Consumer Behavior Trend and its Implications to Marketing Strategies of Tmall.com in China

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Consumer Behavior Trend and its Implications to Marketing Strategies of Tmall.com in China

 

Abstract

 

This research proposal report is targeting at offering an in-depth understanding of the project paper titled “Consumer Behavior Trend and Implications to Marketing Strategies of Tmall.com in China”. With case study of Tmall.com and based in the market of China, this study would be dedicated to the research of the relationship between the changing customers behaviors and the e-marketing strategies to be adopted by the markers in mainland China. With the surge of the e-business in China, this study will be of great significance by looking into the impacts of the consumer behaviors among the online buying deals.

 

Looking back on the history of various business activities and forms, we can safely come to a conclusion that the changing of consumer behaviors always bring about and push forward the changes that are required and needed by the customers and to further enhance the customer experience. Therefore, when we play with our smart phone, we can so much certain that a new era has come as the small little smart phones bring much advancement in customer experience in shopping online. Therefore, it is necessary to study the trend of the consumer behaviors.

 

 

 

 

Table of Contents

Table of Contents…………………………………………………………………………………………….. 3

List of figures………………………………………………………………………………………………….. 8

List of tables…………………………………………………………………………………………………… 9

List of charts…………………………………………………………………………………………………. 10

  1. Chapter 1: Introduction……………………………………………………………………………. 12

1.1           The background to research………………………………………………………….. 12

1.1.1     Research Problem………………………………………………………………….. 13

1.1.2     Research Gaps………………………………………………………………………. 13

1.1.3     Research Questions……………………………………………………………….. 14

1.1.4     Research Objectives………………………………………………………………. 14

1.1.5     The Research Propositions……………………………………………………… 15

1.1.6     Unit of Analysis……………………………………………………………………. 15

1.1.7     Research Methodology………………………………………………………….. 16

1.1.7.1Research approach………………………………………………………….. 16

1.1.7.2Data collection……………………………………………………………….. 16

1.1.7.3Data analysis………………………………………………………………….. 17

1.1.8     Ethical Considerations…………………………………………………………… 17

1.1.9     Limitations…………………………………………………………………………… 17

1.1.10   Structure of the thesis……………………………………………………………. 18

1.1.11   Conclusion…………………………………………………………………………… 18

  1. Chapter 2: Literature review……………………………………………………………………… 18

2.1           Introduction……………………………………………………………………………….. 18

2.1.1     Briefs of this chapter……………………………………………………………… 18

2.1.2     Basic terms & definitions……………………………………………………….. 20

2.1.2.1Definition of E-commerce……………………………………………….. 20

2.1.2.2Definition of B2C………………………………………………………….. 20

2.1.2.3Definition of Reference group…………………………………………. 21

2.1.2.4Definition of Consumer behaviors……………………………………. 22

2.1.2.5Definition of Culture………………………………………………………. 22

2.1.2.6Definition of Marketing & Marketing Mix………………………… 23

2.1.2.7Definition of marketing segmentation……………………………….. 24

2.1.2.8Location based service (LBS)………………………………………….. 24

2.2           Background for the study…………………………………………………………….. 25

2.2.1     Macro areas………………………………………………………………………….. 25

2.2.1.1Political and legal areas…………………………………………………… 25

2.2.1.2Economic areas………………………………………………………………. 26

2.2.1.3Social areas – Common feature of the Chinese customers and the Chinese culture   28

2.2.1.4Technological areas…………………………………………………………. 30

2.2.2     Industrial factors…………………………………………………………………… 31

2.2.2.1Bargaining power of suppliers………………………………………….. 31

2.2.2.2Bargaining power of customers………………………………………… 32

2.2.2.3Threat of new entrants…………………………………………………….. 33

2.2.2.4Threat of substitute products……………………………………………. 35

2.2.2.5Competitive rivalry within the industry…………………………….. 35

2.2.3     Relevant surveys & data in respect of online customer behaviors in China  36

2.2.3.1Online shopping frequency………………………………………………. 36

2.2.3.2Expenditure spent on online shopping………………………………. 37

2.2.3.3Time spent online shopping……………………………………………… 38

2.2.3.4Product types shopped online in 2012 in China………………….. 38

2.2.3.5The popularity of holiday big sale…………………………………….. 39

2.2.3.6The trend of LBS (Location Base Service) in China……………. 40

2.2.3.7The payment methods adopted in China for online shopping in China40

2.2.3.8Social media and online shopping…………………………………….. 41

2.3           GAPS in the relevant literature……………………………………………………… 42

2.4           Model of consumer behaviors in E-Commerce………………………………… 43

2.4.1     Consumer behaviors and marketing stimuli………………………………. 44

2.4.1.1Product marketing mix and consumer behaviors…………………. 46

2.4.1.2Price marketing mix and consumer behaviors…………………….. 48

2.4.1.3Place marketing mix and consumer behaviors…………………….. 50

2.4.1.4Promotion marketing mix and consumer behaviors……………… 50

2.4.2     Consumer behaviors and internal influencing factors…………………. 51

2.4.2.1Personal needs and motives……………………………………………… 51

2.4.2.2Attitudes……………………………………………………………………….. 53

2.4.2.3Perceptions……………………………………………………………………. 54

2.4.2.4Personality…………………………………………………………………….. 54

2.4.2.5Lifestyle………………………………………………………………………… 55

2.4.2.6Knowledge……………………………………………………………………. 55

2.4.3     Consumer behaviors and extenral influencing factors………………… 55

2.4.3.1Demographic…………………………………………………………………. 57

2.4.3.2Social class…………………………………………………………………….. 60

2.4.3.3Reference groups & the rise of social media………………………. 61

2.4.4     Consumer behaviors and e-commerce contributors……………………. 62

2.4.4.1Logistics……………………………………………………………………….. 62

2.4.4.2Technical support……………………………………………………………. 63

2.4.4.3Customer service……………………………………………………………. 63

2.4.5     Consumer behaviors and decision making process: Online shopping decision making      64

2.4.6     Buyer’s response…………………………………………………………………… 64

2.4.6.1Product choice……………………………………………………………….. 65

2.4.6.2Brand attitude……………………………………………………………….. 65

2.5           The Research Problem, Research Gap, Research Questions, Research Objectives & Research Propositions……………………………………………………………………………………………. 65

2.5.1     The Research Problems………………………………………………………….. 65

2.5.2     The Research Gaps………………………………………………………………… 66

2.5.3     The Research Questions…………………………………………………………. 66

2.5.4     Research Objectives………………………………………………………………. 67

2.5.5     The Research Propositions……………………………………………………… 67

2.6           Conclusions………………………………………………………………………………… 67

  1. Chapter 3: The research methodology………………………………………………………… 68

3.1           Research approach………………………………………………………………………. 68

3.2           Data collection……………………………………………………………………………. 68

3.3           Data analysis………………………………………………………………………………. 69

3.4           Ethical issues………………………………………………………………………………. 69

3.5           Research Limitations……………………………………………………………………. 70

  1. Data Analysis and Findings………………………………………………………………………. 71

4.1           Part A: Profile of Respondents……………………………………………………… 71

4.2           Part B: General Consumption Behaviors………………………………………… 80

4.3           Part C: Consumer behaviors and marketing stimuli………………………….. 91

4.4           Part D: Consumer behaviors and internal influencing factors……………. 92

4.5           Part E: Consumer behaviors and extenral influencing factors……………. 94

4.6           Part F: Consumer behaviors and e-commerce contributors………………… 96

4.7           Part G: Buyer’s response………………………………………………………………. 99

  1. Conclusion and Recommendations…………………………………………………………… 100

5.1           Introduction……………………………………………………………………………… 100

5.2           Conclusions on research questions……………………………………………….. 101

5.3           Conclusion on the research propositions……………………………………….. 102

5.3.1     Conclusion on the research proposition 1……………………………….. 103

5.3.2     Conclusion on the research proposition 2……………………………….. 103

5.3.3     Conclusion on the research proposition 3……………………………….. 103

5.4           Conclusion on the Research Problem……………………………………………. 103

5.5           Recommendations……………………………………………………………………… 104

5.5.1     Business strategies………………………………………………………………. 104

5.5.1.1Don’t miss the aged groups……………………………………………. 104

5.5.1.2Don’t miss the less educated people as potential online customers       104

5.5.1.3Seek business opportunities and growth in LBS sector……… 105

5.5.2     Product strategies………………………………………………………………… 105

5.5.3     Promotion strategies…………………………………………………………….. 106

5.5.4     Place strategies……………………………………………………………………. 106

5.6           Meeting the research objective…………………………………………………….. 106

5.7           Implications………………………………………………………………………………. 107

5.8           Limitations……………………………………………………………………………….. 107

5.9           Further research…………………………………………………………………………. 107

5.10        Conclusions………………………………………………………………………………. 107

Appendix 1. Online Survey on the Trend of Ecommerce Consumer Behavior in China121

Appendix 2. SPSS analysis of answers in respect of age groups…………………………. 132

Appendix 3. SPSS analysis of customers’ rating of 11 major online shopping website134

 

 

 

List of figures

 

Figure 1 The e-commerce business scale and growth trend…………………………… 13

Figure 2 Conceptual Framework……………………………………………………………….. 20

Figure 3 Porter’s Five Forces model (1980)………………………………………………… 32

Figure 4 The marketing 4Ps……………………………………………………………………… 46

Figure 5 The 3 levels of product……………………………………………………………….. 47

Figure 6 The Adidas original packing………………………………………………………… 48

Figure 7 Maslow’s hierarchy of needs……………………………………………………….. 53

Figure 8 Maslow’s hierarchy and the relevant product / service in need…………. 54

List of tables

 

Table 1 GAPS in the relevant literature……………………………………………………… 43

Table 2 Types of Pricing Strategies……………………………………………………………. 49

Table 3 SPSS analysis of answers in respect of online purchasing possibility from Monday to Sunday      89

Table 4 SPSS analysis of answers in respect of online purchasing possibility in major Chinese holidays     90

Table 5 Performance of Tmall.com out of 11 websites…………………………………. 97

 

 

 

List of charts

 

Chart 1 Differences between B2B, B2C and C2C e-commerce……………………. 22

Chart 2 China GDP from 2004 to 2012……………………………………………………… 28

Chart 3 China disposable income per capita from 2004 to 2012……………………. 28

Chart 4 China’s cultural system in Hofstede’s Five Cultural Dimensions……….. 29

Chart 5 Online shopping frequencies among Chinese online customers in 2011 and 2012      37

Chart 6 Chinese online customers’ expenditure spent on online shopping in 201238

Chart 7 Product types shopped online in 2012 in China (percentage)…………….. 40

Chart 8 Payment methods adopted by Chinese online consumers (CNNIC 2012)41

Chart 9 Frequency of checking the social networks before purchasing…………… 42

Chart 10 Online purchasing led by social networks in half a year………………….. 43

Chart 11 The model of e-commerce consumer behavior……………………………….. 44

Chart 12 Branded markets vs commodity market……………………………………….. 49

Chart 13 Sex ratio of Internet users in China……………………………………………… 58

Chart 14 Age groups of Internet users in China………………………………………….. 59

Chart 15 Education background of Internet users in China………………………….. 60

Chart 16 Income levels of Internet users in China……………………………………….. 61

Chart 17 Answers to question “What is the gender group that you are belonging to?”    72

Chart 18 Answers to question “What is your age group?”……………………………. 73

Chart 19 Answers to question “What is your ethnic group?………………………….. 75

Chart 20 Answers to respondents’ individual monthly income……………………… 76

Chart 21 Respondents’ education level in comparison with the same of the Internet users in 2012      78

Chart 22 Answers in respect of location / area of the respondents…………………. 80

Chart 23 Answers in respect of online shopping frequency…………………………… 81

Chart 24 Answers in respect of Chinese online customers’ expenditure in comparison with 2012 data       83

Chart 25 The length of time the respondents spend on online shopping in a typical day85

Chart 26 The length of time the respondents spend on online shopping per day (By different areas)  86

Chart 27 Products purchased online by respondents against 2012 CNNIC data (%)       91

Chart 28 Effectiveness of various ad forms (ranked by Mean)……………………… 92

Chart 29 Survey results in respect of frequency of checking the social networks before purchasing    95

Chart 30 Survey results in respect of online purchasing led by social networks in half a year       97

Chart 31 Online shopping payment methods in comparison with the 2012 data99

 

 

 

1.        Chapter 1: Introduction

 

1.1    The background to research

 

Figure 1 The e-commerce business scale and growth trend

 

With the rapid popularization of computer, smart mobile phone as well as convenient internet access, the e-commerce[1]in China in recent years has witnessed a very fast growth. According to Q2 2013 China Online Shopping Report released by iResearch, GMV of China Online Shopping market hit 437.13 billion Yuan, up 45.3% compared with the same period of 2012. As to online shopping market structure, B2C accounted for 36.1%, up 2% over Q1 2013. According to the share of online shopping market, Tmall and JD took a combined share of 67.9%, leading B2C and independent B2C market separately (iresearchchina.com 2013). While the market volume of the B2C e-commerce is still growing strongly, the intensified competition among large platforms has further narrowed the retail profit margins (goldsea.com 2013). Therefore, growing business opportunities as well as intensified competition presses the players in the B2C sector to become more considerate about the changing E-Commerce consumer behaviors in China in order to find new ways out and seek further and endurable growth.

 

1.1.1            Research Problem

 

The main purpose of this study is to explore and identify the consumer behavior trends in the e-commerce industry in China and to analyze the implications to the marketing strategies of Tmall.com. Therefore, the research problem is:-

 

What are the consumer behavior trends in China and the implications to marketing strategies of Tmall.com?

 

1.1.2            Research Gaps

 

From the above reviews of the major theories in relate to the topic of the relationship online consumer behavior, we have come to the below conclusions:-

 

There aren’t sufficient literatures about the Chinese market which are up to date;

 

Though there are enough of theories about the consumer behaviors in term of decision making and influential factors, these theories are not fully and systematically tested in the new industry, i.e. the e-commerce in a raising market, China.

 

The up to date research on the Chinese online consumer behaviors is required to test the traditional theories in China where the culture is different.

 

1.1.3            Research Questions

 

This paper addresses the said research problem with the following research questions:-

 

What are the basic profile details / features of the online customer in China in term of age, gender, income, and etc?

 

What are the external and internal factors that affect online purchasing decision?

 

This research question helps researcher to focus on the effect of the external factors such as political, social and economical factors and the internal factors such as shopping seasonality over the online shopping behavior pattern in China.

 

How internal factors affect online purchasing decision making in China?

 

This research question helps researcher to focus on the effect of the internal factors such as personality over the online shopping behavior pattern in China.

 

What are the implications to marketing strategies of Tmall.com?

 

This research question helps researcher to come back to one major targets of this study which is to provide suggestions in term of marketing strategies making and improve the marketing effectiveness and efficiency to the Tmall.com.

 

1.1.4            Research Objectives

 

To identify the literatures to be applicable in the consumer behavior study of the B2C E-Commerce sector in China;

 

To identify the trend of consumer behavior with respect to the B2C E-Commerce sector in China;

 

To offer suggestions regarding how the Tmall.Com should react to the changing online consumer behaviors trend.

 

1.1.5            The Research Propositions

 

The above mentioned 3 research questions should be based on the following 3 Research Propositions in order for the research to be viable according to the empirical testing:

 

Research Proposition 1: Consumers behavioral trends have significant impact over their online consumption behaviors and decision making;

 

Research Proposition 2: B2C service providers are facing challenges due to the changes of the consumers behavioral trends;

 

Research Proposition 3: B2C companies are eager to study the changes of the consumer behaviors and generate their marketing strategies to reflect these changes.

1.1.6            Unit of Analysis

The unit of analysis refers to ‘the level of aggregation of the data collected during

the subsequent data analysis stage’ (Cavana, Delahaye & Sekaran, 2001, p. 119). Because constraining by cost, time and resource limitation, it was necessary to only investigate the international sourcing in china.

 

1.1.7            Research Methodology

 

The process of collecting data and the data analysis for research projects is known as research methodology.

 

1.1.7.1          Research approach

 

Research methodologies can be quantitative (for example, measuring the number of times someone does something under certain conditions) or qualitative (for example, asking people how they feel about a certain situation). Ideally, comprehensive research should try to incorporate both qualitative and quantitative methodologies (blurtit.com 2010). Bryman (1988) argued for a `best of both worlds’ approach and suggested that qualitative and quantitative approaches should be combined.In this project, quantitative will be used for research about consumer behavior trend, and qualitative will be used for research about Marketing Strategies of Tmall.com in China. Qualitative will be the primary research approach while the quantitative methodologies will be used as the supplementary method to be applied to further strengthen the qualitative findings.

 

1.1.7.2          Data collection

 

The choice of method is influenced by the data collection strategy, the type of variable, the accuracy required, the collection point and the skill of the enumerator. Links between a variable, its source and practical methods for its collection, the main data collection methods include registration, questionnaires, interviews, direct observation and reporting (fao.org 2009).

 

The primary data collection method to be adopted will be the original online survey targeting at a sampling volume of 100 people and these people will be based on random selection from the online customers;

 

The secondary data collection method to be adopted will include text book, journals and news which are generated after year of 2005.

 

1.1.7.3          Data analysis

 

Data analysis refers to the use of descriptive and inferential statistical techniques to transform raw data into information that is easier to manipulate, understand, and report (nrepp.samhsa.gov 2010). Both qualitative analysis and quantitative analysis (such as contingence and correlation) will be adopted to test the research propositions as well as achieving the research targets.

 

1.1.8            Ethical Considerations

 

Invitation of the online survey should be offered to the public with no discrimination to gender difference, job title, age groups and ect so long as they are online customers;

 

The online survey should begin only the online customers is informed about the content and intension of the survey and the agreement of attending the survey is offered by the respondents;

 

The name and other private information should not be disclosed to any parties and the use of the collected data should be confined in this research only.

 

1.1.9            Limitations

 

The sample size (100) and the range of coverage of the survey may offer some limitations to the comprehensiveness of the report; and at the same time, due to the limitation of human resource, some points and issues may be ignored.

 

1.1.10        Structure of the thesis

 

In term of the structure of this report, it begins with the introduction of the research background followed by the literature review of the consumer behavior theories as well as e-marketing related theories; also the research problems and the research methodology, would be stated with respect to this study; the major part of this study is the findings and data analysis based on an original survey focusing on online consumer research as well as the literatures referred and discussed followed by the relevant conclusions made and recommendations offered to the online B2C giant, the Tmall.com which makes the report and study meaningful and useful.

 

1.1.11        Conclusion

 

This chapter provides the foundation of this chapter. It covers the research reasons, questions and objectives of this study. In the next chapter, Chapter 2, this paper will review the existing literature. This will establish the hypothesis and conceptual research framework by which the research findings will be critiqued

 

2.        Chapter 2: Literature review

2.1    Introduction

 

2.1.1            Briefs of this chapter

Figure 2 Conceptual Framework

Figure 2 Conceptual Framework

Source: Developed for this research

 

The second chapter will review the literature relating to the Chinese B2C industry as well as the theoretical framework for the customer behavior in respect of B2C online shopping in China. It will go through the basic terms and definitions, background for the study including macro environment in China, industrial environment of B2C industry in China, relevant parent discipline, immediate discipline as well as some conclusions made based on the literature reviews.

 

2.1.2            Basic terms & definitions

 

2.1.2.1          Definition of E-commerce

 

E-commerce refers to commercial trade activities carried out by electronic methods, the electronicization of traditional trades. The electronic means refer to electronic technologies, tools, equipments and systems, including telephone, telegram, television, mobile phone, facsimile, E-mail, electronic data interchange, computer, the communication network and so on. And in a narrow sense, e-commerce refers to various online commercial activities focusing on commodity exchanges by electronic methods, computer network in particular (Qin 2009, p. 8). The survey of literature on the features of e-commerce identified three types of benefits by approaching e-commerce mode; these benefits include perceived economic, relationship-related and strategic benefits (Ratnasingam 2002). Perceived economic benefits arise from improvements and efficiencies in business processes, such as time saving and a reduction of transaction costs as a result of speed and automation of e-commerce applications. Perceived relationship-related benefits are derived from positive opportunities that arise from economic benefits such as increased operational efficiencies, better customer service, improved inter-organizational relationship, better customer service and so on. Perceived strategic benefits refer to the long-term gains and benefits a company obtains from developing closer ties with its trading partners by approaching e-commerce to improve its competitive position, these benefits include a compressed business cycle and intensified relationships with the trading partners (Fearon and Philip 1998).

 

2.1.2.2          Definition of B2C

 

Business to Consumer or B2C refers to e-commerce activities that are focused on consumers rather than on businesses. For instance, Amazon.com which is a book retailer company is a B2C company. B2C applications are directed towards the average consumer. As a type of e-commerce, B2C e-commerce applications has grown rapidly during the past decade, especially after the widespread use of the Internet and the improvement of the services offered over the Internet (Sharma 2008).

Chart 1 Differences between B2B, B2C and C2C e-commerce

Source: Sharma 2008

 

Different from other two major sale channels, i.e. B2B (business to business) and C2C (consumer to consumer), the B2C e-commerce provides more customized product offering (compared to B2B) and in respect of the complexity of buying process the B2C e-commerce offers relatively simple, limited discussion over price and payment and delivery options.

 

2.1.2.3          Definition of Reference group

 

A reference group in the consumer behavior context is referred to as a group to which an individual belongs. Such membership is preferred as a point of comparison with another possible group. The said group becomes the individual’s frame of reference and source for sharing his or her experiences, perceptions, cognition, and idea of self. It becomes the basis of referece in making comparisons or contrasts and in evaluating his decisions (Majumdar 2010).

 

2.1.2.4          Definition of Consumer behaviors

 

By definition, consumer behavior reflects the totality of consumer’s decision with respect to acquisition, consumption and disposition of goods, services, activities, experiences, people and ideas by human decision-making unit over time (Hoyer & MacInnis 2010, p. 3). James McNeal in his classic book Consumer Behavior, advances the idea that human behavior including consumer behaviors are affected by server factors which include social setting, social forces and roles. In term of social setting, all consumers make decisions and take actions within the larger social setting and in doing so, are influenced by their peers; in term of social forces, forces in a society set the standard of acceptable behavior, and the rules set which could be written as well as unwritten are established by those within the society with the most influence; in term of roles, a role is a pattern of behavior associated with a specific position within a social setting and each role brings with it a set of expectation for behavior. Functioning of social role is well within our common sense and general understanding, for instance, a father is expected to nurture the children and thus bears the responsibility of taking care as well as monitoring the growing up of the child. This social role establishes the actual father and son / daughter relationship and at the same time enables the father with the high possibility to purchase relevant products for his children.

 

2.1.2.5          Definition of Culture

 

Culture is defined as the patterns of behavior and social relations that characterize a society and separate it from others. Culture conveys values, ideals, and attitudes that help individuals communicate with each other and evaluate situations. It is important in viewing culture to draw legitimate generalizations about a given culture or subculture without resorting to stereotyping. And individual’s culture provides a frame of reference concerning acceptable behaviors, and as such, culture is a learned set of arbitrary value (Reid & Bojanic 2009).

 

2.1.2.6          Definition of Marketing & Marketing Mix

 

According to the Chartered Institute of Marketing (www.cim.co.uk), marketing is a management process that is involved in identifying, anticipating, satisfying customers needs and wants through the efficient and effective use of the company’s resources (Wright 1999). Dr. Philip Kotler defines marketing as “the science and art of exploring, creating, and delivering value to satisfy the needs of a target market at a profit.  Marketing identifies unfulfilled needs and desires. It defines, measures and quantifies the size of the identified market and the profit potential. It pinpoints which segments the company is capable of serving best and it designs and promotes the appropriate products and services” (Kotler 2012)

 

“Marketing mix” is a general phrase used to describe the different kinds of choices organizations have to make in the whole process of bringing a product or service to market. The 4Ps is one way – probably the best-known way – of defining the marketing mix, and was first expressed in 1960 by E J McCarthy.

 

The 4Ps are:

 

l  Product (or Service)

l  Place

l  Price

l  Promotion

 

2.1.2.7          Definition of marketing segmentation

 

Market segmentation involves splitting a target market up into clusters of people likely to respond in a similar, positive way to the marketing mix presented to them. The main ways of segmenting a consumer market are on the basis of geography, demography, psychographic factors and behavior. To be worthwhile, market segments should be accessible, measurable, substantial and viable.

 

There are four common bases for segmenting a market:

 

Geographical – By country, or region.

Demographic – By age, gender, occupation, and so on.

Psychographic – By lifestyle, values, interests, and so on.

Behavioral – What product is used for, brand loyalty, the benefit is expected from the product, and so on.

 

Market segmentation aims to make the marketing more effective, and it can also help to serve the customers’ needs better. And by applying its principles within an organization, it is likely to increase the likelihood that people will be interested in them (mindtools.com 2013).

 

2.1.2.8          Location based service (LBS)

 

Location-Based Services refers to a broad range of services that are based on (or enhanced by) information about the physical location of a user and/or device. Typical location-based services for consumers might include real-time turn-by-turn directions, the location of the nearest gas station or motel, or social networking services. What makes the service location-based is that it knows your location automatically, without entering a zip code. Location-based services can also be business-oriented. Location-based services are typically made available to the user via a WAP site, or downloadable software (Java, BREW, Symbian, etc.) (phonescoop.com 2012).

 

The first truly mobile computing devices began to appear in the 1980s (in my own case, in the form of a Hyperion portable computer with two 5.25 inch floppies, an alphanumeric screen, an Intel 8088 processor, and no hard drive). Since then mobility has become an increasingly important factor in computing, and today we expect to find far more powerful performance available in devices that can fit in a pocket. Mobility has in turn opened up the possibility of providing information about the user’s location in space and time, about the user’s surroundings, and about features in the environment that are nearby but beyond the user’s own sensory perception. This information may be useful to the user, and it may also be useful to someone else, such as the user’s employer, bank, parents, or government (geog.ucsb.edu 2012).

 

2.2    Background for the study

 

2.2.1            Macro areas

 

2.2.1.1          Political and legal areas

 

Political factors refer to the stability of the political environment and the attitudes of political parties or movements. This may manifest in government influence on tax policies, or government involvement in trading agreements. Political factors are inevitably entwined with Legal factors such as national employment laws, international trade regulations and restrictions, monopolies and mergers’ rules, and consumer protection. The difference between Political and Legal factors is that Political refers to attitudes and approaches, whereas Legal factors are those which have become law and regulations (oxlearn.com 2013).

 

In term of the legal system, the Chinese legal framework for e-commerce is still in its nascent stage and has already experienced several problems. China has limited experience with drafting e-commerce legislation for issues such as transactional security, intellectual property rights protection and tax. And regulations supporting areas critical to the development of e-commerce such as the privacy, consumer rights, and validation of electronic contracts and recognition of digital signatures have yet to be written (Kariyawasam 2011, p.270). But the legal system of China does develop quickly to meet the needs of the e-commerce.

 

2.2.1.2          Economic areas

 

Though the economic environment is influenced by domestic economic policies, it is also dependent upon world economic trends. Rates of the economic growth, inflation, consumption patterns, income distribution and many other economic trends determine the nature of the products and services required by consumers, as well as how difficult it becomes to supply then (Dransfield  2005, p. 59)! Influences may include:

 

l  GDP;

l  Disposable income per capita;

l  Price Volatility;

l  Inflation

Chart 2 China GDP from 2004 to 2012

Source: tradingeconomics.com 2013

 

The gross domestic product (GDP) measures of national income and output for a given country’s economy. The gross domestic product (GDP) is equal to the total expenditures for all final goods and services produced within the country in a stipulated period of time. The Gross Domestic Product (GDP) in China was worth 8230 billion US dollars in 2012. The GDP value of China represents 13.27 percent of the world economy. China GDP reached an all time high of 8230.00 USD Billion in December of 2012.

 

Chart 3 China disposable income per capita from 2004 to 2012

Source: tradingeconomics.com 2013

 

Disposable Income is the amount of money that households or persons have available to spend and save after paying income taxes and pension contributions to the government. The revenue may include employees’ compensation, property income, social benefits, money earned abroad and other incomes (tradingeconomics.com 2013). From the above chart we can learn that the disposable income per capita in China witnesses a stable growth with the continual growth of its economy.

 

2.2.1.3          Social areas – Common feature of the Chinese customers and the Chinese culture

 

The Chinese culture – Analysis with Hofsteede’s Cultural Dimensions

 

In 1980, the Dutch management researcher Geert Hofstede first published the results of his study of more than 100,000 employees of the multinational IBM in 40 countries. Hofstede was attempting to locate value dimensions across which cultures vary. His dimensions have been frequently used to describe cultures, they include: Power Distance, Uncertainty Avoidance, Individualism, Masculinity and Long-Term Orientation.

 

Chart 4 China’s cultural system in Hofstede’s Five Cultural Dimensions

Source: Geert-hofstede.com 2013

 

In collectivist society people belong to ‘group’ that takes care of them in exchange for loyalty. At a score of 20 China is a highly collectivist culture where people act in the interests of the group and not necessarily of themselves. In-group considerations affect hiring and promotions with closer in-groups (such as family) are getting preferential treatment. Employee commitment to the organization (but not necessarily to the people in the organization) is low. Whereas relationships with colleagues are cooperative for in-groups they are cold or even hostile to out-groups. Personal relationships prevail over task and company (Geert-hofstede.com 2013). The low level of Individualism (IDV) or high degree of collectivism is said to be linked with the long tradition of the Chinese social system. Another major feature of the Chinese culture is the high priority of long term orientation which implies that values such as perseverance and thriftiness are highly appreciated in the Chinese cultural system.

 

Online customer behavior: China vs United States

 

According to the most recent report released by the China Internet Network Information Center (CNNIC), the total number of the internet users throughout the year of 2012 reached 564 million out of which 74.5 per cent use mobile devices to surf the internet and use the other internet apps (chinaabout.net 2013). Also there are three features that describe maybe the future mainstream of the online shopping behaviors in the People’s Republic of China which are quite different from that of the other parts of the world. First of all, Chinese people spend a large share of their income online. Research finds out that, US consumers spend 23% of their disposable income online, slightly higher than the 22% average among 15 studied countries with consumers in India were found out to be spending the largest share of their disposable income online which was 33%, and China with 31% was ranked the second (marketingcharts.com 2012). Secondly, Chinese people are found to be of great love of shopping online. According to the reports from KPMG China, the most popular good to be shopped by Chinese online customers are CDs/DVDs/books and video games with 79%of the Chinese respondents expressed that they loved buying goods online instead of 65% average of the global respondents (kpmg.com 2012).

 

2.2.1.4          Technological areas

 

According to Ron Basu and J. Nevan Nevan Wright (2008), technology plays an important part in the supply chain. And it is true to say that the supply chain of today would not function without information technology to provide point of sale, electronic data interfaces, electronic funds transfer, manufacturing resource planning, enterprise resource planning and CRM. One major problem in the e-commerce field in relate to the technological area is the payment security issue. But this payment security issue seems to be well addressed in recently years in China with the rapid development of the relevant technologies. According to the most recently published “China B2C E-commerce and Online Payment Report 2013”, The total online payment transaction value in China demonstrated strong quarter-on-quarter growth in 2011-2012.Third-party online payments, which are used by around a third of online shoppers in China, were especially on the rise. Also, Alipay launched a mobile wallet application in China, offering online-to-offline payments. According to forecasts, it is expected to grow by more than 30 percent annually between 2010 and 2016. A growing number of Chinese residents use social networks to purchase products online. Social Commerce is expected to become even more significant in China than in the USA. M-Commerce is also gaining in importance. Between 2011 and 2012, M-Commerce grew approximately fivefold (ystats.com 2013).

 

2.2.2
Industrial factors

Figure 3 Porter’s Five Forces model (1980)

Source: Adapted from Porter 1980 (The Free Press/Macmillan)

 

Around 1980, Michael Porter in his book Competitive Strategy: Techniques for Analyzing Industries and Competitors, first introduced his highly influential concepts of the five forces model and the value chain, and the five forces model according to him could determinate an industry’s attractiveness by looking into five external forces:

 

  1. Competitive rivalry within an industry
  2. Bargaining Power of Suppliers
  3. Bargaining Power of Customers
  4. Threat of New Entrants
  5. Threat of Substitute Products

 

The five forces are also the five key players in most business markets, the interaction and bargaining between these five forces is believed to be critical and of great importance to the determination of the competitive extent of an industry or market.

 

2.2.2.1          Bargaining power of suppliers

 

The bargaining power of suppliers comprises one of the five forces that determine the intensity of competition in an industry. The presence of powerful suppliers reduces the profit potential in an industry. By threatening to raise prices or reduce the quality of goods and services, suppliers increase competition within an industry. As a result, they reduce profitability in an industry where companies cannot recover cost increases in their own prices. In the B2C market sector in China, the supplier bargaining power can be considered as being ranged in a low level for the below three factors: firstly, the input contributed by the suppliers is not indefensible to the B2C websites. Most customers who go the B2C websites are seeking for products with similar functional features and thus they may not name a specific brand which means the input offered by the suppliers is not a must factor to the B2C platforms; the 2nd reason is that there is small amount switching cost for B2C companies to shift to other product suppliers since the products are not in a real store. In addition, the fact that there are usually no substantial warehouses held by the B2C companies actually reduce the switching cost of the B2C companies and also it reduces the bargaining power of the suppliers; last but not least, the B2C firms are very important customers of the suppliers. Considering the frequent large amount of the B2C companies in term of large volume of purchasing such as Taobao Mall, the importance of these B2C firms to suppliers is thus enhanced greatly.

 

2.2.2.2          Bargaining power of customers

 

The bargaining power of buyers of firms in an industry constitutes the ability of the buyers, individually or collectively to force a reduction in the prices of products or services, demand a higher quality or better service, or to seek more value for their purchases in any way (Kozami 2006, p. 269). Indicators of a high level of bargaining power of buyers:

 

l  Few buyers and a lot of sellers

l  Switching costs are low

l  Buyer is highly price sensitive

l  Buyer has a lot of knowledge about the product

l  Buyer purchases products in high volume

l  Low level of uniqueness among products in the market

l  Substitutes are available (ecommerce-startup.org 2013).

 

In the B2C market in China, the customers are the individual customers who tend to have medium bargaining power for the following four reasons:

 

Firstly, the non-business customers or sometime referred as individual customers are not buying goods at the same time which means that their purchasing are highly random and are not related. Therefore, they would small bargaining power by forming a large sum of purchasing power because they are not united;

 

Secondly, the goods to be purchased can not be classified as big business purchasing which means that individual buyers’ shopping online are basically done to meet personal needs in the B2C market;

 

Thirdly, but still the customers in the B2C market could enhance their bargaining power by not selecting a single website for all time, the customers could choose between several websites so that competition of price could still exist.

 

Fourthly, the individual customer could have increased their bargaining power by using group purchasing.

 

2.2.2.3          Threat of new entrants

 

New entrants are those who are not currently competing in the market, but may enter the market to offer the same product. As such, a high threat of new entrants is an undesirable factor, since it means that the potential for increased competition is great. And increased competition is almost certain to bring with it lower prices, lost sale, and lower market share. An industry’s barriers to entry are a critical factor. Government rules or environmental regulations can also pose significant bareers to entry (Klein & Iammartino 2010, p. 138).

 

First of all, the capital requirements to open a B2C website are relatively low. B2C websites could be in large scale or in a smaller scale depending on the visiting volume of the customers, for example the investment of a large scale B2C website could begin with a smaller scale one because in the beginning the visitors are small in numbers. And to many companies that have their own products, they could also enter into the B2C market by adding the B2C functions into their official websites; secondly, many exiting successful C2C website with the current popularity could easily enter into the B2C market using their existing customer base. For example, Taobao has long been China’s large C2C website but its growth into the B2C experiences a rapid process. As an independent B2C platform, Taobao Mall (Tmall) on April 10, 2008 on the line, now attracts more than 70,000 brands to enter, the highest single-day turnover reached 936 million yuan. Research released in April 2010, top 30 online retailers, B2C rankings, Taobao Mall 30 billion in annual trade among the first in the absolute superiority over the other 29 combined turnover of B2C Web site. Last year, Taobao Mall occupies nearly 50% of China’s B2C market share, ranking Jingdong Mall Second, the market share of 18.1%, Dangdang only 2.2% (taobaotrading.com 2012). Thirdly, the distribution channel is not proprietary to the existing players. The distribution channels are the pathways that companies use to sell their products to end-users. Both B2C and B2B companies can sell through a single channel or through multiple channels that may include: direct/sales team, Internet, Value-added reseller and dealers and etc (marketingmo.com 2010). And to most B2C websites, Internet will be the major distribution channel and it would not be priertary to the B2C websites. What is more the logistic service is also open for all B2C companies and C2C shops.

 

2.2.2.4          Threat of substitute products

 

Of all the above elements, threat of substitutes is deadly for an organisation as it is the most unexpected and it might take up the market by storm, thereby posing a potential danger to companies. Thus, it is extremely important that companies make breakthrough products and, creative and innovative efforts to tackle the possible hacking by substitute products and services (mbaskool.com 2013). Customers have several other choices when they do not choose to visit the B2C websites. The customers could straight go to the physical stores and other various retailers to purchase the products rather than buying on line through which the customers could only view by pictures and descriptions but cannot touch the product by their hands.

 

2.2.2.5          Competitive rivalry within the industry

 

Competitive rivalry among the existing firms in an industry is the extent to which firms respond to competitive moves of other incumbent firms (Stahl & Grigsby 1997, p.147). Various data and new reports suggest that with the further spread of Internet applications, B2C and other e-commerce model are in the booming in these few years in term of a dramatic growth.

 

China’s e-commerce sector raked in 4.98 trillion yuan (807 billion U.S. dollars) in revenue in the first half of 2013, up 45.3 percent year on year as official data showed. Rapid Internet development boosted e-commerce and information consumption in China, home to the world’s largest number of Internet users. Information consumption has become a new engine driving economic invigoration and boosting domestic demand. In the first six months, consumption of information products and services jumped 20.7 percent year on year to 2.07 trillion yuan. The output of smartphones surged 120 percent to 214 million units (peopledaily.com.cn 2013).

 

Another factor that increases the intensity of the internal competition in the B2C industry is the existence of the large B2C websites such as the largest Taobao Mall, QQ Mall, Jingdong, Amazon and Dangdang. But the rapid growing market size contributes to the medium level of rivalry within the exiting companies in the B2C market in China rather than a seeming high level of competition. According to iResearch’s data as the chart shows, each segment of China online shopping market has the growth of different level and the fastest growth rate belongs to the B2C sector (including tmall.com and shop.qq.com) which has a growth rate of 19.5% (iresearchchina.com 2011). With an enlarging market size, it is expected that many B2C companies will focus on getting the attention of the new customers rather than gaining the competitors’ share by intense marketing efforts

 

2.2.3            Relevant surveys & data in respect of online customer behaviors in China

 

2.2.3.1          Online shopping frequency

1 – 2 times     3 – 4 times     5 to 10 times    11 times or more

 

Chart 5 Online shopping frequencies among Chinese online customers in 2011 and 2012

Source: cnnic.cn 2013

 

According to the “2012 China’s online shopping market research report” released by China Internet Network Information Center (2013) in March this year, in 2012, online shopping frequency among the online customers had witnessed significant improvement with the average online purchasing reaching 18 times in six months, an increase by 3.5 times compared to the same data of 2011. Meanwhile, there is a rapid growth of percentage of online customers whose online shopping in six months exceed 10 times. The said percentage had reached 54.5%, an increase of 23.8 percentage points.

 

2.2.3.2          Expenditure spent on online shopping

Chart 6 Chinese online customers’ expenditure spent on online shopping in 2012

Source: China Internet Network Information Center (2013)

 

Based on the “2012 China’s online shopping market research report” released by China Internet Network Information Center (2013), in year of 2012 China’s online shoppers are spending 5,203 yuan per capita on average, an increase of 1,302 yuan and an increase of 25%. At the same time, the online customers demonstrated strong spending power on online shopping with 56% of users of online shopping spending over 1,000 yuan annually; most users spend 2001-5000 yuan accounting for 22.6% of the total sample; followed by 501-1000 yuan (accounting for 22.3%). What’s more, key consumers in online shopping was also increasing, the data showed that about 6.8 percent of online shopping users spent more than 10,000 yuan in the year of 2012.

 

2.2.3.3          Time spent online shopping

 

With the popularity of mobile, online shopping is rapidly growing among the Chinese customers. At the same time, the average time people spend on browsing the good on the various online platforms also witnesses a rapid growth. Data show that users from third-tier and fourth-tier cities such as Jinzhou, Tangshan, Datong and other regions, users spend over 20 minutes on average per day on online shopping, while users from Beijing, Shanghai, Guangzhou and other cities spent a average duration of 18.5 minutes on online shopping, and online customers from second-tier city spent approximately 19 minutes on online shopping. For example, customers from Wuhan city spent 18.6 minutes on doing online shopping per day. The analysis also explains such differences as people from third and fourth tier cities are living an ease and relatively more leisure life so that they have more time to be spent on online shopping. And in contrast, though with similar demand, customers are forced to reduce the time on online shopping due to higher living and work pressure (huaxia.com 2013).

 

2.2.3.4          Product types shopped online in 2012 in China

Chart 7 Product types shopped online in 2012 in China (percentage)

Source: CNNIC 2012

 

2.2.3.5          The popularity of holiday big sale

 

Tmall has promotion activities in all major holidays or festivals in China, such as Teacher’s Day, Mid Autumn Festival, National Day, Double Ninth Festival and Spring Festivals, not to mention some popular western festivals like Christmas and Thanksgiving. One of the biggest promotion activity is not from the festivals we mentioned above. Tmall created Double 11, which is on Nov 11, nicknamed after Bachelor’s Day by Chinese. Taobao normally can expect sales of over billion yuan on one single day. Last year, the total sales exceed 3 billion yuan (USD 490 million). Last year’s Double 11 promotion realized 19.1 billion yuan (USD 3 billion) Gross Merchandise Volume (chinainternetwatch.com 2013). Therefore, the popularity of the holiday big sale on one hand shows the promotional efforts of the business entities but also the Chinese customers’ great love of online shopping during major festivals.

 

2.2.3.6          The trend of LBS (Location Base Service) in China

 

Analysts forecast the Location-based Service (LBS) market in China to grow at a CAGR of 25.93 percent over the period 2012-2016. One of the key factors contributing to this market growth is the increasing adoption of mobile broadband. The LBS market in China has also been witnessing the growing use of mobile LBS. However, the increasing concern over privacy of the users could pose a challenge to the growth of this market (sbwire.com 2013).

 

2.2.3.7          The payment methods adopted in China for online shopping in China

Chart 8 Payment methods adopted by Chinese online consumers (CNNIC 2012)

Source: CNNIC 2012

 

In 2012, according to CNNIC data the most used payment method adopted in the online shopping in China is Internet banking payment (including debit cards, credit cards) with 63.1 per cent of the online customers choosing it to settle the payment issue; Third-party payment / account balance payment such as Alipay has occupied a usage rate of 61.6%; the proportion of users using cash on delivery has also reached 32.4%.

 

2.2.3.8          Social media and online shopping

 

Chart 9 Frequency of checking the social networks before purchasing

Source: CNNIC 2012

 

CNNIC data suggested that about 80% of the online customers had once check the social network before they made the purchasing decision and 43% of them are either sometimes or often have similar behaviors before they make the final decision regarding what products and which brand they should choose.

Chart 10 Online purchasing led by social networks in half a year

Source: CNNIC 2012

 

Even when the customers had no intention to buy any specific products on the internet, still 29% of them and 18% of them will visit the social networks and browse or check the promo information there.

 

2.3    GAPS in the relevant literature

 

According to Hossein Bidgoli (2004) understanding a customer culture and their online behaviors are crucial for effective global as well as national e-business sector. As in the case of China, with the rapid grown up of the internet user group, their behavioral trends are subject to fast changes as well. Also by reviewing the relevant major literatures some limitations are found as follows:-

 

Authors Research Topics Limitation
Marcus (2011) E-Commerce in China & Website localization Focused more on technical perspective
Yan & Dai (2009) Consumer’s Online Shopping Influence Factors and Decision-Making Model Based on outdated data and there was no concerning over the B2C business sector
Hasan (2010) Exploring gender differences in online shopping attitude Focused only on the consumer behavior difference between men and women
Wood & Solomon 2009 Research on the online consumer behavior Not enough of focus regarding the online behaviors of Chinese users

Table 1 GAPS in the relevant literature

Source: Summarized from the review of research literature

 

2.4    Model of consumer behaviors in E-Commerce

Chart 11 The model of e-commerce consumer behavior

Source: Botha 2004, p. 124

 

According to the model of electronic commerce consumer behavior, the purchasing decision process in basically the reaction of the consumer to the different stimuli as shown in the chart above. First of all, the buying decision making is influenced by internal / personal factors such as motivation, perception, learning ability, attitude as well as lifestyle; secondly, external factors such as family and reference group also impact the consumer behaviors; thirdly, marking elements in term of 4Ps of the particular product also influence the evaluation of the buying option of the consumers; lastly but not least, the features of E-commerce also play a role in shaping the final decision of the consumers in term of payment and deliveries and technological support provision.

 

2.4.1            Consumer behaviors and marketing stimuli

 

According to S. Ramesh Kumar (2009 p. 72), marketing mix elements which include product, price, promotion and place form the foundation of any marketing strategy. The marketing mix established the strategy of the brand in a systematic and structured manner. It helps the marketers to establish linkages with the focused target segment and thus finally lead the potential customers into actual purchasing behavior. Therefore there is a chital linkage between marketing mix and consumer behavior.

Figure 4 The marketing 4Ps

Source: learningmarketing.net 2012

 

Marketing stimuli is about information offering communicated either by the marketers via ads, salespeople, brand symbols, packages, signs, prices and so on or by non-marketing sources such as the media or world of mouth. According to C. L. Tyagi and Arun Kumar (2004) marketing stimuli consist of the four elements: product, price, place and promotion. And based on the view of Ramanuj Majumdar (2010) stimulus discrimination through ways such as product differentiation strategies (product differentiation strategies usually try to establish the product on the basis of an attribute or discriminator that is relevant, meaningful and valuable to consumers) provide unique ways in fulfilling consumer needs as well as creating unique branding and image for the company among the mind of the customers. Laura Lake (2009) suggested that the educating consumers can be achieved by effective marketing stimuli. According to him, education is seen as a value-add by most consumers. Companies should take the time and initiative to objectively educate the consumers about the products or services that their offer builds trust and confidence. Consumers want to make informed buying decisions, so that they need guidance on how the purchase the relevant products and service.

 

2.4.1.1          Product marketing mix and consumer behaviors

Figure 5 The 3 levels of product

Source: Young 2008

 

A product could be defined as anything that can be offered in the market for attention, acquisition, use, or consumption to satisfy a need, want, demand or expectation. It may include physical objects, persons, ideas, and service. According to Young (2008), there are three levels that a product shall be viewed: the first level is the core product which refers to the total product that the consumer is actually buying; the second level is known as tangible or the actual product which is the physical and touchable property of a product such as the product name, label, brand name and so on; the third level is called the augmented product which is the part where the seller provides additional services and privileges to consumers of the product in term of discounts and maintenance service.

 

Brands and purchasing decision making

Figure 6 The Adidas original packing

 

From a legal point of view brands are defined as protected trademarks that are adequate to differentiate legally goods and services of one company from those of its competitors. These trademarks must be graphically represented. These graphical representations are words, good figures, letters, numbers, audible signals, three-dimensional designs and include good design, good packaging as well as other formats, such as colors and combination of colors (Tatiana 2008). According to Watt (2008), “brand is not just a logo on a product” and “branding is the process of making products and companies into a brand”. Blynthe (2006, 160) also pointed out that branding is a process by adding value to a product. By adding value to a product, it actually helps to persuade the consumers into the purchasing behaviors. This last aspect determinates the existence of two main categories of goods: commodity products and branded products. Commodity goods have the tendency to be undifferentiated in price, product characteristics and they also do not mirror any company image. On the other hand, branded goods gain those factors.

Chart 12 Branded markets vs commodity market

Source: tutor2u 2010

 

A McKinsey study from 2008 reveals that 63 % of the Chinese consumers have a shortlist of preferred brands when planning to buy a product. It is important to get on that shortlist, but companies need to take into account that Chinese consumers on average are only willing to pay a premium of 2.5 % for branded products. Additionally Chinese consumers have become more wary in 2008 to buy unfamiliar products and it is therefore more difficult to launch new brands (Giele 2009).

 

2.4.1.2          Price marketing mix and consumer behaviors

 

The term price refers to the exchange value of a product expressed in money. Pricing is the process of fixing the price of a product or service. Pricing is of crucial importance to a business concern because the prices of the firm’s products determine the volume of sale and the amount of profit. To the consumer, in most case only when the price is acceptable to them they will consider the purchasing. It is therefore necessary to fix the price of a product in a careful manner. It is learned that product and service providers usually based their pricing decisions on three factors, i.e. the cost of production, the demand for the product and the nature of competition for the product. It is within our understanding that to survive in a competitive market a firm should adopt a price which enable to the company to attract different types of target customers for its products as well as for its business as a whole. Based on the view of Tobias Richter (2012, p. 35), from the perspective of the consumer, the price a customer is willing to pay for a product depends to a large extent on two variables: The customers’ perception of a product’s utility and the expectations toward the product. Both variables operationalize the perceived product value which varies from country to country due to social-economical or cultural reasons. An organization can adopt a number of pricing strategies. The pricing strategies are based much on what objectives the company has set itself to. The usual pricing strategies include:

Pricing Strategy Definition
Penetration pricing: Here the organization sets a low price to increase sales and market share.
Skimming pricing: The organization sets an initial high price and then slowly lowers the price to make the product available to a wider market.
Competition pricing Setting a price in comparison with competitors.
Bundle Pricing: The organization bundles a group of products at a reduced price.
Psychological pricing: The seller here will consider the psychology of price and the positioning of price within the market place
Premium pricing The price set is high to reflect the exclusiveness of the product.
Optional pricing: The organization sells optional extras along with the product.
Cost Plus Pricing: Here the firm adds a percentage to costs as profit margin to come to their final pricing decisions.

Table 2 Types of Pricing Strategies

Source: learnmarketing.net 2013

 

The detail adoption of different pricing is sourced from and will in return affect the consumer behaviors. For example, premium pricing is a marketing tool to set higher prices for certain goods in the hope that the higher price will give the impression the good is of a higher quality. And of course, the company has to provide the relevant product and promotion strategies so that the good under the premium pricing can actually make the customer feel that the product sustain the sufficient value against the consumer expectation.

 

2.4.1.3          Place marketing mix and consumer behaviors

 

Place (or its more common name “distribution”) is about how a business gets its products to the customers.  The objective of distribution is clear.  It is to: to make products available in the right place at the right time in the right quantities.  Distribution matters for a business of any size – it is a crucial part of the marketing mix (tutor2u.net 2013). Distribution is achieved by using one or more distribution channels, including:

 

  •      Retailers
  •      Wholesalers
  •      Distributors / Sales Agents
  •      Direct (e.g. via e-commerce)

 

From the view of cost, the distribution system performs all the transactional, logistical, and facilitating functions between middle men and retailer which brings them the best deals and the most effective profit. Distribution decisions include market coverage, channel member selection, logistics, and levels of service. And place strategy decision making could contribute to the costs which will be reflected in final price and sustained by the suppliers as well as the customers.

 

2.4.1.4          Promotion marketing mix and consumer behaviors

 

According to Jim Blythe (2008), promotion is not something which is done to consumers; it is something which they consume. People buy magazines, watch TV shows, go to the cinema and ride on public transport. Although they do not usually do these things in order to be exposed to advertisement, they usually pay at lest some attention to them and frequently they enjoy the experience. Furthermore, people often use media such as classified advertisements and directories in active search information about goods they might like to buy; these certainly include goods to be sold online.

 

2.4.2            Consumer behaviors and internal influencing factors

 

According to Mukesh Trehan and Ranju Trehan (2009, p. 197) important personal factors affecting consumer behavior include age, education, occupation and sex. For instance, age is an important factor affecting needs for the products. The need for products varies with age e.g children have special liking for toys, chocolates, milk, sweets, but old people have no liking for toys, chocolates; children and young people prefer color, design, style, to quality, but mature persons prefer quality. Based on the empirical study, Kim et al (2000) discovers that customer lifestyle and income are significantly related to purchasing behavior on the Internet. Customers who are more price oriented and/or time oriented perceive more benefit and less risk through online shopping. Customers who have more disposable income perceive have more preference to purchase online.

 

2.4.2.1          Personal needs and motives

Figure 7 Maslow’s hierarchy of needs

Source: Merrick & Maher 2006

 

Abraham Maslow put forth his theory of hierarchy of needs in 1954. Maslow pointed out that the most basic human needs are physiological needs such as hunger and thirst. Freedom from danger is the next need to emerge. When both physiological and safety needs are gratified, the needs arise to love and to be loved and thus to belong. If needs for love and friendship are met and the need for esteem of others emerges. When all the lower deficiency needs are met, the higher level growth needs become a concern. The first of these is the cognitive need to know, understand and explore. When this is met the aesthetic need arises for symmetry, older and beauty. Finally an individual will seek self-actualization and the need to find self-fulfillment and self-transcendence (Merrick & Maher 2006).

 

Figure 8 Maslow’s hierarchy and the relevant product / service in need

Source: Samli 1995

 

Maslow’s hierarchy of needs has been widely accepted in marketing circles. According to Samli (1995) attempts in general to understand if Maslow’s hierarchy holds true in the international settings have indicated that in all societies higher level needs become more dominant as lower level needs are satisfied which is true at all stages of the social hierarchy. As shown in the figure above, from the categories of products, product offering targeting at meeting the particular needs which are common in a particular society shall certainly receive more purchasing orders. For instance, club, groups and family relative business may not have sufficient customers if they are set up in a country where the economy development is still at the very beginning and people are still focusing on meeting the basic physiological needs.

 

2.4.2.2          Attitudes

 

An attitude is generally understood to refer to a predisposition to respond in a consistent manner to a stimulus, i.e. a tendency to act or behave in some predictable way. Attitudes are usually represented as being positive or negative, favorable or unfavorable to an object, idea, or other entity; indeed, Hughes (1971) defines attitude as an ‘individual’s favorable or unfavorable inclination towards an attribute of an object’. In the marketing context, consumers hold attitudes toward brands, products, companies, stores, or advertisement; consumer attitude are their linking or disliking of these stimuli. Attitudes are learned or acquired rather than inborn; they are formed as result of personal experience, reasoning or information, the communicated experience of others (Foxall, Goldsmith & Brown 1998).

 

2.4.2.3          Perceptions

 

Perception in marketing is described as a process by which a consumer identifies, organizes, and interprets information to create meaning. A consumer will selectively perceive what they will ultimately classify as their needs and wants (boundless.com 2012). According to Marieke de Mooij (2010), the implication of selective perception is that people observe some aspects of realty and do not see other aspects. Selective perception is a universal phenomenon, but is reinforced by culture. People who are used to behavior and phenomena in their own cultures tend to expect similar phenomena and behavior in other cultures, which may not exist or exist in limited ways. This selective perception process is stronger in individualist cultures, where people are universalistic and tend to expect that everybody elsewhere has similar values. And people tend to ignore the differences and only perceive the similarities. In individualistic cultures, reality is a subjective observation. In collective cultures, more phenomena influence perception. In particular, the context influences what people see and hear. This can easily lead to miscommunication between members of individualist and collectivist cultures.

 

2.4.2.4          Personality

 

Personality is defined by Schiffman (2008) as that the unique dynamic organization of characteristics of a particular person, physical and psychological, which influence behavior and responses to the social and physical environment. It seems that consumer purchases are always influenced by their personality as many marketers thought. In fact, some parts of personality are unique to certain person while some parts of it can be found from many persons. In general, there are three significant and evitable characteristics to describe the nature of personality. The first one is that personality can reflect the differences of individual. Moreover, personality is consistent and enduring because that a consistent kind of behavior would endure over time. The last point is that personality can change (studymode.com 2008).

 

2.4.2.5          Lifestyle

 

Lifestyle of customers is another import factor affecting the consumer buying behavior. Lifestyle refers to the way a person lives in a society and is expressed by the things in his/her surroundings. It is determined by customer interests, opinions, activities etc and shapes his whole pattern of acting and interacting in the world (aipmm.com 2013).

 

2.4.2.6          Knowledge

 

Guo and Meng (2008) conclude in an international comparison that Chinese consumers are more inclined to make stereotype judgments than French consumers. At the same time the Chinese consumers remembered more of the product attributes than the French. According to the authors this is attributable to the particularities of the Chinese language: much more categorization leads to more stereotype judgments and they claim the use of Mandarin asks more of the human brain and therefore Chinese are better in remembering product attributes.

 

2.4.3            Consumer behaviors and extenral influencing factors

 

Referring to Robert D. Reid and David C. Bojanic (2009), external influences include culture, socioeconomic status, reference groups and household. All of these entities have an influence on the way a consumer progresses through the decision making process. In term of reference group, based on the view of Paul Baines, Chris Fill and Kelly Page (2012, p. 91), reference groups including such role models as parents, entertainers, and athletes have an important socializing influence on consumers’ behaviors, in particular in adolescence. However, where people live, what social class people come from, what lifestyle people lead, and in what stage of the lifecycle people are all having an impact on the behaviors of the consumers. Celebrity endorsers are especially powerful influencers in this regard, because frame is becoming an increasingly attractive quality to many modern consumers. In term of family, Yet McKinsey’s 2010 survey of China’s consumers found out that increasingly, Chinese consumers are behaving like their counterparts in the developed world. They are more demanding and pragmatic than ever as their horizons expand beyond basic concerns about product features. Also, they are willing to pay for better value and quality and are spending more time researching and are exploring product nuances. Also, while Chinese consumers remain brand conscious but, unlike shoppers elsewhere, they focus on value so intensely that brand loyalty is often secondary. The needs or interests of their families have greater importance for them than for their counterparts in the developed world. Word of mouth has become a more significant source of product information than it is elsewhere, thanks largely to fast-growing use of the Internet, which Chinese consumers see as a credible information source (csi.mckinsey.com 2010). Products and services are purchased to support consumers’ lifestyles. Marketers have worked hard researching how consumers in their target markets live their lives since this information is key to developing products, suggesting promotional strategies and even determining how best to distribute products (knowthis.com 2013).

 

2.4.3.1          Demographic

 

Four of the most common demographic variables employed in market segmentation are age, gender, income, and education. It is believed that the types of goods and services sought by individuals change as they age and pass through the various life cycle stages. In comparison with their older counterparts, younger individuals are less committed to definite patterns and are more open to new perspectives and products (de Mooij, 2004), particularly those involving advanced technology. Income also strongly affects product choice, as higher-income consumers are better able to purchase expensive. Higher education levels expose individuals to different cultural perspectives and make them less likely to follow local behavioral norms and more global as consumers (Keillor et al., 2001). Lastly, males and females differ on many aspects of consumer behavior, such as responses to advertising, and the products they tend to buy (Cleveland et al., 2003).

 

Gender percentage

Chart 13 Sex ratio of Internet users in China

Source: CNNIC 2013

 

As of the end of December 2012, the sex ratio of Internet users was 55.8:44.2, similar to that of 2011. Gaps remain in use rate of Internet for male and female residents (CNNIC Statistical Survey on Internet Development in China 2013).

 

Age groups

 

Below 10  10-19     20-29   30-39     40-49    50-59  60 & above

Chart 14 Age groups of Internet users in China

Source: CNNIC 2013

 

Percentage of Internet users between the age of 10 and 19 has declined from 26.7 % in 2011 to 24.0%, relating to the population decline of that age. Besides, percentage of Internet users above the age of 40 increased at different levels for different age sections. And penetration speed of Internet among these groups accelerated.

 

Education background of Internet users in China

Chart 15 Education background of Internet users in China

Source: CNNIC 2013

 

Internet penetration rate among people with high school and junior college degree and above education background has attained a comparatively high level, and among people with junior college degree and above in particular, was saturated. The growth momentum of Internet users is from people with low education background. By the end of 2012, percentage of Internet users with the education background of elementary school and below has grown up to 10.9%.

 

Income structure

Chart 16 Income levels of Internet users in China

Source: CNNIC 2013

 

Internet users with monthly income of above RMB 3,000 increase steadily, accounting for 28.8%, which is 6.5% higher than the end of 2011.

 

2.4.3.2          Social class

 

A major influence on one’s purchasing habits and consumer behavior is the social class in which one finds him or herself. Social class can be described as “divisions within society composed of individuals sharing similar values, interest and behavior.” Social class is considered an external influence on consumer behavior because it is not a function of feelings or knowledge (managementparadise.com 2013).

 

In 2010, Forbes defined the concept of middle class in China as: Urban professionals and entrepreneurs from all walks of life, who have college degrees and earn an annual income from $10,000 to $60,000. In 2010 over three hundred million people, or about 25 percent of China’s population, met these criteria (forbes.com 2010).

 

2.4.3.3          Reference groups & the rise of social media

 

As introduced earlier, a reference group is a concept referring to a group to which an individual or another group is compared. Sociologists call any group that individuals use as a standard for evaluating themselves and their own behavior a reference group. They are groups that people refer to when evaluating their [own] qualities, circumstances, attitudes, values and behaviors (Thompson & Hickey 2005). Reference groups can be a critical source of brand meanings. Consumers use others as a source of information for arriving at and evaluating one’s beliefs about the world, particularly others who share beliefs and are similar on relevant dimensions. Consumer research on reference groups has demonstrated congruency between group membership and brand usage (e.g., Bearden and Etzel 1982) and has defined several types of social influence (e.g., Bearden and Etzel 1982; Park and Lessig 1977).

 

Reference groups can be divided into primary and secondary reference group. A primary reference group is one with which an individual interacts on a regular basis and whose opinion are of importance to him, family, neighbors, close friends, colleagues and co- workers are examples of primary reference groups. Secondary reference groups are those with which an individual interacts only occasionally and does not consider their opinion very important. Also reference groups can be membership and symbolic reference groups. A membership reference group is one to which a person belongs or qualifies for membership (marketing91.com 2013).

 

A consumer may interact with several individuals on a daily basis, and the influence of these people constitutes the social factors that affect the buying process. Social media are self-selecting groups of individuals engaged in sustained computer-mediated interactions around common interests or goals, governed by shared norms and values, and serving individual and shared needs. Virtual communities and social media represent a new type of social formation on the Internet. They expand the power of technology to connect individuals by providing unprecedented opportunities of social interaction and relationships development among people with shared interests irrespective of geography and time (Zhang 2012). Social networks’s influence on consumer behavior is supported by research that indicates that online communities engender a sense of community that moves beyond mere interactions to include affective or emotional attachment.  Also the social structure of online communities follows a typical pattern: More experienced members serve as experts and leaders and newer members seek advice and information (ecopreneurist.com 2013).

 

2.4.4            Consumer behaviors and e-commerce contributors

 

According to the survey carried out by iResearch (2012), in 2012 third-party payment enterprises transform their service from online payment to integrated payment. The maturing online market makes payment enterprises resort to the huge offline market for an integrated business to cater market demands. Also the enterprises expand services from ecommerce to traditional industry. Since there are less space and profit left in ecommerce market, they penetrate into the ecommerce of traditional industry to promote its development. Their strategies transform to create demands instead of simply meeting demands. While the online payment security has been increasingly enhanced by the advanced technologies, preserving privacy of online shopping was other important factors. Online transmission of consumer individual information was easily intercepted and illegal utilized. Out of personal privacy considerations, a great portion of the consumers had a wait and see attitude towards online shopping.

 

2.4.4.1          Logistics

 

The logistics integrates, in addition to the physical tasks, market forecasting, services offered to customers and the location’s choice of factories and warehouses (Akbari Jokar et al., 2000). It is a group of functions related to the flow of goods, information and payment between suppliers and customers, since the acquirement of raw materials until the recycling of finished products. The logistical sensitivity is multi-dimensional. It integrates the sensitivity to the merchandising, to the availability of products and associated information, the shelves logistics sensitivity and the sensitivity to tills logistics. These dimensions are influenced by the loyalty to the retailer and the consumer’s mood (Garrouch 2011).

 

2.4.4.2          Technical support

Lee (2002) presented a behavioral model for the online consumer based on three distinct phases: building trust and confidence, online purchase experience, and after-purchase needs. The first phase examines issues connected to the Web site’s brand name, authentication, reliability, credibility, privacy and security. Intuitive navigation, searching facilities, product information, payment modes, usability and convenience are consumer requirements that affect the second phase. The last phase relates to on-time delivery, customer support, technical support, availability of product warranty, and so forth. Since the combination of three phrases releases a behavioral model that may increase consumer trust and lead to more online purchase, technical support and customer service is necessary to ensure the customers are coming back again and again with good confidence over the brand and platform.

 

2.4.4.3          Customer service

 

Customer service is especially important in today’s socially connected world as consumers can harm the image of a company with a single comment. The customer support personnel can find out customers’ needs according to the sources of visitors and their footprints in the website and then take the initiative to send invitations to customers to offer personalized service to them. The customer support personnel can refer customers to the relevant department or person, thereby realizing barrier-free direct communication between customers and company staff. They can also invite multiple departments to serve customers together and provide the customers with professional, authoritative, and satisfactory solutions. Furthermore, if customer support personnel can see customers’ messages with the real-time viewing function before such messages are submitted, they will be able to prepare the answer in advance, thereby enhancing customer support personnel’s response speed and improving service quality (practicalecommerce.com 2013).

 

2.4.5            Consumer behaviors and decision making process: Online shopping decision making

 

Online shopping decision-making includes information seeking, comparison of alternatives, and choice making. The results bearing on this factor directly influence consumers purchasing behavior. According to Haubl and Trifts (2000), potential consumers appear to use a two-stage process in reaching purchase decisions. Initially, consumers typically screen a large set of products in order to identify a subset of promising alternatives that appears to meet their needs. They then evaluate the subset in greater depth, performing relative comparisons across products based on some desirable attributes and make a purchase decision. Using a controlled experiment, these authors discover that the interactive tools designed to assist consumers in the initial screening of available alternatives and to facilitate in-depth comparisons among selected alternatives in an online shopping environment may have strong favorable effects on both the quality and the efficiency of purchase decisions (Haubl and Trifts 2000, p. 4).

 

2.4.6            Buyer’s response

 

2.4.6.1          Product choice

 

Liang and Huang (1998) argued that product characteristics play a determinant role in consumer choice of retail channels and concluded that some products such as books and flowers are more suitable for marketing on the internet than others considering the effects of perceived transaction costs on the channel choice and the consumers’ experience.

 

2.4.6.2          Brand attitude

 

Brand attitude is defined as “consumer overall evaluations of a brand” (Keller, 1993, p. 4). Keller (1993) contended that brand attitude was important since consumer behavior such as brand choice was based on brand attitude. In general beliefs about specific behavior are referred to as the extent to which a person believes that the behavior has certain attributes or benefits. Balabanis and Reynolds (2001) found that consumer’s attitude toward the brand positively affected their attitude toward the web site of the same brand in a multichannel environment and that the attitude toward the brand’s website and length time spent browsing at the brand’s website were positively correlated “Principles” brand in their study.

 

2.5    The Research Problem, Research Gap, Research Questions, Research Objectives & Research Propositions

 

2.5.1            The Research Problems

 

The main purpose of this study is to identify the consumer behavioral trends in the B2C sector in China based on which this research would obtain its practical significance which is to offer reliable suggestion to the Tmall.com, the large B2C player in China currently. Therefore the research problem is:-

 

What are the major consumer behaviors trends related the Chinese B2C market?

 

2.5.2            The Research Gaps

 

With the reference to the above literature gaps seems to be the following:-

 

There are not enough of studies targeting at the B2C consumer behaviors in China which are done lately;

 

While there are relatively a number of theories and model about B2C consumer decision making, they are not widely applied in the situation of the Chinese online market;

 

With the fast changing of the Chinese culture and economic situation, the online consumer behaviors may be subject to further changes which may not be reflected in the past literature timely.

 

2.5.3            The Research Questions

 

In response to the above research gaps, the research questions will be as following:-

 

What are the latest online consumer details in term of income level, gender ratio and etc.?

 

What are the factors shaping the online consumer behaviors and decision making process in term of both external and internal factors?

 

What Tmall.com shall react to the most recent consumer behaviors?

 

2.5.4            Research Objectives

 

To verify the typical consumer behaviors theories in the Chinese market;;

 

To find out the most recent trend of development of consumer behaviors in the B2C market in China;

 

To identify the marketing implications to the Tmall.com;

 

2.5.5            The Research Propositions

 

The above research objectives shall set with the following research propositions:-

 

Research Proposition 1: Consumers behavioral trends have significant impact over their online consumption behaviors and decision making; the similar situation shall be the same in China;

 

Research Proposition 2: B2C service providers are facing challenges due to the changes of the consumers behavioral trends in China;

 

Research Proposition 3: B2C companies are eager to study the changes of the consumer behaviors and generate their marketing strategies to reflect these changes.

 

2.6    Conclusions

 

This chapter reviewed the literature pertinent to the immediate disciplines and parents disciplines over the research “Consumer Behavior Trend and Implications to Marketing Strategies of Tmall.com in China”. While the major paper and literatures we have reviewed in the above does provide support us to understand the major concepts and theories in relate to this topic as well as the major tools suitable to be applied, we still identified some research gaps such as the lack to updated research over the online consumer behaviors in China.

 

These research gaps have led to the construction of the above research questions, and the setting of the research objectives and research propositions could be used to examine and ensure the consistence of the paper in a whole to finally resolve the said research questions.

 

3.        Chapter 3: The research methodology

 

3.1    Research approach

 

Research methodologies can be quantitative (for example, measuring the number of times someone does something under certain conditions) or qualitative (for example, asking people how they feel about a certain situation). Ideally, comprehensive research should try to incorporate both qualitative and quantitative methodologies (blurtit.com 2010). Bryman (1988) argued for a `best of both worlds’ approach and suggested that qualitative and quantitative approaches should be combined. In this project, qualitative will be the primary research approach while the quantitative methodologies will be used as the supplementary method to be applied to further strengthen the qualitative findings.

 

3.2    Data collection

 

The choice of method is influenced by the data collection strategy, the type of variable, the accuracy required, the collection point and the skill of the enumerator. Links between a variable, its source and practical methods for its collection, the main data collection methods include registration, questionnaires, interviews, direct observation and reporting (fao.org 2009).

 

The primary data collection method to be adopted will be the original online survey targeting at a sampling volume of 100 people and these people will be based on random selection from the online customers;

 

The secondary data collection method to be adopted will include text book, journals and news which are generated after year of 2005.

 

3.3    Data analysis

 

Data analysis refers to the use of descriptive and inferential statistical techniques to transform raw data into information that is easier to manipulate, understand, and report (nrepp.samhsa.gov 2010). Both qualitative analysis and quantitative analysis (such as contingence and correlation) will be adopted to test the research propositions as well as achieving the research targets. In term of the specific analytical tool to be adopted in this project paper is the famous SPSS which is an application of statistic elaboration. It is made for the professional usage. It allows to conceive one’s graphs, diagrams, boards, pie chart and to announce one’s results. It has simple and intuitive interface. With various versions of SPSS, here we selected the IBM SPSS Statistics which is a comprehensive, easy-to-use set of data and predictive analytics tools for business users, analysts and statistical programmers (ibm.com 2013).

 

3.4    Ethical issues

 

Invitation of the online survey should be offered to the public with no discrimination to gender difference, job title, age groups and etc so long as they are online customers;

 

The online survey could start only when the online customers is informed about the content and intension of the survey and the agreement of attending the survey is offered by the respondents. This is to ensure that the survey participants are well informed about what they would be asked about and it is their true willing to join the survey.

 

The name and other private information should not be disclosed to any parties and the use of the collected data should be confined in this research only.

 

3.5    Research Limitations

 

Like most research papers, this project paper is subject to several major limitations listed as followings:-

 

  1. Limited to the Chinese market: As mentioned at the introduction sections, this study is limited to the research of the consumer behavioral trends targeting at the consumers doing regular shopping in China; thus our study here is limited to the research of the Chinese market rather than research of any other markets. On the other hand, this limitation means that the conclusions drawn from this study is approved to be typically applicable and meaningful in the Chinese e-commerce sector (though we do not exclude the possibility that these conclusions could act as important and most recent traits of the e-commerce market for other markets outside of China). Nevertheless, we do use widely applicable theories and models which are mostly developed in the western countries in this paper. However, these theories are generally considered as targeting at the e-commerce business sector itself rather than any specific countries / markets, thus they are to a large degree universally applicable, therefore it would be appropriate to apply them in this paper with scope limited to the Chinese market.

 

  1. Limited to the B2C market: As said at the beginning that here in this research paper we will focus only on the B2C sector while the data of C2C and B2B business sectors would be reviewed for reference only.

 

4.        Data Analysis and Findings

 

4.1    Part A: Profile of Respondents

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Valid Male 52 52.0 52.0 52.0
Valid Female 48 48.0 48.0 100.0
Total 100 100.0 100.0

Chart 17 Answers to question “What is the gender group that you are belonging to?”

 

As reviewed in the literature review part, as of the end of December 2012, the sex ratio of Internet users was 55.8 : 44.2, similar to that of 2011. Gaps remain in use rate of Internet for male and female residents (CNNIC Statistical Survey on Internet Development in China 2013). Speaking from the answers submitted by 100 nos of random respondents, from the above chart and SPSS data, we can see that 52 male respondents and 48 female counterparts are found in this survey which is in high consistence with the user data released by the CNNIC as of at the end of 2012. On the other hand, it also in return reflects the fact that the sampling of the survey is normal and typical. Assume the online user gender percentage is still 55.8:44.2 when this survey was performed, since all the participants of this survey are required to be online customers another major conclusion that is reached is that the penetration rate of online shopping among the male internet user group is a little bit lower than that of the female counterparts.

Chart 18 Answers to question “What is your age group?[2]”

 

From the analysis of the answers to question “What is your age group?, there are four major conclusions that could be drawn:-

 

  1. First of all, the age group distribution of the 100 nos of online customers surveyed is similar to the Internet user age groups reviewed in the literature part. The common major feature of the age group distribution among the online users and age group distribution among the online customers is that the young age groups account for the major part of the total sample. As for the age group distribution among the online users, percentage of online users between the age of 10 and 39 is 80.95%; in comparison for the age group distribution among the online customers, percentage of online customers between the age of 10 and 39 is 80%. Therefore, we can see that online customer age structure affected by the internet user age structure tend is marked with a large proportion of young users. This conclusion is also well within our common understanding as young people in their young age tend to be susceptible to new technology (e.g. online payment & internet surfing).

 

  1. Very young users aged from 10 ~ 19 and very old users aged from 50 and above account for very little share of the total sample. This may be understood in two aspects: the young age users do not have the spendable income in online shopping even though they may know how to surf the internet; on the other hand, the old users may have sufficient income but their demand in online shopping could be constrained by the old thinking as well as reduced and abated physical demand.

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Valid Han 92 92.0 92.0 92.0
Valid Menggu 2 2.0 2.0 94.0
Valid Hui 2 2.0 2.0 96.0
Valid Zang 1 1.0 1.0 97.0
Valid WeiWuEr 1 1.0 1.0 98.0
Valid Miao 0 0.0 0.0 98.0
Valid Others 2 2.0 2.0 100.0
Total 100 100.0 100.0

Chart 19 Answers to question “What is your ethnic group?

 

In term of the ethnic structure of the surveyed online customers, we can see that with a 90 percentage, the Han Chinese is the majority race in China which is accordance with the national demographic detail: The People’s Republic of China (PRC) officially recognizes 55 ethnic minority groups within China in addition to the Han majority. As of 2010, the combined population of officially recognised minority groups comprised 8.49% of the population of mainland China (china.org.cn 2010).

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Over RMB 8000 5 5.0 5.0 5.0
5001 – 8000 6 6.0 6.0 11.0
3001 – 5000 16 16.0 16.0 27.0
2001 – 3000 19 19.0 19.0 46.0
1501 – 2000 9 9.0 9.0 55.0
1001 – 1500 9 9.0 9.0 64.0
501 – 1000 13 13.0 13.0 77.0
Below 500 13 13.0 13.0 90.0
No Income 10 10.0 10.0 100.0
Total 100.0 100.0

Chart 20 Answers to respondents’ individual monthly income[3]

 

From individual monthly income data collected from the surveyed respondents, we can see that the there are two major income groups found:-

 

  1. Monthly income level ranged from RMB 0 to RMB 1000 (36%); &
  2. Monthly income level ranged from RMB 2001 to RMB 5000 (35%).

 

For the first income group, we can understand them as low income customers. They either have no income or have little income, i.e. a monthly incommme lowered than RMB 1,000 (or US $163.4). Though some of the respondents under this income group may have expenditure more than their actual income level by depending on others such as wives and children depending on the husbands and parents. And in general, the ability of expenditure of this income group is considered as little and in a low level.

 

For the second income group, we can understand them as middle income[4] group whose monthly incomes are ranged between RMB 2001 and RMB 5000 and this group account for 35% of the total sample.

 

These two largest income groups found in the 100 number of respondents surveyed are also in consistence with the internet user income structure discussed in the relevant contents in the literature part.

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Elementary education and below 9 9.0 9.0 9.0
Junior high school 29 29.0 29.0 38.0
Senior high school 33 33.0 33.0 71.0
Junior college 14 14.0 14.0 85.0
University and above 15 15.0 15.0 100.0
Total 100.0 100.0

 

Chart 21 Respondents’ education level in comparison with the same of the Internet users in 2012

 

In term of educations of the Chinese online customers, we can see that users who shop online tend to have higher education degree in comparison with the average level among the national internet users:-

 

  1. Here we group respondents with education degree of “University and above”, “Junior college” and “Senior high school” as having high education degree. Then we can see that the 15% respondents are having an educational degree of University and above, 3.7 percentage points higher than that among the internet users; the 14% respondents are having an educational degree of Junior college, 4.2 percentage points higher than that among the internet users; 33% respondents are having an educational degree of Senior high school or equal, 0.8 percentage points higher than that among the internet users in 2012. In total, the percentage of respondents under the group of high education degree is 8.6 percentage points higher than that among the internet users.

 

  1. Here we group respondents with education degree of “Junior high school” and “Elementary education and below” as having low education degree. Then we can see that the 29% respondents are having an educational degree of Junior high school, 6.6 percentage points lower than that among the internet users; 9% respondents are having an educational degree of Elementary education and below, 1.9 percentage points lower than that among the internet users.

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
First-tier cities 24 24.0 24.0 24.0
Second–tier cities 19 19.0 19.0 43.0
Third–tier cities 18 18.0 18.0 61.0
Fourth–tier cities 5 5.0 5.0 66.0
Rural areas and other regions 34 34.0 34.0 100.0
Total 100.0 100.0

Chart 22 Answers in respect of location / area of the respondents

 

From the above chart we can see that the 100 respondents cover all the 1st to 4th tier cities and rural areas. And based on the results of the survey, the majority of the online customers (66 per cent of the total sample) are from various cities while those from the rural areas account for only 34 percentages of whole 100 respondents. One more trend that could be observed from the above results is that areas with higher degree of economic development tend to demonstrate higher penetration rate of online shopping. It could be understand that people from more developed cities and areas are more acceptable to new things such as online shopping.

 

4.2    Part B: General Consumption Behaviors

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
1 – 2 times 8 8.0 8.0 8.0
3 – 4 times 13 13.0 13.0 21.0
5 to 10 times 23 23.0 23.0 44.0
11 times or more 56 56.0 56.0 100.0
Total 100.0 100.0

Chart 23 Answers in respect of online shopping frequency

 

As reviewed in the literature review part earlier, according to the “2012 China’s online shopping market research report” released by China Internet Network Information Center (2013) in March this year, in 2012, there were 54.5% of online customers whose online shopping in six months exceed 10 times. And based on our survey, this number has grown from 54.5% in 2012 to 56%. And as for other ranges of shopping frequencies, the percentage of online customers whose online shopping in six month ranges from 5 to 10 times increases from 2012’s 21% to 23%. And in particular the percentage of online customers whose online shopping frequency in six month ranges from 1 to 2 times reduce from 10.5% in 2012 to 8%. Based on the above comparison and analysis, assume that the survey truly reflects the average situation of online shopping, it is found out that more Chinese online customers are shopping more and their buying frequency is growing in a fast speed.

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
More than 50000 2 2.0 2.0 2.0
20001 – 50000 3 3.0 3.0 5.0
10001 – 20000 6 6.0 6.0 11.0
8001 – 10000 13 13.0 13.0 24.0
5001 – 8000 10 10.0 10.0 34.0
2001 – 5000 26 26.0 26.0 60.0
1001 – 2000 13 13.0 13.0 73.0
501 – 1000 16 16.0 16.0 89.0
301 – 500 5 5.0 5.0 94.0
101 – 300 4 4.0 4.0 98.0
Less than 100 2 2.0 2.0 100.0
Total 100.0 100.0

Chart 24 Answers in respect of Chinese online customers’ expenditure in comparison with 2012 data

 

As pointed out in the literature review, based on the “2012 China’s online shopping market research report” released by China Internet Network Information Center (2013), in year of 2012 China’s online shoppers are spending 5,203 yuan per capita on average, an increase of 1,302 yuan and an increase of 25%. At the same time, the online customers demonstrated strong spending power on online shopping with 56% of users of online shopping spending over 1,000 yuan annually; most users spend 2001-5000 yuan accounting for 22.6% of the total sample; followed by 501-1000 yuan (accounting for 22.3%). Through a comparison of the respective survey results and the “2012 China’s online shopping market research report” results, we may come to some more findings in respect of the annual expenditure over online shopping in various groups in China:-

 

  1. We categorize respondents with annual expenditure exceeding RMB 10,000 as extremely high end customers. Speaking from the extremely high end customers, last year according to the “2012 China’s online shopping market research report” results, the percentage of customers in this group accounted for 6.8%; in contrast, the percentage of customers with the same level of expenditure based on our original survey is 11%. In another world, the survey results show an increase of 4.2 percentages points in the extremely high end online customers.

 

  1. We categorize respondents with annual expenditure between RMB 5,000 and RMB 10,000 as high end customers. Speaking from the high end customers, last year according to the “2012 China’s online shopping market research report” results, the percentage of customers in this group accounted for 12.1%; in contrast, the percentage of customers with the same level of expenditure based on our original survey is 23%. In another world, the survey results show an increase of 10.9% percentages points in the high end online customers.

 

  1. We categorize respondents with annual expenditure between RMB 2,000 and RMB 5,000 as middle end customers. Speaking from the middle end customers, last year according to the “2012 China’s online shopping market research report” results, the percentage of customers in this group accounted for 22.6%; in contrast, the percentage of customers with the same level of expenditure based on our original survey is 26%. In another world, the survey results show an increase of 3.4 percentages points in the middle end online customers.

 

  1. We categorize respondents with annual expenditure no more than RMB 2,000 as lower end customers. Speaking from the lower end customers, last year according to the “2012 China’s online shopping market research report” results, the percentage of customers in this group accounted for 58.8%; in contrast, the percentage of customers with the same level of expenditure based on our original survey is 40%. In another world, the survey results show a decrease of 18.8 percentages points in the lower end online customers.

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Less than 5 minutes 16 16.0 16.0 16.0
6 – 10 minutes 14 14.0 14.0 30.0
11 – 15 minutes 22 22.0 22.0 52.0
15 – 30 minutes 32 32.0 32.0 84.0
More than 30 minutes 16 16.0 16.0 100.0
Total 100.0 100.0

Chart 25 The length of time the respondents spend on online shopping in a typical day

 

In a typical day, on average the respondents spend 22.3 minutes on online shopping including time spent on browsing of various goods by visiting various platforms. Looking at the detailed groups, those spend over 15 minutes account for 48 percentages of the 100 respondents; in particular most respondents spend 15 to 30 minutes on the online shopping. Therefore, looking at the surveyed online customers over 84% of them have regular shopping behaviors which is highly positive to the e-commerce sector indicating a well developed customer base in China.

1st-tier cities 2nd-tier cities 3rd–tier cities 4th–tier cities Rural areas and other regions
Less than 5 minutes 16.67% 15.79% 11.11% 0.00% 20.59%
6 – 10 minutes 25.00% 15.79% 11.11% 20.00% 5.88%
11 – 15 minutes 33.33% 26.32% 22.22% 0.00% 14.71%
15 – 30 minutes 20.83% 31.58% 33.33% 40.00% 38.24%
More than 30 minutes 4.17% 10.53% 22.22% 40.00% 20.59%
Mean
Mean of length of time 16 minutes 21.5 minutes 23 minutes 25.5 minutes 24.7 minutes
Mean among 100 respondents 22.3 minutes

Chart 26 The length of time the respondents spend on online shopping per day (By different areas)

 

The above chart further looks at the 100 respondents in respect of their time spent on online shopping by considering their respective geographical location, there facts could be concluded:-

 

  1. Looking at the answers in respect of the length of time the respondents from the 1st tier cities spend on daily online shopping, 16.67 per cent of the respondents from the 1st tier cities spend less than 5 minutes on online shopping, 25 per cent of the respondents from the 1st tier cities spend 6 to 10 minutes on online shopping, 33.33% of the respondents from the 1st tier cities spend 11 to 15 minutes on daily online shopping, 20.8% of the respondents from the 1st tier cities spend 15 to 30 minutes on online shopping while only 4.17 per cent of the respondents from the 1st tier cities spend over half an hour on online shopping through various online shopping platforms. Among the five sub groups, the largest single group of respondents from the 1st tier cities spend 11 to 15 minutes in daily online shopping while the mean for all respondents from 1st tier cities spend 16 minutes per day on e-commerce, 6.3 minutes or 28.25 per cent less compared with the total average. Also with reference to the reviewed data in the literature part that customers from the 1st tier cities such as Beijing, Shanghai, Guangzhou were found to spend an average duration of 18.5 minutes on online shopping; and according to our survey, the same digit has shifted from 18.5 minutes to 16 minutes representing a decline of 13.5 per cent. This may be explained by higher and higher working and life pressure for respondents from the 1st tier cities whose leisure time has been further reduced.

 

  1. Looking at the answers in respect of the length of time the respondents from the second tier cities spend on daily online shopping, 15.79 per cent of the respondents from the second tier cities spend less than 5 minutes on online shopping, 15.79 per cent of the respondents from the second tier cities spend 6 to 10 minutes on online shopping, 26.32% of the respondents from the second tier cities spend 11 to 15 minutes on daily online shopping, 31.58% of the respondents from the second tier cities spend 15 to 30 minutes on online shopping while only 10.53 per cent of the respondents from the second tier cities spend over half an hour on online shopping through various online shopping platforms. Among the five sub groups, the largest single group of respondents from the 2nd tier cities spend 15 to 30 minutes in daily online shopping while the mean for all respondents from 2nd tier cities spend 21.5 minutes per day on e-commerce, 0.8 minutes or 3.6% per cent less compared with the total average (22.3 minutes). Also with reference to the reviewed data in the literature part that customers from the 2nd tier cities were found to spend an average duration of 19 minutes on online shopping; and according to our survey, the same digit has shifted from 19 minutes to 21.5 minutes representing a rapid growth of 13.16 per cent. This hints that online customers tend to have more time (5.5 minutes) in online shopping compared to respondents from the 1st tier cities.

 

  1. Looking at the answers in respect of the length of time the respondents from the 3rd tier cities spend on daily online shopping, 11.11 per cent of the respondents from the 3rd tier cities spend less than 5 minutes on online shopping, 11.11 per cent of the respondents from the 3rd tier cities spend 6 to 10 minutes on online shopping, 22.22% of the respondents from the 3rd tier cities spend 11 to 15 minutes on daily online shopping, 33.33% of the respondents from the 3rd tier cities spend 15 to 30 minutes on online shopping while only 22.22 per cent of the respondents from the 3rd tier cities spend over half an hour on online shopping through various online shopping platforms. Among the five sub groups, the largest single group of respondents from the 3rd tier cities spend 15 to 30 minutes in daily online shopping while the mean for all respondents from 3rd tier cities spend 23 minutes per day on e-commerce, 1.5 minutes or 7% per cent more compared with the overall average (22.3 minutes). Also with reference to the reviewed data in the literature part that customers from the 3rd tier cities were found to spend an average duration of more than 20 minutes on online shopping; and according to our survey, the same digit is about 23 minutes which in consistency with the previous data.

 

  1. Looking at the answers in respect of the length of time the respondents from the 4th tier cities spend on daily online shopping, 0 per cent of the respondents from the 4th tier cities spend less than 5 minutes on online shopping, 20 per cent of the respondents from the 4th tier cities spend 6 to 10 minutes on online shopping, 0% of the respondents from the 4th tier cities spend 11 to 15 minutes on daily online shopping, 40% of the respondents from the 4th tier cities spend 15 to 30 minutes on online shopping while only 40 per cent of the respondents from the 4th tier cities spend over half an hour on online shopping through various online shopping platforms. Among the five sub groups, the largest single group of respondents from the 4th tier cities spend 15 to 30 minutes (tired with those spending over “more than 30 minutes”) in daily online shopping while the mean for all respondents from 4th tier cities spend 25.5 minutes per day on e-commerce, 2.2 minutes or 9.87 per cent more compared with the total average (22.3 minutes). Also with reference to the reviewed data in the literature part that customers from the 4th tier cities were found to spend an average duration of more than 20 minutes on online shopping; and according to our survey, the same digit is now 25.5 minutes which in consistency with the previous data.

 

  1. Looking at the answers in respect of the length of time the respondents from the rural areas and other regions spend on daily online shopping, 20.59 per cent of the respondents from the rural areas and other regions spend less than 5 minutes on online shopping, 5.88 per cent of the respondents from the rural areas and other regions spend 6 to 10 minutes on online shopping, 14.71% of the respondents from the rural areas and other regions spend 11 to 15 minutes on daily online shopping, 38.24% of the respondents from the rural areas and other regions spend 15 to 30 minutes on online shopping while only 20.59 per cent of the respondents from the rural areas and other regions spend over half an hour on online shopping through various online shopping platforms.

 

Among the five sub groups, the largest single group of respondents from the rural areas and other regions spend 15 to 30 minutes in daily online shopping while the mean for all respondents from rural areas and other regions spend 24.7 minutes per day on e-commerce, 2.7 minutes or 9.87 per cent more compared with the total average (22.3 minutes). From the above data, we can see that the average time spend on online shopping seems to increase with the when the degree of economic development decreases though the digit of the customers from rural areas and other regions seems to be a little less compared with that of the fourth–tier cities.

 

Item Percentages of respondents 2012 Data (CNNIC) Difference
Jewelry Accessories 9 6.7 2.3
Stationery 6 6.8 -0.8
Baby products 7 6.9 0.1
Movie tickets 10 8.5 1.5
Food and beverage service 9 8.5 0.5
Handbags, luggage 14 12.8 1.2
Food, health products 14 14.5 -0.5
Cosmetics and Beauty Products 15 15.2 -0.2
Prepaid cards, game cards and other virtual cards 18 16.6 1.4
Books audio and video products 20 18.4 1.6
Household appliances 25 22.9 2.1
Computers, digital communications products and accessories 34 29.6 4.4
General merchandise 35 31.6 3.4
Clothing and shoes 85 81.8 3.2
Average 21.5 20.06 1.44

Chart 27 Products purchased online by respondents against 2012 CNNIC data (%)

 

In term of product types, we can see that the top three product types that people would shop online are Clothing and shoes, General merchandise and Computers, digital communications products and accessories with 85%, 35% and 34% respondents saying that they had ever purchased them via an online purchasing platform. As for other products types, their penetration rate among the surveyed respondents is much lower in particular the Jewelry Accessories, Stationery, Baby products, Movie tickets Food and beverage service. With reference to the 2012 Data (CNNIC), we can see that:-

 

  1. In general, the findings of our original survey which had been participated by 100 respondents are in consistency with the 2012 results released by CNNIC in term of the respective penetration rate in major product categories;

 

  1. On the other hand, by looking at the difference between the survey findings and the published data, from a general perspective the overall penetration rate has further increased. Among the 14 selected / listed product categories, the average online penetration rate among the surveyed customers is 21.5 while the same according to the published data by CNNIC in 2012 was 20.06 indicating that the overall online penetration rate in the 14 listed product types have on average increased by 1.44 percentage points.

 

4.3    Part C: Consumer behaviors and marketing stimuli

SPSS ANALYSIS newspaper magazines television radio_ad outdoor_ad direct_mail
Mean 1.6000 2.0000 2.4000 1.4000 2.1000 1.3000
N 100 100 100 100 100 100
Std. Deviation .84327 1.05409 1.50555 .69921 1.28668 .67495
e_mail text_messages mobile_app_ads website_ads blog_ads
Mean 1.7000 2.2000 3.0000 2.5000 2.4000
N 100 100 100 100 100
Std. Deviation .82327 1.03280 1.24722 1.64992 1.50555

Chart 28 Effectiveness of various ad forms (ranked by Mean)

 

By summarizing the answers from respondents in respect of effectiveness of various ad forms in leading the respondents into the online shopping behaviors, we can see that the traditional advertising methods as the earliest and endurable forms of communication for marketing which are used to encourage, persuade, or manipulate the target audience have shown some degree of ineffectiveness in comparison with the new media including the text messages, mobile app adds, website ads & blog ads. For instance, radio and direct have become the least effective methods in pushing the customers into the actual online purchasing behaviors. Ranked by the value of mean in respect of the various form of ads, the most effective methods are: Mobile app ads, television, blog ads & website ads. But it should also be noted that the standard deviations of the website ads and blog ads are higher than most other forms, meaning to say that these ad form are very effective to some customers while at the same time they are not really effective to others; and in comparison, the mobile app ads seems to be a relatively better advertising tool to promote the products with a relatively lower standard deviation value.

 

4.4    Part D: Consumer behaviors and internal influencing factors

 

Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
monday 100 100.0% 0 0.0% 100 100.0%
tuesday 100 100.0% 0 0.0% 100 100.0%
wednesday 100 100.0% 0 0.0% 100 100.0%
thursday 100 100.0% 0 0.0% 100 100.0%
friday 100 100.0% 0 0.0% 100 100.0%
saturday 100 100.0% 0 0.0% 100 100.0%
sunday 100 100.0% 0 0.0% 100 100.0%

 

Report
monday tuesday wednesday thursday friday saturday sunday
Mean 3.0000 3.4000 3.5000 3.5000 3.1000 3.6000 4.0000
N 100 100 100 100 100 100 100
Std. Deviation 1.33333 1.07497 .97183 1.17851 1.28668 1.50555 1.33333

Table 3 SPSS analysis of answers in respect of online purchasing possibility from Monday to Sunday

 

From the above SPSS analysis of the answers in respect of online purchasing possibility from Monday to Sunday, we can see that even though weekend (Sat & Sun) still act as regularly important timing for the customers to make online shopping. But based on the analysis of mean of each day’s possibility of purchasing, the rest days (Monday to Friday) are all convenient days for the respondents to do online shopping. In another word, there are not major differences between weekend (Saturday and Sunday) and other days.

 

Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
new_year_day 100 100.0% 0 0.0% 100 100.0%
spring_festival 100 100.0% 0 0.0% 100 100.0%
qingming 100 100.0% 0 0.0% 100 100.0%
may_day 100 100.0% 0 0.0% 100 100.0%
dragon_boat 100 100.0% 0 0.0% 100 100.0%
mid_autumn 100 100.0% 0 0.0% 100 100.0%
national_day 100 100.0% 0 0.0% 100 100.0%
Report
new_year_day spring_

festival

qingming may_day dragon_boat mid_autumn national_day
Mean 3.2000 4.1000 3.1000 3.2000 3.8000 4.2000 3.9000
N 100 100 100 100 100 100 100
Std. Deviation 1.22927 .99443 1.37032 1.47573 1.03280 1.03280 .99443

Table 4 SPSS analysis of answers in respect of online purchasing possibility in major Chinese holidays

 

From the above analysis, the collected answers tell that people have a relatively very high possibility to do their purchasing around these holidays online. In particular the major holidays such as Spring Festival and Mid Autumn Day, people are even more confirmable and likely to do shopping online.

 

4.5    Part E: Consumer behaviors and extenral influencing factors

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Never 15 15.0 15.0 15.0
Often 23 23.0 23.0 38.0
Rarely 35 35.0 35.0 73.0
Sometimes 27 27.0 27.0 100.0
Total 100.0 100.0

Chart 29 Survey results in respect of frequency of checking the social networks before purchasing

 

From the above chart, we can learn that Chinese online customers are having regular check on the various social network platforms when they have an intention to buy something online. Among the 100 surveyed respondents who have online shopping experience, only 15 per cent of them never seek reference from other users including friends, families and colleagues and so on before they make any purchasing online; with another 35 per cent of the respondents only have experience in seeking suggestions and ideas from the social media in a rare case; 27 per cent of the respondents actually do this sometime and the most importantly 23 per cent of the total respondents often seek reference before doing online purchasing from social networks and this percentage has exceeded that of those who never visit the social network for online purchasing assistance. Also in comparison with the data published by CNNIC which had been discussed and reviewed in the literature part, the percentage of those who had never checked the social networks before purchasing online has decreased by 5 percentage points while the percentage of those who either sometimes or often check the social websites before making the purchasing decision has increased from 43% to 50%.

 

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Never 18 18.0 18.0 18.0
Often 23 23.0 23.0 41.0
Rarely 23 23.0 23.0 64.0
Sometimes 36 36.0 36.0 100.0
Total 100.0 100.0

Chart 30 Survey results in respect of online purchasing led by social networks in half a year

 

From the above chart, we can learn that Chinese online customers are having regular check on the various social network platforms even when they do not have an intention to buy anything from online. Among the 100 surveyed respondents who have online shopping experience, only 18 per cent of them never seek reference from other users including friends, families and colleagues and so on even when they do not have an intention to buy anything from online; with another 23 per cent of the respondents only have experience in seeking suggestions and ideas from the social media in a rare case; 36 per cent of the respondents actually do this sometime and the most importantly 23 per cent of the total respondents often seek reference even when they do not have an intention to buy anything from online and this percentage has exceeded that of those who never visit the social network in case with no purchasing intention. Also in comparison with the data published by CNNIC which had been discussed and reviewed in the literature part, the percentage of those who had never checked the social networks before purchasing online has decreased by 7 percentage points while the percentage of those who either sometimes or often check the social websites even when they do not have an intention to buy anything from online has increased from 47% to 59%.

 

4.6    Part F: Consumer behaviors and e-commerce contributors

 

E-commerce payment methods Percentages of respondents 2012 Data (CNNIC) Difference
Post office or bank counter transfer / remittance 2% 4.80% -2.80%
Quick payment[5] 24% 18.50% 5.50%
Cash on delivery 29% 32.40% -3.40%
Third-party payment / account balance payment 70% 61.60% 8.40%
Internet banking payment (including debit cards, credit cards) 68% 63.10% 4.90%
Average 38.6% 36.08% 2.52%

Chart 31 Online shopping payment methods in comparison with the 2012 data

 

In term of the payment methods, we can see that the top three payment methods adopted by the online customers in China based on the survey results are Third-party payment / account balance payment, Internet banking payment (including debit cards, credit cards) and Cash on delivery with 70%, 68% and 29% respondents saying that they had ever purchased good using the respective payment method while doing their online shopping. In contrast, the traditional payment method of Post office or bank counter transfer / remittance had used by very small percentage of respondents. Comparing with the published data in 2012, we can see that:-

 

  1. In general, the findings of our original survey which had been participated by 100 respondents are in consistency with the 2012 results released by CNNIC in term of the respective usage rate of the major payment methods;

 

  1. On the other hand, by looking at the difference between the survey findings and the published data, from a general perspective the overall usage rates have further increased. Among the 5 selected / listed payment modes, the average usage rate among the surveyed customers is 38.6% while the same according to the published data by CNNIC in 2012 was 36.08% indicating that the average usage rate among the surveyed customers in the 5 selected / listed payment modes have on average increased by 2.52 percentage points.

 

  1. Third-party payment / account balance payment as an payment method has surpassed Internet banking payment (including debit cards, credit cards) and becomes the most used payment method for the online shopping.

 

4.7    Part G: Buyer’s response

 

In order to survey the online customers’ level of satisfaction with the major online shopping platforms, in our survey we listed out 11 main online shopping websites and invited the respondents to rate the performance of these websites in China in 5 aspects:

 

  1. Range of products;
  2. Product quality;
  3. Price competitiveness;
  4. After sale; &
  5. Delivery;

 

Since respondents were only required to rate those website where they had purchasing experiences with, the number of respondents vary from website to website. Below are the records of the respect number of respondents recorded for the selected 11 main online shopping websites. On the other hand, the numbers of respondents for each website to some extent also reflect the respective penetration rate of the websites among the Chinese online customers:-

 

  1. Tmall.com (54 nos of respondents rated the website)
  2. suning.cn (7 nos of respondents rated the website)
  3. JD.com (32 nos of respondents rated the website)
  4. Amazon China (12 nos of respondents rated the website)
  5. Paipai.com (6 nos of respondents rated the website)
  6. Taobao.com (89 nos of respondents rated the website)
  7. Vancl.com (13 nos of respondents rated the website)
  8. Dangdang.com (17 nos of respondents rated the website)
  9. Yihaodian.com (9 nos of respondents rated the website)
  10. m18.com (5 nos of respondents rated the website) &
  11. VIP Shop (6 nos of respondents rated the website).

 

Based on the SPSS analysis (see appendix 3), the following results have been obtained:

 

Aspect Average Score (1-5) Ranking in 11 websites
Range of products 3.45 3
Products quality 3.6 2
Price competitiveness 3.65 5
After sale 3.35 7
Delivery 3.45 7

Table 5 Performance of Tmall.com out of 11 websites

 

From the above table and the attached SPSS analysis, we can see that Tmall.com has a strong performance in offering wide range of products (more options) as well as excellent product quality in comparison with other competitors. It is ranked as 2nd place and 3rd place in the aspect of Products quality and Range of products respectively. And in term of price competitiveness, Tmall.com is only ranked at the 5th place indicating that the good sold in Tmall.com are not priced as competitively as like some other competitors. And the most important issue is that the after sale and delivery have not received good evaluations from the respondents.

 

5.        Conclusion and Recommendations

 

5.1    Introduction

 

In this part of the study, we will try to drawing some conclusions in response to our research questions, research problems and research objectives as well as analyzing the implications to the marketing strategy setting for Tmall.com.

 

5.2    Conclusions on research questions

 

The online consumer behavioral trends in China found out above are not exactly consistence with the literature; our survey results show that Chinese online customers have the particular features in term of General Consumption Behaviors, Consumer behaviors and marketing stimuli, Consumer behaviors and internal influencing factors, Consumer behaviors and extenral influencing factors, Consumer behaviors and e-commerce contributors and Buyer’s response.

 

With the above statement and analysis, this research has achieved its destination in offering detailed analysis in respect of the topic of “online customer behaviors in China” with the following conclusions that could be drawn from the above theoretical review as well as discussion and analysis of an original survey participated by 100 Chinese online customers:-

 

  1. In term of market segmentations, we can segment a market with gender, age, educational background and income level and so on. For instance, online customers with an annual expenditure exceeding RMB 10,000 can be classified as extremely high end customers; online customers with an annual expenditure between RMB 5,000 and RMB 10,000 can be classified as high end customers; online customers with an annual expenditure between RMB 2,000 and RMB 5,000 can be classified as middle end customers and online customers with an annual expenditure no more than RMB 2,000 can be classified as lower end customers.

 

  1. With a 80% young customers aged from 10 ~ 39 among the surveyed 100 participants, the online customer group in China is sharing with strong features of purchasing behaviors of the young customers such as high possibility of impulse buying behavior.

 

  1. In term of educational background, in comparison with the internet users, the surveyed respondents are found to have received relatively higher education. In another perspective, it may also be understood that there are difficulties for the less educated internet users to do their first shopping online which to some degree explains the reason why there is a lower penetration rate of online shopping among the respondents with relatively lower educational degree.

 

  1. In term of promotion, new media ads such as blogging and text message are surpassing the traditional ad forms though the effectiveness of the new media ads seems to vary and fluctuate in comparison with the traditional ways.

 

  1. In term of the social media’s influence on consumer behaviors, various social networks have played an increasingly important role as a reference group as the percentage of those who either sometimes or often check the social websites before making the purchasing decision has reached 50% according to our report and percentage of those who either sometimes or often check the social websites even when they do not have an intention to buy anything from online has increased from 47% (CNNIC 2012 data) to 59% based on our survey results.

 

  1. Based on the buyers’ response, Tmall.com has done a good job in offering wide range of product choices and good product quality, but there is still much space for improvement in particular for the improvements in delivery and after sale service in order to keep and enhance core competitiveness in competing with other opponents.

 

5.3    Conclusion on the research propositions

 

5.3.1            Conclusion on the research proposition 1

 

Consumer behaviors theories are found to be applicable in the B2C market in China and the research proposition 1 which suggest that “Consumers behavioral trends have significant impact over their online consumption behaviors and decision making” is considered as true.

 

5.3.2            Conclusion on the research proposition 2

 

With increasing competition and consumers’ various choices of online shopping platforms, it is also true that “B2C service providers are facing challenges due to the changes of the consumers behavioral trends in China”.

 

5.3.3            Conclusion on the research proposition 3

 

With the increasing competition, it is also safe to say that “B2C companies are eager to study the changes of the consumer behaviors and generate their marketing strategies to reflect these changes”.

 

5.4    Conclusion on the Research Problem

 

In response to the research problem that “What are the major consumer behaviors trends related the Chinese B2C market?”, the young Chinese online consumers affected by the new marketing stimuli and various internal and external influential factors and e-commerce contributors, this research problems are addressed in our detailed analysis and various conclusions obtained. Setouts hereunder are the recommendations on marketing strategies offered to Tmall.com.

 

5.5    Recommendations

 

5.5.1            Business strategies

 

5.5.1.1          Don’t miss the aged groups

 

With a young population of online customers but an aging population trend, the penetration of internet as well as online shopping among the elders would be critical in the future e-business. On one hand, the Chinese population is rapidly aging due to a lower mortality rate and the one child policy. At the same time, this creates a growing market for healthcare products and services in China. Just as the rise of the Baby Boomers had placed an indelible mark on the U.S. economy, China’s demographic shift to an older society will have a profound impact on the Chinese economy (wikinvest.com 2012), no doubt that this include the aspect of online shopping. Therefore, while the young age population tends to dominate the online shopping market in term of percentage, the aging population would also tend to alter the whole situation in particular when the current young customers grow old. Though it may take some time for such change, the aging population will certainly become a more and more customer group in the Chinese B2C market speaking from the long term perspective.

 

5.5.1.2          Don’t miss the less educated people as potential online customers

 

As concluded, with a relatively lowered penetration rate of online shopping among the less educated internet users (education degree being below high school) and it is possible that these less educated people encounter difficulties to utilize the internet access to start their online purchasing. Therefore, the Tmall.com as the market leader shall initiate programs or actions to offer instructions, education or special service for these less educated potential customers to perform their very first online shopping. For example, there are at least two aspects where the Tmall.com could make its contributions: on one hand, the company shall simplify the account registration process or even simply enable a non-member to place an order without going through all the procedures which may discourage the less educated people from doing their first purchasing; on the other hand, the company shall provide simplified online payment or provide payment on arrival or other special care services to the first-time visitors to encourage them to place the first online order in Tmall.com.

 

5.5.1.3          Seek business opportunities and growth in LBS sector

 

As found out in the survey, the online penetration rate of Jewelry Accessories, Movie tickets and Food and beverage service are among the least purchased good among the surveyed respondents. But based on the relevant literature that analysts forecast the Location-based Service (LBS) market in China to grow at a CAGR of 25.93 percent over the period 2012-2016 and one of the key factors contributing to this market growth is the increasing adoption of mobile broadband, therefore, the market for these location based services such as tickets and food and beverage shall be expected to witness a rather rapid growth in the coming years. Therefore, the Tmall.com shall consider the seeking of business opportunities in LBS market sector.

 

5.5.2            Product strategies

As evaluated by the surveyed respondents in comparison with other major competitors, Tmall.com has provided excellent products in term of range of products and product quality, but the after-sale service and delivery speed has been evaluated as worse than some other websites. Therefore, the Tmall.com should redefine the concept of quality product by considering more customer experience address the found issues such as the lack of sufficient and quality after sale service.

5.5.3            Promotion strategies

As pointed out by in the finding and analysis part by comparing the various form of major ads, it is believed that the new media ads are playing an increasing important role in persuading customers into actual buying behavior online, therefore, it is recommended that Tmall.com should tailor its advertising strategy to reflect such trend through ways such as enforcing close cooperation with the major websites and blog platforms. In particular the social networks have been proved as playing an active role in lead the customers who either have an intention to buy or do not have an intention to buy anything from online into regular online purchasing behaviors. Therefore, the promotion activities shall be better integrated into major social networks platforms.

 

5.5.4            Place strategies

 

As found out through the SPSS analysis of the answers in respect of online purchasing possibility from Monday to Sunday and in major festivals, it is proved that people tend to do their online purchasing on the weekends and major holidays. Therefore, it is important for the Tmall.com to further prepare the distribution and cooperate with the partners in solving the possible jam in the distribution channels, in particular when the major holiday comes together weekend under which the peak of online purchasing would happen.

 

5.6    Meeting the research objective

 

The study has met the previously set research objective by verifying the typical consumer behaviors theories in the Chinese market and identifying the most recent trends of development of consumer behaviors in B2C market. Also the marketing implications to the B2C company, Tmall.com have been offered.

 

5.7    Implications

 

This research offers a latest testing of the consumer behaviors trends in the B2C market in China, it implies that:-

 

Consumer behaviors have influential impacts over the final purchasing behaviors;

 

Market strategy setting shall take into consideration of the latest consumer behaviors trends;

 

Theories are not always reflecting the true in the market; they shall be tested from time to time.

 

5.8    Limitations

 

With a small sampling volume, it is true that the survey results may be to some extent different from the reality and a larger scale of study could be done to test conclusions and thus revise the marketing suggestions to Tmall.com.

 

5.9    Further research

 

Further researches could be done not only in B2C market but also in C2C market and also the future studies could cover other Chinese communities such as Taiwan of China and Hong Kong of China.

 

5.10              Conclusions

 

E-commerce has not only become a raising market but also a dominating market in China in particular the B2C market. Consumer behaviors are subject to macro environment, industrial environment as well as personal and group features and profiles of the Chinese consumers. Any companies which are engaging in the B2C market shall have study into this market and a key subject will be to study the consumer behaviors.

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Appendix 1. Online Survey on the Trend of Ecommerce Consumer Behavior in China

SEGi UNIVERSITY | PROJECT PAPER

 

Welcome to take part in this questionnaire. This questionnaire is to study the trends and traits of consumer behavior in B2C sector of the Chinese market intended for the project paper named “Consumer Behavior Trend and its Implications to Marketing Strategies of Tmall.com in China” from the SEGi UNIVERSITY. SEGi University, founded in 1977 as Systematic College, is located in Kuala Lumpur. SEGi University is a private university in Malaysia with more than 23,000 students at its campus in UEP Subang Jaya, Kota Damansara, Kuala Lumpur, Penang and Sarawak.

 

Your information will be kept in secret and be limited to the usage of study, i.e.  “Consumer Behavior Trend and its Implications to Marketing Strategies of Tmall.com in China” only. Thank you very much for you time and contribution to this paper.

 

Please take note that only online individual customers are invited to participate this survey.

 

Please select the answer(s) best fitting your individual profiles / situations:

 

Part A: Profile of Respondents

 

What is the gender group that you are belonging to?

Female

Male

 

What is your age group?

10 and below

10 – 19

20 – 29

30 – 39

40 – 49

50 – 59

60 & above

 

What is your ethnic group?

Han

Menggu

Hui

Zang

WeiWuEr

Miao

Others

 

What is your current highest level of education?

 

Elementary education and below

Junior high school

Senior high / vocational secondary school / technical school

Junior college

University and above

 

 

Please choose the income category which best represents your individual monthly income.

 

Over RMB 8000

5001 – 8000

3001 – 5000

2001 – 3000

1501 – 2000

1001 – 1500

501 – 1000

Below 500

No Income

 

Where are you living in?

 

First – tier cities (Beijing, Shanghai, Guangzhou, Shenzhen)

Second – tier cities (Chengdu, Chongqing, Shenyang, Hangzhou, Tianjin, Dalian, Wuhan, Suzhou, Nanjing. Qingdao, Xiamen, Xi’an, Ningbo, Changsha, Hefei, Zhengzhou, Wuxi, Dongguan, Jinan.)

Third – tier cities (Fuzhou, Kunming, Changchun, Harbin, Foshan, Shijiazhuang, Nanning, Changzhou, Nanchang, Hohhot, Wenzhou, Yantai, Nantong, Zhuhai, Guiyang, Taiyuan, Urumqi, Shaoxing, Zhongshan, Jiaxing, Tangshan, Xuzhou, Jinhua, Quanzhou, Luoyang, Lanzhou, Haikou, Jilin, Yangyang, Shantou, Weifang.)

Fourth – tier cities (The rest cities except ones already listed above)

Rural areas and other regions / areas

 

Part B: General Consumption Behaviors

 

How often will you do the shopping online for half a year?

 

1 to 2 times per half a year

3 to 4 times per half a year

5 to 10 times per half a year

11 or more per half a year

 

Please select the range of expenditure which best describes your spending on online shopping estimated for 12 months based on your current consumption behaviors?

 

More than 50000

20001 – 50000

10001 – 20000

8001 – 10000

5001 – 8000

2001 – 5000

1001 – 2000

501 – 1000

301 – 500

101 – 300

Less than 100

 

In a typical day, what is the length of time you will spend on online shopping?

 

Minute(s)

 

Please select types of products that you would purchase (multiple choices) online:-

 

Jewelry Accessories

Stationery

Baby products

Movie tickets at

Food and beverage service

Handbags, luggage

Food, health products

Cosmetics and Beauty Products

Prepaid cards, game cards and other virtual cards

Books audio and video products

Household appliances

Computers, digital communications products and accessories

General merchandise

Clothing and shoes

 

Part C: Consumer behaviors and marketing stimuli

 

How effective do you think the following ad forms is to lead to into online shopping behaviors? (the higher the number, the higher the ad form is effective)

 

Ineffective        Highly effective

Newspaper                           1     2     3     4      5

Magazines                           1     2     3     4      5

Television                           1     2     3     4      5

Radio advertisement                   1     2     3     4      5

Outdoor advertising                    1     2     3     4      5

Direct mail                           1     2     3     4      5

Text messages                        1     2     3     4      5

Mobile app ads                       1     2     3     4      5

Website ads                          1     2     3     4      5

Blog ads                             1     2     3     4      5

 

Part D: Consumer behaviors and internal influencing factors

 

Based on your individual conditions, on which day / festival you would like to do the online shopping (multiple choices; the higher the number, the higher the purchasing possibility)?

 

Low possibility      High Possibility

Monday                          1     2     3     4      5

Tuesday                          1     2     3     4      5

Wednesday                        1     2     3     4      5

Thursday                          1     2     3     4      5

Friday                            1     2     3     4      5

Saturday                          1     2     3     4      5

Sunday                            1     2     3     4      5

New Year’s Day (Jan. 1)              1     2     3     4      5

Spring Festival (subject to lunation)     1     2     3     4      5

Qingming Apr. 4 or 5                 1     2     3     4      5

May Day (May 1)                    1     2     3     4      5

Dragon Boat (5th of 5th lunar month)     1     2     3     4      5

Mid-Autumn (Aug. 15 of lunar calendar)  1     2     3     4      5

National Day (Oct. 1)                  1     2     3     4      5

 

Part E: Consumer behaviors and extenral influencing factors

 

When you want to buy something online, would you like to check the social website before you make the final decision in purchasing it online?

 

Never

Often

Rarely

Sometimes

 

In case you don’t have a plan to buy something, how often will you browse or check the promo information and finally make final purchasing?

 

Never

Often

Rarely

Sometimes

 

Part F: Consumer behaviors and e-commerce contributors

 

Please select the payment methods you adopt when you did the purchasing online (multiple choices)?

 

Post office or bank counter transfer / remittance

Quick payment

Cash on delivery

Third-party payment / account balance payment

Internet banking payment (including debit cards, credit cards)

 

 

 

Part G: Buyer’s response

 

Please rate the performance of the following online shopping platforms (skip those which you don’t have enough of purchursing experience to make judgements):

 

Tmall.com

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

JD.com

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

Amazon China

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

Paipai.com

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

Taobao.com

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

Vancl.com

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

Dangdang.com

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

Yihaodian.com

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

m18.com

 

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

VIP Shop

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

suning.cn

 

Low performance   High performance

Range of products                   1     2     3     4      5

Product quality                     1     2     3     4      5

Price competitiveness                1     2     3     4      5

After sale                          1     2     3     4      5

Delivery                           1     2     3     4      5

 

 

Appendix 2. SPSS analysis of answers in respect of age groups

SPSS ANALYSIS Frequency Percent Valid Percent Cumulative Percent
Valid Male –   10 and below 1 1.0 1.0 1.0
Valid Male –     10 – 19 13 13.0 13.0 14.0
Valid Male –       20 – 29 17 17.0 17.0 31.0
Valid Male –         30 – 39 12 12.0 12.0 43.0
Valid Male –         40 – 49 7 7.0 7.0 50.0
Valid Male – 50 – 59 2 2.0 2.0 52.0
Valid Male –  60 & above 0 0.0 0.0 52.0
Valid Female –   10 and below 0 0.0 0.0 52.0
Valid Female –     10 – 19 13 13.0 13.0 65.0
Valid Female –       20 – 29 16 16.0 16.0 81.0
Valid Female –         30 – 39 9 9.0 9.0 90.0
Valid Female –         40 – 49 6 6.0 6.0 96.0
Valid Female – 50 – 59 3 3.0 3.0 99.0
Valid Female –  60 & above 1 1.0 1.0 100.0
Total 100 100.0 100.0

 

 

 

Appendix 3. SPSS analysis of customers’ rating of 11 major online shopping website

Report
tmall.com suning.cn jd.com amazonchina paipai.com taobao.com
Mean 3.4500 2.4286 3.3750 2.9167 3.8333 3.9000
N 54 7 32 12 6 89
Std. Deviation 1.31689 .97590 1.20416 1.50504 1.16905 1.02084
vancl.com dangdang.com yihaodian.com m18.com vipshop
Mean 2.6923 3.3333 3.0000 3.2000 2.5000
N 13 17 9 5 6
Std. Deviation 1.18213 1.17514 1.22474 1.30384 1.04881

 

Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
tmall.com 54 54.00% 46 46.00% 100 100.00%
suning.cn 7 7.00% 93 93.00% 100 100.00%
jd.com 32 32.00% 68 68.00% 100 100.00%
amazonchina 12 12.00% 88 88.00% 100 100.00%
paipai.com 6 6.00% 94 94.00% 100 100.00%
taobao.com 89 89.00% 11 11.00% 100 100.00%
vancl.com 13 13.00% 87 87.00% 100 100.00%
dangdang.com 17 17.00% 83 83.00% 100 100.00%
yihaodian.com 9 9.00% 91 91.00% 100 100.00%
m18.com 5 5.00% 95 95.00% 100 100.00%
vipshop 6 6.00% 94 94.00% 100 100.00%

Customer rating on 11 shopping websites: Range of products

 

 

 

 

 

Report
tmall.com suning.cn jd.com amazonchina paipai.com taobao.com
Mean 3.6000 3.2857 3.8125 3.5833 3.5000 3.3500
N 54 7 32 12 6 89
Std. Deviation 1.09545 .95119 1.37689 1.08362 1.04881 1.08942
vancl.com dangdang.com yihaodian.com m18.com vipshop
Mean 2.5385 3.1765 3.4444 3.2000 3.5000
N 13 17 9 5 6
Std. Deviation 1.12660 1.13111 .88192 1.09545 1.04881

 

Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
tmall.com 54 54.00% 46 46.00% 100 100.00%
suning.cn 7 7.00% 93 93.00% 100 100.00%
jd.com 32 32.00% 68 68.00% 100 100.00%
amazonchina 12 12.00% 88 88.00% 100 100.00%
paipai.com 6 6.00% 94 94.00% 100 100.00%
taobao.com 89 89.00% 11 11.00% 100 100.00%
vancl.com 13 13.00% 87 87.00% 100 100.00%
dangdang.com 17 17.00% 83 83.00% 100 100.00%
yihaodian.com 9 9.00% 91 91.00% 100 100.00%
m18.com 5 5.00% 95 95.00% 100 100.00%
vipshop 6 6.00% 94 94.00% 100 100.00%

Customer rating on 11 shopping websites: Products Quality

 

 

 

Report
tmall.com suning.cn jd.com amazonchina paipai.com taobao.com
Mean 3.6500 3.7143 3.2500 3.3333 3.8333 4.0500
N 54 7 32 12 6 89
Std. Deviation 1.13671 .75593 1.39044 1.23091 1.16905 1.14593
vancl.com dangdang.com yihaodian.com m18.com vipshop
Mean 4.0000 3.4118 3.1111 3.8000 3.3333
N 13 17 9 5 6
Std. Deviation 1.00000 1.12132 .92796 .83666 .81650
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
tmall.com 54 54.00% 46 46.00% 100 100.00%
suning.cn 7 7.00% 93 93.00% 100 100.00%
jd.com 32 32.00% 68 68.00% 100 100.00%
amazonchina 12 12.00% 88 88.00% 100 100.00%
paipai.com 6 6.00% 94 94.00% 100 100.00%
taobao.com 89 89.00% 11 11.00% 100 100.00%
vancl.com 13 13.00% 87 87.00% 100 100.00%
dangdang.com 17 17.00% 83 83.00% 100 100.00%
yihaodian.com 9 9.00% 91 91.00% 100 100.00%
m18.com 5 5.00% 95 95.00% 100 100.00%
vipshop 6 6.00% 94 94.00% 100 100.00%

 

Customer rating on 11 shopping websites: Price competitiveness

 

 

Report
tmall.com suning.cn jd.com amazonchina paipai.com taobao.com
Mean 3.3500 4.2857 3.6875 3.3333 3.5000 3.2000
N 54 7 32 12 6 89
Std. Deviation 1.08942 1.11270 1.13835 1.07309 1.04881 1.32188
vancl.com dangdang.com yihaodian.com m18.com vipshop
Mean 3.6154 3.3529 3.3333 3.6000 2.8333
N 13 17 9 5 6
Std. Deviation 1.04391 1.11474 .70711 .54772 .75277
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
tmall.com 54 54.00% 46 46.00% 100 100.00%
suning.cn 7 7.00% 93 93.00% 100 100.00%
jd.com 32 32.00% 68 68.00% 100 100.00%
amazonchina 12 12.00% 88 88.00% 100 100.00%
paipai.com 6 6.00% 94 94.00% 100 100.00%
taobao.com 89 89.00% 11 11.00% 100 100.00%
vancl.com 13 13.00% 87 87.00% 100 100.00%
dangdang.com 17 17.00% 83 83.00% 100 100.00%
yihaodian.com 9 9.00% 91 91.00% 100 100.00%
m18.com 5 5.00% 95 95.00% 100 100.00%
vipshop 6 6.00% 94 94.00% 100 100.00%

 

Customer rating on 11 shopping websites: After sale

 

 

 

Report
tmall.com suning.cn jd.com amazonchina paipai.com taobao.com
Mean 3.4500 3.8571 3.2500 3.4167 3.3333 3.5000
N 54 7 32 12 6 89
Std. Deviation .99868 1.06904 1.00000 .79296 1.21106 1.19208
vancl.com dangdang.com yihaodian.com m18.com vipshop
Mean 3.0769 3.4706 4.1111 3.8000 3.5000
N 13 17 9 5 6
Std. Deviation .95407 1.00733 .78174 1.09545 1.04881
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
tmall.com 54 54.00% 46 46.00% 100 100.00%
suning.cn 7 7.00% 93 93.00% 100 100.00%
jd.com 32 32.00% 68 68.00% 100 100.00%
amazonchina 12 12.00% 88 88.00% 100 100.00%
paipai.com 6 6.00% 94 94.00% 100 100.00%
taobao.com 89 89.00% 11 11.00% 100 100.00%
vancl.com 13 13.00% 87 87.00% 100 100.00%
dangdang.com 17 17.00% 83 83.00% 100 100.00%
yihaodian.com 9 9.00% 91 91.00% 100 100.00%
m18.com 5 5.00% 95 95.00% 100 100.00%
vipshop 6 6.00% 94 94.00% 100 100.00%

 

Customer rating on 11 shopping websites: Delivery

 

 

 

[1] Here we will focus only on the B2C sector while the data of C2C and B2B business sectors would be reviewed for reference only.

[2] As for the SPSS analysis of answers to this question, please refers to “Appendix 2. SPSS analysis of answers in respect of age groups”

[3] 1 USD = 6.1215 CNY according to the updated exchange rate on 1st Oct 2013. (xe.com 2013)

[4] The middle income group has not relation to the general understanding of another term, middle income class which is cleared defined by organizations or countries.

[5] Introduced by Alipay late last year, Quick Pay was developed to address an impediment with China’s banking networks that hinders online shopping. Users of the service who have credit or debit cards through participating banks are able to enter their account information just once, without logging onto a bank website, to complete purchases. The service also eliminates obstacles such as transaction amount limits and browser compatibility issues among various websites. According to Alipay, a user’s chance of completing a successful online credit card payment in China was below 70%; using Quick Pay the success ratio is approximately 95% (alizila.com 2012).