Mediating Effect of Social Media Marketing Adoption Towards Business Performance: An Approach of Structural Equation Modelling Partial Least Square (SEM-PLS)
Bibliographic record
Abstract
Business environment in Malaysia has become more competitive due to the advancement of technology and globalisation including the small and medium enterprises (SMEs). SMEs have so long devised various marketing strategies in order to penetrate the online advertisement clutter alongside improving their performance. Past studies have shown that using ICT such as social media help to improve SMEs’ performance. The owner-managers themselves determine the adoption of social media in their marketing due to the process of information collection and analysis are highly relied on them. Most of the SMEs have their own websites but these are primarily used as an information tool only. Thus, this study investigated the mediating role of social media marketing adoption between technological support, organisational support, government support and competitive intelligence towards the performance of SME. The theoretical framework of this study is substantiated by Resource-based View (RBV) theory and supported by Technology, Environment and Organisation (TOE) framework. This study has been carried out in the East Coast region of Malaysia consisting of Kelantan, Terengganu and Pahang states and also employed a questionnaire survey method. From 1920 questionnaires distributed, 339 responses were returned and found usable for the final analysis using the structural equation model partial least square (SEM-PLS). The findings revealed that the adoption of social media marketing mediates the relationship between these technological, organisational, and government supports, the competitive intelligence, and SME performance. Finally, the study’s theoretical and practical implications as well as the limitations and directions for future research were provided and discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".