The social context for value co-creations in an entrepreneurial network
Bibliographic record
Abstract
Purpose – The purpose of this paper is to study micro-level research into the social dimensions of entrepreneurial partnerships assessed by the influences of: the degree of interpersonal attraction, the strength of relational norms and the level of partner trustworthiness on value co-creations in an emerging biotechnology network. Design/methodology/approach – Financial and scientific partnerships were investigated by structured interviews with entrepreneurs. Financial partnerships were also studied using interviews with lead investors. Research design and analyses were based on a Conditional Process Model. Findings – Partner trustworthiness was found to be critical for the co-creation of value in both types of partnerships. In financial partnerships, the level of interpersonal attraction and relational norms strength acted independently as antecedents of partner trustworthiness. Only the entrepreneur linked interpersonal attraction directly to value co-creation. Both entrepreneurs and lead investors perceived the association between interpersonal attraction and co-created value to be mediated through partner trustworthiness. Only the lead investor perceived this mediation to be moderated by relational norms strength. However, in scientific partnerships, relational norms strength, but not interpersonal attraction, contributed to partner trustworthiness that subsequently effected value co-creation. The entrepreneur’s trustworthiness perception in both types of partnerships was mainly due to a partner’s reputation, whereas for lead investors it was primarily the perceived reliability of the entrepreneur. Originality/value – This research points out the challenges of measurement and interpretation of network research. Theoretical conclusions based on only one partner’s perspective and in one context would not be sufficient to describe the complexity of value co-creations in entrepreneurial networks. Also, the cooperative social, rather than competitive opportunistic nature of entrepreneurial knowledge-intensive networks was confirmed.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".