Determinants of mall attractiveness: meta-analytical review and future directions
Bibliographic record
Abstract
The resilience of physical malls in the digital age is a testament to their adaptability and unique value proposition as entertainment and social hubs. This research aims to explore the determinants influencing mall attractiveness by conducting an exploratory meta-analysis. It focuses on four key themes: mall patronage, loyalty, experience, and attitudes toward malls, seeking to understand the critical factors driving mall patronage. The study employs an exploratory meta-analysis to systematically review and synthesize existing literature on mall attractiveness. By examining previous research, it identifies and categorizes the significant determinants impacting consumer behaviours and perceptions of malls. Key findings reveal that behavioural intentions, customer support, emotional and psychological factors, merchandise and product-related aspects, service quality, social factors, utilitarian value, and mall environment and atmospherics significantly influence mall patronage and loyalty. The study also highlights the role of socioeconomic status through a moderator analysis in influencing mall attractiveness. The research presents a Mall Attractiveness Value Framework (MAVF) to guide mall management and marketers in creating appealing and engaging environments. This study contributes to the retail literature by providing empirical insights into the dynamics of mall attractiveness amidst the increasing debate about the ‘death of the mall.’ The MAVF offers a unifying perspective on the drivers of mall attractiveness and suggests future research directions to address emerging challenges and opportunities in the retail landscape.
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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 | 0.031 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".