Bridging the information asymmetry in e-commerce: an intercultural perspective on sustainable clothing
Bibliographic record
Abstract
Purpose This study addresses the information asymmetry issue in e-commerce, particularly prevalent in the clothing industry, leading to high product returns and environmental harm. The research aims to fill gaps in the existing literature by holistically examining multiple information cues and considering intercultural differences, utilizing signaling theory. Design/methodology/approach An Adaptive Choice-Based Conjoint experiment involving German (n = 332) and Chinese (n = 331) respondents from Generation Y is conducted. This cross-cultural comparison explores consumer preferences for sustainable clothing and analyses factors influencing their choices, including price, shipping costs, sustainability labels and online customer reviews. Findings German online shoppers exhibit a stronger preference for sustainable clothing compared to their Chinese counterparts, with notable differences in the emphasis placed on various factors. Chinese respondents prioritize monetary aspects (e.g. price and shipping costs), while Germans attach greater importance to sustainability labels and online customer reviews. Originality/value This research contributes to the existing literature by providing a comprehensive analysis of information cues in e-commerce, considering cultural variations. The findings shed light on the distinct preferences of German and Chinese respondents from Generation Y, offering valuable insights for businesses aiming to address information asymmetry and enhance environmental sustainability in online clothing retail.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".