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Open Access

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

Humanities and Management Department, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144027, India
Department of Commerce and Business Administration, Kanya Maha Vidyalaya (KMV), Jalandhar 144004, India
Mittal School of Business, Lovely Professional University, Phagwara 144402, India
Government College for Women, Indra Gandhi University, Ateli 123021, India
Institute of Management Studies, Kurukshetra University, Kurukshetra 136119, India
Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
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Abstract

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 ( R2=0.589) and furthermore attitude was found to be a stellar antecedent to purchase intentions ( R2=0.540).

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Big Data Mining and Analytics
Pages 491-503
Cite this article:
Kumar V, Preeti, Saheb SS, et al. A PLS-SEM Based Approach: Analyzing Generation Z Purchase Intention Through Facebook’s Big Data. Big Data Mining and Analytics, 2023, 6(4): 491-503. https://doi.org/10.26599/BDMA.2022.9020033

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Received: 25 May 2022
Revised: 09 September 2022
Accepted: 26 September 2022
Published: 29 August 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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