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Methodological considerations on the means-end chain analysis revisited
Psychology and Marketing ( IF 5.507 ) Pub Date : 2021-05-29 , DOI: 10.1002/mar.21521
Fleur B. M. Kilwinger 1 , Ynte K. Dam 2
Affiliation  

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.

中文翻译:

重新审视手段-目的链分析的方法论考虑

手段-目的链分析已被广泛应用于理解消费者行为的学科。尽管它被广泛接受,但没有标准化的方法来分析数据。不同分析对结果的影响在很大程度上是未知的。本文通过比较分析手段-目的链数据的不同方法,为方法论辩论做出了贡献。我们发现 (1) 未提及的结构对受访者仍然很重要;(2)同一基础层的编码结构或上层结构的浓缩导致不同的结果,信息的增减并存;(3) 聚合数据可以基于影响结果的不同算法。在可用的软件包中,使用的算法没有一致性;(4) 在新的研究领域应用手段-目的链分析之前,应该评估研究模型所依据的假设的有效性。我们得出结论,手段-目的链分析没有通用的“最佳方法”,最合适的方法取决于研究问题。关于如何评估产品的研究可以最好地应用基于受访者数量的聚合和低水平的凝聚。关于产品价值的研究可以最好地应用基于响应频率的聚合和高水平的冷凝。关于如何评估产品的研究可以最好地应用基于受访者数量的聚合和低水平的凝聚。关于产品价值的研究可以最好地应用基于响应频率的聚合和高水平的冷凝。关于如何评估产品的研究可以最好地应用基于受访者数量的聚合和低水平的凝聚。关于产品价值的研究可以最好地应用基于响应频率的聚合和高水平的冷凝。
更新日期:2021-08-03
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