From strong ties to no ties: Configurations for first-customer acquisition in tech startups
Bibliographic record
Abstract
Attracting customers is one of the most important milestones for technology ventures. Ties are generally considered as beneficial when attracting start-up resources, such as first customers, because they can mitigate information asymmetry to overcome liabilities of newness and smallness. Despite this, when and how entrepreneurs use different network approaches to attract their ventures' first paying customers remains understudied. We rely on the concept of tie strength to distinguish between ventures acquiring customers via pre-existing strong ties, weak ties, or no ties (i.e., through market-based mechanisms). Using fuzzy-set Qualitative Comparative Analysis on 72 entrepreneurs from 72 Flemish technology ventures, complemented by extensive qualitative data, we identify distinct, equifinal configurations of founder, firm, and environmental attributes that are associated with acquiring customers through strong, weak, or no ties. Our post-hoc performance analyses further reveal performance differences: while attracting customers through no ties is associated with higher revenues, only using strong ties to attract first paying customers is associated with higher survival at scale. Our findings have important practical implications for entrepreneurs and technology commercialization policies. Overall, our study contributes a network-based perspective to customer acquisition to the literatures on entrepreneurial resource acquisition, entrepreneurial marketing and technology entrepreneurship.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".