When Wiki Technology Meets Corporate Knowledge Management Routines: A Sociomateriality Perspective
Bibliographic record
Abstract
There seems to be an inherent tension between wiki affordances—open boundaries, unconstrained editing, and transparency—and traditional knowledge management (KM) routines used in firms. The objective of this study is to investigate how users respond to these tensions during adoption of wiki technology at the workplace. The theoretical lens of sociomateriality highlights the manner in which routines and materiality (namely, technology) relate to one another, providing a useful conceptualization for our investigation. In particular, we adopt Leonardi’s theory of human and material imbrication, which stresses the importance of a worker’s past experiences with technology in determining his future adoption decisions. Extending Leonardi’s conceptualization, we suggest that out-of-work experiences are also influential. Namely, we argue that attitudes towards Wikipedia influence one’s response to wiki deployment in the workplace. Using an online survey containing four open-ended questions, we assessed the perceptions of employees towards wiki deployment. Results from our qualitative analysis of 1032 responses reveal five approaches users take in responding to the tensions between wiki affordances and existing KM routines, highlighting the effect of users’ dispositions towards Wikipedia. Our findings inform the sociomateriality literature and shed light on the challenges faced by organizations trying to adopt social media tools.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".