The Effects of Technology Adoption on Firms, Supply Chains, and Rivals
Bibliographic record
Abstract
We study the effects of technological change and adoption timing on firms’ liquidity management practices and subsequent product market outcomes. We first posit that investment in Enterprise Resource Planning (ERP) software improves trade credit and inventory efficiencies between supply chain partners. We identify exogenous variation in the timing of a firm’s incentives to implement ERP by using a firm’s ex-ante exposure to the Year 2000 bug (Y2K). Our results show that a firm’s average accounts receivable collection period and inventory turnover improve following ERP implementation in the supply chain. Interestingly, this is due to adoption by either the focal firm or its key customer. ERP adoption also increases a firm’s subsequent market share, but only for non-early adopters. This pattern suggests that early adopters of transformational technologies bear higher implementation costs that spill over to the advantage of both trading partners and competitors. Documenting these mechanisms yields insight into the boundary conditions of first-mover advantage, particularly in the early years of a significant technological advance.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".