Small and medium-sized enterprises perceived trust towards social media: applying the extended technology acceptance model
Bibliographic record
Abstract
Purpose This study aims to examine the perceived ease of use (PEU), perceived usefulness (PU) and perceived trust (PT) in influencing attitude (ATT) and behavioural intention to use (BIU) social media among Malaysian small and medium sized Enterprises (SMEs), the effects of gender on PT and ATT and the mediating effect of SME’s ATT towards social media. Design/methodology/approach Data was collected from 317 SMEs in Malaysia from the manufacturing, services and other sectors using a structured questionnaire with a seven-point Likert scale. Findings The findings confirm that PEU, PU and PT positively affect SMEs ATT towards using social media. The mediating effect of SMEs’ ATT towards social media also has a positive effect. This study found that female and male SME owners trust social media platforms for business, indicating no significant gender differences. Research limitations/implications The study relied on a quantitative approach using close-ended questions, which may limit the depth of understanding regarding the experiences of SMEs. Future research should incorporate qualitative methods for a comprehensive understanding of SMEs PT. Practical implications The study highlights that governments and policymakers customise their policies to SMEs’ unique characteristics and needs, fostering a secure and cohesive environment. Collaborative efforts between policymakers and social media companies are essential to create a conducive ecosystem for SMEs to thrive in social commerce. Social implications Wider use of social media by SMEs can contribute to economic growth. This is relevant for Malaysia, where SMEs play a significant economic role. By addressing trust issues, policymakers and social media platforms can fan inclusive and beneficial digital environment for businesses. Originality/value Previous research used consumer trust in social media, and this study explores SMEs’ trust in social media through technology acceptance model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".