Bibliographic record
Abstract
Purpose The purpose of the paper is to examine the underlying components of information technology (IT) that support different models of knowledge management (KM). Design/methodology/approach This empirical study is conducted in the management consulting industry to examine the important link between IT and KM. Based on previous research, four knowledge models were developed for the management consulting industry based on the knowledge type and service type. Data collected through a survey from 115 management consulting firms in the USA and Canada were analyzed. Findings Regardless of the type of KM model utilized, the most widely used IT by management consulting firms was the internet‐related technology (e‐mail, internet, and search engine). The second important IT component was data management technology (document management, data warehousing, data mining, knowledge repositories, and database management). The third important IT was collaborating technology (videoconferencing, workflow management, groupware, group decision support systems, and knowledge maps). The least important IT was artificial intelligence (expert systems, case‐based reasoning systems, intelligent agent, and neural network). Originality/value This paper develops a new topology of KM models based on the knowledge type (exploitive and explorative) and service type (standardized and customized). Thus, four KM models are developed: reuser (exploitive/standardized); stabilizer (exploitive/customized); explorer (explorative/standardized); and innovator (explorative/customized). While IT has been widely accepted as an enabler for KM, its application for a different focus of KM has not been explored.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".