How to Promote Adoption of Complex E-Services Innovation? An Institutional Factor Perspective
Bibliographic record
Abstract
By leveraging advanced information and communication technologies to provide innovative services through Internet, manufacturing service companies successfully integrate and coordinate the value chain activities to cost-effectively produce quality products for global customers. However, those complex electronic service (e-service) innovations usually are not necessarily adopted soon by customers as expected, which leads to slower investment returns and lower online service level. This study tried to understand the relationship between industry institutional factors and adoption intention of e-service innovation, and consider the moderating effect of e-service complexity. A survey included 263 respondent companies was conducted to collect empirical data from semiconductor industry. Research results shown that three institutional factors (technology standardization, propagating institution and institutional pressure) all have positive influence on the e-service innovation adoption intention. The e-service complexity has negative influence on the adoption intention and negatively moderates the effect of institutional pressure and propagating institution, while positively moderates technology standardization effect. Institutional factors can be useful strategic tools for promoting e-service innovation diffusion but need to consider innovation complexity. With the findings, this study contributes to academic understandings and provides several managerial implications for practitioners.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| 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".