Effects of Online Shopping Values and Website Cues on Purchase Behaviour: A Study Using S–O–R Framework
Bibliographic record
Abstract
Executive Summary The e-commerce industry in India has seen unprecedented growth in last few years. Eyeing India’s substantial e-retail opportunity across multiple segments, investors have been aggressively funding the e-commerce sector. This growth has been fuelled by rapid adoption of technology, improving standards of living, an increasing young population, and economically advancing middle class, besides increasing access to the Internet through broadband and use of smartphones and tablets. The entry of global e-commerce giants has intensified the competition for home-grown players. E-retailers use web atmospherics to differentiate themselves from their competitors and evoke positive cognitive and emotional states of online consumers. However, though this Indian online market is growing at an exponential rate, it is still unexplored in terms of its shopping behaviour. Using structural equation modelling, this study applies the concept of the stimulus–organism–response to explain Indian buyers’ online shopping behaviour, besides examining the importance of design elements in enabling website satisfaction (WS). Using a survey method to test the research model, primary data were collected from five Indian metropolitan cities of Delhi, Mumbai, Kolkata, Bengaluru, and Hyderabad during the months of May and June 2015. Confirmatory factory analysis (CFA) was used to estimate the measurement model with respect to convergent and discriminant validities. This was followed by testing the structural model framework and research hypotheses. Findings suggest that both internal and external elements have direct influence on WS. As the mediating variable, WS affects purchase intention. This research highlights on why and how ‘satisfaction with website’ matters in the contribution of shopping values and website atmospherics to behavioural outcomes by presenting its mediating role.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".