Articlediamond2022

An integrated model for the usage and acceptance of stickers in WhatsApp through SEM-ANN approach

Khadija Farhan Alhumaid · Noha Alnazzawi · Iman A. Akour · Osama Al Khasoneh · Raghad Alfaisal · Said Abdelrahim Salloum

45 citations0/0 files storedInternational Journal of Data and Network Science

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Technology Adoption and User Behaviour Innovative Human-Technology Interaction Digital Communication and Language

Abstract

This analysis integrates the “technology acceptance model (TAM)” with the “use of gratifications theory (U&G)” to develop an embedded model that predicts the use and satisfaction of emotional icons called stickers through WhatsApp. The explanation for combining these two theories is that U&G offers accurate information and a thorough knowledge of use, while TAM theory has been firmly established in several technical implementations. A newly developed hybrid analysis procedure has been applied within this research. Using an artificial neural network (ANN), and the structural equation model (SEM) have been combined. The research also uses the importance-performance map analysis (IPMA) to present each factor’s performance as well as importance. The ANN and IPMA research have both indicated that for sticker use intention, a highly essential predictor is Socialization. An online questionnaire survey was developed to assess the recommended model. The intention to use stickers was significantly affected by “Socialization, Self Presentation, Enjoyment, Novelty, Unique Function, Perceived Ease of Use, and Perceived Usefulness”. The research's main achievement is the convergence of two separate theories into a single conceptualization to accurately calculate the TAM components when it comes to the usage of stickers in WhatsApp. Theoretically, the recommended model provides enough insight for aspects which affect the intention to use stickers with relevance to the socialization’s factors considering interpersonal aspects. Practically, the higher education decision-makers along with professionals would extract variables that are important as compared to others and policies would be developed accordingly. The deep ANN model competence has been analyzed within the research to decide upon the non-linear associations between variables of the theoretical model, methodologically.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 45 citations exist upstream.

Cited by

2024Forecasting the Acceptance of ChatGPT as Educational Platforms: An Integrated SEM-ANN Methodology 2024Envisioning ChatGPT's Integration as Educational Platforms: A Hybrid SEM-ML Method for Adoption Prediction 2024Exploring the Acceptance of ChatGPT for Translation: An Extended TAM Model Approach 2023The Theory of Planned Behavior Regarding Artificial Intelligence in Recommendations and Selection of YouTube News Content 2022The Adoption of Metaverse Systems: A hybrid SEM - ML Method 2024Use Chat GPT in Media Content Production Digital Newsrooms Perspective 2024The Impact of Educating Managers in Adopting AI Applications on Decision Making Development: A Case Study in the U.A.E 2024Metaverse in Supply Chain Management: Predicting Suppliers’ Intention to Use Metaverse for Educating Suppliers Through Perceived Usefulness, Training Value and Ease of Use (A Case Study in UAE)

Imported from OpenAlex (CC0) — nothing here is generated.