The Moderating Effect of Religiosity on the Relationship between Technology Readiness and Diffusion of E-Commerce
Bibliographic record
Abstract
Developing states, following the theory of “the survival of the fittest”, are severely in need of building up their electronic commerce for their economic survival on the globe. Beside other challenges, developing states are lagging behind in terms of technology readiness (TR). Another leading factor, particularly within the practicing Muslim communities, could be the impact of religiosity that has been unanimously found playing an immense role in buyers’ buying attitude, judgment of product price and quality. Having an immense influence, it affects both intra-personally and interpersonally. Furthermore, religiosity shapes consumers’ mind-set, learning and life style and is also considered as one of the significant factors with regards to hi-tech innovations adoption. This paper puts light on the relevant and valuable perspectives: technology readiness, religiosity and diffusion of electronic commerce, in the perspective of Muslim majority developing countries. While contributing to the field of knowledge, the study highlights the importance of technology readiness and trust in the diffusion process of electronic commerce. Looking into the moderating effect of religiosity in this regard, it underlines the unique features (completeness, universality, ever-greenness and applicability) of Islam including the moderate approach of Islam, toward technologies including e-commerce, thus boosting up electronic commerce trade. While, possessing the practical, educational and theological implications, the study will be helpful to all the stakeholders including; prospective consumers, governmental concerned authorities and e-commerce global community.
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.
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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".