Social Influence and Knowledge Management Systems Use: Evidence From Panel Data1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Theory suggests that coworkers may influence individuals’ technology use behaviors, but there is limited research in the technology diffusion literature that explicates how such social influence processes operate after initial adoption. We investigate how two key social influence mechanisms (identification and internalization) may explain the growth over time in individuals’ use of knowledge management systems (KMS)—a technology that because of its publicly visible use provides a rich context for investigating social influence. We test our hypotheses using longitudinal KMS usage data on over 80,000 employees of a management consulting firm. Our approach infers the presence of identification and internalization from associations between actual system use behaviors by a focal individual and prior system use by a range of reference groups. Evidence of these kinds of associations between system use behaviors helps construct a more complete picture of social influence mechanisms, and is to our knowledge novel to the technology diffusion literature. Our results confirm the utility of this approach for understanding social influence effects and reveal a fine-grained pattern of influence across different social groups: we found strong support for bottom-up social influence across hierarchical levels, limited support for peer-level influence within levels, and no support for top-down influence.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it