Corporate‐startup partnering: Exploring attention dynamics and relational outcomes in asymmetric settings
Bibliographic record
Abstract
Abstract Research Summary Startups that partner concurrently with a large corporation must compete for the latter's attention. We extend the attention‐based view from an intraorganizational to an interorganizational context, exploring how startups differ in the amount of attention they receive, their actions to attract and sustain attention, and the impact of attention dynamics on the relational outcome of the partnership. Our research uncovers two separate contests for attention involving corporate and divisional managers, highlighting the distributed nature of attention. Reflecting these, our findings reveal how startups' responsiveness to the respective cognitive schemas and corresponding stimuli of corporate and divisional managers is critical to understanding their distinct relational trajectories and disparate outcomes. Our focus on attention is complementary to the focus on trust that has hitherto dominated research on relational dynamics. Managerial Summary Startups partner with large corporations to access needed complementary resources. However, truly benefiting from such partnerships is challenging and requires them to attract as well as sustain the latter's attention. Our study reveals two contests for attention: one with corporate managers tasked with running a startup partnering initiative and the other with divisional managers in business units with whom actual commercial joint activity is forged. These two sets of managers have different priorities (schemas) that result in differences in the nature and amount of attention they pay to startups' actions (stimuli). Startups seeking corporate partnerships would do well to recognize this heterogeneity within large corporations and accordingly manage the attention–attraction process through suitable partner‐centric behaviors. On their part, large corporations need to be aware of and sensitive to the challenges such disparate schema of corporate and divisional managers pose for successful partnering outcomes as the relationship transitions from the early to later stages.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".