Talk less and listen more? The effectiveness of social media talking and listening tactics on export performance
Bibliographic record
Abstract
This research investigates the effect of social media talking and listening tactics on customer performance through firms’ networking capabilities and promotion adaptation strategies among entrepreneurial, emerging market, small and medium-sized enterprises (ESMEs). From a survey of 169 ESME managers, the study tests whether firms should use social media to listen and adapt to the foreign market or disseminate and network in the foreign market. The study's ultimate aim is to guide managers on how best to leverage social media in their international export campaigns. The results were analyzed using a series of nested structural equation models. We found that using social media tactics combining both talking and listening leads to significantly higher levels of customer performance than using talking or listening strategies singly. Moreover, we found that each tactic's mechanisms operating these effects differed. By showing, first, the mechanisms through which social media tactics affect customer performance and, second, the superiority of an ambidextrous social media strategy, the study provides ESME entrepreneurs with an understanding of how best to leverage social media to facilitate international exporting.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".