An Empirical Investigation of Knowledge Management Strategy and Information Technology Strategy on Performance
Bibliographic record
Abstract
Recently, a great number of theoretical frameworks have been proposed to develop the linkages between knowledge management (KM) and organizational strategy. While there has been much theorizing and case study in the area, validated research models integrating KM strategy and information technology (IT) strategy for empirical testing of these theories have been scarce. It is thought that the rapid progress of IT has been provided a good solution to support KM practices. Choosing the proper ITs to fit with different KM strategies is critical for organizations. Effective KM activities require employing KM strategies, as well as IT, appropriately. That is, as long as the KM strategy has been determined within an organization, the IT strategy must be followed. In this present research, we try to develop and examine a research model for explaining the relationships between KM strategy, IT strategy, and their effects on performance. Empirical data for hypotheses testing are collected from top-ranked companies in Taiwan; yielding 161 valid samples. The findings showed that KM strategy has a positive direct effect upon IT strategy; KM strategy and IT strategy have significant positive effects upon KM performance and IT performance respectively, and then collectively, have the impact upon business performance. Finally, from the empirical data analysis, meaningful findings and conclusions are derived, and suggestions for future research are proposed and discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".