Creating occupational groups using visual job classification

Abstract

Purpose

The purpose of this paper is to assess the use of two innovative job analysis techniques. First, a graphic‐based approach is used to collect job classification data. Second, the results are presented in a graphical representation to decision makers. In addition, the paper examines two concepts, similarity and relatedness, often confused by subject matter experts (SMEs) and decision makers in the context of job classification.

Design/methodology/approach

A case study approach was used. Focus groups of SMEs used a graphic‐based tool to group jobs into occupational clusters based on the concepts of similarity and relatedness. To effectively communicate the results to organizational decision makers a graphic presentation technique was used.

Findings

The paper found that SMEs were highly engaged in the graphical approach. Decision makers were also intrigued by the graphical presentation. In addition, the paper found confusion between the concepts of similarity and relatedness throughout the process. This confusion had important implications for the grouping of jobs into occupational clusters.

Practical implications

The graphic presentation of results highlighted issues around which the agency had been previously struggling. The approach allowed decision makers to examine and understand meaningful data and reach consensus on complex, multi‐faceted issues. The results also showed that people often confuse the similarity and relatedness of jobs, and that this confusion should be taken into consideration when communicating with non‐job analysts.

Originality/value

Job analysis and classification has changed little over the past several decades. This paper applies innovative ideas to job classification which are equally applicable to job analysis offering interesting avenues for future research and practice.

Citation

Stetz, T.A., Button, S.B. and Scott, D.W. (2011), "Creating occupational groups using visual job classification", Management Research Review, Vol. 34 No. 3, pp. 294-310. https://doi.org/10.1108/01409171111116312