The Dark Side of Technological Modularity: Opportunistic Information Hiding During Interorganizational System Adoption
Bibliographic record
Abstract
To drive competitive advantage in today’s fast-paced and disruptive business environment, firms are increasingly investing in the modularization of their technology infrastructure. In a rapidly changing and interconnected business environment where flexibility is key, modularity is often hailed as a foundational pillar of information technology systems of the future. For networked firms, modularity has been traditionally viewed as unambiguously beneficial because it allows for closer alignment with partner firms and also mitigates risk by lowering partner switching costs. However, we find that in interfirm networks undergoing technology transitions in the form of adoption of new interorganizational systems such as blockchains, modularity can also engender additional risks. Specifically, the early stages of IOS adoption are characterized by information asymmetries, and we find that high levels of technological modularity can render firms more susceptible to opportunistic information withholding by network partners. Our findings run counter to the traditional view of modularity as a capability that can improve the efficiency of IOS adoption, or as a governance mechanism that reduces risks associated with IOS adoption. As optimism and investments toward modularity grow, by identifying associated risks, our work cautions managers to adopt a more qualified view of this capability during technological transitions.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".