The Eureka moment in understanding luxury brand purchases! A non-linear fsQCA-ANN approach

定性比较分析 计划行为理论 消费(社会学) 营销 集合(抽象数据类型) 消费者行为 因果模型 广告 心理学 业务 控制(管理) 经济 社会学 计算机科学 社会科学 管理 机器学习 程序设计语言 医学 病理
作者
Fajin Li,Eugene Cheng‐Xi Aw,Garry Wei‐Han Tan,Tat‐Huei Cham,Keng‐Boon Ooi
出处
期刊:Journal of Retailing and Consumer Services [Elsevier BV]
卷期号:68: 103039-103039 被引量:55
标识
DOI:10.1016/j.jretconser.2022.103039
摘要

Although substantial research has been conducted to understand the luxury brand consumption at a global level, especially in the Western market, far less attention has been dedicated to the identification of factors driving such purchases in the Asia-Pacific region. This gap is addressed in the present study, which draws upon the extended theory of planned behavior (TPB) and complexity theory (CT) to identify the non-linear causal pathways pertaining to Chinese consumers’ luxury brand purchase intention. An online survey was conducted with a sample of Chinese consumers to gather data related to their attitudes, subjective norms, perceived behavioral control, self-congruity, power distance beliefs, and gender. An asymmetrical-based approach was adopted by combining fuzzy-set qualitative comparative analysis (fsQCA) with artificial neural network (ANN) analysis. The fsQCA results showed that there are seven solutions (i.e., various combinations of causal antecedents) that can facilitate the formation of luxury brand purchase intention, four of which are gender-specific. After completing the fsQCA, the ANN analysis was conducted to rank the causal conditions in terms of their importance, indicating that attitude and subjective norms are the most relevant antecedents. The present study contributes to the existing luxury branding literature by reevaluating and extending the well-established TPB with new variables and new research lens (i.e., asymmetric thinking). More specifically, this study represents the pioneering effort to apply set theoretic approach in understanding luxury goods consumption, thereby yielding more insightful results that reflect the complexity of reality, and thus advance the findings yielded by past studies that relied on variance-based methods.
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