Developing a strategy map for environmental consulting firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify key performance indicators (KPIs) for environmental consulting firms, explore their causal linkages and develop a strategy map around the balanced scorecard (BSC) perspectives. Design/methodology/approach Relevant KPIs are identified through interviews and secondary data. Causal relationships between KPIs are explored by using the decision-making trial and evaluation laboratory (DEMATEL) method to analyze survey responses from senior partners and industry experts. Findings The results suggest that the learning and growth perspective plays a pivotal role for consulting firms. In addition, and contrary to views held by some, internal process perspective can play a significant cause factor role for service businesses. Among the KPIs which were identified as important, acquiring new skills/techniques, increased customer value proposition, personnel utilization, new product solutions and start to end solutions as KPIs exhibited both cause and effect characteristics. Practical implications The results isolate core KPIs which self-reinforce, complement each other and form a feedback loop. Active management and monitoring of these KPIs is likely to result aid a consulting firm in achieving strategic objectives. The strategy map developed in this study can also serve as a reference point for similar businesses. Originality/value This is the first known study to develop a strategy map for a consulting business by adopting a structured approach and identify causal link among BSC perspectives and their respective KPIs. The study provides further empirical evidence for usefulness of a structured approach such as the DEMATEL.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".