Rethinking Technical Debt: A Strategic Tool for New Venture Creation in the XaaS Era
Bibliographic record
Abstract
Technical debt (TD) has become an increasingly nuanced concept in entrepreneurship, particularly in the era of Everything-as-a-Service (XaaS). Initially introduced as a metaphor to describe the trade-offs in software development when shortcuts are taken for immediate gains at the expense of long-term maintainability, the concept has since evolved to encompass broader contexts, including digital infrastructure design and management. Its core premise of making visible the balance between short-term benefits with long-term costs has significant importance with the resource-constrained decision-making often observed in new venture creation. This study empirically examines the emerging types and sub-dimensions of TD encountered by startups in the XaaS context. Our findings identify dependency debt and integration debt as two salient forms of TD in this era. Additionally, we uncover two emerging entrepreneurial practices—microsourcing and surface innovation—that illustrate how XaaS is reshaping entrepreneurship. We conclude by emphasizing the growing role of TD as a strategic tool for driving new venture creation.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".