Analysis of Factors Affecting Decision-Making Process of Offshore Application Maintenance using ISM Approach
Bibliographic record
Abstract
Software maintenance has the longest lifespan and requires roughly 60% of the total budget of software development life cycle.Organizations are seeking ways to reduce the software maintenance costs.Therefore, companies use offshore outsourcing to reduce costs by using low-cost countries' cheaper and more skilled labour.This research seeks to analyse the factors impacting the decisionmaking process and also identifies their structural associations.To fulfil the research objectives, first, the factors are evaluated by the IT specialists using online survey.Second, an ISM approach is implemented creating an ISM model based on factors' interrelationships.Five elements are prioritized in the first level: cost savings, infrastructure, domain expertise, project management, and requirement adjustments.These depend on second-level variables including employee skills, inadequate communication, and language barrier.The third level includes legal requirements and maturity.Further, this study classifies elements into three tiers based on their impact on the decision-making process.The findings of this study help service providers and clients to adopt effective sourcing strategies, increasing project success and saving projects' costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".