Exercising Due Diligence in Studies of Duration of Competitive Advantage Due to Emerging Technology Adoption
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Motivated by the study of Reinking et al. (2015), the study proposes a due diligence process for future studies aiming to investigate the duration of competitive advantage due to emerging technology adoption. The proposed process is based on the following premise: Predictions related to rate of adoption are useful to IT business value researchers because technology adoption remains a potential source of competitive advantage until adoption rate has reached approximately 50 percent. Based on a comparison of two technologies (ERP and e-commerce), the study provides the following three recommendations for researchers interested in productivity and financial performance-related payoffs due to emerging technology adoption: (1) apply the resource-based view analysis on the emerging technology to see if the duration of competitive advantage is worth exploring; (2) leverage the synthesis done by Stratopoulos (2016) to develop an a priori testable benchmark duration; and (3) contrast adopters with a matched sample of non-adopters or late adopters to establish a duration advantage.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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 it