Enterprise 2.0 Technologies for Knowledge Management: Exploring Cultural, Organizational & Technological Factors
Bibliographic record
Abstract
This paper reports findings from a recent empirical study conducted to explore sociological and technological factors that affect the use of enterprise 2.0 (E2.0) technologies for knowledge management (KM). To help organizations adopt and institutionalize effective KM strategies, this study aims to highlight the effects of national and organizational cultural differences among operating environments of different firms, and to identify how these differences translate into varying knowledge management behaviors and use of E2.0 technologies for KM in firms.The study utilized a quantitative empirical research design to collect and analyze quantitative data from employees of various organizations in different countries and industries. A web-based survey data was collected from various countries including Canada, USA, and Saudi Arabia. Exploratory factor analysis and structural equation modeling techniques were used to estimate a structural model among factors impacting the use of E2.0 technologies for KM.The key findings from this study validate the role of technology perceptions including ease of use, usefulness, media richness and technology sophistication in improving the use of enterprise 2.0 technologies in the workplace. Furthermore, the use of these technologies was shown to have a positive effect on the knowledge management environment of the organization. In terms of cultural differences, the knowledge management environment of firms was shown to be affected by long-term orientation of the national culture. This study offers recommendations for companies operating in global cultural contexts on how to approach KM strategies differently according to national culture and organizational environments of firms.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".