E-Grocery Retailing Mobile Application: Discerning Determinants of Repatronage Intentions in an Emerging Economy

Singh, Pratibha; Gupta, Manali; Kumar, Amresh; Sikdar, Pallab; Sinha, Neena

doi:10.1080/10447318.2021.1912471

Cited by 10 publications

(10 citation statements)

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“…Retailers mainly provide SST to reduce costs and improve customer experience [2]. e most commonly cited advantages of SST are convenience and speed [1]. e users' negative experience in the process of use may mainly include the experience of forced use of self-checkout, the closure of self-checkout terminals at certain times of the day (such as night), and the fact that these terminals happen to be slow when queuing [1].…”

Section: Related Workmentioning

confidence: 99%

“…e most commonly cited advantages of SST are convenience and speed [1]. e users' negative experience in the process of use may mainly include the experience of forced use of self-checkout, the closure of self-checkout terminals at certain times of the day (such as night), and the fact that these terminals happen to be slow when queuing [1]. Studies have shown that SST customers tend to use selfcheckout terminals to check out smaller baskets and may avoid items (such as fruits and vegetables) that require additional steps in the checkout process [15,16].…”

Section: Related Workmentioning

confidence: 99%

“…e retail sector has been playing a dominant role in economic activities, and it is a strategic industry of the national economy in China. China's total volume of retail sales increased from 9.4 trillion in 2012 to 21 trillion in 2016 [1]. e supermarket dominates the retail sector [2], and the chain operation development mode is widely adopted.…”

Section: Introductionmentioning

confidence: 99%

“…SST has been proven to be an effective way to increase consumer satisfaction and convenience at checkout by avoiding the long queues associated with traditional checkout methods. [1,2] However, SST using barcode scanning and self-service payment devices is mainly used in large shopping centers [12] and has a limited reach. In these cases, the shopping basket is transferred to a self-checkout terminal, where the actual payment and delivery of the merchandise occurs, but in practical terms, the checkout capacity remains limited during peak periods [1].…”

Section: Introductionmentioning

confidence: 99%

“…[1,2] However, SST using barcode scanning and self-service payment devices is mainly used in large shopping centers [12] and has a limited reach. In these cases, the shopping basket is transferred to a self-checkout terminal, where the actual payment and delivery of the merchandise occurs, but in practical terms, the checkout capacity remains limited during peak periods [1]. Current self-checkout solutions can be very appropriate for the digital transformation of general grocery stores, and they are already well adopted in these stores [12].…”

Section: Introductionmentioning

confidence: 99%

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Applying Deep Learning-Based Personalized Item Recommendation for Mobile Service in Retailor Industry

Xiao

Zhou

Lü

et al. 2022

Mobile Information Systems

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Various kinds of mobile services allow integrating terminal customers as important coproducers into the whole retailer’s business processes. People have enjoyed increasing popularity in the past years since they allow saving costs and increasing satisfaction. However, in some retail settings, as the technology relies on retailers providing terminals, it does not yet fully utilize the possibilities provided by mobile service, which until recently have mostly served as shopping aids. Recommendation systems can provide accurate recommendation services to users, especially in the field of e-commerce. In this study, a mobile retail terminal, Kkbox, leverages deep learning-based recommendation and self-service technologies to provide an express and personalized self-checkout retail environment without the engagement of storekeepers and cashiers. An attention-based mechanism for product personalization recommendation model is adopted, and it models the intrinsic relationship between users’ historical interactions with products through a multilayer self-attentive network and then feeds the output of the multilayer self-attentive network into a GRU network with attention scores to model the evolution of users’ interests. We analyze the performance of the product recommendation module based on user data from multiple perspectives, such as purchase frequency, purchase time, and product category. In the comparison experiments with some traditional recommendation methods, the recommendation accuracy of the model used in this study achieves better results. Besides, it significantly reduces the labor cost and provides enough flexibility. The time performance of app users is independent of store rush. The time for a transaction is significantly lower for app users than the regular shoppers during peak periods. The Kkbox has been deployed in several communities in Taizhou, China, to provide fast and convenient mobile retail services to residents.

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Section: Related Workmentioning

confidence: 99%