Conventionally adopted means-end chain (MEC) methodology uses product attributes, consequences and values to indicate consumption behavior hierarchies regarding specific products. These hierarchies are useful for elucidating consumer product knowledge and devise effective marketing strategies. In the MEC literature, the qualitative laddering scheme is the main approach used to identify the contents of consumer cognitive structures. However, MEC suffers limitations associated with the subjective research judgment. To overcome these weaknesses of MEC analysis, this work presents a novel laddering-matrix analysis (LMA) based on the quantitative matrix algorithm. The analytical results demonstrated that by integrating LMA and MEC it is possible to explore the information of the summary implication matrix without bias, thus providing extremely useful material for developing MEC computer software.
