小编导读

如今，选择网上购物的人越来越多，尤其是在Covid-19疫情的影响下，电子商务变得越来越流行。因此，分析顾客的网购行为对电子商务企业来说是至关重要的。来自土耳其Beykent大学的M. Çağrı Budak和土耳其国际电联工业工程部的Sezi Cevik Onar在期刊 International Journal of Computational Intelligence Systems (IJCIS) 上发表了题为“Analyzing Online Shopping Behaviors via a new Data-Driven Hesitant Fuzzy Approach”的文章，提出了一种新的基于数据驱动的犹豫模糊算法对网上购物行为进行分析。

要点介绍

了解网上购物行为对许多公司的生存至关重要。消费者网上购物行为的建模是一个复杂的问题，涉及到不确定性、犹豫性和不精确性。在这项研究中，我们提出了一个新的基于数据驱动的犹豫模糊认知地图方法，评估了不同世代群体的网上购物行为。该模型建立在技术接受模型、创新扩散理论和技术接受与技术利用扩展统一理论的基础上。利用数据驱动的方法定义了参数之间的关系和关系层次。

本研究的目的在于揭示不同条件对消费者网上购物行为的影响，帮助决策者制定网上购物策略。统计模型有局限性在于它没有反映出消费者网上购物行为固有的犹豫和不精确。因此，我们利用犹豫模糊认知图来反映不确定性和犹豫，并用它来分析不同的情景。针对每一代人开发了不同的认知地图和三种情景，并通过这些模糊认知地图观察顾客行为。

图1. 顾客的网上购物行为模型。

研究结论：我们提出了一种数据驱动结合犹豫模糊认知图的方法来处理网路购物行为评估中的不确定性和不精确性。研究重点在于影响网上购物的因素。为此，我们采用PLS-SEM和HFCM方法。首先建立9因素模型，通过研究它们之间的关系，发现每一代之间的关系在统计学上是显著的。从PLS-SEM得到的解用于生成场景。在HFCM方法中，显著关系之间的效应大小被转化为语言术语。世代类型对网购有显著影响。基于数据驱动模型，开发了三种不同的犹豫模糊认知图，以识别不同世代的影响。通过情境观察不同情境对世代行为的影响。

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原文信息

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M. Çağrı Budak, Sezi Cevik Onar, "Analyzing Online Shopping Behaviors via a new Data-Driven Hesitant Fuzzy Approach",  International Journal of Computational Intelligence Systems, 2021, DOI: 10.2991/ijcis.d.210205.003.

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International Journal of Computational Intelligence Systems(IJCIS)是欧洲模糊逻辑和技术学（EUSFLAT）会刊，主要刊载有关应用计算智能各个方面的原创性研究，尤其是针对证明使用了计算智能理论的技术和方法的研究型论文及综述等，由西班牙哈恩大学Luis Martínez Lopez教授和澳大利亚悉尼科技大学路节教授担任共同主编。本刊目前已被DOAJ, Science Citation Index Expanded (SCIE), Ei Compendex and Scopus等数据库收录。

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