The effects of social media attributes on customer purchase intention: The mediation role of brand attitude
Bibliographic record
Abstract
Social media influencers have proved to be a major influence on customers’ purchasing decisions, especially with the increased usage of social media platforms. Social media influencers provide many opportunities for companies to increase their customer base and sales, and to enhance the attitude towards a brand. Despite the advantages of utilizing social media influencers, there are factors that social influencers need to take into consideration in order to engage customers and influence their decisions to purchase. The purpose of this study is to examine the factors that influence customers’ intention to purchase based on social media influencers. An online questionnaire was used to collect data from 439 Instagram platform users. A partial least square-SEM (PLS-SEM) approach was used to analyze and examine the proposed model. The results indicate that all constructs, namely Information Quality (IQ), Trustworthiness (TRU), Attractiveness (ATT), Meaning Transfer (TRA) and Expertise (EXP) significantly influence customers’ purchase intentions. This finding could provide insights for companies’ decision-makers when it comes to promoting their brands and increasing their sales. In addition, it could provide insights for social media influencers in terms of recognizing the important factors that encourage customers to engage and purchase.
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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 | 0.002 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".