Exploring the Long Shadow of IT Innovation Adoption Decisions on IT Value
Bibliographic record
Abstract
Much research has been conducted to understand the value of IT innovations. However, research has examined such value primarily at the ex post stage, independently of the ex ante conditions that lead to adopting such innovations. This paper argues that there is a long shadow cast by past adoption conditions and decisions over the present assessment of value. We develop a conceptual framework that ties IT innovation value to the original motives underlying the adoption. The main premise is that the initial conditions that exist at the adoption stage (ex ante) can be used to understand the emphasis that should be placed on the different types of realized IT innovation value (ex post). Specifically, we develop a typology of four motivational forms of adoption that result from combining two dimensions of environmental uncertainty. We then develop propositions that relate each form of adoption to different components of IT innovation value. This paper extends the extant IT value literature by providing an account of IT innovation value that is consistent with the original motives of adoption. It also provides one way to integrate between the IT adoption and IT value streams, which hitherto have been treated separately.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| 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".