Bibliographic record
Abstract
Purpose Porter's value chain has been a keystone of strategic analysis. However, because of processes associated with economic globalization: outsourcing, brand marketing and “knowledge economy” phenomena, value drivers have changed dramatically over the last 20 years. The added‐value chain provides an expanded mental model for practitioners and academics to develop and communicate strategies for value creation. Design/methodology/approach The expanded set of activities in the added‐value chain was developed based on experience using the value chain in real world situations and analyzing leading business and strategy models that are commonly used by firms today. Findings The added‐value chain incorporates new sources of value creation such as the firm's brand, reputation and “social capital” or goodwill in addition to profit margin. The Added‐Value Chain also adds three primary activities. Practical implications Managers performing value‐chain analysis need to take into account newly important business drivers. Originality/value Expanding the value chain ensures that no potential strategic activity is forgotten and no opportunity for enhancing value is over‐looked.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".