An analysis of sustainable change management for quality 4.0: Evidence from hybrid project management adoption in the Malaysian FinTech context
Bibliographic record
Abstract
In this paper, the authors aim to analyse organisational intention and focus on hybrid project management (HPM) methodology adoption in FinTech system software development. It is important to ascertain the internal and external factors that affect organisational decision-makers’ intentions towards HPM adoption. This study aims to apply a theoretical approach integrating Technology-Organisation-Environment (TOE), which examines the factors that impact FinTech organisations’ decisions to adopt HPM into their software development projects, together with the Theory of Planned Behaviour (TPB) which examines the behavioural intention. It addresses those factors that form organisational decision-makers’ readiness for HPM implementation and enable their intention to use it. When combining the independent, dependent, and moderating variables, the results show that the effect of relative advantage, top management support, and industry pressure have a positive influence on individual’s attitude towards HPM adoption in FinTech Malaysia and sustainability in Quality 4.0. The authors also considered the influence of attitudes and perceived behavioural control variables having a positive influence on sustainable intention of HPM adoption in the FinTech industry. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to verify the proposed hypotheses, with the exception of the direct influence of top management support or attitude on intention to adopt.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".