基於時間觀點探勘不同群組使用淘寶App功能之購物行為

透過購物App成功銷售商品已是行動商務的重要議題。本研究以購物模擬任務，追蹤34名消費者之淘寶App功能使用差異，進而採用分群法辨識出五種購物群組，後續輔以滯後序列分析之移動路徑與生活風格問卷調查深入探索不同群組特性。研究結果發現購物時間因素反應購物行為，購物時間短之交易與推薦群組有簡化的移動路徑；時間長之價格敏感、資訊消費與商品比較三群各有較多元與獨特的搜尋樣式，並分別重視價格、資訊與品牌的比較。研究之生活風格分析，發現不同購物群組對於資訊尋求構面存在顯著差異，也呼應本研究透過購物App功能使用行為進行群組分析之研究結果。研究結果有助於購物App經營者透過理解不同型態消費者之購物行為，以進行設計App之參考指南。

關鍵字

集群分析；生活風格分析；搜尋樣式；購物群組；時間因素

並列摘要

How to successfully sell products through shopping apps has become an important topic in mobile commerce. This study recruited 34 Taobao App consumers to participate in simulated shopping tasks to explore differences in App functionality usage behaviors. We used clustering methods to identify five unique shopping groups and then applied lag sequential analysis (LSA) to analyze search paths. Additionally, we augmented the explanations of these approaches with lifestyle analysis to explore the characteristic of five unique shopping groups. The research findings indicate that time factors significantly influence shopping behaviors. Transactional-oriented group and recommendation-adopting groups with shorter shopping times have simplified search moves. In contrast, price sensitive, information-consuming, and product-comparing in longer time groups demonstrate more diverse and unique search patterns and place emphasis on comparing prices, information, and brands, respectively. The lifestyle analysis in the study revealed significant differences in the information-seeking (IS) dimension among different shopping groups. This finding aligns with the research results obtained through segmentation analysis based on shopping App functionality usage in this study. The findings contribute to helping shopping App managers understand various consumer shopping behaviors for enhancing design of Apps functionalities and interfaces.

並列關鍵字

Clustering ； Lifestyle Analysis ； Search Patterns ； Shopping Group ； Time Factor

參考文獻

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