An Empirical Study of Factors Influencing Consumers’ Purchasing Behaviours in Shopping Malls
Bibliographic record
Abstract
Since a couple of years ago, the development of shopping malls is booming in the Klang Valley-Kuala Lumpur area in Malaysia. Motivating consumers for frequent visits to shopping complexes is imperative in order to run a successful shopping mall in such a competitive retail environment like the Klang Valley-Kuala Lumpur with over 100 shopping plazas. Getting knowledge of the elements attracting consumers to visit a shopping mall and make purchases is of greatest importance in order to achieve high profit return and increase economic growth and development of a nation. The objective of this research paper is to study the factors influencing the consumers’ buying behaviours in the shopping malls. The environmental related factors (building structure, atmosphere, sounds and music and fragrance and smell), services related factors (personal services, price, advertising and promotion), administrative related factors (tenant mix, anchor tenant, entertainments) as well as transportation and location related factors (parking, location, accessibility) were identified as independent variables and consumer’s buying behaviour within the malls as a dependent variable. A research framework was developed based on a thorough literature review. There were 200 responses collected from consumers in four shopping malls in Klang Valley-Kuala Lumpur area. Correlation and multiple regression analyses were carried out using the SPSS software package to obtain the results. The results of this research indicate that environmental, transportation and location related factors have significant impact on consumers’ buying behaviours in the shopping malls. The results congruent with previous studies by Brengman et al. (2012) and Grimmer et al. (2016) that indicated that environmental related factors have positive effects on consumers’ purchase behaviours. Additionally, this study also found that transportation and location related factors have significant relationship with consumers’ purchase behaviours as mentioned by Saber et al. (2017) and Samiran et al. (2015). The findings can be adopted by the shopping malls’ managers to improve overall shopping malls’ performance as well as by mall developers to evaluate the mall site’s location and construction designs. For academicians, this study could be used as a ground work for further exploration of the possibilities to influence consumers’ purchase behaviours through different marketing strategies to increase sales and profits.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".