e-Commerce and Supply Chain Management in China

China, being the world's largest e-commerce market, exceeding $ 1.15 trillion, will continue significantly contribute to the growth of global retail e-commerce sales - including desktop and mobile commerce, which made up to 10.2% of total retail sales worldwide in 2017 (in 2015, this figure was 7.4%; Emarketer.com 2018). The Digital analytics firm eMarketer projects that online retail sales will more than double between 2015 and 2019 and account for more than 12% of global retail sales by 2019. Regardless that the online retail ecosystem is evolving fast, the convergence between online and offline shopping behaviour remains one of the topical issues among researchers and professionals. Particularly, the location selection is a significant problem that needs to be addressed. For example, only the right location of bonded warehouses (e.g. in cross-border comprehensive e-commerce zones) can reduce the delivery time to 5 days, compared to the direct shipping model that requires 7–30 days for delivery.

With the notion of the cycles in the market economy (manufacturing-distribution-consumption), the right evaluation of factors that favour the integrated development of offline and online channels in e-commerce is essential. Many authors studied this topic, identifying the factors that influence e-commerce development. However, research requires broader analysis of supply chain designing in e-commerce cross-border trade. This type of e-commerce takes place between companies/consumers mainly of neighbouring countries without the need of physical travel across the borders for buying/selling activities. China's market is already responsible for 40% of overall e-commerce transactions globally. A growth rate of Chinese cross-border e-commerce has been impressive, on the average being more than 30% p.a. in 2010–2016.

In the past, e-commerce saw slower growth than current levels mainly for two reasons: Shipping costs were added to order costs and there was a reluctance to divulge credit card information. However, consumer concerns waned as online sales grew rapidly, doubling in the 2011–2014 period. Two studies were conducted to understand the shift in customer tendency to "try" new ways of shopping in particular, the market for online shopping. The outcome of the studies showed a correlation between role clarity, motivation and ability of usage of online ordering. This means that customers who understand the process of online ordering and have access to the Internet may opt for this model instead of the classical brick and mortar retail model.

From a logistics point of view, however, the growing number of channels (online and offline) also increases complexity. The fulfillment process is no longer linear, because brick and mortar retailing increasingly overlaps with distance retail. In the past, supply chain management was responsible for delivering goods to a retail store with the store being the end point of the transaction. Online retailing has now placed distribution systems on the front line, since retailers need to offer a variety of options for finding, buying, and returning goods. This topical issue partly can be considered in terms of the location problem. In this regard, different methods are available (the analytic hierarchy process, linear, non-linear programming, greenfield analysis, network optimization experiments, etc.), which proved their applicability to identical problems in scholar studies.

This research work consists of two parts: (1) it contains analytical model using pairwise comparison, which is based on second-hand data arising from number of different sources and (2) case study to confirm, if the reasons from general choice algorithm (GCA) are consistent to validate the model. The considered model uses this data to build-up information concerning better cities to locate e-commerce and omnichannel warehousing and supply chain operations in China.

Yin suggests case selection based on the following criteria. A single case can serve as a critical example (1) if it forms an extreme or unique case, e.g. if not many cases are available; (2) if it forms a typical or representative case, standing as an example of a wider group of cases; (3) if it is a revelatory case, where the investigator has an opportunity to observe and analyze a phenomenon so far inaccessible to scientific investigation; (4) if it provides a longitudinal case studying two or more points in time; or (5) if it stands as a pilot in a multi-case setting. In contrast, multiple cases often use a replication logic, but can also be used to select typical cases within a certain domain. In our case, we have chosen two case studies completed in China, where two key persons were interviewed from these reported companies (rather typical case amount as compared to large-scale analysis of Hilmola, 2018). Company interaction and interviews took place during June-Sept.2017. Interviews were part of larger assignment, which was examining Asian retail market through case studies. Research regarding to cases is used here to serve the understanding of Chinese market, and it is descriptive with systems approach. Chinese retail market has become increasingly important over the years as in largest cities (so called tier 1 cities) purchasing power (especially in purchasing power parity terms) has improved to the level of old west (e.g. regional GDP in 2017 in Beijing, Shanghai and Tianjin was around 20,000 USD per person; Babones 2018). Tan et al.  research work indicated in logistics industry responses that some Chinese cities, like Shenzhen or Shanghai, could even act as regional distribution centers of Asia.

The purpose of this study is to evaluate the e-commerce enterprise location in China. In this regard, this article answers the following research questions: 1) What are the success factors of e-commerce zones in China? and 2) Which locations are favourable for omnichannel shoppers' development? To reveal the essence of the research, the study is structured as follows: In Section 2, several theories have been considered to create the framework for studying the e-commerce enterprise location in China. Section 3 provides systematization of factors that support the choice for favourable Chinese provinces, which were ranked based on general choice algorithm. After these, in Section 4, two completed case studies are analyzed together, which give perspective for theoretical second-hand data based evaluation model. In final Section 5, we conclude study, and provide avenues for further research.