Exploring the use of social capital to support technology adoption and implementation
Bibliographic record
Abstract
Information System (IS) implementations are a risky business with studies showing only a 16%-29% success rate. This research explores the use of social capital to support technology implementations. This research brings together two distinct bodies of knowledge: social network analysis (SNA) and technology acceptance models, in order to better understand the relationship between social capital and technology acceptance. The first aspect of the research looks at social network centrality and influence measures as an alternative means to measure social influence in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The social influence construct has proven to be inconsistent in past research. An individual‟s decision to adopt a new technology is influenced by their social context or the informal social network within which they work. The social capital of others influences their attitudes and decision to adopt a new technology. Social Capital, as measured through social network analysis, could be substituted for the social influence construct of the UTAUT model. Two revised UTAUT models are developed and tested. The second aspect of this research uses social capital to inform membership of a Community of Practice (CoP) to support a Finance Management System implementation in a higher education organization. SNA can be used to gain an understanding of the social network and identify individuals with high social capital. There is growing evidence that CoP support successful organizational change initiatives but it is less clear how CoP membership might be determined. SNA provides an evidence-based approach to CoP formation. The IS implementation cases described in the paper demonstrate an innovative approach to IS implementation grounded in social capital and technology acceptance research that add to the body of knowledge in both theory and practice.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".