Does Facebook Commerce Enhance SMEs Performance? A Structural Equation Analysis of Omani SMEs
Bibliographic record
Abstract
This research aims to identify the factors affecting the adoption of Facebook commerce, in specific the adopted Facebook advertisements for both small and medium enterprises (SMEs) and how this contributes to enhancing such SMEs’ organizational performances. Specifically, ease of use, demographic targeting, interaction, and brand awareness are regarded as the key factors that can influence Facebook’s advertisements adoption. However, three organizational performance dimensions (efficiency, flexibility, and responsiveness) are considered to be the most significant areas of focus in this research. In this study, both the quantitative research approach and the descriptive research design were employed. Data were collected from different SMEs in Muscat and Dhofar in Oman, and the total valid questionnaires suitable for analysis reached 342. The participants were primarily from those who publicize their services through Facebook. Many statistical techniques including exploratory, confirmatory, and structural equation modeling have been adopted in this study; meanwhile, the quantitative data were analyzed using SPSS 25 and AMOS 25 softwares. The findings of this study suggested that the ease of use, demographic targeting, interaction, and brand awareness interpreted 20% of the variance in the Facebook advertisements as the dependent variable. However, Facebook advertisements as an independent variable were found to have a statistically significant effect on the SMEs’ performance dimensions (efficiency, flexibility, and responsiveness) with standard regressions of 0.66, 0.51, and 0.74, respectively, thereby explaining 44% of the variance in the efficiency, 26% of the variance in the flexibility, and 55% of the variance in the responsiveness. Regarding the researchers’ knowledge, this research stands out as the first research to highlight SMEs that measure statistically the relationship between the organizational performance dimensions and Facebook advertising as key social media tools within a unique context—such as Oman as an example of developing countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".