To propose a structured framework for measuring white‐collar workforce.
The proposed framework, designated as the multi‐dimension measurement process or the MDMP, is based on the nature of white‐collar work as well as on the strengths of current measurement techniques such as Zigon's. The experiment on comparing the MDMP with several techniques was conducted. The analytic hierarchical process (AHP) has been adopted to determine the usefulness and applicability of the MDMP. The follow‐up discussions with the participants and the surveys to external experts have also been made.
The research results imply potential usefulness and applications for the MDMP. Relatively to others, the MDMP has performed very highly with the set of prioritized criteria (from the AHP) that is used for this comparison, e.g. reliability, strategic congruence, measurement coverage, and user acceptance. Based on this experiment, the MDMP appears to ensure the alignment between strategies and measurement, and also to gain the acceptance by both workers and supervisors.
Given the small size (16 participants) and the limited scope (participants mainly from the accounting and finance areas), it is not possible to provide a definite conclusion on the effectiveness of the MDMP. More experiments and tests will be needed to determine the level of the MDMP generalization.
A basis or a starting point to help develop a tool that can be used to measure white‐collar workforce.
This research incorporates several aspects relating to the work performed by white‐collar workforce from outputs/outcomes, skills, behavior, and organizational goals. The needs to develop the framework that measures and captures the performance of white‐collar workforce have been cited by several sources for many years. The focus on measuring the workforce level is mainly for appraisal/administrative purposes. As a result, the information may not reflect all aspects relating to the white‐collar work.
Takala, J., Suwansaranyu, U. and Phusavat, K. (2006), "A proposed white‐collar workforce performance measurement framework", Industrial Management & Data Systems, Vol. 106 No. 5, pp. 644-662. https://doi.org/10.1108/02635570610666421Download as .RIS
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