The Honeycomb and the SME: Facilitating Growth Through Social Media in a High Technology Enterprise
Bibliographic record
Abstract
The purpose of this paper is to analyse how a SME owner manager utilised the LinkedIn social media platform to create strategic growth for a new high technology firm in the business-to-business market. The primary research draws on a series of in-depth phenomenological interviews carried out with the owner manager. Findings, when viewed in the context of the owner managers local logic of action and the related opportunity focus, indicate that LinkedIn can provide a valuable conduit for the development of strategic growth opportunities in an SME. Interview analysis details a series of successful routes to growth that could provide the basis for analysis and action for similar firms, in similar situations. In terms of originality, the social media honeycomb construct is deployed as a theoretical and practical device, through which to structure and explore key issues. These include the importance of individual subjectivity in strategy making, a firm’s use of resources, and its growth in terms of the adoption and use of social media, in a dynamic product and market setting. Findings will be of value to other researchers interested in the areas of SME strategy, social media and the use of phenomenology as a research tool.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".