ArticleHeliyon2024
Understanding Trust Drivers of S-commerce.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Generic Requirement Engineering Framework for Social Commerce Platforms: Front-end and Back-end Features.F1000Research · 2025Article
- Parasocial interactions and parasocial relationships on Instagram: An in-depth analysis of fashion and beauty influencers.Heliyon · 2024Article
- Purchasing in the digital age: A meta-analytical perspective on trust, risk, security, and e-WOM in e-commerce.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trust has emerged as a pillar in the acceptance and use of new technologies in the ever-changing digital landscape, notably in the booming field of social commerce. The importance of this study lies in the fact that it explores in-depth the aspects of customer trust in Instashopping using new constructs that have yet to be explored in s-commerce literature. Focusing on Instashopping, the research proposed a multi-dimensional model of trust to examine the dynamics of user trust in social commerce platforms and analyses the effects of various factors, including institution-based trust, disposition to trust, personal inventiveness, perceived page quality, and overall web experience. Structural equation modelling and confirmatory factor analysis were used to examine data from 267 responses in a survey of university students in the United Arab Emirates who have used Instagram for shopping. The analysis showed that user trust and trusting beliefs were significantly influenced by the disposition to trust, institution-based trust, and general web experience. Still, no significant association was found between perceived site quality and trusting beliefs. These findings highlight the crucial part that user trust plays in social commerce platform success and how important it is for online platforms to build and maintain user trust. The work also contributes theoretically to the knowledge body by comprehensively analysing trust dynamics in social commerce. In practice, the knowledge gained can help organisations plan their strategy for gaining and keeping client trust, which is essential for long-term success in the digital arena. To ensure long-term success, organisations must emphasise building and maintaining customer trust.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.