Role of socio-cultural capital and country-level affluence in ethical consumerism
Bibliographic record
Abstract
So far, most ethical consumerism research has been contained within Western countries, thus limiting our understanding of the concept in emerging markets. Given the call for extending empirical-based knowledge for a better understanding of peculiarities, dynamics and country-level variations (i.e. social, cultural) in the context of ethical consumerism in emerging markets, this research cross-examines the interactive nature of individual- and country-level predictors of ethical consumerism in emerging and developed markets, employing a multilevel approach. At the individual level, we posit that ethical consumerism is motivated by social and cultural capital. In contrast, at the contextual level, we choose country-level afuence as an infuential factor that might impact the relationship between socio-cultural capital and ethical consumerism. The study uses the International Social Survey Programme’s (ISSP) 2014 citizenship module data set (including 34 countries) for investigating individual-level predictors (of social and cultural capital). The GDP per capita data from the International Monetary Fund’s (IMF) Economic Outlook database was used to examine the cross-level interactions between individuallevel predictors and country-level afuence. The fndings suggest that social and cultural capitals positively infuence ethical consumerism in emerging and developed markets. Further, country-level afuence moderates the relationship between socio-cultural capital and ethical consumerism for both markets. However, cultural capital proved to be a stronger predictor of ethical consumerism as country-level afuence increases. The research fndings highlight meaningful cross-country-level interactions that help further understand the basis of ethical consumerism from a global perspective.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".