Economies Before Scale: I.T. Strategy and Performance Dynamics of Young U.S. Businesses
Bibliographic record
Abstract
We examine how dimensions of information technology (IT) strategy affect the performance of young businesses, as well as dynamics as they age. Drawing from lifecycle theory and firm boundary research, we derive the relationship between age-based performance differences and IT sourcing decisions. We highlight the dynamic tension between outsourcing’s support for accessing frontier inputs in the short term and ownership’s advantages for developing organization-specific resources and capabilities over time. Leveraging a large panel of Census Bureau microdata from 2006 to 2014, we provide the first systematic evidence that young manufacturing establishments (both startups and new units of existing firms) disproportionately benefit from modern IT outsourcing (ITO). The young also enjoy productivity benefits from owned IT capital (ITK), despite high uncertainty, smaller operational scale, and less complementary organizational capital. Although these returns appear commensurate with those of older producers, they are conditional on survival, which is improved by ITO but harmed by ITK accumulation. Combining flexibility-related gains from ITK with vintage-related advantages in early ITK investment, the young are revealed to have significantly greater IT productivity than older incumbents. A large battery of tests supports a causal interpretation, as well as mechanisms rooted in mitigating the effects of uncertainty (as opposed to size- or cost-related reasons). These findings illuminate an often-overlooked pattern of young-business dynamism relevant to economic trends and management practices in an increasingly digital age. This paper was accepted by DJ Wu, information systems. Funding: This work was supported by the Social Sciences and Humanities Research Council (SSHRC) of Canada, the National Bureau of Economic Research, and the Stanford Digital Economy Lab. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2019.03116 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".