Multi‐channel communication and consumer choice in the household furniture buying process
Bibliographic record
Abstract
Purpose Furniture is among the important personal consumption expenditures for durable goods in the USA. Retailers and manufacturers offer different communication channels to assist consumers all through the process of acquiring furniture. The objective of the present study is to evaluate US consumers' channel use at different steps of the residential furniture‐buying process. Design/methodology/approach A cross‐section study was conducted by taking advantage of the structured nature of quantitative methods using a questionnaire for data collection and a socio‐demographic representative sample of US citizens. Consumers' use of six communication channels through five buying decision steps was assessed. Findings Results showed that the furniture retail store is the most important communication channel at each of the five considered buying process stages. Overall score of that channel was higher for females than males, indicating that women care more about communication when buying furniture. The internet was not of significant importance when buying furniture. Advertising was perceived as a significant means to gather information. Practical implications The study will help to orient companies' marketing strategies by making proper use of communication channels. It also shows marketing students the present state of consumers' communication channel preferences. Originality/value This paper gives unique insights into consumers' buying behavior that will help to design communication channels properly.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".