MétaCan
Menu
Back to cohort
Record W1500169884 · doi:10.1108/14637150710721159

An assessment of facilitators and inhibitors for the adoption of enterprise application integration technology

2007· article· en· W1500169884 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBusiness Process Management Journal · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnterprise resource planningKnowledge managementComputer scienceProcess managementEnterprise application integrationProcess (computing)Enterprise systemBusinessEnterprise architectureEnterprise systems engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.338
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it