What makes neighbourhood-level commercial centres attractive for neighbourhood residents?

ABSTRACT

Neighbourhood commercial centres (NCC) are dynamic hubs that provide essential goods, promote community bonds and drive economic growth. Despite numerous studies on shopping behaviour, there remains a notable gap in understanding how transportation and shopping centre-related factors collectively shape the attractiveness of the NCCs, especially in developing countries like India. This research aims to understand why some NCCs outperform others in terms of attractiveness. This study proposes the neighbourhood commercial centre attractiveness index (NCCAI), which is developed by collecting 455 samples from 13 NCCs with the help of a primary survey to understand how different factors influence the attractiveness of NCCs in the Indian planned city context. The study used principal component analysis (PCA) and identified five major components that significantly contribute to NCC’s attractiveness. Further, the weightage of the components was determined through two distinct approaches: one based on PCA results and the other utilising the analytic hierarchy process (AHP) with expert opinion data. Finally, the more reliable weights from these two methodologies were selected using analysis of covariance (ANCOVA). The NCCAI can help urban planners, store owners and retail managers, providing data-driven insights to enhance overall NCC attractiveness. The decomposable nature of NCCAI allows for component score breakdown, enabling planners and policymakers to allocate resources strategically.

KEYWORDS:

• Commercial centre attractiveness
• neighbourhood sustainability
• neighbourhood commercial centre
• brick-and-mortar stores
• ANCOVA
• expert opinion survey
• PCA

ACKNOWLEDGEMENTS

This work is part of ongoing PhD research funded by the Ministry of Education (MoE), Government of India.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 To ensure dataset robustness and address errors, unengaged responses and outliers, we aimed to collect 20% more samples than the calculated size, targeting a minimum of 384 samples post-cleaning. This led to a goal of 384 × 1.2 = 461 samples. From each of the 13 NCCs, 461 ÷ 13 = 35 samples were collected through systematic random sampling. This approach ensured a representative and evenly distributed collection over the 14-hour operational period, resulting in an average sampling interval of every 14 × 60/ 35 = 24 min.

2 Items 17 and 19 are almost equally loaded in components 1 and 5. As these components are more related to the customers, these are considered under customer component.

Additional information

Funding

This work was supported by Ministry of Education, India: [Grant Number Nil].