Am I making a difference? A scoping review of the consultant’s individual characteristics fostering organizational consulting effectiveness
Bibliographic record
Abstract
Purpose The field of organizational consulting is often criticized for the lack of data supporting the practice and limited understanding of individual characteristics required for consultants to make a positive impact on organizations. The aims of this study were (1) to identify existing evidence on consultants’ knowledge, skills, abilities and other personal attributes (KSAO) related to organizational consulting effectiveness and (2) to lay the foundation of an empirically derived competency framework for effective consulting. Design/methodology/approach A scoping review of scientific peer-reviewed papers published between 1973 and 2023 and exploring attributes of the consultant related to consulting effectiveness was carried out among three academic databases and one consulting-specific journal. Content analysis was conducted in NVivo using an inductive/deductive approach. Findings In total, 32 single individual characteristics were extracted from 13 empirical papers and organized into 3 broad categories: (1) knowledge, (2) skills and abilities, and (3) other personal attributes. Results showed that skills and abilities have received the most attention from scholars, emphasizing the importance for consulting training programs to focus on the development of process-related and relational skills. Knowledge and personal attributes, including personality traits, were marked by a paucity of research. Originality/value The findings laid the foundation of a first data-based competency model for consulting effectiveness, useful for both researchers and practitioners. The current review identified gaps in the literature and highlighted opportunities for consolidating research in the field of organizational consulting.
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 | 0.029 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".