Unveiling consumer adoption intentions towards AI-powered home appliances in emerging economy
Bibliographic record
Abstract
Purpose The purpose of the present study is to investigate the consumer intention to use artificial intelligence (AI)-powered home appliances in an emerging economy with influences of perceived usefulness, novelty value, perceived value, hedonic motivation and attitude. This study also measures the indirect influences of perceived usefulness, novelty value, perceived value and hedonic motivation on the intention to use AI-powered home appliances through the mediating effect of attitude. Design/methodology/approach The present study applied a purposive sampling method to collect data from 358 respondents using a self-administered survey questionnaire. The data were analysed using partial least squares structural equation modelling (PLS-SEM) to determine the construct reliability, validity and path coefficients. Findings The study's findings revealed that perceived usefulness, novelty value, hedonic motivation, and attitude significantly and positively influence the intention to use AI-powered home appliances. The findings also indicate that perceived value does not significantly impact the intention to use AI-powered home appliances, but it indirectly influences the intention to use them through the mediating effect of attitude. Originality/value The present research findings provide valuable insights to service providers who want to adopt artificial intelligence in home appliances to offer better services towards consumers. This study enhances theoretical depth by incorporating attitude as a mediating variable and sheds light on its pivotal role in shaping users'’ adoption intentions. It also brings much-needed attention to the emerging economy context and offers valuable insights into consumer behaviour in regions with unique challenges and opportunities.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".