基于PLS-SEM的方法：通过Facebook的大数据分析Z世代的购买意愿

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2023-08-29 DOI:10.26599/BDMA.2022.9020033

Vikas Kumar;Preeti;Shaiku Shahida Saheb;Sunil Kumari;Kanishka Pathak;Jai Kishan Chandel;Neeraj Varshney;Ankit Kumar

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引用次数: 0

摘要

本文的目的是通过Facebook更好地再现Z世代对零售产品的购买意图。这项研究围绕着Z世代形成的有利态度转变为通过Facebook购买零售产品的意图展开。还探讨了态度的前因，即享受、可信度和同伴交流的作用。主要目的是分析F商务在印度Z世代中的普遍性（通过Facebook进行零售购买），以及如何有效地实现它。一个概念性的外观是在抛出德国和城市文学之后提出的。这项研究只关注Z世代人群。使用偏最小二乘结构方程模型对数据进行统计分析。研究发现，所提出的概念模型对Z世代通过Facebook购买零售产品的意图具有很高的预测力，验证了F-commerce的物化。乐趣、可信度和同伴交流被证明是态度的良好预测因素（R2=0.589），此外，态度被发现是购买意愿的主要前提（R2=0.540）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A PLS-SEM Based Approach: Analyzing Generation Z Purchase Intention Through Facebook's Big Data

The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook. The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook. The role of antecedents of attitude, namely enjoyment, credibility, and peer communication was also explored. The main purpose was to analyze the F-commerce pervasiveness (retail purchases through Facebook) among Generation Z in India and how could it be materialized effectively. A conceptual façade was proposed after trotting out germane and urbane literature. The study focused exclusively on Generation Z population. The data were statistically analyzed using partial least squares structural equation modelling. The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce. Enjoyment, credibility, and peer communication were proved to be good predictors of attitude (R ² =0.589) and furthermore attitude was found to be a stellar antecedent to purchase intentions (R ² =0.540).

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.