Ranking lecture-room furniture used in a Ghanaian University with students’ anthropometric data via multi-criteria decision making analysis

K. Kan-Dapaah, P. Normeshie, R. Tetteh, C. Owusu, J. L. Zedek

Research output: Contribution to journalArticlepeer-review

Abstract

None of the furniture – either the imported Table/Chair (TC) pairs or locally made Bench (BN) – found in the lecture rooms of Ghanaian public universities is purchased according to students’ physical attributes (anthropometrics). This situation exposes the students to the side effects associated with non-ergonomic furniture. Therefore, there is a need for models that can be used to compare and rank the different furniture types presented to procurement units of universities. In this study, we used a Multi-criteria Decision Making (MCDM) analysis to compare and rank available furniture for engineering students at the University of Ghana. We collected six dimensions of six different types of furniture and eight anthropometric parameters of 120 students. Six standard equations were used to assess mismatch or fit, and the results used in the MCDM model to obtain Furniture Utility Value and Furniture Anthropometric Match (FAM) scores. There were significant differences between genders for four anthropometric measures (popliteal height, hip width, subscapular height and sitting shoulder height). Seat width and depth had the highest standard deviations (8.01 and 8.92, respectively) and the degrees of mismatch (4%–100%) were associated with the type of furniture. MCDM analysis showed that respectively the TC, TC1 (FAM scores: females=0.81, males=0.88) was the most favorable, and the BNs (FAM Scores: 0.44) were the least favorable. The results’ implications were discussed for the development of an ergonomic MCDM tool to aid furniture procurement by public universities.

Original languageEnglish
Article numbere02610
JournalScientific African
Volume27
DOIs
Publication statusPublished - Mar 2025

Keywords

  • Anthropometry
  • Ergonomic mismatch
  • Furniture dimension
  • Multi-criteria decision making

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