Planning for resilient central-city shopping districts in the post-Covid era: an explanatory case study of the Hoddle Grid in Melbourne

This research explores how central-city shopping districts could be transformed to support a post-Covid lifestyle, where people re-embrace community, local streets and walking while relying more on online shopping. By reviewing metropolitan/city development plans since the 1980s and mapping changes of retail provisions, urban environment and pedestrian movements in Melbourne's Hoddle Grid in the twenty-first century, this paper shows that planning policies focusing on people-centred experiences in the central-city shopping district helped to improve retail resilience. This paper thus adds insights to understanding the relentless retail landscape changes and has implications for central-city retail planning in the post-Covid era. © 2022 The Author(s). Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved.

Multi-Channel and Omni-Channel Retailing in the Scientific Literature: A Text Mining Approach

Electronic commerce appeared as a new way of managing businesses in the digital era. However, it has also been accelerated by the recent pandemic situation. Retailers had to find new strategies of reaching customers in the online environment. Thus, concepts such as multi-channel and omni-channel retailing have gained the attention of both retailers and researchers in this field. This paper aims at using a text-mining approach in order to reveal the researchers focus on this theme in a period that also precedes and covers the COVID-19 pandemic. The research methodology follows five steps that are necessary in order to obtain a relevant collection of documents that will further provide the content to be analyzed. These steps refer to: (1) Creating the database of documents for analysis purposes;(2) identifying geographic areas for separating the collection's documents;(3) framing a thematic dictionary of descriptors;(4) exploring the text using text mining approach;and (5) correspondence analysis. The discussion of the main findings is constructed starting with the geographic and the temporal distribution of documents and the design of a thematic dictionary of descriptors. Then, exploring the content of the documents provides information on the frequency of descriptors and reveals clusters of descriptors along with a link analysis. All of them are presented separately on geographic regions. Finally, the correspondence analysis of descriptors versus years provides the proximity maps and reveals the preferred topics and less approached themes. Among the main findings, one can highlight: (1) The greatest contributor in terms of documents related to the theme of interest is the United States;(2) a higher number of connections (and stronger) among descriptors for America as compared to the other two regions;(3) some categories of descriptors are specific to a particular year, which means that there are different themes under the researchers lens depending on the period;(4) the most frequently used descriptors are included in the following categories from the dictionary: Online retail environment and Consumer behavior, regardless of the region. In the end of this paper, research limitations and guidelines for future research are elaborated. © 2022 by the authors.

When Offline Stores Reduce Online Returns

Among the dark sides of contemporary multi-channel retailing are the vast amounts of product returns, especially in the online channel. High product returns not only put pressure on the retailers’ profitability, but also come at high societal and environmental costs. A central question then is whether multi-channel retailers can use their offline stores to help reduce product returns in the online channel without harming online sales. In an empirical study, we address this issue using data from a large Dutch shoe retailer. We develop a novel spatial model to estimate the influence of proximate retail stores on customers’ online shopping behavior, while controlling for spatial and customer heterogeneity. Results demonstrate that an increased offline channel presence indeed reduces online returns, depending on the product’s risk profile, without significantly lowering online sales. Offline stores can thus be an effective and appealing way for retailers to mitigate the negative impact of online shopping related to product returns.

The impact of fear on the effectiveness of customer empowerment-messages in online retailing

We explore the effectiveness of empowerment-messages in two contexts of fear caused by an existential threat: COVID-19 pandemic and economic fear. We show in a field experiment that an empowerment-message, which increased sales of an online retailer by 13% before COVID-19 lockdowns when fear was low, does not have the same effect post-COVID-19 lockdowns when fear is high. Three follow-up experiments show that when fear is low, an empowerment-message makes individuals feel responsible and increases purchase. When fear is high, an empowerment-message does not make individuals feel responsible and does not improve purchase. We find that personal control that is generally desired, does not resonate with individuals in the context of fear caused by an existential threat. This implies that marketers should reconsider their use of marketing efforts that empower consumers in high fear contexts. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

A Hybrid Machine Learning Approach for Customer Segmentation Using RFM Analysis

Due to COVID-19 situation, online retailing (electronic retailing) for purchasing goods has recently increased which leads to the need of customer segmentation. Customer segmentation is done based on customers’ past purchase behavior and then divide them into different categories, i.e., loyal customer, potential customer, new customer, customer needs attention, customers require activation. This paper uses recency, frequency, monetary value (RFM) analysis and K-means clustering technique for grouping the customers. Further to enhance the efficiency of segmentation, a decision tree is used to create nested splitting (based on Gini index) inside the each cluster. The implementation of proposed hybrid approach is showing promising results for customer segmentation to take better management decisions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 pandemic and behaviour of Indian consumers: a sustainable transition

In this paper, we offer some preliminary examinations of how COVID-19 pandemic affects Indian consumers' behaviour. In this paper, data is collected from various Indian consumers from different cities. 300 consumers are randomly selected to fill the questionnaire. (76 responses are from a semi-urban city in Uttar Pradesh, Aligarh, 70 were from Delhi/NCR, 100 were from Hyderabad and 54 were from Bangalore). We argue that COVID-19 pandemic offers great opportunities for businesses. We also discuss some potential directions of how consumer sustainable decision making has been shifted due to this pandemic. The results show that Indian consumers' interests have been shifted towards online retailing, online payments, sustainable food habits, and a sustainable lifestyle. This study will help the businesses as well the academicians in determining what consumers are preferring during this COVID-19 pandemic. This is the first study conducted in India that addressed the consumers' behaviours at the best knowledge of the authors.

The Boosting Effect of Online Retailing on Household Consumption during Covid-19

The outbreak of Covid-19 has posed severe negative impact on household consumption. This paper investigates the boosting effect of online retailing on household consumption during the epidemic period. Based on the data of Anhui Province in China, this paper show that during the epidemic period, every 1% increase in the growth rate of online retail sales could increase the proportion of total retail sales of consumer goods above the quota in GDP by 4.27%. Therefore, we provide reliable empirical evidence of promoting consumer consumption through the development of online retail under the normalization of the epidemic situation. © 2021 ACM.

Coordinated inventory control and pricing policies for online retailers with perishable products in the presence of social learning

During the period of COVID-19, restrictive social distancing measures have stimulated retailers to increasingly turn to online selling platforms and apply social technologies in marketing even for food and grocery products. Social technologies create opportunities for online retailers to effectively facilitate word-of-mouth communication by allowing consumers to share their consumption experiences. It makes consumers’ behavior more observable to potential customers and leads to social learning. Social learning has a notable impact on purchasing decisions of potential customers, influencing the demand for the product. In this regard, this paper aims to examine the coordinated dynamic pricing and inventory control problem for a perishable product under social learning. The idea is that online retailers who sell a perishable product under Expiration Date-Based Pricing (EDBP) policy counteract the negative quality inference of this practice through social learning. To formulate the problem, a mathematical model is developed and its structural properties are analyzed for a two-period lifetime product. Furthermore, numerical analysis is conducted in a real case study to derive some managerial insights. The obtained results show that the online retailer can promote the EDBP by adopting a consumer-generated online review system. As well, to better exploit the system, the online retailer should adjust the product pricing and inventory control policies with the evolution of consumers’ ratings. Finally, the firm’s profit and waste avoidance are improved by incorporating consumers’ social learning behavior into the pricing and inventory policies.

How should local Brick-and-Mortar retailers offer delivery service in a pandemic World? Self-building Vs. O2O platform.

The Covid-19 pandemic has dramatically changed consumer purchase behavior, and the "stay-at-home order" policy has altered the operations of brick-and-mortar (B&M) retail stores. These changes have induced local B&M retailers to start online retailing with home delivery as an added option. B&M retailers can choose to offer online retailing on their own (referred to as self-building mode) or via a third-party online-to-offline (O2O) platform (referred to as platform mode). This paper investigates how the interplay between capacity, pricing, and online retailing mode is affected by the absence/presence of the pandemic. We characterize the equilibrium between the B&M retailer and the O2O platform provider. We find that the impact of the "stay-at-home orders" on B&M retailers differs by the online retailing mode. Interestingly, we find that the "stay-at-home orders" does not necessarily lower the B&M retailer's profit if they engage in online retailing. Under self-building mode, the stay-at-home order leaves the B&M retailer with just the online channel. We identify the threshold delivery cost above (below) which the B&M retailer's profit is lower (higher) than before. Under the platform mode, the "stay-at-home order" alters the retailer's sales channel from dual channel to single channel, which mitigates the competition between the retailer and the O2O platform. The retailer's profit increases if it has sufficiently high capacity. Finally, we extend the model to examine the effect of a "reopening policy" with a government subsidy. We find that although the subsidy improves the B&M retailer's profitability, it may hurt the consumer surplus under some conditions. We suggest that governments take the B&M retailer's capacity and operations mode into account when designing subsidy policies.