Is co-created value the only legitimate value? An institutional-theory perspective on business interaction in B2B-marketing systems
Bibliographic record
Abstract
Purpose This paper aims to deal with the concepts of “institutions” and “institutional logics” in the context of business-to-business (B2B) marketing systems and uses institutional theory as a framework to look at value co-creation. Design/methodology/approach By integrating the literature on value co-creation, institutional theory and institutional entrepreneurship, the paper argues that the boundaries of B2B marketing systems are continuously reshaped through legitimation processes occurring through actors’ institutional work, thus making co-created value the only legitimate value. Findings The paper proposes a conceptual framework and furthers the conceptual development of value co-creation and augments the literature on service-dominant logic and the notion of co-created value by assuming a legitimacy-based B2B market systems perspective. Practical implications This paper presents a number of propositions that serve to illustrate several managerial implications. These arise from organizations co-creating value by conforming to the various institutional logics that maximize their legitimacy. Originality/value The paper makes a contribution by developing a critical theoretical framework based on the application of institutional theoretical constructs/concepts (e.g. ceremonial conformity, decoupling, considerations of face, confidence and good faith).
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".