing two different levels of strategic analysis: competitive priorities and a specific structural decision that (should) support these priorities. This should give us a broader understanding of the measurement issues we are facing as a field. We argue that while many of the commonly employed measures display many worthwhile properties, further refinement of measures and methods is needed to improve the field as a whole. Suggestions to improve measures and methods used in the field are then offered.

There has been a substantial increase in the use of field-based, empirical research methods in operations management over the past decade. Research methodologies such as case studies, panel studies, focus groups and surveys have been successfully applied to a variety of topic areas. While each of these methodologies has unique strengths and weaknesses that make them most appropriate for certain applications, by far the most commonly applied methodology has been surveys. Our objective in this paper is to examine measures used in survey-based research in operations management. We use commonly accepted measures of competitive priorities and advanced manufacturing technology to explore the state of development for measures used in survey-based research in OM. We focus on these measures for three reasons. First and foremost, these measures provide a broad spectrum of the type of measures used in survey research. The competitive priorities measures address the macro-level construct of strategy. The AMT measures address a micro-level construct that is representative of the many structural or infrastructural investments a firm must make to support its strategy. While the AMT measures focus on a single structural decision area, they are among the better developed measures and the characteristics associated with the AMT measures are readily generalizable. Secondly, both sets of measures have been used in numerous replications and are deemed reliable and valid. Our final reason for choosing these specific measures is the authors' familiarity with them. Many of the issues we raise are the result of our own work with these measures.

As our discussion indicates, researchers have started to develop measures with good psychometric properties. In addition, these measures are used by a number of researchers, allowing for comparisons across various studies. However, our discussion also indicates that a great deal more refinement of measures and methods is needed before we can be comfortable that research is actually addressing the constructs of interest both today and in the future. This study addressed four primary areas where deficiencies exist. There is a need for greater variance in the methods we use to develop and validate measures — rather than simply using mail surveys over and over, researchers should consider using some finer grained methodologies such as case studies or focused interviews. In some ways, surveys are similar to a shotgun — guaranteed to hit something, but as knowledge in areas such as operations strategy and AMT improves, researches should use finer grained methodologies which are similar to a rifle — more accurate and likely to uncover more complex insights. Secondly, researchers should strive to better assess managerial perceptions by including multiple and/or more precisely targeted respondents. Third, many constructs should be examined in a more dynamic manner in order to assess how strategy or technology investment changes over time. Finally, researchers should carefully consider the structure of existing measures. Many measures are designed to be generalizable to many environments/industries. Yet, better data can be obtained by carefully crafting measures for specific situations and/or focusing studies more precisely on similar organizations to pinpoint finer grained difference between individual organizations.