Web 3.0 Adoption Behavior: PLS-SEM and Sentiment Analysis

Sheikh M. Hizam, Waqas Ahmed*, Habiba Akter, Ilham Sentosa, Mohamad N. Masrek

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

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 3.0 through 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.

Original languageEnglish
Pages (from-to)113-126
Number of pages14
JournalCEUR Workshop Proceedings
Volume3158
StatePublished - 2022
Externally publishedYes
EventJoint International Baltic Conference on Digital Business and Intelligent Systems 2022 Doctoral Consortium and Forum, Baltic-DB and IS-DC-Forum 2022 - Riga, Latvia
Duration: 3 Jul 20226 Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 Copyright for this paper by its authors.

Keywords

  • Adoption Behavior
  • PLS-SEM
  • Sentiment Analysis
  • Web 3.0

ASJC Scopus subject areas

  • General Computer Science

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