Abstract
AbstractMeans‐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.
Highlights
The means‐end chain model and related laddering methodology were developed in the 1980s to understand how, and why, consumers value products or services (Grunert & Grunert, 1995; Gutman, 1982; Reynolds & Gutman, 1988)
Means‐end chain theory is based on several influential theories in psychology (Reynolds & Olson, 2001), such as personal construct theory (Kelly, 1955), attribute theory and cognitive structure (Scott, 1969), and human values (Rokeach, 1973)
We discuss three issues regarding coding where means‐end chain analysis diverges from the underlying personal construct theory
Summary
The means‐end chain model and related laddering methodology were developed in the 1980s to understand how, and why, consumers value products or services (Grunert & Grunert, 1995; Gutman, 1982; Reynolds & Gutman, 1988). Means‐end chain analysis accommodates these individual differences by inviting individual respondents to select and verbalize their own constructs to describe how products are linked to their personal goals (Walker & Olson, 1991). A means‐end chain analysis starts with the elicitation of personally relevant attributes that a respondent uses to evaluate a product or service.
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