Web 3.0 Adoption Behavior: PLS-SEM and Sentiment Analysis

TL;DR

This study investigates Web 3.0 adoption behavior using PLS-SEM and sentiment analysis, revealing key factors influencing behavioral intention and sentiment trends on Twitter, contributing novel insights to web informatics.

Contribution

It introduces a multi-analytical approach combining PLS-SEM and sentiment analysis to understand Web 3.0 adoption, with novel findings on influential factors and sentiment distribution.

Findings

Digital Dexterity and eWOM significantly impact behavioral intention

Sentiment analysis shows 56% positive and 7% negative tweets about Web 3.0

PE has no significant effect on Web 3.0 adoption

Abstract

Web 3.0 is considered as future of Internet where decentralization, user personalization and privacy protection would be the main aspects of Internet. Aim of this research work is to elucidate the adoption behavior of Web 3.0through a multi-analytical approach based on Partial Least Squares Structural Equation Modelling (PLS-SEM) and Twitter sentiment analysis. A theoretical framework centered on Performance Expectancy (PE), Electronic Word-of-Mouth (eWOM) and Digital Dexterity (DD), was hypothesized towards Behavioral Intention (INT) of the Web 3.0 adoption. Surveyed data were collected through online questionnaires and 167 responses were analyzed through PLS-SEM. While 3,989 tweets of Web3 were analyzed by VADER sentiment analysis tool in RapidMiner. PLS-SEM results showed that DD and eWOM had significant impact while PE had no effect on INT. Moreover, these results were also validated by PLS-Predict method. While sentiment analysis explored that 56% tweets on Web 3.0 were positive in sense and 7% depicted negative sentiment while remaining were neutral. Such inferences are novel in nature and an innovative addition to web informatics and could support the stakeholders towards web technology integration