E‐readiness and trust: macro and micro dualities for e‐commerce in a global environment
Bibliographic record
Abstract
Purpose The paper's aim is to create a framework for national readiness and receptivity to e‐commerce at both the business to business (B2B) as well as business to consumer (B2C) levels. Design/methodology/approach Relevant literature on e‐readiness is discussed in light of research on corruption and national values. A model is formulated at the macro level in which e‐readiness is predicted to be related to national culture values and corruption. Analysis at the micro level rests on existing literature related to trust and web site usability. Findings At the macro level of analysis, levels of perceived corruption within a country, and overarching national values are identified as significant contributors to e‐readiness especially in the B2B realm. At a more micro level, it is proposed that individual expectations regarding ability to trust an online vendor, and the suitability of usability characteristics of web site design contribute to e‐readiness at the B2C level. Taken together, macro and macro factors jointly contribute to a nation's readiness and receptivity to e‐commerce. Research limitations/implications The empirical work presented is based on aggregate level data from only one point in time. Results only provide generalized trends that may not be representative of all firms in a country or still applicable in the present time. Practical implications Practitioners are challenged to think beyond technological readiness and address factors such as corruption, national culture, and web design before entering new markets. Originality/value This paper identifies aspects of e‐readiness beyond purely technical infrastructure and provides a fresh empirical model. This study uniquely considers both micro and macro level characteristics that contribute to e‐readiness.
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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 | 0.002 | 0.007 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".