Value creation logics and resource management: a review
Bibliographic record
Abstract
Purpose The purpose of this paper is to locate different value creation logic contingencies within the resource management framework. While Sirmon et al. discuss how external environmental contingencies, such as environmental munificence, impact resource management, this paper aims to discuss a second key contingency; that is how the firm's choice of value creation logics impacts its resource management choices. This paper seeks to argue that management of the firm's resources and capabilities is contingent on the value creation logic employed by the firm. Design/methodology/approach This paper reviews three value creation logics: value shop, value network, and value chain and then integrates them within the resource management framework. Findings A review of extant literature indicates that value shop firms, value network firms, and value chain firms enact very different environments and thus require very different resources and capabilities to support their value creation approaches. It is argued that Sirmon et al.'s resource management framework should reflect these differences. Research limitations/implications This paper points to new directions for research in value creation logic theory and provides a basis for future empirical work. Practical implications This paper argues that a mismatch between a firm's value creation logic and its resource management practices will have an adverse impact on the firm's performance. Originality/value This study is one of the first to integrate Stabell and Fjeldstad's value creation logic theory with Sirmon et al.'s resource management framework.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".