Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective1
Bibliographic record
Abstract
This study used institutional theory as a lens to understand the factors that enable the adoption of interorganizational systems. It posits that mimetic, coercive, and normative pressures existing in an institutionalized environment could influence organizational predisposition toward an information technology-based interorganizational linkage. Survey-based research was carried out to test this theory. Following questionnaire development, validation, and pretest with a pilot study, data were collected from the CEO, the CFO, and the CIO to measure the institutional pressures they faced and their intentions to adopt financial electronic data interchange (FEDI). A firm-level structural model was developed based on the CEO’s, the CFO’s, and the CIO’s data. LISREL and PLS were used for testing the measurement and structural models respectively. Results showed that all three institutional pressures— mimetic pressures, coercive pressures, and normative pressures—had a significant influence on organizational intention to adopt FEDI. Except for perceived extent of adoption among suppliers, all other subconstructs were significant in the model. These results provide strong support for institutional-based variables as predictors of adoption intention for interorganizational linkages. These findings indicate that organizations are embedded in institutional networks and call for greater attention to be directed at understanding institutional pressures when investigating information technology innovations adoption.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".