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Record W2533289375 · doi:10.6000/2371-1647.2016.02.07

A Stage Model of Social Media Adoption

2016· article· en· W2533289375 on OpenAlexvenueno aff
Aidan Duane, Philip O’Reilly

Bibliographic record

VenueJournal of Advances in Management Sciences & Information Systems · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsStage (stratigraphy)Social mediaBusinessComputer scienceWorld Wide WebGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.006
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.307
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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