Exercising Due Diligence in Studies of Duration of Competitive Advantage Due to Emerging Technology Adoption
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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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 | 0.046 | 0.214 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".