How is the Appropriate Use of Enterprise Social Media Determined? The Case of GCConnex Within the Canadian Federal Government
Bibliographic record
Abstract
The success of enterprise social media depends on users' social interactions.Individual users must determine how to use the system and whether or not their usage is appropriate.Governments are generally risk-averse, and missteps by public servants can have negative implications on their careers.This research explores how the appropriate use of enterprise social media is socially constructed in the use of GCConnex within the Canadian federal government.It addresses two research questions: What influences a public servant's individual judgment that a specific action is appropriate when using enterprise social media?And How are actions taken by public servants using ESM validated as appropriate?The use of an interpretive case study approach allowed for data collection from various sources, including organizational data, to capture the complexity of enterprise social media use in the Canadian federal government.Data sources included the results of 22 semi-structured interviews with federal public servants plus policy and help documents.This research is guided by a conceptual process model that draws on the literature on legitimacy, IT value, IS use, enterprise social media and the social construction of reality.This study explores the first three stages of the Appropriate Use process model -Individual Judgment, Innovation, and Local Validation.The final two stages, Diffusion and General Validation, were out of scope for this study.Our findings indicate that appropriate use is a continuous and dynamic concept that is socially constructed.Appropriate use also has multiple dimensions, and in this research, three were discovered: Task, Feature and Form.Employee judgment of iii appropriate use is influenced by the affordances of the technology and the employee backgrounds and environment.Federal public servants carefully partition their use of social media.GCConnex is strictly for the benefit of federal public servants to share information and to collaborate.A specific appropriate use policy is not essential if there are other means to communicate and develop organizational norms.The Values and Ethics Code for the Public Sector is foundational in forming the organizational norms of behaviour.Although employees may be reticent to comment on other users' use, explicit validation is needed to legitimize new actions.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".