Predicting Indian Shoppers’ Malls Loyalty Behaviour
Why this work is in the frame
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Bibliographic record
Abstract
Executive Summary Mall managers tend to believe that purchasing decisions are made inside the shopping malls. These decisions, however, are influenced by various antecedent factors. This implies that shoppers look beyond the basic chore of shopping and experience while shopping plays a vital role. To attract the attention of shoppers, mall developers make huge investments in mall promotion and ambient factors in order to enhance the shopping experience. As the Indian shoppers’ euphoria about shopping malls gets toned down with time, mall managers need to focus on something more substantive. Such fundamental benefits can be offered to shoppers only if mall managers know what is more relevant for the shoppers visiting the malls. Past studies have identified a number of factors such as ambience, physical infrastructure, convenience, safety, and marketing activities. This research posits that a more optimal and focused approach in mall management requires identification of relative significance of various influencing factors. This way, mall managers would be able to offer the most meaningful benefits to shoppers at a very optimal level of investment. Once shoppers get what they value the most, they are expected to be more loyal to the shopping mall. Despite the development of various forecasting techniques, predicting mall loyalty has remained under-explored in marketing literature. This article addresses the gap by using neural network model to predict shoppers’ loyalty towards a particular mall. To gain more insights from the model, the authors have also identified relative significance of the factors impacting shoppers’ mall selection. This study establishes that mall shoppers value ‘convenience’ as the most influencing factor in their selection of malls. This factor alone garners one-third of the total weightage among the five factors, which reflects that significance of convenience is 66 per cent more than what is expected in a scenario when all determinants contribute equally. This strongly indicates that Indian mall shoppers are more utilitarian than hedonic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it