Value co-creation: a metatheory unifying framework and fundamental propositions
Bibliographic record
Abstract
Purpose Value co-creation (VCC) represents actors’ joint, communal or shared value-creating processes. However, while existing research has advanced important VCC-based insight, the use of differing metatheoretical lenses to study VCC incurs a risk of theoretical fragmentation, thus potentially hampering this research stream’s continued development. We, therefore, undertake an in-depth review of the corpus of VCC research that focuses on its common conceptual underpinnings as anchored in differing perspectives. Design/methodology/approach To explore this objective, we undertake an extensive review of extant VCC literature, based on which we develop an integrative conceptual framework of VCC. Findings We propose an integrative, metatheory-unifying definition and framework of VCC that reflect its core hallmarks and dynamics across its adopted theoretical perspectives. Based on the framework, we also derive a set of fundamental propositions (FPs) that synthesize VCC’s core tenets. Research limitations/implications VCC conceptualizations grounded in differing metatheoretical perspectives reveal the concept’s core interactive, value-creating nature across metatheoretical perspectives. Though VCC emanates from interactivity between any actor constellation, unifying different metatheories of VCC uncovers important insight. Practical implications The study suggests that for effective value co-creation, managers need to establish agreed-upon institutional arrangements, facilitate positive actor relationships and experiences and address challenges like collaboration, transparency, empathy and skill development while ensuring that affective, cognitive, economic and social dimensions of success are met for all actors involved. Successful initiatives require seamless communication, mutual understanding, cost-benefit favorability and public recognition of contributions. Originality/value Given VCC’s rising strategic importance, a plethora of studies have investigated this concept from differing metatheoretical perspectives, yielding potential VCC-based fragmentation. Addressing this gap, we take stock of the VCC literature with a view to distilling the concept’s core, trans-metatheoretical hallmarks, as synthesized in the proposed framework and FPs of VCC.
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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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".