From strategic orientation to social media orientation
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop and test a framework of small and medium enterprises’ (SMEs) strategic orientation (SO) and its impact on social media performance. Moreover, it introduces a new concept, social media orientation (SMO) (composed of sales and business development (SBD) and visibility) to add in the model. Design/methodology/approach A quantitative approach was used and, based on a study of 257 SMEs, analyses were performed. A smartPLS analysis was judged appropriate regarding the sample size. Findings Results show that entrepreneurial orientation (EO) and customer orientation have a positive influence on SBD which in turn has a positive influence on social media performance. Visibility is positively influenced by EO and has an indirect effect on social media performance. Social media performance is therefore directly influenced by SBD and indirectly by visibility. Research limitations/implications The authors complete previous research that called for the introduction of different SO on a same study and go further as the author highlight the role of EO on visibility (and not only on business or performance). A second contribution lies in the conceptualization of SMO (defined here with SBD and visibility) and third in the measurement of social media performance through growth and attention. Practical implications SMEs first need to develop their visibility, and then link it to SBD. Originality/value This research is one of the first to explore SMEs’ SO on social media and proposes a new concept defined as SMO. It gives SMEs future direction on how to perform on these platforms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".