Investigating the impact of social and product opinions on online shopping behaviors
DOI:
https://doi.org/10.51359/2526-7884.2025.265959Keywords:
Online Shopping, Consumer Fulfilment, Principal Component Analysis, Analysis of Variance, Partial Least SquareAbstract
The study investigates the role of eigenvalues, eigenvectors, and summarized social and product review data using advanced software technologies to understand their influence on consumers’ purchase intents in social shopping contexts. It examines online shopping behavior based on various factors, including environmental consciousness, socio-economic status, product attributes and marketing tactics. The research highlights how technical expertise aids in evaluating product topographies and enhances credibility and trust in online shopping behavior. Data were collected through Google Forms surveys targeting youth, students and employees to gain insights into their online shopping perceptions. Analytical methods such as Principal Component Analysis, Partial Least Squares and Analysis of Variance were conducted using ORIGINPRO 2024 software. The proposed hypotheses were validated based on the analysis results. The study concludes that customer fulfilment – particularly among technically skilled students and employees – is the most influential and essential factor driving online shopping behavior.
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