The purpose of this paper is to explore how the sophistication of a quality costing system depends on the quality management maturity (QMM) level in Food and Beverage (F&B) enterprises. Since no previous research has taken place in this area, the paper aims at analyzing the relationships between quality costing and the specific variables that define the various maturity stages.
A structured questionnaire was used to survey 457 F&B companies. This produced 104 usable responses (23 percent response rate). Multidimensional correspondence analysis (MCA) with hierarchical cluster analysis (HCA) were used to detect and represent underlying structures in the categorical data set and to detect possible clusters between variables.
The more mature a company’s QMM, the more emphasis they placed on appraisal quality costs and effective use of quality costs information. Prevention costs have no statistically significant connection with the level of maturity. A generalized “expensive” use of quality costing, with no focus on problematic areas and possible solutions, does not always lead to the resolution of problems.
A complicated – and some think unfair – tax system, combined with limited cash liquidity constitutes an unstable environment for Greek companies, in which they have to survive and develop. This environment does not support quality costing, thus resulting in limited interest by company management in participating in the authors’ research. Furthermore, the Greek Uniform Chart of Accounts and the Greek Accounting Standards do not include specific quality-related accounts, making it difficult for companies to measure quality costs and for researchers to investigate the quality costing field.
It is the first time that QMM levels of Greek F&B companies have been reported. The research explores the characteristics that a quality costing system of Greek F&B organizations develop at the various maturity levels. The analysis uses an exploratory method – MCA – which can highlight intense correspondences of characteristics and clusters, which cannot be predicted in advance.
