Endogenous Adaptation: The Effects of Technology Position and Planning Mode on IT‐Enabled Change*
Bibliographic record
Abstract
ABSTRACT The redesign of information technology (IT)‐enabled work processes often necessitates fundamental design changes to the intended work process, the IT platform hosting the work process, or both. Research suggests that such design changes often can be traced to earlier decisions involving endogenous adaptation or internal organizational change. Two such decisions are a firm's technology position and planning mode. This study examines the relationship between technology position and planning mode in predicting the magnitude of design change in process redesign projects. The conceptual frame applied in examining these relationships involves a synthesis of Miles and Snow's adaptive cycle with elements central to concurrent engineering. Our results indicate that the magnitude of design change is related to differences in technology position and planning mode. To effectively implement organizational change, firms must leverage their IT platform by carefully timing IT investments in accordance with their adopted technology position. Directing the trajectory of a firm's IT platform and deploying it so as to complement the firm's technology position reduces design uncertainty, promoting reengineering success.
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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 | 0.005 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".