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CHAPTER 4

RESEARCH AND ANALYSIS

4. 1 Case Background Analysis

4. 1.1 Amazon Company Development

Amazon, the largest Internet e-commerce company in the United States, is

located in Seattle, Washington State. One of the first companies to start an

e-commerce business on the Web, founded in 1995, Amazon began operating only

online book sales business, now expanding to a wide range of other products, has

become the world's largest online retailer of goods and the world's second largest

Internet companies, under the company's name, also includes Subsidiaries such as

Alexa Internet, A9, lab126, and Internet Movie databases (Internet Movie database,

IMDB). Amazon and other vendors offer customers millions of unique, brand new,

refurbished and second-hand commodities, such as books, film and television, music

and games, digital downloads, electronics and computers, home gardening, supplies,

toys, baby products, food, apparel, footwear and jewellery , health and personal care

products, sports and outdoor products, toys, Automotive and industrial products. The

August 2004 Amazon wholly-owned acquisition network, the Amazon World's

leading online retail expertise with the excellent network of Chinese market

experience to further enhance customer experience, and promote the growth of

e-commerce in China. February 2017, Brand Finance released the 2017 global Top

500 brand list.

In addition to e-commerce, Amazon has become one of the world's best

cloud providers, creating an Amazon Web Service platform that attracts large

numbers of high-quality users. In the field of terminals, Amazon has released several

hardware capacity, such as the Kindle, the Kindle Fire tablet, and the Fire Phone

smart shopping. Meanwhile, Amazon is accelerating its expansion in the area of cloud

storage. By April 2017, Amazon's market value had surpassed $43 billion,, almost

twice as much as Wal-Mart's market value.

4. 1. 2. Amazon's Cultural Interpretation

The culture of a company is also the company's code of soul and behavior,

in the industry, Amazon has been in customer service long, from the original is not

recognized, to the present scramble to imitate. It hasn't been profitable for more than a

decade, but the share price has continued to rise. It's all connected to Amazon's

20-year low profile, deep-rooted customers, creating value for clients, focusing on

long-term strategies and culture, and taking Amazon's mobile platform as an example

to study customers Satisfaction is also an important part of Amazon culture.

Providing the best customer experience is Amazon's core culture. 'We seek

to be the world most customer-company company in the world,' Mr. Bezos once said.

"This starts with Amazon's mission statement and runs through all the work of all the

employees of the company. Inside Amazon, every new employee at the company

knows that Amazon's first rule of leadership is" Customer Propulsion ". Bezos once

made it clear that Amazon's customer-supremacy culture was even higher than

Amazon's innovative culture. It has also been expressed repeatedly that Amazon's

strategy is aimed at customers, not rivals. The customer's first rule effectively

dominates all aspects of Amazon's operations. So even the company Logo, also

contains the customer's smile.

To facilitate the customer's culture. In fact, this paper is also a derivative of

the customer's first culture. In the Amazon's all system process phase settings, it is

hard to simplify, let the customer get the simple function service, simple shopping

process, the heart-saving after-sales service. Convenient customer culture , Also

embodied in the Amazon platform all aspects. As a key purchase function, convenient

product search function and so on, no one embodies a 'Simple' word.

Innovation culture. Amazon's technological innovations have proven

worldwide and now account for 4 of the world's Internet Company. And he has been

acknowledged as the most strategic-minded technology company. Innovation is also

to serve customers, emphasize service function with the progress of science and

technology. As a result, Amazon has always attached great importance to investment

in the field of technology, which accounts for a very high level of revenue.

The Amazon flywheel theory. The flywheel theory is described in detail in

Fig. 3.1. This is actually a closed loop that can start at any point but has no end.

Amazon platform to do is to provide consumers with more choices and the

convenience of the selection process, and the rich selection and convenience is to

achieve a good customer experience, when the customer in the Amazon shopping

experience is good enough, the customer will become the Amazon free evangelist,

through Word-of-mouth legend, Influence the people around you to join the Amazon's

shopping force, Thus achieving the increase in traffic and even geometric

multiplication. In this way, the Amazon platform, with enough traffic, can naturally

attract more suppliers to join in, and the addition of more sellers, not only enrich the

product category, but also from the level of competition, reducing the platform on the

price of products, sellers more, lower prices, and lower prices, but also allow

consumers to further improve the degree of satisfaction, this ring process held

Continued, the Amazon platform continues to grow along this flywheel-like cycle.

Figure 3. 1 Amazon "flywheel theory"

The flywheel principle, in fact, is Amazon's business model, relying on a

loss-based proprietary business to attract users, create moat, gain profits on

e-commerce platforms and other revenues, and then devote all of their profits to the

innovation of new products and new services. This is true both in e-commerce

platforms and cloud computing services.

With regard to Amazon's strategy, we can see from above that the Amazon's

competitive strategy is to focus on customers, adhere to long-term development, do

not value short-term benefits. In the marketing tactics, in the brand publicity to make

their own service design unique, the main value of the brand is consumer products

and services evaluation. He designed the Review mechanism and module on the

product page, initially to allow readers to share their reading experiences and

experiences, and to evaluate any product on the Amazon platform. To create a better

ecological environment for evaluation, he allows any URL to be linked to Amazon

free of charge. Even the reviewers commented on the quality of the performance

grading, for honest and reliable rich product reviews to share the staff, their rankings

will be up front, in the product Review score on the algorithm to give weight tilt.

Amazon's product strategy, the focus is on the full range and variety of products,

expanding the scale, so that the variety of goods, to provide customers with more

choice of goods, hope they can buy on the Amazon platform any goods and services

they want to buy. Around the world to open a site, the nearest service to local

customers, inviting all over the third-party sellers to join the Amazon platform, and

strive to achieve a "wide" from the product strategy Word. Pricing strategy, Amazon

began to use Low-cost strategy to intervene in the market, and has been practicing this

important business strategy. Amazon's membership service is also famous. Amazon

Prime, once launched in February 2005, has been very popular and has been working

to develop a wide range of membership services for his loyal member customers to

enhance platform stickiness. Amazon's $99-a-year membership service includes free

courier fees, as well as complimentary appreciation of the huge amount of video,

music books. According to statistics, these Prime members of the consumption

amount and frequency are higher than ordinary consumers. The current number of

members has reached 80 million, which tells the growing number of members that

Amazon's future sales will provide a sustained source of growth. Complete logistics

system, because the online purchase of consumers are most concerned about the

timeliness of logistics delivery, accurate and convenient, in the past 20 years, Amazon

has been committed to logistics development, and establish a complete logistics

system, which will help the Amazon in the retail aspect of the exhibition. 2007 Third

party sellers outsourcing logistics services Fulfillment by Amazon (FBA) opened, that

is, Amazon will own warehousing and distribution resources to third-party sellers, the

core advantage of fast logistics services to third-party sellers, the overall upgrading of

Amazon customer satisfaction, It also makes Amazon's logistics business a new

business and profit growth point.

Due to the company's first corporate culture and its outstanding performance

in the international market, this paper considers the world's largest comprehensive

e-commerce platform as an object of empirical research, and hopes to find out the

influence factors of mobile shopping customer satisfaction.

4. 1. 3 Introduction to the Features of the Amazon Mobile Platform

The 12-country site style is consistent with the PC end purchase page. As

long as you download an Amazon APP, you can switch and purchase different sites

via voice-based choices or the choice of the country.

Automatic synchronization of mobile platform and PC terminal information.

The technology that has headaches for two years has now been conquered by most

comprehensive e-commerce platforms.

Powerful search and forecasting functions. Key words prediction function is

very powerful and convenient, in the search results correlation, Amazon also has good

performance, search intelligent error correction function also can be very good service

customer.

Large mobile purchase buttons. Amazon's mobile purchase buttons on its

mobile platform are designed to make sure that customers can quickly find it.

One-button purchase function, also called One-click function. This makes it

easy to buy and leads customers to repeat purchases. This is basically what other

e-commerce platforms can do now, and this article does not taunt.

In the US, Amazon is their first choice when consumers buy daily consumer

goods online. More than half of Americans use Amazon as their main shopping site.

51% of the growth in U.S. electricity is from Amazon, while retail sales, including the

real retail sector, have 24% from Amazon. Amazon's 42% increase in traffic comes

from Web pages--indicating that the remaining 58% traffic is growing from his own

mobile client app. In the course of Amazon's internationalization, he also quickly

established a market leadership position in the UK and Germany, followed by the

most popular and visited online retailer across Europe. But in China, Alibaba

dominates the electricity industry, and Amazon's position in the country after Beijing

east and Suning. This article also attempts to explore the reasons for the impact of

mobile shopping satisfaction factor one or two.

4. 2 Project Analysis

In this paper, two methods are used to analyze the project.

First, correlation method is used to calculate the correlation coefficient

between the score of the title and the total questionnaire. The common standard is that

the identification Force index under 0.2 should be eliminated, the discriminant index

in the 0.2-0.4 problem, the Discriminant Force index in more than 0.4 of the problem

is better. The statistical analysis results of this questionnaire show that the

discriminant strength of each problem is greater than 0.70, and the correlation

between the title and total score is higher (p<0.001), which indicates that the topic is

better, so all the questions continue to participate in the exploratory factor analysis.

Table 4. 1 A correlation coefficient between the total score and the total score of 19 subjects

Order Total score Order Total score Order Total score

Order Total score

1 .710*** 6 .793*** 11 .811*** 16 .798***

2 .833*** 7 .841*** 12 .826*** 17 .739***

3 .762*** 8 .843*** 13 .704*** 18 .729***

4 .791*** 9 .810*** 14 .782*** 19 .773***

5 .744*** 10 .794*** 15 .817***

Secondly, the critical ratio value (CR) of each item should be obtained, and

the items that did not reach significant levels were deleted. That is to sort by the total

score of the test volume, each take its first 27%, the latter 27% as a high-group and

sub-group, then carry out the independent sample t test of the difference between the

high-group and sub-group on each item, and delete the outstanding items.

Table 4.2 t Test (M + SD) on 19 subjects on high and low levels

Number Low score (N=37)

High score (N=36)

t（CR） df Sig.

1 3.37±0.49 4.62±0.53 -10.110 71 .000

2 3.04±0.48 4.71±0.46 -15.443 71 .000

3 3.07±0.69 4.62±0.59 -10.171 71 .000

4 3.15±0.58 4.77±0.43 -13.728 71 .000

5 3.29±0.57 4.82±0.39 -13.025 71 .000

6 3.07±0.65 4.81±0.48 -13.117 71 .000

7 3.04±0.72 4.91±0.38 -14.088 71 .000

8 3.05±0.63 4.76±0.43 -13.304 71 .000

9 3.04±0.79 4.82±0.38 -12.017 71 .000

10 3.07±0.56 4.71±0.52 -13.216 71 .000

11 3.10±0.75 4.74±0.56 -10.734 71 .000

12 2.85±0.60 4.68±0.63 -12.925 71 .000

13 2.98±0.59 4.54±0.87 -7.934 71 .000

14 3.10±0.47 4.57±0.66 -10.792 71 .000

15 3.15±0.53 4.63±0.55 -13.032 71 .000

16 3.12±0.56 4.68±0.54 -12.562 71 .000

17 3.21±0.62 4.57±0.56 -11.241 71 .000

18 3.21±0.64 4.63±0.60 -9.934 71 .000

19 3.31±0.52 4.76±0.43 -12.497 71 .000

In this study, the total score is more than 79 points, (about 27% of the

group's being tried) as a group, the total score is less than 65 (about 27% of the

sub-group being tried) as a sub-group, the total score is less than 65 (about 27% of the

sub-group's being tried) as a sub-group, the results show that the total score is

independent sample t test on the high and low group, and the results show that the

total score is less than 65 (about 27% of the sub-group) The CR value of the project

reached p<0.001 (Table 5.2) It indicates that the regional division of each item of this

questionnaire is good.

4.3 Exploratory Factor Analysis

Using data (N = 186), the exploratory factor analysis was carried out on all

19 subjects. Prior to factor analysis, proper sampling (KMO) and Bartlett spherical

test were performed first. The greater the KMO value, the greater the common divisor

of the variable, the more suitable for factor analysis. Generally, the value of KMO is

less than 0. 50 is not suitable for factor analysis, in More than 0.80. In the case of

factor analysis, the Bartlett sphere test rejects the original assumption, then a factor

analysis can be performed. If the original hypothesis is not rejected, it is stated that

these variables may be independent of some information and are not suitable for

factor analysis. Table 4. 3 KMO and Bartlett spherical test

Kaiser-Meyer-Olkin sampling suitability test .946

Bartlett spherical test

Approximateχ2 2196.489

D f 170

Sig .000

Sample proper and Bartlett globular examination were carried out in this

study, and the KMO value was 0. 945,

It shows that the sample size is sufficient, suitable for factor analysis;

meanwhile Bartlett spherical inspection, χ 2 = 2196. 489, p <0. 001, to the

significance level of 0.001, rejecting the original hypothesis, there is a common

factor in the correlation matrix of the parent group, therefore this questionnaire is

suitable for factor analysis.

According to the factor analysis, the principal component analysis method

(PCA) is used to extract the five factors according to the theoretical foundation of the

questionnaire, and the ultimate variance method is adopted to obtain the final result

Factor-load matrix. Finally, according to the factor analysis theory, the post-rotation

question item is filtered to determine the number of public factors and named. After

the first exploratory factor analysis, the first factor was found to be more subject than

the number of other factors, so the sixth problem with lower load was deleted, then

the second exploratory factor analysis was carried out. After the second exploratory

factor analysis, a factor was found only one topic (topic 3), and therefore deleted it

and then carried out a third exploratory factor analysis. After the third exploratory

factor analysis, it was found that the topic was reasonable and the number of factors

was more balanced. The exploratory factor analysis is finished, as shown in table 4.4.

Table 4.4 shows the cumulative variance contribution rate of five factors,

which can be seen in 17 projects. The five-factor load is reasonable and the factors are

clear and the characteristic root is 10.569、1.027、0.813、0.782、0.529. The cumulative

contribution rate of variance is 80. 71%.

Table 4. 4 total variance of interpretation

Ingredient

Initial eigenvalue The square sum of loads to be extracted

Eigenvalues

The proportion of the total variance

Cumulative variance contribution rate

Eigenvalues

The proportion of the total variance

Cumulative variance contribution rate

1 10.468 62.166 62.166 10.568 62.166 62.166 2 1.026 6.0043 68.211 1.026 6.0043 68.211 3 .812 4.782 72.994 .836 4.782 72.994 4 .781 4.601 77.596 .781 4.601 77.596 5 .528 3.112 80.708 .528 3.112 80.708 6 .506 2.983 83.693 7 .400 2.355 86.048 8 .389 2.290 88.339 9 .325 1.918 90.258 10 .299 1.763 92.022

11 .277 1.626 93.649 12 .236 1.394 95.042 13 .228 1.344 96.387 14 .192 1.136 97.525 15 .164 .958 98.483 16 .152 .889 99.380 17 .106 .631 100.000

Table 4. 5 Rotation component matrix

Ingredient

1 2 3 4 5 9 .792 8 .714 10 .695 7 .588 17 .818 18 .817 19 .618 16 .565 13 .861 14 .679 12 .669 11 .604 4 .628 15 .553 2 .465 5 .768 1 .615

Seventh question, eighth question, ninth question, tenth entitled Factor One,

the contents of these topics are: can provide efficient search, payment convenience, at

any time to buy the right goods, process design is not cumbersome, interface design

(keys, etc.) suitable for mobile devices, mainly for customers to provide a kind of

convenience, so it is named "convenience";

Question 16th, question 17th, 18th, 19th, factor two, the contents of these

topics are: the delivery of goods and network display consistent, has been placed in a

single commodity will not or rarely out of stock, can be delivered in the agreed time

of goods or services, delivery of goods and orders consistent, indicating that the

business can be agreed to complete the transaction, named as " Compliance ";

Question 11th, question 12th, 13th, 14th, Factor III, the content of these

topics is: whether the interface design is satisfied, to provide a convenient and

unobstructed interactive communication mode, the pre-sale consultation is satisfied,

the seller can quickly and effectively deal with customer problems, explaining the

commodity or business interaction with customers, named it "interactive";

The second question, the fourth question, the 15th is entitled Factor Four,

the content of these topics are: the timeliness of information update, the satisfaction of

payment security, the satisfaction of the return service, mainly explained the safety

and reliability of the commodity, named it "System reliability";

The first and fifth, "factor Five ," the contents of these topics are: the

authenticity of information, the confidentiality of user information, will not reveal the

privacy of the issue, it is named "Privacy and security."

4. 4 Reliability and validity analysis

4. 4. 1 Reliability Analysis

The internal consistency coefficient (Cronbach α coefficient) is used as the

index of test reliability. The internal consistency coefficient is greater than 0.7. The

reliability of the scale is higher; in the exploratory study, the internal consistency

coefficient can be less than 0. 7, but should be greater than 0.6. If the number of

subjects is less than 6, the internal consistency coefficient is greater than 0.6. It is

shown that the scale is effective.

Cronbach α coefficient of five factors was found in this study0. 758~0.961,

more than 0.7 Acceptable levels indicate that the whole questionnaire and five factors

have a higher reliability, as shown in Table 4. 6. Table 4.6

The whole questionnaire

Conveniences Practicability Interactivity System reliability Privacy security

α 0.960 0.917 0.889 0.895 0.847 0.757

4. 4. 2 Validity Analysis

Validity analysis

The method used to investigate structure validity is factor analysis. Based on

exploratory factor analysis, we conclude that the questionnaire consists of five factors:

convenience, performance, interactivity, system reliability, privacy and security. This

is basically the same as our theoretical conception dimension, which shows that the

structure is better.

In addition, according to the factor analysis theory, each factor should have

a moderate degree of correlation, if the correlation is too high to indicate that there is

overlap between the factors, some factors may not be necessary; if the correlation

between factors is too low, it is indicated that some factors may measure the exact

difference with the desired measurement. The correlation between the factors of the

questionnaire and the correlation between the factors and the scores of the

questionnaires are shown in table 4.7, and the correlation coefficients in the table are

very significant. You can see that correlation between factors (r=0.636~0.840) is less

than the correlation between the factor and the total score (r=0.807~0.928), and there

is also a correlation between the factors (r=0.636~0.840), indicating that different

factors measure different contents, these factors collectively measure the same content.

Therefore, the structure validity of this questionnaire is higher. Table 4.7 Correlation between factors and factors and total scores


Conveniences

Practicability

Interactivity

System reliability


1

Conveniences .751*** 1

Practicability .768*** .683*** 1

Interactivity .841*** .782*** .767*** 1

System reliability .674*** .691*** .635*** .774*** 1

Total points .922*** .880*** .893*** .927*** .808***

Validate factor analysis

In order to further examine the structure validity of the questionnaire, this

study used another set of data (n=186) to analyze the confirmatory factor of the

questionnaire, and the maximum fitting method (maximum likehood estimation) was

used to test the degree of fit. With MPLUS7.4 analysis results are as follows:

Figure 4.2 confirmatory factor analysis model coefficient of service quality

This study selects the following indices to test the stability and accuracy of

the scale structure. (1) The card square test (chi-square), that is, Χ2/DF, generally,

think that the value of Χ2/DF is considered a good model and data fitting standard

between 1-3. (2) Compare the fitted index CFI (compare to fit index), and generally

think that CFI>0.90 is considered to have a good explanatory power to the model, and

≥ 0.85 indicates that the model is acceptable. (3) Tucker-Lewis index (TLI) > 0.90 is

considered to be a good model, and ≥ 0.85 shows that the model is acceptable. (4)

Approximate error mean square root RMSEA (root mean square error of

approximate), RMSEA<0.05, indicates that the model fit well, while RMSEA<0.08 is

also acceptable, and RMSEA is less affected by the sample, is a good absolute fitting

index. (5) Standardized residual mean square root (standardized root mean square

residual, SRMR), SRMR 0.05, indicates that the model is well fitted and that SRMR

0.08 is acceptable.

After running, the Model fitting index of the questionnaire can be obtained,

as shown in table 4.8, it can be seen that the model is well fitted. Table 4. 8 model fitting index

Fitting index

/df CFI TLI RMSEA (90%C.I.) SRMR

Estimated value

2.18

0.932 0.915 0.094（0.078，0.110） 0.044

Therefore, the model of five factors can be seen as a good model by

analyzing the factorial factor analysis of the questionnaire.

4. 5 Population Studies Statistical Analysis

4. 5.1 Population Learning Statistics

Use SPSS 19. A descriptive statistical analysis of 186 official questionnaires

was carried out, and the descriptive statistical analysis of population characteristics

was shown in table 4. 9.

Sample basic information

Types Number of samples Percentage

Gender Male 87 46.7%

Female 99 53.2%

Age

Under 20 years old 3 1.6%

21-30 years old 85 45.7%

31-40 years old 86 46.2%

41-50 years old 9 4.84%

Over 50 years old 3 1.61%

Education

Master and above 41 22.0%

College or undergraduate 82 44.1% High school or secondary school

53 28.5%

Elementary or junior high school

2 1.07%

none of the above 5 2.69%

Occupation

Worker 2 1.07%

Farmer 5 2.69%

Staff 74 39.8%

Medical staff 2 1.08%

Service industry 9 4.84%

Business or self-employed 22 11.8%

Teacher 16 8.60%

Institutions / civil servants 17 9.14%

Technology workers 6 3.23%

Freelancer 15 8.06%

School 8 4.30%

Other 10 5.38%

Monthly income

2000 yuan or less 23 12.4%

2001-5000yuan 86 46.2%

5001-10000yuan 41 22.0%

10,000 yuan or more 36 19.4%

Table 4. 9 Population Descriptive Analysis Results

The above table is available: In the gender aspect, the male and female

proportion is 46.7%, 53.2% respectively, in the age aspect, the data distribution

conforms to the normal distribution, 21-40 years old distributes the most samples,

explained that the mobile network buys the consumer to concentrate here. In terms of

education, higher education accounted for higher than the obvious, and more open

attitude towards new things. From a career perspective, the company's staff have

relatively stable time and atmosphere. From a revenue perspective, the

2001-10000-yuan Range distributes the vast majority of consumers.

4. 5. 2 Effects of Demographic Variables on Service Quality and Customer

Satisfaction

Impact of gender on service quality and customer satisfaction

To study the impact of gender on service quality and customer satisfaction,

an independent sample T-test method was used to analyze the obtained data and the

results were shown in table 4.10.

Table 4. 10 Comparison of factors and total scores of different gender-based

subjects (M ± SD) Factors and total scores Male（n=87） Female（n=99） t df p

Convenience 15.15±2.89 15.94±2.90 -1.411 133 .162

Fulfillment

14.85±2.76 15.75±2.57 -1.763 133 .081

Interactivity

14.06±3.34 15.52±2.96 -2.451* 133 .015

Reliability

10.91±1.99 11.81±2.06 -2.227* 133 .029

Privacy and security

7.74±1.14 7.89±1.33 -.594 133 .555

Service 62.74±10.57 66.88±10.66 -2.027* 133 .045

quality

Customer satisfaction

10.82±2.12 11.86±2.14 -2.573* 133 .012

Table 4. 10. There were significant differences in the interactivity, system

reliability, quality of service and customer satisfaction (p <0.05) There is no

significant difference in convenience, performance, privacy and security (p> 0.05),

indicating gender factors influencing the interactivity, system reliability, quality of

service, customer satisfaction.

Impact of different age on service quality and customer satisfaction

Because fewer than 20 years of age, 50 years of age and 41-50 years old,

therefore, the "under-20" study was merged into the "20-30-year-old" group, which

constituted the "30-year-old" group, merging the "50-year-old and 41-50-year-olds"

into the "31-40-year-old" group, which constituted " 30 years old and above "group,

the effect of age factor on service quality and customer satisfaction was analyzed by

the method of independent sample T test, the result is shown in table 4.11. Table 4. 11 Comparison of factors and total scores of subjects of different age

groups (M ± SD)

Factors and total scores

Under 30 years

old（n=88） Over 30 years old （n=98） T df P

Convenience 16.06±3.09 15.46±2.75 1.195 133 .233

Fulfillment 15.87±2.77 15.22±2.50 1.472 133 .142

Interactivity 15.56±3.36 14.74±2.93 1.518 133 .133

Reliability 11.79±2.23 11.35±1.95 1.227 133 .223


8.00±1.36 11.35±1.95 1.229 133 .221

Service quality 67.31±11.65 64.48±9.90 1.523 133 .129

Customer satisfaction 11.81±2.17 11.38±2.19 1.143 133 .254

Table 4.11 shows that the subjects of different ages are in convenience, there

is no significant difference in performance, interactivity, system reliability, privacy

and security, service quality and customer satisfaction (P<0.05), indicating that age

factors do not affect convenience, performance, interactivity, system reliability,

privacy and security, Quality of service and customer satisfaction.

Impact of education level on service quality and customer satisfaction

Because in the master and above, college or undergraduate, high school or

technical secondary school, primary or secondary school, is not these options, the

number of primary or junior high school, the merger in the high school or secondary

schools, categorized as "high school or the following" group, will choose "None of

the above" survey results as a lack of Then the difference of service quality and

customer satisfaction in different educational level is tested, the result is shown in

table 4.12.

Table 4.12 Comparison of factors and total scores of different academic subjects (M ± SD)


Group 1: master's and above （n=30）

Group 2: college or undergraduate （n=63）

Group 3: high school or secondary school and below

（n=38）

F P Multiple comparisons

Convenience

15.02±2.55 15.36±2.83 16.75±3.16 3.372* .036

Group1Group3; Group2 Group3

Fulfillment

14.58±2.63 15.43±2.46 16.15±2.76 3.183* .046 Group1Group3

Interactivity

13.68±2.87 14.67±3.10 16.75±2.73 10.258*** .000

Group1Group3 Group2Group3

Reliability

10.79±1.84 11.26±2.05 12.41±2.02 6.379* .002

Group1Group3 Group2Group3 Group 1Group2

Privacy 7.42±1.26 7.74±1.21 8.19±1.34 3.097* .048 Group1Group3

and security

Service quality

61.49±9.76

64.62±10.04

70.17±11.17 6.458* .002



10.82±1.99 11.41±2.16 12.33±2.15 4.337* .016


Table 4.12 show that the research objects of different degrees have

significant differences in convenience, performance, interactivity, system reliability,

privacy and security, quality of service and customer satisfaction (p <0.05). It

explains the factors that affect convenience, performance, interactivity, system

reliability, privacy and security, quality of service and customer satisfaction.

Due to significant differences in convenience, performance, interactivity,

system reliability, privacy and security, quality of service and customer satisfaction,

there are significant differences between the two groups or groups.

The test results showed significant difference in system reliability, service

quality and customer satisfaction. In other words, there is a difference in the system

reliability, service quality and customer satisfaction between the master's and the

above group in the system reliability, service quality and customer satisfaction in the

system reliability, service quality and customer satisfaction. (p <0.05). The difference

in system reliability, quality of service and customer satisfaction (p <0.05) was found;

There was a difference between group 1 and group 3 and group 2 and group

3 in convenience and interactivity (p <0. 05). In other words, there are differences (p

<0.05) between master's and above group in convenience and interactivity. No

difference (p <0.05) between college or undergraduate or college or technical

secondary school and the following groups is found in the convenience and

interactivity. 05 = There is no difference (p> 0.05) in the convenience and

interactivity between master's and above group;

There is a difference between group 1 and group 3 in terms of performance,

privacy and security (p <0.05) There is no difference in comparison among other

groups. In other words, there are differences in performance, privacy and security

between masters and above in the performance, privacy and safety of high school or

secondary school and the following groups (p <0.05). No difference (p> 0.05) in

performance, privacy and safety between a college or undergraduate or a junior or

technical secondary school and the following groups. There is no difference (p> 0.05)

in the performance, privacy and safety of a master's or above group in the

performance, privacy and safety.

Impact of monthly income on service quality and customer satisfaction

In order to study the effect of monthly income on service quality and

customer satisfaction, a single factor variance analysis method is used to analyze the

obtained data, as shown in table 4. 13.

Table 4.13 Comparison of factors and total scores of research objects of different monthly income (M ± SD)


Group

one：2000

yuan or less

（n=24）

Group

two：

2001-5000

yuan（n=45）

Group three ：

5001-10000元（n=30）

Group four：10,000 yuan or more（n=36）

F P Multiple comparisons

Convenience

16.57±3.41 16.32±2.75 15.38±2.19 14.71±3.02

3.082* .029

Group1 Group3；Group2 Group3

Fulfillment

16.53±2.46 15.83±2.77 14.82±2.08 14.96±2.83 2.724

* .046 Group1

Group3

Interactivity

16.26±2.91 16.33±2.84 13.41±2.41 14.24±3.33

8.418* .000

Group1 Group3； Group2 Group3

Reliability

12.28±2.08 12.08±2.08 10.79±1.93 11.05±1.91

4.238*

.007

Group1 Group3 Group2 Group3

Group1 Group2


8.22±1.34 7.79±1.41 7.41±1.06 7.74±1.21 2.165*

.095

Group1 Group3

Service quality

69.87±11.15

68.57±11.09

61.81±7.23 62.76±10.89

4.892*

.003

Group1 Group3 ；Group2 Group3； Group1 Group2


12.47±1.99 11.99±2.23 11.04±1.55 10.91±2.42 3.849*

.012

Group1 Group3；Group2 Group3 Group1 Group2

Table 4. The results show that different research objects of monthly income

differ significantly in convenience, performance, interactivity, system reliability,

quality of service and customer satisfaction (p <0.05). It shows that monthly income

affects convenience, performance, interactivity, system reliability, quality of service

and customer satisfaction, while the differences in research objects on the basis of

privacy and security are not significant (F = 2.165, p =. 095), showing that different

income groups are more concerned about privacy and security.

Because there are significant differences in convenience, performance,

interactivity, system reliability, service quality and customer satisfaction in different

monthly income research objects, it is necessary to make a multiple comparison

between the two groups or groups.

Postmortem examination found that in the interactive, system reliability, in

the quality of service, there is significant difference between Group 1 and Group 3,

Group 1 and Group 4, Group 2 and Group 3, Group 2 and Group 4 (p >0.05).

There were significant differences between group 1 and group 3, group 1

and group 4, group 2 and group 4. There was no difference between group 1 and

group 2, group 2 and group 3 and group 3 and group 4 (p> 0.05). There were

significant differences between group 1 and group 4, group 2 and group 4 in

convenience and performance, and there was no difference between group 1 and

group 2, group 1 and group 3, group 2 and group 3, group 3 and group 4 (p> 0.05)

4.6 Analysis of the Effects of Service Quality Factors on Customer Satisfaction

4. 6. 1 A Regression Analysis of the Effect of Convenience on Customer

Satisfaction

In order to study the effect of convenience on customer satisfaction, for

convenience as the independent variable, the regression equation was established with

the satisfaction of the customer, and the regression equation was tested, as shown in

table 4.14.

Table 4. 14 Coefficient of determination R ²

Model R R² Adjusted R² Standard error

1 .831* .693 .688 1.21149

Table 4.14. The coefficient of determination R2 = 0. 693, adjusted R2 = 0.

688. Therefore, the self-variable convenience can explain the satisfaction of the

variable customer 68. 9% variation.

Table 4.15 significance test of regression equation Model SS D f MS F Sig.

1 Regression 437.723 1 437.723 298.229 .000 Residual 195.208 133 1.467 Total 632.932 134

Table 4. 15 shows the significance test results of the regression equation.

The results show that F (1,133) = 298.229, p <0. 001, it is noted that the regression

equation established is significant.

Table 4. 16regression coefficient

Model Unstandardized Standardization t Sig

coefficient factor

B Standard error

β

1 （Constant） 1.778 .576 3.082 .002 Convenience

.624 .037 .833 17.268 .000

Table 4.16. The coefficient of regression equation established, including the

standardized regression coefficient and the unstandardized regression coefficient, and

the significance test of the regression coefficient, were obtained by the

non-standardized regression equation: customer satisfaction = 1.778+0.624 ×

convenience, standardized regression equation: customer satisfaction = 0.833 ×

convenience. Standardized regression equation shows the convenience increases a

unit, customer satisfaction increases 0. 833 units, At the same time, the significance

test results showed that the regression coefficient was significantly different from 0 (t

= 17.268, p <0. 001).

4. 6. 2 A Regression Analysis of the Effect of Performance on Customer

Satisfaction

In order to study the effect of performance on the satisfaction of customers,

the regression equation is established with the satisfaction of the customer as the

dependent variable, the regression equation is established, and the results are shown

in Table 4.17.

Table 4. 17 Coefficient of determination R2


1 .773* .598 .597 1.38214

Table 4. 17. The coefficient of determination R2 = 0. 598, adjusted R2 = 0.

597. Thus, the self-variable performance can explain the satisfaction of the variable

customer. 59.66% variation.

Table 4. 18 significance test of the regression equation

Model SS D f MS F Sig

1 Regression 378.857 1 378.857 198.318 .000* Residual 254.077 133 1.910 Total 632.932 134

Table 4.18 shows the significance test results of the regression equation. The

results show that F (1,133) = 198.318, p <0. 001, it is noted that the regression

equation established is significant.

Table 4. 19 Regression Coefficient

Model

Unstandardized coefficient

Standardization factor

T Sig

B Standard error

β

1 （Constant） 1.688 .713 2.369 .020

Fulfillment .639 .046 .775 14.082 .000

The coefficient of regression equation established, including the

standardized regression coefficient and the unstandardized regression coefficient, and

the significance test of the regression coefficient, were obtained by the


performance, standardized regression equation: customer satisfaction = 0.775 ×

performance. Standardized regression equation describes the performance increase of

a unit, customer satisfaction increases 0. 775 units, At the same time, the significance

test results showed that the regression coefficient was significantly different from 0 (t

= 14.082, p <0. 001）

4. 6. 3 A Regression Analysis of the Influence of Interactivity on Customer

Satisfaction

In order to study the influence of interactivity on customer satisfaction, with

interactivity as the independent variable, customer satisfaction is the dependent

variable, the regression equation is established, and the regression equation is checked

and the result is shown in Table 4. 20-4.22.


Model R R² AdjustedR² Standard error

1 .752* .564 .561 1.44159

Table 4. 20 show the coefficient of determination R2 = 0. 564, adjusted R2

= 0. 561. Therefore, the self-variable interactivity can explain the satisfaction of the

variable customer 56. 0% variation.

Table 4. 21 significance test of the regression equation


1

Regression 356.541 1 356.541 171.567 .000*

Residual 276.395 133 2.079

Total 632.932 134

Table 4.21 show results of the significance test of the regression equation

are shown in 21. The results show that F (1,133) = 171. 567, p <0. 001, it is noted that

the regression equation established is significant.

Table 4. 22 regression, coefficient

Model



T Sig B Standard

error β

1 （Constant） 3.717 .614 6.065 .000 Interactivity .521 .041 .752 13.099 .000

Table 4.22 show the coefficient of regression equation established, including

the standardized regression coefficient and the unstandardized regression coefficient,

and the significance test of the regression coefficient, were obtained by the


interactivity, standardized regression equation: customer satisfaction = 0.752 ×

interactive. Standardized regression equation shows that the interactivity increases

one unit, customer satisfaction increases 0. 752 units, At the same time, the

significance test results showed that the regression coefficient was significantly

different from 0 (t = 13. 099, p <0. 001).

4.6.4 The Regression Analysis of the Effect of System Reliability on

Customer Satisfaction

In order to study the effect of system reliability on customer satisfaction, the

regression equation is established by using the system reliability as the independent

variable and the customer satisfaction as the dependent variable, and the regression

equation is tested, and the results are shown in table 4.23-4.25.



1 .746* .556 .553 1.45480

Table 4. 23 show the coefficient of determination R2 = 0. 556, adjusted R2

= 0. 553. Therefore, the reliability of the independent variable system can explain the

satisfaction of the variable customer55.2% variation.

Table 4.24 significance test of the regression equation


1

Regression 351.448 1 351.448 166.057 .000*

Residual 281.486 133 2.117

Total 632.932 134

Table 4.24 show the results of the significance test of the regression

equation are shown. The results showed that F (1,133) = 166. 057, p <0. 001, it is

noted that the regression equation established is significant.

Table 4. 25 Regression Coefficient

Model



T Sig B

Standard error β

1 （Constant） 2.545 .711 3.575 .000

Reliability

.781 .061 .746 12.885 .000

Table 4.25 show the coefficient of regression equation established, including

the standardized regression coefficient and the unstandardized regression coefficient,

and the significance test of the regression coefficient, were obtained by the

non-standardized regression equation: customer satisfaction = 2.545+0.781 × system

reliability, standardized regression equation: customer satisfaction = 0.746 × system

reliability. The standardized regression equation shows that the system reliability is

increased by one unit, and customer satisfaction is increased by 0.746 units, At the

same time, the significance test results showed that the regression coefficient was

significantly different from 0 (t = 12.885, p <0. 001).

4.6.5 Analysis of the Influence of Privacy and Security on Customer

Satisfaction

In order to study the impact of privacy and security on customer satisfaction,

the paper establishes the regression equation and verifies the regression equation with

the privacy and security as the independent variable, the customer satisfaction as the

dependent variable, and the result is shown in table 4.26-4.28.

Table 4.26 Coefficient of determination R2


1 .683* .467 .463 1.59094

Table 4.26 show the coefficient of determination R2 = 0. 467, adjusted R2 =

0. 463. Therefore, the self-variable privacy and the security. It is possible to interpret

the customer's satisfaction46.4% variation.

Table 4.27 significance test of the regression equation


1

Regression 296.295 1 296.295 117.061 .000*

Residual 336.636 133 2.532

Total 632.932 134

Table 4.27 shows the results of the significant test of the regression equation.

The results show that f (1, 133) =117.061, p. 001, the regression equation established

is significant. Table 4. 28 Regression Coefficient

Model



T Sig B Standard

error β

1 （Constant） 2.533 .848 2.988 .000 Reliability 1.155 .108 .685 10.821 .000

Table 4.28 shows the coefficient of regression equation established,

including the standardized regression coefficient and the unstandardized regression

coefficient, and the significance test of the regression coefficient, and the

non-standardized regression equation is obtained as follows: customer satisfaction =

2.533+1.155 × Privacy and Safety. The regression equation of standardization is:

customer satisfaction = 0.685 × Privacy and Security. Standardized regression

equation shows a unit of privacy and security, and customer satisfaction increases by

0. 685 units, At the same time, the significance test results showed that the regression

coefficient was significantly different from 0 (t = 10. 821, p <0. 001)

4. 6. 6 The regression coefficient summary of the five factors of service to

customer satisfaction

In order to clarify the effect of five factors on customer satisfaction, this

paper summarizes the regression coefficients of customer satisfaction by five factors

of service.

Table 4.29 Summary of regression coefficient of five factors of service to customer satisfaction

Argument

unnormalized regression coefficients Normalized regression coefficients

T P Constant term

Standard error

Regression coefficients

Standard error

Convenience→ customer satisfaction

1.778 .578 .624 .035 .831 17.268 .

000

Fulfillment→ customer satisfaction

1.686 .711 .637 .046 .773 14.082 .

000

Interactivity→ customer satisfaction

3.715 .614 .521 .039 .752 13.099 .

000

Reliability→ customer satisfaction

2.547 .711 .781 .060 .746 12.887 .000

Security→ customer satisfaction

2.533 .848 1.155 .108 .683 10.821 .000

The five elements of convenience, performance, interactivity, system

reliability, privacy and security are significant for customer satisfaction.

4.7 Structural Equation Model Analysis

In order to study the relationship between service quality and customer

satisfaction, this paper puts forward the hypothesis that the quality of service can

influence customer satisfaction, so the latent variable structure equation model is

established, and the model is tested by collecting data.

Before the latent variables are modeled, we validate the questionnaires used

to ensure that the research tools have a high degree of validity. The validity of the

service questionnaire has been discussed in the previous article. The customer

satisfaction questionnaire is tested below. Using SPSS19.0, the reliability analysis of

the customer satisfaction questionnaire showed that the Cronbach α coefficient was

0.905, and the reliability of the questionnaire was very good. Using MPLUS7.4 To do

confirmatory factor analysis of customer satisfaction questionnaire, the result is

shown in Figure 4.3.

Figure 4.3 The confirmatory factor analysis model and model parameters

estimation of customer satisfaction questionnaire

Fit index of confirmatory factor analysis model of customer Satisfaction

Questionnaire: Χ2= 0, D f=0. RMSEA = 0, CFI = 1.00, TLI=, SRMR= 0.000. It is

generally believed that the value of χ2/d F is between 1-3 and the GFI and TLI are

more than .90, the model fitting is very good, and the value of RMSEA and SRMR is

less than. 05model fitting is very good, when the value is greater than. 05 is less than.

08model fit well, greater than .08 less than .10 indicates that the model is passable.

Therefore, the model and data are fitted perfectly, which shows that the single factor

model and data fitting are very good and the questionnaire structure is reasonable.

The model of latent variable path analysis is established. Using MPLUS7.4

to test the hypothesis model, the maximum likelihood estimation method (maximum

likelihood) is used to estimate the path analysis model diagram shown in Fig. 2. The

fitting indices of the model are good: χ2=64.694 D f=19, χ2/d f=3.405, RMSEA =

0.133, 90%of the Confidence interval is (0.099, 0.170), CFI = 0.956, TLI = 0.935,

SRMR= 0.026. It is generally believed that the value of χ2/d F is between 1-3 and the

GFI and TLI are more than .90, the model fitting is very good, and the value of

RMSEA and SRMR is less than .05 model fitting is very good, when the value is

greater than .05 is less than. 08model fit well, greater than .08 less than .10 indicates

that the model is passable. Therefore, the latent variable path analysis model we have

built is a good model.

Figure 4. 4 Service quality to Customer Satisfaction Measurement Model

In the model, with the increase of service quality, customer satisfaction also

increased significantly (β=0.765r, = 41.36, P. 001), the quality of service increased by

1 standard deviation, subjective increased by 0.921 standard deviation, indicating the

strong impact of service quality on customer satisfaction.

Based on the above analysis, we can conclude that the H0 Hypothesis, is set

up.

Hypothesis of H0: The service quality of mobile shopping platform is

directly related to customer satisfaction

chapter 4 research and analysis€¦ · smart shopping. meanwhile, amazon is accelerating its...

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