The Impact of Perceived Social Media Marketing Activities: An Empirical Study in Saudi Context
Bibliographic record
Abstract
Purpose – The purpose is to investigate the impact of social media marketing activities in the context of Saudi consumers of social media. A research model is developed in this study to examine the relationships. Design/methodology/approach – This research is quantitative and uses the probability sampling technique, simple random sampling. Data is collected through a questionnaire in a survey of 241 Saudi social media users. Structural equation modeling (SEM) with PLS 3 was used with SPSS 22.0 for statistical data analysis. Chi–square and overall model fit indices further confirm the structural model fit. Findings – The results indicate that social media marketing activities significantly influence brand loyalty, purchase intentions, value consciousness and brand consciousness; brand loyalty has a significant statistical impact on eWOM; eWOM influences purchase intention significantly; brand consciousness does not mediate the relationship between perceived social media marketing and brand loyalty, while value consciousness mediates this relationship. Research limitations/ future research – The research is limited to Saudi social media users and this limits the results from being generalized. Future research must be conducted in other countries. Moreover, limited research is conducted with these variables in previous studies. Originality/value – This article is pioneering in that it investigates the effects of social media marketing in the context of Saudi consumers, a topic of relevance for both marketers and scholars in the era of social media. It provides empirical evidence and valuable insights through a proposed 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".