Bibliographic record
Abstract
There is very little understanding of how organisations manage social media. In particular, there is no established path of activities that guide a company down the path of social readiness, and the management and organisation of social media is under-researched. Consequently, many organisations experience significant problems with their social media business profiles (SMBP). Stage of growth (SoG) models represent a picture of evolution, where the current stage can be understood in terms of history and future, providing an opportunity to identify the stages, paths of evolution, benchmark variables, and dominant problems experienced by organisations at each stage. Following a review of four decades (1974-2014) of SoG model research, and a review of existing social media research and practitioner insight across multiple domains, the authors adopt Gottschalk and Solli-Saether’s (2010) [1] five step Stage Modelling Process as a research methodology to develop a stage model of SMBP implementation and management. The paper analyses the findings from Step 1 (Suggested Stage Model) and Step 2 (Conceptual Stage Model) of the Stage Modelling Process, before concluding with the key findings. This research contributes to academia by enhancing the existing four decades of knowledge of SoG models, extending it to the management of social media in an organisational context. This research is also a critical piece of research from a practitioner perspective, as organisations struggle to devise tactics and strategies to manage social media adoption and use.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".