Trust, ICT and income: their relationships and implications
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.
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship and interactions among trust, information and communication technologies (ICT) and country income levels. Design/methodology/approach This study adopts the standardization method by Osberg and Sharpe (2005) and analyzes the changes in global trends, coefficient of variations, and correlations. The statistical data consist of panel data for 28 countries from 2007 to 2014. Findings Trust in people (TP) and institutional confidence (IC) have different shapes of movement over the period and the change speed of IC has decreased much faster than that of TP. While TP in high income countries is positioned in relatively high ranks, IC of middle income countries tends to be ranked in higher ranks. While the telecommunication infrastructure index (TII) has continuously increased in all countries for the entire period, open service index (OSI) has not increased at the same rate since improving OSI is not easier than TII. As OSI increases, IC may affect an increase to a certain point and then decrease in an inverted U-shape. The result of this relationship emphasizes on the importance of OSI along with TII in building trust, particularly with institutions. Research limitations/implications The examination of the relationship of trust, ICT and income in quantifiable values can contribute to understanding the direction of movement and change speed toward trust building with people and institutions. Practical implications To promote levels of trust, countries should consider different strategies for growing TP and institutions and concentrate on improving ICT-mediated services more than installing ICT facilities. Originality/value Quantifying the interactions of a qualitative concept of trust with ICT facilities, online services, and income levels presents an in-depth analysis of TP and with institutions.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it