虚拟零售大数据采用的关键成功因素:路易莎杂志案例研究

IF 0.7 4区 管理学 Q4 BUSINESS
Bruno Felix, Elaine Tavares, Ney Wagner Freitas Cavalcante
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引用次数: 10

摘要

本研究的目的是通过对Luiza杂志的案例研究,确定在虚拟零售中采用大数据的关键成功因素。在这个案例中,大数据的使用重点是改进其推荐系统,以便更好地了解消费者的行为。设计/方法论/方法——案例研究是通过访谈、对购物推荐系统的观察和对Bob项目的机构演示的分析来完成的。访谈采用半结构化访谈脚本。调查结果-基于结果,我们概述了一些与大数据在巴西虚拟零售中实施的机会和障碍相关的命题。其中一些命题与文献(尤其是BI)一直在讨论的内容是一致的。然而,它们仍然需要在大数据的背景下进行讨论。原创性/价值——该研究的主要贡献是确定了采用大数据的相关因素,而这些因素对于采用以前的技术并不重要。portuguesObjetivo - O objetivo比伊·德斯塔尽管信息自由要区分因子criticos成功德一书对位adocao de大数据没有varejo虚拟,所以meio de嗯estudo Luiza卡索做杂志。使用大数据,不需要进行数据分析,也不需要进行数据预处理、数据系统、数据推荐、数据综合、数据处理和数据处理。方法学-研究如何为企业家提供有效的方法,观察如何为系统提供建议,比较和分析如何为机构提供建议,以及如何为Bob提供项目。企业的半结构和利用是企业的基础。结果-结果的中介,结果的代表,tracamos算法提出的关系和机遇,以及在巴西实施大数据的过程中遇到的困难。本文从文献、原理、理论、理论等方面探讨了数学数学的发展趋势。不合格的形式、数据和精确的数据是不适合大数据的。贡献——主要贡献是为大数据领域的相关人士提供了一种独特的数据分析方法,同时也为大数据领域的批评者提供了一种新的技术分析方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fatores críticos de sucesso para adoção de Big Data no varejo virtual: estudo de caso do Magazine Luiza
Purpose – The objective of this research was to identify critical success factors for the adoption of Big Data in the virtual retail, through a case study of Magazine Luiza. The use of Big Data, in this case, focused on improving its system of recommendations for a better understanding of consumer behavior. Design/methodology/approach – The case study was done through interviews, observation of the shopping recommendation system and analyzes of institutional presentations of the Bob project. A semistructured interview script was used in the interviews. Findings – Based on the results, we outline some propositions related to the opportunities and obstacles for the implementation of Big Data in the virtual retail in Brazil. Some of these propositions are in line with what literature, especially BI, has been discussing. However, they still need to be discussed in the context of Big Data. Originality/value – The main contribution of the research was the identification of relevant factors to the adoption of Big Data that were not considered as critical for the adoption of previous technologies.
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来源期刊
CiteScore
2.10
自引率
9.10%
发文量
16
审稿时长
50 weeks
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