How customers respond to the assistive intent of an E-retailer?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how an E-retailer's assistive intent impacts the perceptions and behaviours of online shoppers. The paper introduces a model that explains and examines the process through which the perceived assistive intent of an E-retailer leads to improved patronage intentions towards the web site. Design/methodology/approach – A survey on the most recent e-purchase experiences of more than 600 individuals in North America was conducted. Structural equation modelling based on EQS 6.1 was used to assess the measurement and structural models. Findings – Results indicated that customers’ impressions of an E-retailer's assistive intent positively impact web site patronage intentions both directly and indirectly through two key constructs of e-shopping, including web site involvement and web site attitudes. Research limitations/implications – The student sample is not representative of the population. Students are familiar with internet and feel less need for assistance online. Another shortcoming might be its settings. Since the survey was on the respondents’ most recent online experiences, the data quality depends on the amount and accuracy of the information they could retrieve from memory. Practical implications – The findings suggest that E-retailers would highly benefit from investing in the development of an assistive image. To do so, E-retailers should leverage the interactive nature of the web and provide supportive tools that facilitate the e-shopping task of clients. Social implications – Developing impressions of the site's assistive intent is highly rewarding for E-retailers that are new to the business. Originality/value – This paper represents the first effort to link the newly developed construct of E-retailer's assistive intent to two fundamental variables of online shopping, including web site involvement and web site attitudes. This work would also be an extension of the past studies that call for further investigation of the link between customer orientation and customer's loyalty intentions.
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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 | 0.002 | 0.012 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".