IT for KM in the management consulting industry
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it