Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the impacts of innovation-adoption characteristics on Chinese consumers’ adoption of online food shopping. It also examines consumers’ online purchase preferences for specific food categories and the consumer segments shopping for food online in China. Design/methodology/approach The data were collected through a web-based survey ( n =643, in three cities: Beijing, Guangzhou and Chongqing). Descriptive analysis, cluster analysis, factor analysis and structural equation modeling were employed for data analysis. Findings Participants had strong online purchase intentions toward snack and imported food, while they had weak online purchase intentions toward fresh food products such as meat, eggs, vegetables, fish and seafood. Two consumer segments were found: online-food-conservative (42 percent) and online-food-pioneer (58 percent). Factor analysis resulted in an adjusted factorial structure of the innovation-adoption characteristics, which was considered more appropriate within the context of Chinese consumers when shopping for food online. Path analysis found that Chinese consumers’ attitudes and/or purchase intentions were positively linked to their perceived incentives and negatively associated with their perceived complexity for online food shopping. Originality/value This is the first study to explore consumer segments, consumption psychology (innovation-adoption characteristics) and product preferences related to online food shopping with a sample from China, the largest e-commerce country. The findings can help food producers and marketers to better understand Chinese consumers’ online food shopping behaviors in order to meet the needs of consumers and have further success in this major market.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".