An assessment of facilitators and inhibitors for the adoption of enterprise application integration technology
Bibliographic record
Abstract
Purpose Enterprise application integration (EAI) aims to integrate various enterprise applications, such as legacy systems, enterprise resource planning systems, and best‐of‐breed business applications, to aid in promoting organizational goals. EAI is a relatively new area of concern for researchers and practitioners and research on its adoption by organizations remain to be examined. Design/methodology/approach This paper extends prior research by providing a systematic examination of both generic and specific dimensions of facilitators and inhibitors for the adoption of EAI technology. A rigorous validation of these factors was established. A case study was conducted to refine the developed instrument. Findings The results indicate that EAI adoption is facilitated by generic as well as specific factors to this technology. Research limitations/implications Several limitations of the study need to be mentioned at this stage. First, the research design of this study has incorporated only one site to examine and enrich the list of facilitators and inhibitors of EAI adoption. It is not known whether these results would apply to other organizations, other technologies and whether the project size has some influence on the results. More empirical work is needed to increment the developed instrument. The results of this study have three specific implications for future research. First, this study can be replicated to examine the effect of these facilitators on EAI project performance. Second, more research can be conducted to validate dimensions identified in this study. A survey may strengthen the validation process of the developed instrument and the structure of the dimensions and constructs used. Finally, the results of this study and the developed instrument can be applied on other technologies such as web services, etc. Practical implications The paper extends King and Teo's list to include EAI‐specific factors. Second, it validates the instrument through the card sorting procedure and a case study. The identified dimensions can be used in future research on EAI adoption. The results have also important managerial implications. Managers who are planning to adopt EAI technology can use the developed instrument to assess systematically the facilitators and inhibitors of this technology in their organizational context. Originality/value This study extends and accumulates on Teo's framework for inhibitors and facilitators of IT adoption in the EAI context.
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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 | 0.033 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".