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Methodological considerations on the means‐end chain analysis revisited
Author(s) -
Kilwinger Fleur B. M.,
Dam Ynte K.
Publication year - 2021
Publication title -
psychology and marketing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.035
H-Index - 116
eISSN - 1520-6793
pISSN - 0742-6046
DOI - 10.1002/mar.21521
Subject(s) - respondent , superordinate goals , computer science , consistency (knowledge bases) , coding (social sciences) , chain (unit) , data science , data mining , psychology , artificial intelligence , social psychology , mathematics , statistics , physics , astronomy , political science , law
Means‐end chain analysis has been applied in a wide range of disciplines to understand consumer behavior. Despite its widespread acceptance there is no standardized method to analyze data. The effects of different analyses on the results are largely unknown. This paper makes a contribution to the methodological debate by comparing different ways to analyze means‐end chain data. We find that (1) a construct that is not mentioned can still be important to a respondent; (2) coding constructs at the same basic level or condensing constructs at a superordinate level lead to different results and both an increase and decrease of information; (3) aggregating data can be based on different algorithms which influences the results. Among available software packages there is no consistency in the used algorithm; (4) before applying means‐end chain analysis in a new research area the validity of assumptions underlying the research model should be evaluated. We conclude there is no universal “best way” to means‐end chain analysis, the most suitable approach depends on the research question. Research concerning how products are evaluated can best apply number‐of‐respondents‐based aggregation and low levels of condensation. Research concerning why products are valued can best apply frequency‐of‐responses‐based aggregation and high levels of condensation.