Exploring the roots of Porter's activity‐based view
Bibliographic record
Abstract
Purpose Porter's activity‐based view of the firm is a comprehensive strategic framework which analyzes firm‐level competitive advantage. Although Porter's activity‐based view is widely cited by academics, taught to students, and applied by practitioners, little is known about its intellectual roots. Given that a framework's intellectual antecedents not only determine its current content, but also its future development, this paper aims to examine the intellectual roots of Porter's activity‐based view and the value chain. Design/methodology/approach The paper examines Porter's writings in an effort to document his influences while developing the activity‐based view and value chain. Porter's and other scholars' explanations are found to be lacking, so the paper ventures further down paths first suggested by Porter and others. Findings Whereas Porter's five forces framework built on the existing industrial organization paradigm, the activity‐based view is not derived from any existing paradigms. While consultants of the 1970s impacted Porter's development of the value chain and the activity‐based view, its deeper roots lay in operations research, particularly activity analysis; and the work of Arch Shaw, who was the first to teach a business policy course at Harvard Business School. Porter's contribution is to bring the diverse threads together into a coherent whole which managers can apply to analyze and improve their competitive positions. Practical implications Following Porter, the authors argue that activities are a key link between resource holdings and strategic positions. Therefore, it is only when the activity‐based and resource‐based views are integrated that they provide a comprehensive explanation of firm value creation. Originality/value The paper is the first to critically examine the intellectual antecedents of the activity‐based view.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".