e-Commerce and Supply Chain Management in China
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Description
Read this article. The authors provide a broad view of decisions on supply chain management concerning e-commerce in China, considering several factors, including consumer access to the internet and telecommunication infrastructure for a given location. Two case studies are included to evaluate the models included in the article. Pay particular attention to the intersection of IT and non-IT considerations for firm supply-chain management strategies.
Abstract
E-commerce is experiencing strong global growth, and leading market is nowadays that of China, whether it is evaluated from the perspective of domestic market size or cross-border volumes. In this research work further understanding and knowledge is built from Chinese market using general choice algorithm (GCA) and two real-life case studies. The outcome of GCA model shows the list of the most preferable locations for e-commerce enterprise development. Findings of this study are also compared to two case studies of e-commerce entrance to Chinese market, where one of these two is operating in an omnichannel environment. Market entrance in these two was implemented through very simplified and centralized distribution structure, and using location of Shanghai. Actual operations are either outsourced or in-house produced. Cash on delivery (COD) is still the preferred payment method in e-commerce order fulfilment. One factor being rather important in case companies was distribution prices, where courier services all over the country play an important role. For logistics in general, and sea ports, research reveals that currently dominating areas of the Pearl and Yangtze River Delta face the challenge to some extent from other places of China as favourable locations for e-commerce and consumption are also at north and western parts.
Source: Panova, Y., Tan, A., Hilmola, OP. et al. , https://jshippingandtrade.springeropen.com/articles/10.1186/s41072-019-0045-6#Tab1
This work is licensed under a Creative Commons Attribution 4.0 License.
Introduction
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.
Theoretical background
Relevance of international trade models and transport cost theories
E-commerce
is one of the prospering and competitive spheres of businesses in China. Jiang and Prater correctly estimated that e-commerce
adaptation would take time from Chinese logistics system and
infrastructure, however, once done, it would change significantly this
country. The long-term expectations for this digital market remain high
not only within one country, but across the global market, where a full
potential is far from being realized, especially in the sphere of
cross-border trade. Absolute e-commerce cross-border trade was estimated
to be 6.66 trillion yuan (around one trillion USD) in 2016, and this
mostly to export direction. Based on the statistics of
China's Custom, absolute e-commerce cross-border trade is 8.06 Trillion
yuan in 2017 (approximately 1.2 trillion USD). In the first half of
2018, absolute e-commerce cross-border trade reaches 4.5 trillion yuan
(almost 0.67 trillion USD; 100ec.cn 2019). One of the driving factors of
its growth is in the development of platform-based services, and particular, shopping platforms of Alibaba Group, such as
Aliexpress, Pandao, Joom, Wish that are used in Russia. For some other
foreign markets is used Lazada in Vietnam, Indonesia, Malaysia,
Singapore and the Philippines, as well as widespread growth of mobile
users contributing to m-commerce evolvement.
The current study
looks into the invisible part of the online trade, such as logistics,
purchasing and distribution practices, as well as initial manufacturing
phases that represent the offline side of omnichannel shoppers and
should be considered from an integral point of view.
Before any goods (tangible or intangible) appear on the company's
websites and purchased by consumers, they have to be produced, undergo
the value-added operations and then delivered to the required locations.
Forward-thinking companies may be aware of this need for simultaneous
developments, but do not know where to start and make the right
decisions on the online business development that drive margin and
profitability growth in both channels.
One of the sources for
gaining financial benefits is hidden in the economic concept, which
dates back to Smith. In particular, the economies of scale theory
can be applied to a variety of organizational and business situations,
because when the economy of scale is realized, the economic growth can
be achieved. According to the theory, when average costs starts falling
as output increases, then economies of scale are occurring. For doing
so, it is possible to purchase inputs at a lower per-unit cost when they
are purchased in large quantities, or use other sources of economies.
For example, in wholesale and retail distribution, the increase of the
speed of operations, such as order fulfilment, can lower the overall
costs of doing business. Continuing notions from the landmark book of A.
Smith 'The Wealth of Nations', where transport supports the
trade in goods on the national level, the article takes logistics and
trade perspective under the focus.
Logistics is considered as
indispensable part of studies on location of e-commerce enterprises. For example, the
research on dual-channel pricing under deterministic demand discusses
the various logistical determinants of price. Authors,
in turn, apply location-based pricing of a food retail company taking
into account spatial distribution of customers on logistics costs,
customers' channel preference and service providers' pricing. Li et al. studied the phenomenon of returns in e-commerce through the
location-inventory problem, which was solved by lagrangian relaxation
combined with ant colony algorithm (LRCAC). XiaoYan et al. also
developed reverse logistics network model, but under the assumption of
uncertain demand and return. Other recent studies likewise focus on logistics-oriented decisions for online
shopping, providing location orientation of E-shops and joint
distribution center by a new hybrid fuzzy multiple-criteria
decision-making methods.
Basic approaches for the analysis of international e-commerce
Apart from logistics and supply chain theories, the feasibility studies on the e-commerce enterprise location in China include other well-tested approaches and methods of scientific analysis. Due to the fact that omnichannel enterprises in cross-border trade are represented by large ecosystems processing of goods in international trade, the explanation of the arrangement of their multi-functional structure must be found additionally in the theories of trade and transport cost theories (Fig. 1). Specifically, the figure schematically shows the relationship of the basic approaches that were taken into consideration to justify the Chinese provinces advantageous for omnichannel shoppers, with the allowance for logistics and trade perspective of cross-border trade.
Fig. 1 Theories advocating for omnichannel shopper's development
![](https://learn.saylor.org/pluginfile.php/5619169/mod_book/chapter/50156/01.jpeg)
Despite
the fact that some of the theories could be called by the authors as
approaches or simple models, they are still fundamental in the
international business. For the development of trade theory, first and foremost,
Smith made significant contribution. Adam Smith argued that
countries should specialize in production of goods for which they have
absolute advantage (e.g. French in production of wine). Depending on
countries absolute advantages in producing specific types of products,
it is possible to find out what products are profitable to export and
what is useful to import. On the other hand, countries should import
products, which can be made with lower cost in the partner country.
Specifically, David Ricardo's theory of comparative advantage took one
step further from A. Smith's theory, suggesting in his book 'Principles
of Political Economy' that countries should specialize in the
production of those goods they produce most efficiently and buy goods
they produce less efficiently from other countries. These theories
include many unrealistic assumptions and naturally have a lot of
limitations that make them suitable mainly for explaining trade flows
between the nations.
Unlike Ricardo's theory, the Heckscher-Ohlin
argues that the pattern of international trade is determined by
differences in factor endowments (like land, labour, and capital),
rather than differences in productivity. Thus, Swedish economists Eli
Heckscher (in 1919) and Bertin Ohlin (in 1933) put forward a different
explanation of comparative advantage. Another
famous Harvard strategy professor, Michael Porter, has also written
extensively on international trade. Porter theorizes that four broad
attributes (factor endownments; demand conditions; related and
supporting industries; firm strategy, structure, and rivalry),
constituting a diamond, bring the creation of competitive advantage. In the framework
of Porter' work "National Demand of Competitive advantage", Chen and
Ning evaluated China's progress towards building electronic
commerce.
It should be noted that these international trade
theories, at the same time, have important implications for business
practice (location, first-mover, and government policy implications).
Specifically, many economists believe and stress in their classical
trade theories (Smith, Ricardo, and Heckscher-Ohlin) that unrestricted
free trade between nations will raise the economic welfare of countries,
which participate in a free trade system. In light of current
governmental policy, e.g. tariffs, subsidiaries, import quotas, export
restraints, local content requirements, administrative policies, and
antidumping duties, which frequently intervene and influences the
international trade, the attention should be drawn to benefits of the
free trade explained by the theories (higher level of domestic
consumption, more efficient utilization of resources that stimulate
growth and creation of wealth of nations). Still, the critics, for
example from Paul Samuelson, show that free trade has historically
benefited rich countries, and, of course,
the political reality of international trade is beyond from the books'
models and approaches.
Moreover, in the works of authors on
international trade the attention to transport costs and related
barriers is scarce. Meanwhile, transport significantly determines the
trade in goods and services, therefore, the location of e-commerce
enterprise was considered with the accessibility to the transport
infrastructure and with the allowance for transport costs. Economic
analysis of the transport sector is elaborated in the works of Oliver E.
Williamson and Peter Nijkamp, theoretical and empirical studies of
transport costs were provided by Paul A. Samuelson, D. Hummels, P-P.
Combes and M. Lafourcade, while Russian school in the sphere of
transport and logistics is represented by K.V. Kholopov, L.B. Mirotin,
E. L. Limonov, E.G. Efimova, N.A. Zhuravleva, and A.S. Balalaev.
Further on, going down from the national
level to the regional, the omnichannel ecosystem was regarded as a
network of various companies, referring to logistics and supply
management theory. The question of the formation of the scientific base
of the integrated supply chain management is reflected in many works of
B. A. Anikin, V. V. Dybskaya, V. S. Lukinskiy, L. B. Mirotin, Yu. M.
Nerush, O. D. Protsenko, Yu. I. Ryzhikov, V. I. Sergeev, S. A. Uvarov
and others. The theories of logistics and supply
chain management are correlated with the 40 theories of marketing,
management and organization theory.
The scientific
basis for the management of material, information, financial, service
and personnel flows is, first of all, is grounded on models and methods
of the theory of logistics, management and marketing: transport models; network models; deterministic and probabilistic
models of dynamic programming; deterministic and probabilistic models
of inventory management; Markov decision-making processes; Queuing
systems; theory of games and decision-making, respectively, in terms of
certainty, risk, uncertainty; simulation modeling; method of dynamics of
averages; forecasting methods.
Location problem and multi-criteria decision-making methods
To
define the advantageous location of the enterprise in the sphere of
e-commerce cross-border trade, depending on the type or complexity of
location problem (single facility location problem, multi-facility
allocation or location-allocation problem)
different methods can be applied (e.g. linear, non-linear programming,
simplex algorithm, lagrangian relaxation, branch & cut methods,
branch and bound, local beam search, tabu search, artificial neural
network, expert systems, fuzzy control, generic algorithms, multi-agent
systems, and so on). In the current study, the general
choice algorithm is applied, which was for the first time proposed by
Lukinskiy et al. and further used by Lukinskiy and Lukinskiy for the selection of intermediaries in the supply chain. In the
earlier publication, Lukinskiy and Katkova provided the
comparative evaluation of the analytic hierarchy process (AHP),
point-rating assessment method (PRA) and proposed general
intermediaries' choice algorithm (ICA).
According to the authors, the ICA
method is more difficult than the PRA one, but its objectivity is
higher. At the same time, the ICA method produces almost the same
evaluations as the AHP method does. In spite of AHP method popularity, it is often criticized because of a
series of drawbacks. It allows some
small inconsistency in augments because human is not always consistent,
therefore, there is an inability to adequately handle the inherent
uncertainty with the AHP method. Moreover, the AHP method possesses an
advantage only when there is no any quantitative (tangible) information
except for the experts' evaluations. In the below analyses, the tangible
information was collected about all relative factors for the choice of
alternatives.
The choice was made among 31 Chinese provinces
based on the 15 factors that were partly mentioned in the similar
studies: (1) Number of cross-border
e-commerce zones, (2) Access to the sea, (3) Number of postal offices,
(4) Number of outlets for express services, (5) Length of postal routes,
(6) Retail purchase value, (7) Retail sales value, (8) Retail stock
value, (9) Number of corporate enterprises of retail trade, (10) Number
of websites, (11) Number of Internet users, (12) Broadband subscribers
port of Internet, (13) Labour productivity, (14) Wages level, and (15)
Unemployment rate.
For multi-criteria decision-making, the
general choice algorithm (GCA) was used to rank the most advantageous
regions for the development of e-commerce enterprise. GCA, as AHP, is
grounded on the pairwise comparison. In a pairwise comparison, two
alternatives are compared according to criterion and one is preferred.
The objective of AHP is to choose the best alternative. The decision
maker selects the alternative that best meets the decision criteria. As a
rule, the general mathematical process includes several steps. In the
first step, it is required to mathematically determine preferences (e.g.
for the site) with respect to each criterion. In the second step,
mathematically identify preferences for criteria (rank order of
importance). Then, combine these two sets of preferences to
mathematically derive the composite score for each site. Finally, select
the site with the highest score.
According to GCA, at the
beginning, the criteria should be divided into three groups:
quantitative, qualitative and relay (or 'killer-evaluation'). Then, the
pairwise comparison is used to rank the criteria. Quantitative data
processing is carried out by the qualimetry methods, and to obtain
quality criterion values authors suggested to use Harrington
desirability function. The calculation of
the integral estimates is a sum of qualitative and quantitative
criteria evaluation. In the current research, all data were of the
quantitative nature, which again justifies the use of GCA, because AHP
method 'possesses substantial advantages without having any quantitative
(tangible) information except for the experts' evaluations'.
GCA and AHP both utilize pairwise
comparison. In this regard, GCA is similar to AHP. Pairwise comparison
was carried out based on the mathematical steps. Firstly, a pairwise
comparison matrix for each decision alternative for each criterion was
developed. Afterwards, the synthesization was done in several sub-steps.
That is the row value of the pairwise comparison matrix was summed up
in the last column. Next, the sum of the last column was found.
Afterwards, each value in last column was divided by its column sum.
Finally, the value in each row of normalized matrices was averaged, and
the sum of the row averages in the normalized matrix equaled to 1. The
row average values represented the preference vector. Preference vectors
for other criteria are computed similarly, resulting in the preference
matrix (that is step 1 in AHP).
As a result, the final criteria
preference matrix was used for the overall ranking for the decision
alternatives, assuming the equal importance of the criteria. So, with
GCA, preferences for the site with respect to each criterion were
determined mathematically. GCA did not utilize preference scale, which
assigns numerical values (as a rule, from 1 to 9) to different levels of
importance. GCA did not resort to preference scale for pairwise
comparison because quantitative (tangible) information was available for
each criterion. In conclusion, it is worth to mention that the research
was partly done in the Henan Key Laboratory for Big Data Processing
& Analytics of Electronic Commerce, which has an affiliation to
Luoyang Normal University.
E-commerce enterprise development in the prominent regions of China
According
to Digital analytics firm eMarketer, an improving
export business, higher employment and wages as well as rising
e-commerce and cross-border sales across Asia-Pacific, North America and
Western Europe were all factors in driving retail spending. Thus, the
location of omnichannel shoppers is determined by multi-criteria that
support online and offline trade development. The factors for such
decisions are mainly related to the economic benefits of both
enterprises and mentioned in earlier research (Panova and Hilmola,
2018): (1) National economic; (2) Industrial; (3) Logistical; (4)
Economical; and (5) Regulative.
Many sub-factors influence the
location decision. Their relevance depends on the scope of the location
problems (whether it is viewed from international, national, statewide
or communitywide level). From the
international level, factors such as the access to the natural
resources, provision of labour forces, foreign exchange rate, business
climate, components of the multiplier effect, inflation level, duties
and taxes are essential. If the scope of location problem is narrower,
then the factors like property tax incentive, government regulations,
transport tariffs, the selling price of goods, and business climate on
local basis play a role.
Authors stress that the development of technical infrastructure and
regulatory frameworks stimulate e-commerce growth.
The research
of critical e-commerce factors in France by Colla and Lapoule
revealed that an integrated multichannel approach for online business
requires more differentiated logistical system and improved customer
relations marketing. The number of internet users in less developed
provinces was considered as a constraint for e-commerce growth. Meanwhile, Internet usage is beginning to increase outside
of the wealthy and educated urban elite as more people go online in
China. The overall trend of the expected growth of middle- and
upper-income categories to some 190 million by 2020 from 17 million in
2010 makes the whole market attractive for foreign direct investments.
Unlike a few decades ago when low-cost production attracted companies to
enter the market, nowadays, the consumption forecast opens up new
opportunities due to triple growth of Chinese economy during 2000 to
2010. So as to identify the most attractive region
for the investors in China, 15 factors were chosen (Fig. 2) for the
general choice algorithm.
Fig. 2 Factors taken into general choice algorithm
![](https://learn.saylor.org/pluginfile.php/5619169/mod_book/chapter/50157/2.jpeg)
In
support of e-commerce enterprise location in China, governmental
regulations trigger the business activity at local level in many
regions. In particular, the government of China develops comprehensive
cross-border e-commerce zones. These zones are designated exclusively
for the development of cross-border e-commerce industry, as they target
preferential tax policies and streamlined customs clearance procedures. Such zones have been developed in three stages,
starting from zone Hangzhou, home to the e-commerce giant Alibaba that
was established in 2015 (first phase). The latest, third phase, which
was announced recently, in July 2018 foresees the creation of the
additional 22 zones). Thus, according to Acolink.com, in China, 35 cross-border e-commerce zones will be developed.
Their location in regard to 22 Chinese provinces, as well as to five
Autonomous Regions (Inner Mongolia, Guangxi, Tibet, Ningxia, and
Xinjiang) and four Chinese municipalities (Beijing, Shanghai, Tianjin,
and Chongqing), which are under the direct administration of central
government, has been summarized in Table 1. Additionally, at this table,
the "E-commerce development plan" in Xinjiang is depicted. According to
Hongfei, this plan was designed to facilitate exports to the
Middle East and East Asia. Hongfei also noted that Guangxi
Province promoted cooperation between China and Vietnam through
E-commerce trade and cooperation. Heilongjiang Province listed the
Suifenhe border economic cooperation zone as a base of cross-border
e-commerce development and focused on opening cross-border trade with
Russia. Recent news also mentioned about the creation of free trade zone
in Luoyang in addition to already established zone in Zhengzhou.
Table 1 Provinces of China with cross-border comprehensive e-commerce zones (number of zones in each province)
Province | Administrative center | Number of Zones (*E-commerce development plan) | Row average values from normalized matrix |
---|---|---|---|
Anhui | Hefei | 1 | 0.027 |
Fujian | Fuzhou | 1 | 0.027 |
Gansu | Lanzhou | 1 | 0.027 |
Guangdong | Guangzhou | 4 | 0.108 |
Guizhou | Guiyang | 1 | 0.027 |
Hainan | Haikou | 1 | 0.027 |
Hebei | Shijiazhuang | 1 | 0.027 |
Heilongjiang | Harbin | 1 | 0.027 |
Henan | Zhengzhou | 1 | 0.054 |
Hubei | Wuhan | 1 | 0.027 |
Hunan | Changsha | 1 | 0.027 |
Jiangsu | Nanjing | 3 | 0.081 |
Jiangxi | Nanchang | 1 | 0.027 |
Jilin | Changchun | 1 | 0.027 |
Liaoning | Shenyang | 2 | 0.054 |
Qinghai | Xining | 0 | 0.000 |
Shanxi | Taiyuan | 0 | 0.000 |
Shandong | Jinan | 2 | 0.054 |
Shaanxi | Xi’an | 1 | 0.027 |
Sichuan | Chengdu | 1 | 0.027 |
Yunnan | Kunming | 1 | 0.027 |
Zhejiang | Hangzhou | 3 | 0.081 |
Beijing | Beijing | 1 | 0.027 |
Shanghai | Shanghai | 1 | 0.027 |
Tianjin | Tianjin | 1 | 0.027 |
Chongqing | Chongqing | 1 | 0.027 |
Inner Mongolia | Hohhot | 1 | 0.027 |
Guangxi | Nanning | 1 | 0.027 |
Tibet | Lhasa | 0 | 0.000 |
Ningxia | Yinchuan | 0 | 0.000 |
Xinjiang | Urumchi | 1 | 0.027 |
The availability and closeness to these zones is essential in terms of logistics processes. Specifically, the bonded warehouse model can be used for the delivery of the products to final clients from abroad. Investors may set up a warehouse within their respective e-commerce zone. Goods will then be transported and stored temporarily within the warehouse under the Customs supervision, before they are delivered to domestic customers. In this case, the delivery time can be reduced to 5 days, compared to the direct shipping model that requires 7–30 days for delivery. The reason for long deliveries is that under the direct sale model, foreign manufacturers maintain warehouses in their home countries and send goods to customers after they have made orders online that involves a relatively more complicated customs clearance procedure. As can be seen from Table 1 and after a provided pair-wise comparison, the most preferred regions are Guangdong, Jiangsu, and Zhejiang while Qinghai and Shanxi regions are less prioritized by the criterion of e-commerce zones availability.
Apart from the regulative preferences, it is essential to take into account the potential basic conditions for the developing of the online retail trade. For these reasons, total sales and purchases value of retail trade, as well as a number of corporate enterprises of retail trade and stock of retail trade across Chinese provinces have been analyzed by the pair-wise comparison. The total purchases value refer to the total value of purchases of commodities by enterprises (establishments) from other establishments or individuals (including direct import from abroad) for the purpose of re-selling, either with or without further processing of the commodities purchased. Sales value of retail trade refers to the value of commodities sold by the establishments to other establishments and individuals. The commodities include: (1) commodities sold to urban and rural residents and social groups for their consumption; (2) commodities sold to establishments in all industries for their production and operation, including commodities sold to wholesale and retail establishments for re-selling, with or without further processing; and (3) commodities for direct export to abroad.
Province | Preference vectors for the criteria | |||
---|---|---|---|---|
Number of corporate enterprises of retail trade (Unit) | Sales value (Yuan) | Purchase value (Yuan) | Stock Value (Yuan) | |
Beijing | 0.020 | 0.063 | 0.069 | 0.057 |
Tianjin | 0.012 | 0.025 | 0.023 | 0.015 |
Hebei | 0.026 | 0.026 | 0.027 | 0.028 |
Shanxi | 0.021 | 0.000 | 0.016 | 0.022 |
Inner Mongolia | 0.014 | 0.014 | 0.013 | 0.012 |
Liaoning | 0.025 | 0.027 | 0.026 | 0.025 |
Jilin | 0.018 | 0.016 | 0.015 | 0.013 |
Heilongjiang | 0.013 | 0.014 | 0.013 | 0.013 |
Shanghai | 0.019 | 0.057 | 0.053 | 0.086 |
Jiangsu | 0.087 | 0.093 | 0.092 | 0.092 |
Zhejiang | 0.057 | 0.066 | 0.064 | 0.069 |
Anhui | 0.047 | 0.033 | 0.033 | 0.031 |
Fujian | 0.057 | 0.038 | 0.036 | 0.030 |
Jiangxi | 0.024 | 0.016 | 0.016 | 0.034 |
Shandong | 0.088 | 0.084 | 0.087 | 0.062 |
Henan | 0.080 | 0.046 | 0.045 | 0.038 |
Hubei | 0.052 | 0.060 | 0.062 | 0.058 |
Hunan | 0.048 | 0.037 | 0.036 | 0.047 |
Guangdong | 0.086 | 0.101 | 0.095 | 0.105 |
Guangxi | 0.020 | 0.014 | 0.014 | 0.019 |
Hainan | 0.002 | 0.004 | 0.004 | 0.005 |
Chongqing | 0.035 | 0.031 | 0.029 | 0.021 |
Sichuan | 0.046 | 0.047 | 0.048 | 0.035 |
Guizhou | 0.021 | 0.014 | 0.013 | 0.013 |
Yunnan | 0.023 | 0.020 | 0.020 | 0.018 |
Shaanxi | 0.034 | 0.028 | 0.028 | 0.027 |
Gansu | 0.011 | 0.010 | 0.010 | 0.008 |
Qinghai | 0.002 | 0.002 | 0.002 | 0.002 |
Tibet | 0.001 | 0.001 | 0.001 | 0.001 |
Ningxia | 0.003 | 0.003 | 0.003 | 0.003 |
Xinjiang | 0.009 | 0.008 | 0.008 | 0.011 |
- Note: In parenthesis, the units are for the initial values of data processed in pair-wise comparison
- Entries in boldface are largest values to identify highest performing regions
Province | Preference vectors for the criteria | ||
---|---|---|---|
Number of postal offices (unita) | Number of outlets for express services (unita) | Length of postal routes (kma) | |
Beijing | 0.033 | 0.004 | 0.091 |
Tianjin | 0.009 | 0.011 | 0.021 |
Hebei | 0.030 | 0.003 | 0.023 |
Shanxi | 0.028 | 0.003 | 0.015 |
Inner Mongolia | 0.019 | 0.021 | 0.040 |
Liaoning | 0.027 | 0.032 | 0.042 |
Jilin | 0.017 | 0.025 | 0.019 |
Heilongjiang | 0.022 | 0.026 | 0.023 |
Shanghai | 0.035 | 0.047 | 0.036 |
Jiangsu | 0.060 | 0.066 | 0.031 |
Zhejiang | 0.055 | 0.071 | 0.105 |
Anhui | 0.035 | 0.042 | 0.009 |
Fujian | 0.034 | 0.039 | 0.035 |
Jiangxi | 0.030 | 0.026 | 0.014 |
Shandong | 0.047 | 0.052 | 0.067 |
Henan | 0.050 | 0.045 | 0.023 |
Hubei | 0.055 | 0.055 | 0.027 |
Hunan | 0.037 | 0.042 | 0.015 |
Guangdong | 0.100 | 0.132 | 0.068 |
Guangxi | 0.028 | 0.029 | 0.038 |
Hainan | 0.008 | 0.009 | 0.014 |
Chongqing | 0.023 | 0.022 | 0.021 |
Sichuan | 0.076 | 0.071 | 0.041 |
Guizhou | 0.028 | 0.029 | 0.015 |
Yunnan | 0.029 | 0.029 | 0.042 |
Shaanxi | 0.030 | 0.026 | 0.032 |
Gansu | 0.021 | 0.024 | 0.029 |
Qinghai | 0.005 | 0.004 | 0.014 |
Tibet | 0.004 | 0.005 | 0.009 |
Ningxia | 0.006 | 0.004 | 0.008 |
Xinjiang | 0.018 | 0.006 | 0.031 |
- Notea: In parenthesis the units are for the initial values of data processed in pair-wise comparison
- Entries in boldface are largest values to identify highest performing regions
Province | Preference vectors for the criteria | ||
---|---|---|---|
Broadband subscribers port of Internet | Number of websites | Number of Internet users | |
Beijing | 0.016 | 0.155 | 0.024 |
Tianjin | 0.010 | 0.013 | 0.014 |
Hebei | 0.054 | 0.032 | 0.055 |
Shanxi | 0.025 | 0.014 | 0.028 |
Inner Mongolia | 0.014 | 0.004 | 0.018 |
Liaoning | 0.033 | 0.030 | 0.038 |
Jilin | 0.015 | 0.007 | 0.020 |
Heilongjiang | 0.019 | 0.010 | 0.026 |
Shanghai | 0.021 | 0.025 | 0.025 |
Jiangsu | 0.090 | 0.065 | 0.063 |
Zhejiang | 0.073 | 0.085 | 0.051 |
Anhui | 0.036 | 0.017 | 0.038 |
Fujian | 0.039 | 0.073 | 0.037 |
Jiangxi | 0.028 | 0.009 | 0.028 |
Shandong | 0.080 | 0.069 | 0.072 |
Henan | 0.059 | 0.051 | 0.057 |
Hubei | 0.038 | 0.026 | 0.042 |
Hunan | 0.036 | 0.018 | 0.042 |
Guangdong | 0.094 | 0.185 | 0.112 |
Guangxi | 0.027 | 0.011 | 0.031 |
Hainan | 0.006 | 0.005 | 0.007 |
Chongqing | 0.024 | 0.013 | 0.022 |
Sichuan | 0.062 | 0.050 | 0.050 |
Guizhou | 0.015 | 0.004 | 0.021 |
Yunnan | 0.022 | 0.006 | 0.026 |
Shaanxi | 0.027 | 0.015 | 0.028 |
Gansu | 0.013 | 0.003 | 0.015 |
Qinghai | 0.003 | 0.001 | 0.004 |
Tibet | 0.001 | 0.000 | 0.002 |
Ningxia | 0.004 | 0.002 | 0.005 |
Xinjiang | 0.016 | 0.003 | 0.000 |
- Entries in boldface are largest values to identify highest performing regions
Province | Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | Preference vectors for the criteria | ||
---|---|---|---|---|---|---|
Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | ||||
Beijing | 119928 | 1.4 | 324301.7 | 0.017 | 0.071 | 0.024 |
Tianjin | 86305 | 3.5 | 625275.8 | 0.024 | 0.028 | 0.047 |
Hebei | 55334 | 3.7 | 501398.5 | 0.037 | 0.027 | 0.038 |
Shanxi | 53705 | 3.5 | 303110.2 | 0.038 | 0.028 | 0.023 |
Inner Mongolia | 61067 | 3.7 | 618200.1 | 0.034 | 0.027 | 0.047 |
Liaoning | 56015 | 3.8 | 396989.6 | 0.037 | 0.026 | 0.030 |
Jilin | 56098 | 3.5 | 458693.2 | 0.037 | 0.028 | 0.035 |
Heilongjiang | 52435 | 4.2 | 362136.4 | 0.039 | 0.024 | 0.027 |
Shanghai | 119935 | 4.1 | 448861.9 | 0.017 | 0.024 | 0.034 |
Jiangsu | 71574 | 3 | 516852.2 | 0.029 | 0.033 | 0.039 |
Zhejiang | 73326 | 2.9 | 445368.4 | 0.028 | 0.034 | 0.034 |
Anhui | 59102 | 3.2 | 472055.3 | 0.035 | 0.031 | 0.036 |
Fujian | 61973 | 3.9 | 430760.9 | 0.033 | 0.026 | 0.033 |
Jiangxi | 56136 | 3.4 | 392368.5 | 0.037 | 0.029 | 0.030 |
Shandong | 62539 | 3.5 | 559660.5 | 0.033 | 0.028 | 0.042 |
Henan | 49505 | 3 | 353468.5 | 0.042 | 0.033 | 0.027 |
Hubei | 59831 | 2.4 | 454114.7 | 0.035 | 0.041 | 0.034 |
Hunan | 58241 | 4.2 | 555081.2 | 0.035 | 0.024 | 0.042 |
Guangdong | 72326 | 2.5 | 413037.1 | 0.029 | 0.040 | 0.031 |
Guangxi | 57878 | 2.9 | 456355.2 | 0.036 | 0.034 | 0.034 |
Hainan | 61663 | 2.4 | 400593 | 0.033 | 0.041 | 0.030 |
Chongqing | 65545 | 3.7 | 429679.1 | 0.032 | 0.027 | 0.032 |
Sichuan | 63926 | 4.2 | 418200.4 | 0.032 | 0.024 | 0.032 |
Guizhou | 66279 | 3.2 | 379307.2 | 0.031 | 0.031 | 0.029 |
Yunnan | 60450 | 3.6 | 352962.4 | 0.034 | 0.028 | 0.027 |
Shaanxi | 59637 | 3.3 | 379350.2 | 0.035 | 0.030 | 0.029 |
Gansu | 57575 | 2.2 | 275918.5 | 0.036 | 0.045 | 0.021 |
Qinghai | 66589 | 3.1 | 407749.2 | 0.031 | 0.032 | 0.031 |
Tibet | 103232 | 2.9 | 365411.0 | 0.020 | 0.038 | 0.028 |
Ningxia | 65570 | 3.9 | 448237.4 | 0.031 | 0.026 | 0.034 |
Xinjiang | 63739 | 2.5 | 301101.5 | 0.032 | 0.040 | 0.023 |
- Entries in boldface are largest values to identify highest performing regions
Province | Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | Preference vectors for the criteria | ||
---|---|---|---|---|---|---|
Wages level (Yuan) | Unemployment rate (%) | Labour productivity (Yuan/person) | ||||
Beijing | 119928 | 1.4 | 324301.7 | 0.017 | 0.071 | 0.024 |
Tianjin | 86305 | 3.5 | 625275.8 | 0.024 | 0.028 | 0.047 |
Hebei | 55334 | 3.7 | 501398.5 | 0.037 | 0.027 | 0.038 |
Shanxi | 53705 | 3.5 | 303110.2 | 0.038 | 0.028 | 0.023 |
Inner Mongolia | 61067 | 3.7 | 618200.1 | 0.034 | 0.027 | 0.047 |
Liaoning | 56015 | 3.8 | 396989.6 | 0.037 | 0.026 | 0.030 |
Jilin | 56098 | 3.5 | 458693.2 | 0.037 | 0.028 | 0.035 |
Heilongjiang | 52435 | 4.2 | 362136.4 | 0.039 | 0.024 | 0.027 |
Shanghai | 119935 | 4.1 | 448861.9 | 0.017 | 0.024 | 0.034 |
Jiangsu | 71574 | 3 | 516852.2 | 0.029 | 0.033 | 0.039 |
Zhejiang | 73326 | 2.9 | 445368.4 | 0.028 | 0.034 | 0.034 |
Anhui | 59102 | 3.2 | 472055.3 | 0.035 | 0.031 | 0.036 |
Fujian | 61973 | 3.9 | 430760.9 | 0.033 | 0.026 | 0.033 |
Jiangxi | 56136 | 3.4 | 392368.5 | 0.037 | 0.029 | 0.030 |
Shandong | 62539 | 3.5 | 559660.5 | 0.033 | 0.028 | 0.042 |
Henan | 49505 | 3 | 353468.5 | 0.042 | 0.033 | 0.027 |
Hubei | 59831 | 2.4 | 454114.7 | 0.035 | 0.041 | 0.034 |
Hunan | 58241 | 4.2 | 555081.2 | 0.035 | 0.024 | 0.042 |
Guangdong | 72326 | 2.5 | 413037.1 | 0.029 | 0.040 | 0.031 |
Guangxi | 57878 | 2.9 | 456355.2 | 0.036 | 0.034 | 0.034 |
Hainan | 61663 | 2.4 | 400593 | 0.033 | 0.041 | 0.030 |
Chongqing | 65545 | 3.7 | 429679.1 | 0.032 | 0.027 | 0.032 |
Sichuan | 63926 | 4.2 | 418200.4 | 0.032 | 0.024 | 0.032 |
Guizhou | 66279 | 3.2 | 379307.2 | 0.031 | 0.031 | 0.029 |
Yunnan | 60450 | 3.6 | 352962.4 | 0.034 | 0.028 | 0.027 |
Shaanxi | 59637 | 3.3 | 379350.2 | 0.035 | 0.030 | 0.029 |
Gansu | 57575 | 2.2 | 275918.5 | 0.036 | 0.045 | 0.021 |
Qinghai | 66589 | 3.1 | 407749.2 | 0.031 | 0.032 | 0.031 |
Tibet | 103232 | 2.9 | 365411.0 | 0.020 | 0.038 | 0.028 |
Ningxia | 65570 | 3.9 | 448237.4 | 0.031 | 0.026 | 0.034 |
Xinjiang | 63739 | 2.5 | 301101.5 | 0.032 | 0.040 | 0.023 |
- Entries in boldface are largest values to identify highest performing regions
Based on the general choice algorithm (GCA) we tried to locate some e-commerce companies in the list of ten preferable locations to conduct interview to understand their supply chain network and their customer distribution. Eventually, two e-commerce companies located in Shanghai were willing to take part in this research.
Two case studies
Company A is an online private sales website for
luxury lifestyle and fashion brands operating in Japan and China and
expanded to Korea in 2012 (see Table 7 concerning supply chain
information from two case studies completed). The company intends to
sell online flash products (for a limited time) at a discounted price
and a diverse range of luxury goods, which belong to five categories as
follows: past season shoes, fashion, cosmetics, accessories, and leather
goods. Company A rotates the brands it works with every week and will
work with each brand 3–4 times per year. Since they began in early 2009
in Japan, Company A has developed working relationships with 450 brands.
Table 7 Two companies operating in Chinese retail market and supply chain strategies
![](https://learn.saylor.org/pluginfile.php/5619169/mod_book/chapter/50158/41072_2019_45_Tab7_HTML.png?time=1645528325779)
Service
level is an important factor for the success of Company A and for this
reason, all customer service is managed by its own employees. Company A
has 35 dedicated customer service employees, who operate out of the
warehouse. One unique market characteristic in China is a large number
of orders that are paid for by cash on delivery (COD). For Company A,
this constitutes half of its sales. Remarkably, its rejection rate, the
rate at which customers either do not pay for the item upon delivery or
are not there to pay for the product is less than 1%. Customer service
agents who actively reach out to customers to make sure they are
informed about their purchase and delivery time. This customer-centric
approach gives people confidence in their purchase and provides the
comfort of knowing they are getting a quality product. Additionally,
Company A will cover the cost of any returned product again easing any
doubt for a customer to purchase a product.
For Company A, 40% of
its customers are based in China's tier 1 cities (Shanghai, Beijing,
Guangzhou, and Shenzhen), 35% are from tier 2 cities, and the remaining
25% are scattered in tier three and four cities. Customers living in
tier 1 cities are primarily business people with white-collar jobs and
the customers residing in the remaining tier 2, 3, and 4 cities are
entrepreneurs who are affluent, but do not have access to luxury goods.
The age of Company A's customer base ranges from 25 to 40-year-olds,
with 40% of them consisting of repeat customers. These customers
purchase on average 900 RMB per transaction. One difference has been
identified, and that relates to the average age of a customer. As
shopping retail stores age was 35 years, but the average age shopping
online was lower, 28 years old.
Company A currently operates a
centralized warehouse based in Shanghai, China. The location in Shanghai
allows it to closely monitor the quality control process and position
itself to service the majority of its customers with short lead times.
Company A has subcontracted its fulfillment operation to German-based
3PL that has been working in this market for a number of years. Third
party operator is responsible for all the receiving, picking and
packing, however, Company A has decided to keep all its customer service
in-house. Company A holds its entire inventory on consignment with 90%
of the goods coming from China and 10% being imported. Acquiring
inventory on consignment allows Company A to keep its working capital
free, manage obsolescence risk, and invest more in sales and marketing.
By sourcing the majority of its products within China, Company A is able
to keep ordering costs down and ensure the products meet customer
preferences for local style, colour and size. Because Company A does not
produce any of its own products, quality control is an important
operation for the company. It checks every inbound piece and before the
product is shipped to a customer, it ensures that the product meets the
quality standards expected by Company A's customer. This process has
proved beneficial as it achieved single-digit percentage in returns.
However, as Company A continues to grow, it looks to introduce regional
distribution centres and have suppliers ship directly to those
warehouses thereby bypassing the central DC in Shanghai. Company A's
warehouse expansion initiative will be guided by regional sales volume
and customer profiling.
Company A's goal is to deliver all its
products within 5 days and for large sales regions such as Shanghai and
Beijing, Company A offers next-day and two-day guaranteed delivery. As a
time to market is an essential component of handling a luxury service,
Company A is looking to expand its warehouse footprint to three or four
regional DC's that will be able to provide even faster time to market.
At the moment, Company A is the collecting and analyzing sales data to
decide, where and when it will open these regional distribution centres.
For distribution, it uses two courier service providers, one local and
one international, depending mostly on delivery costs (typically one
provider is able to give good prices for a particular group of cities,
but not for all).
The use of mobile technology varies in Asia and
is quite different from that of the US or Europe. For example, 25% of
Company A's business in Japan comes through mobile e-commerce. Company A
does not have available mobile e-commerce in China, but it is planning
to in upcoming years and can expect to see similar sales patterns.
As
Company A outsources its warehousing operations, the company does not
have a single integrated IT platform. Company A does manage its
e-commerce platform, allowing the company to manage its orders in real
time.
To date, Company B has operated its e-Commerce business in
Japan for 1 year and is working to launch in China as soon as possible.
Working with luxury products has afforded Company B healthy margins.
Therefore, any incremental supply chain costs the company incurs through
its e-commerce operations will unlikely overshadow the incremental
profits. The reason Company B has involved themselves in e-commerce
business is simply, because the company is growing so fast in China that
there are many other competing opportunities. Company B is opening one
store every 2 weeks in China and plans to continue this pace for 5
years. This type of growth puts a strain on building the organizational
capacity necessary for a different sales channel.
Asian markets
are all the time evolving. For example, mobile technology is taking off
in Japan and now constitutes 50% of Company B's e-commerce sales. One of
the striking differences between Japan and China is that in Japan,
promotions do not play a large role in generating sales. Conversely,
Chinese shoppers go online to find the best deal or a brand name product
at a discount. In Japan, Company B is the number one brand in the
number of bags sold and number two in value.
In general, Company B
prefers to keep its distribution and fulfillment operations in-house,
because it has a very high standard of service. From its experience
managing a 3PL in the past, Company B has learnt that it can take just
as much time to manage labour and operate the distribution and
fulfillment themselves. For these reasons, it operates under a
superstructure that will allow it to keep e-commerce distribution and
fulfillment in-house, but will look at outsourcing options for potential
fulfillment. The company has not yet completed an in-depth study of 3PL
potential partners. It plans to examine, which companies' with other
comparable brand are working with.
A unique characteristic of
e-commerce in China is that the majority of consumers prefer to pay by
COD rather than with credit cards. For products that that cost over
$400USD, this becomes a risk. Therefore, the company has to ensure that
the customer will be present and will be able to pay upon delivery.
Fortunately, Company B has not experienced these problems in Japan as
there are a number of steps a customer has to go through to complete an
online transaction. Returns rates are quite low and Company B remains
confident there will be a similar trend in China.
Another
challenge associated with COD is that a trusted courier company in
delivery and collection of cash in the various regions is critical. This
remains difficult, because there is not a single courier service that
can provide delivery to all postal codes; and the ones that reach more
remote locations do not always provide the level of service Company B
and its customers expect.
China is so vast that Company B does
not expect to reach all the tier 3 and 4 cities. Instead, the company
expects that the majority of their e-commerce business will come from
cities they already have a presence. Company B currently has two
warehouses in Shanghai, one of which is a bonded warehouse and the other
is a duty paid warehouse. Its only other warehouse is in Hong Kong
where currently, Company B runs half of its products through Hong Kong
and the other half through the Shanghai warehouse. However, volume
handled in Shanghai is growing at a much faster rate. In the medium and
long-term as e-commerce is projected to grow in China, company is
looking to couple its e-commerce inventory with that of retail and to
develop a hub-and-spoke model with regional DC's located in the North,
South, and West. This model will evolve over time.
Conclusions
In
this study, we evaluated the e-commerce enterprise location in China
with the allowance of cross-border trade and logistics perspective. The
outcome of the study can be of interest for practitioners, who work on
the convergence between online and offline shopping. The essence of the
conducted analysis was unfolded through the two research questions. The
answer to the first research question (What are the success factors of
e-commerce zones in China?) was gained by the systematization of
factors, taking into account international and national scopes of the
problem. In particular, from the international level, factors such as
political stability, access to natural resources, unemployment rate,
foreign exchange rate, business climate, inflation level, duties and
taxies are essential. If the scope of location problem is narrower, then
the factors like number of cross-border e-commerce zones, wages level,
transport tariffs, labour productivity, number of corporate enterprises
of retail trade, and business climate on local basis play a role.
The
second research question (Which locations are favourable for
omnichannel shoppers' development?) we narrowed down the decision
analysis to 31 Chinese provinces. With the limited number of decision
criteria (15 factors), the answer was found by of multi-criteria
evaluation approach. The outcome shows that the most preferable
locations are Guangdong, Jiangsu, Zhejiang, Shandong, Beijing, Henan,
Fujian, Shanghai, Sichuan, and Hubei (Table 6). On the whole, the
findings of our study may guide the investment funds of companies, who
consider China as one of the several places, in which to invest. Also,
the results of the study can strengthen the governmental support of the
regions that are potentially favourable for e-commerce cross-border
development with neighbouring countries. Therefore, from a practical
point of view, our research facilitates the e-commerce start-ups,
especially of those businesses that regard China e-commerce sphere as
the top investment destination. Interestingly, most highly performing
regions in pairwise comparison of GCA were very seldom showing low wages
or exceptional labour productivity. However, highest performing
locations were those, where Internet development was high, logistics
infrastructure was advancing and supporting parcel deliveries, and
retail trade was already showing its significance.
Two case
studies completed for China- related retail operations, where Company A
was already having e-commerce operations in this vast consumer market,
while Company B was still relying on brick-and-mortar approach, while
being launching e-commerce service in the near future. Both of these
companies had chosen Shanghai as their main warehousing and logistics
hub. However, as case studies indicated, this position is not static.
Company A was planning to expand its warehousing network to other
cities, as serving a larger number of cities requires this. Company B,
in turn, has decided not to serve all cities, and was satisfied to
current structure. Both cases illustrate the specifics of the Chinese
retail market – both companies, A & B, were still selling a lot of
items with cash on delivery payment method, while Company B had found
the demands and awareness of Chinese consumer. Cases also illustrate
that producing services in-house is still very common (company A having
customer service, and company B even warehousing and related functions),
even if this is not necessarily lowest cost approach. Both companies
were also forced to use number of different companies in parcel
deliveries to reach different destinations in China.
Both
empirical data parts of this research highlighted that old the Pearl and
Yangtze River Delta centric warehousing model is about to change. These
regions will of course hold important role in the future too, but
further economic growth, growing cities in other parts of China, and
favourable conditions in numerous different locations for e-commerce are
drivers of change. It could be said based on this study that more
northern and central parts of China will gain larger role in the future.
This means that sea ports of this new emerging e-commerce regions shall
gain some market share from currently dominating sea ports. This same
finding is found in domestic logistics optimization models for lower CO2
emissions in China, where container sea ports play key role in import
and export activity. Another general finding arising
from empirical part is the significance of information technology as
well as supply chain management on overall performance, and location
selection of e-commerce and omnichannel environment. In analyzed market
it seemed to be the situation that low labour cost was not the main
emphasis, but that of getting foothold in the growing markets, and
assuring revenue growth. This is important finding for the e-commerce
and omnichannel research as compared to earlier literature, but also for practice.
As further research, we
would like to continue research work on Chinese omnichannel and
e-commerce branch. One of the interesting avenues to follow would be the
expansion of Chinese e-commerce companies to Russia and other near-by
Eurasian countries. This includes also Europe, where expansion and
deliveries could be supported with railway landbridges (Jiang et al.
2018; actually, railway based prompt delivery is already available in
Alibaba offerings). Another avenue to follow is the expansion of retail
operations to nearly 300 Chinese cities, which have a population of at
least half a million. It is typically forgotten fact that China is
having 15 megacities, but the amount of metro area cities is an overall
277.