Questionnaire data analysis, using SPSS

DefaultReport.csvDefaultReport1 xfinaldraft x
 According to the questionnaire report file and (final draft x), analyze the data according to the literature review data in the (final draft x), and another source from outside. Find the relationship between data from different sources, using graphics from the report, and SPSS results. around 5 references 

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MDT

1 What is the highest level of education you have completed?

.00

4

#

Count

1

from High school

.79%

6

2

11

3 Graduated 28.95% 11
4

8

5

1

6

2.63% 1

0%

38

# Field Minimum Maximum Mean Std Deviation Variance Count

1 What is your age? 1.00

# Answer % Count

1

2

2

3

4

4

5

0

6

0.00% 0

7

0.00% 0

8

0.00% 0

9

0.00% 0

Total

37

# Field Minimum Maximum Mean Std Deviation Variance Count

1 How long is your working age? 1.00

2.54

37

# Answer % Count

1

t yet

9

2

10

3

27.03% 10

4

5

5

3

Total 100% 37

# Field Minimum Maximum Mean Std Deviation Variance Count

1

1.00

38

# Answer % Count

1

14

2

21.05% 8

3 other

Total 100% 38

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Have you ever heard of Social lending (P2P Lending?) 1.00

1.49

37

# Answer % Count

1

19

2

18

Total 100% 37

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Do you often have products that cannot be purchased because of financial pressure? 1.00 2.00

# Answer % Count

1

14

2 No

6

Total 100% 20

# Field Minimum Maximum Mean Std Deviation Variance Count

1 How do you solve the problem mentioned in the previous question? 1.00 3.00

20

# Answer % Count

1

10

2

1

3

9

Total 100% 20

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Have you ever used a social lending product? 1.00 2.00

20

# Answer % Count

1 Definitely yes

5

2 Definitely not

Total 100% 20

# Field Minimum Maximum Mean Std Deviation Variance Count

1

1.00 5.00

20

# Answer % Count

1

30.00% 6

2

3

3

4

4

25.00% 5

5 other

2

Total 100% 20

other – Text

# Field Minimum Maximum Mean Std Deviation Variance Count

1

1.00 4.00

1.22 1.49 20

# Answer % Count

1

50.00% 10

2

10.00% 2

3

20.00% 4

4 other 20.00% 4

Total 100% 20

other – Text

– How did you first get in touch with these information?

# Field Minimum Maximum Mean Std Deviation Variance Count

1

1.00 3.00

16

# Answer % Count

1

6

2

7

3

3

4

0.00% 0

5 other 0.00% 0
Total 100% 16

other – Text

# Field Minimum Maximum Mean Std Deviation Variance Count

1

1.00 4.00

16

# Answer % Count

1

37.50% 6

2

37.50% 6

3

1

4 other 18.75% 3

Total 100% 16

other – Text

# Field Minimum Maximum Mean Std Deviation Variance Count

1 How often would you use a social lending product once? 1.00 5.00

16

# Answer % Count

1

18.75% 3

2

2

3

25.00% 4

4

12.50% 2

5

%

5

Total 100% 16

# Field Minimum Maximum Mean Std Deviation Variance Count

1 How much amount would spend peruse? 1.00 4.00 2.06 0.97

16

# Answer % Count

1

31.25% 5

2

43.75% 7

3

12.50% 2

4

12.50% 2

Total 100% 16

# Answer % Count

1

5

2

4

3

2

4

18.18% 4

5

3

6

1

7 other 13.64% 3
Total 100% 22

other – Text

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Which loan period do you prefer? 1.00 4.00 2.06

16

# Answer % Count

1

37.50% 6

2

31.25% 5

3

18.75% 3

4

12.50% 2

Total 100% 16

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Have you ever experience an overdue from a social lending service? 1.00 2.00

16

# Answer % Count

1 Definitely yes 37.50% 6
2 Definitely not

10

Total 100% 16

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Have you ever encountered improper dunning behavior? (SMS or phone booming, etc,.) 1.00 2.00

0.50 0.25 16

# Answer % Count

1 Yes 43.75% 7
2 No

9

Total 100% 16

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Have you ever acting as a lender in a social lending activity? 1.00 2.00

0.50 0.25 16

# Answer % Count

1 Definitely yes 50.00% 8
2 Definitely not 50.00% 8

Total 100% 16

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Do you ever not having your lending back? 1.00 2.00 1.25 0.43 0.19 8

# Answer % Count

1 Yes 75.00% 6
2 No 25.00% 2
Total 100% 8

# Field Minimum Maximum Mean Std Deviation Variance Count

1 Will you continue support/use social lending service? 1.00 2.00 1.25 0.43 0.19 16

# Answer % Count

1 Yes 75.00% 12
2 No 25.00% 4

Total 100% 16

What would you like to see or improve in this area?
yes

Answer % Count

Total 100% 0

																					Default Report
Last Modified:																															2								0																																														1				9							8		11	22																					3																			4
Q2 –	What is the highest level of education you have completed?
																																								#																				Field																				Minimum																				Maximum																				Mean																				Std Deviation																				Variance																																										Count
																			1.00													6	2.					7	1.	19	1.40				38
																					Answer																						%
	Graduated	1														5
College student		28.95%
Postgraduated		21.05%
PhD or above		2.63%
						other
																					Total							10
Q3 –	What is your age?
				4.00		2.54	0.76	0.57						37
Under	18	5.41%
18 – 24	45.95%	17
25 – 34	37.84%			14
35 – 44	10.81%
45 – 54							0.00%
55 – 64
65 – 74
75 – 84
85 or older
																			100%
Q4 –	How long is your working age?
		5.00		1.22			1.49
				No	24.32%
0-2 years		27.03%
2-5 years
5-10 years	13.51%
above 10 years	8.11%
Q5 – What is your field of work?
What is your field of work? – Selected Choice			3.00	2.05	0.89	0.79
Financial related	36.84%
Information technological related
42.11%																			16
Q5_3_TEXT – other
					other – Text
Mental health related
Art and design
Q6 –	Have you ever heard of Social lending (P2P Lending?)
							2.00			0.50			0.25
			Definitely	yes	51.35%
			Definitely not	48.65%
Q7 –	Do you often have products that cannot be purchased because of financial pressure?
1.30	0.46	0.21										20
			Yes	70.00%
	30.00%
Q8 –	How do you solve the problem mentioned in the previous question?
1.95		0.97	0.95
Get help from friends, family, coworkers…				50.00%
Consider loan	5.00%
Cancel purchase	45.00%
Q9 –	Have you ever used a social lending product?
1.75			0.43			0.19
				25.00%
		75.00%	15
Q10 – Why don’t you want to use a social lending products?
Why don’t you want to use a social lending products? – Selected Choice	2.70	1.38	1.91
Risk/Unsafe
High interest risk	15.00%
Information disclosure			20.00%
Dare not try
	10.00%
Q10_5_TEXT – other
Unnecessary
Q11 – What changes it makes, you will consider using it?
What changes it makes, you will consider using it? – Selected Choice	2.10
Legal Support/Authorization
Reduce interest rate
Improve service support
Q11_4_TEXT – other
preferential policy
Q	12
How did you first get in touch with these information? – Selected Choice	1.81	0.73	0.53
AD					37.50%
From other people			43.75%
Search engine				18.75%
Consumer product loan
Q12_5_TEXT – other
Q13 – For what reason that you would choose to use this product?
For what reason that you would choose to use this product? – Selected Choice			2.06	1.09	1.18
Urgent need
Financial management
Unaffordable	6.25%
Q13_4_TEXT – other
Q14 –	How often would you use a social lending product once?
3.25	1.48	2.19
once 1~2 weeks
once 2~4 weeks					12.50%
once 4~6 weeks
once 6~8 weeks
above 8 weeks			3		1.25
Q15 –	How much amount would spend peruse?
0.93
0~50$
50~100$
100~200$
above 200$
Q16 – What are the main products that you would want to use social lending to purchase?
Electronics/Devices	22.73%
Beauty products		18.18%
Virtual products	9.09%
Daily consumption
Luxury products		13.64%
Cloths	4.55%
Q16_7_TEXT – other
property
Q17 –	Which loan period do you prefer?
1.03	1.06
1~3 months
3~6 months
6~12 months
over 12 months
Q18 –	Have you ever experience an overdue from a social lending service?
1.63	0.48	0.23
62.50%
Q19 –	Have you ever encountered improper dunning behavior? (SMS or phone booming, etc,.)
1.56
56.25%
Q20 –	Have you ever acting as a lender in a social lending activity?
1.50
Q21 –	Do you ever not having your lending back?
Q22 –	Will you continue support/use social lending service?
Q23 –	What would you like to see or improve in this area?
YES
Q16_7_TEXT – Topics

Default Report
Social lending Survey
August

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am MDT

Q2 –

What is the highest level of education you have completed?

																																								#																				Field																				Minimum																				Maximum																				Mean																				Std Deviation																				Variance																																										Count
																																													1	What is the highest level of education you have completed?																				1.00	6 .00	2. 7 4	1. 19	1.40				3 8

#

Count

1

28.95%

11

1

6

2.63%

1

38

																					Answer																						%
Graduated from High school	1 5 .79%												6
																														2	College student		28.95%		11
																				3	Graduated
																		4	Postgraduated		21.05%							8
													5	PhD or above		2.63%
						other
																					Total																						10 0%

Q3 –

What is your age?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

What is your age?

4.00

2.54

0.76

0.57

37

#

Answer

%

Count

1

2

2

3

4

4

5

6

0.00%

0

0.00%

0

8

0.00%

0

0.00%

0

Total

100%

37

Under 18	5.41%
18 – 24	45.95%	17
25 – 34	37.84%			14
35 – 44	10.81%
45 – 54							0.00%								0
55 – 64
				7	65 – 74
75 – 84
			9	85 or older

Q4 –

How long is your working age?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.54

37

How long is your working age?

5.00

1. 22

1.49

#

Answer

%

Count

1

9

2

3

27.03%

10

4

5

5

3

Total

100%

37

No t yet	24.32%
0-2 years		27.03%					10
2-5 years
5-10 years	13.51%
above 10 years	8.11%

Q5 – What is your field of work?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

38

What is your field of work? – Selected Choice

3.00

2.05

0.89

0.79

#

Answer

%

Count

1

14

2

21.05%

8

3

other

Total

100%

38

Financial related	36.84%
Information technological related
42.11%																			16

Q5_3_TEXT – other

			other – Text
Mental health related
Art and design

Q6 –

Have you ever heard of Social lending (P2P Lending?)

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

1.49

37

Have you ever heard of Social lending (P2P Lending?)

2.00

0.50

0.25

#

Answer

%

Count

1

2

Total

100%

37

			Definitely yes	51.35%	19
			Definitely not	48.65%	18

Q7 –

Do you often have products that cannot be purchased because of financial pressure?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

Do you often have products that cannot be purchased because of financial pressure?

1.30

0.46

0.21

20

#

Answer

%

Count

1

14

2

6

Total

100%

20

			Yes	70.00%
			No		30.00%

Q8 –

How do you solve the problem mentioned in the previous question?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

3.00

20

How do you solve the problem mentioned in the previous question?

1.95

0.97

0.95

#

Answer

%

Count

1

10

2

1

3

9

Total

100%

20

Get help from friends, family, coworkers…				50.00%
Consider loan	5.00%
Cancel purchase	45.00%

Q9 –

Have you ever used a social lending product?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

20

Have you ever used a social lending product?

1.75

0.43

0.19

#

Answer

%

Count

1

Definitely yes

5

2

Definitely not

Total

100%

20

				25.00%
		75.00%	15

Q10 – Why don’t you want to use a social lending products?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

5.00

20

Why don’t you want to use a social lending products? – Selected Choice

2.70

1.38

1.91

#

Answer

%

Count

1

30.00%

6

2

3

3

4

4

25.00%

5

5

other

2

Total

100%

20

Risk/Unsafe
High interest risk	15.00%
Information disclosure			20.00%
Dare not try
	10.00%

Q10_5_TEXT – other

other – Text

Unnecessary

Q11 – What changes it makes, you will consider using it?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

4.00

1.22

1.49

20

What changes it makes, you will consider using it? – Selected Choice

2.10

#

Answer

%

Count

1

50.00%

10

2

10.00%

2

3

20.00%

4

4

other

20.00%

4

Total

100%

20

Legal Support/Authorization

Reduce interest rate

Improve service support

Q11_4_TEXT – other

other – Text

preferential policy

Q

12

– How did you first get in touch with these information?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

3.00

16

How did you first get in touch with these information? – Selected Choice

1.81

0.73

0.53

#

Answer

%

Count

1

6

2

7

3

3

4

0.00%

0

5

other

0.00%

0

Total

100%

16

AD					37.50%
From other people			43.75%
Search engine				18.75%
Consumer product loan

Q12_5_TEXT – other
other – Text

Q13 – For what reason that you would choose to use this product?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

4.00

16

For what reason that you would choose to use this product? – Selected Choice

2.06

1.09

1.18

#

Answer

%

Count

1

37.50%

6

2

37.50%

6

3

1

4

other

18.75%

3

Total

100%

16

Urgent need
Financial management
Unaffordable	6.25%

Q13_4_TEXT – other
other – Text

Q14 –

How often would you use a social lending product once?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

5.00

16

How often would you use a social lending product once?

3.25

1.48

2.19

#

Answer

%

Count

1

18.75%

3

2

2

3

25.00%

4

4

12.50%

2

5

5

Total

100%

16

once 1~2 weeks
once 2~4 weeks					12.50%
once 4~6 weeks
once 6~8 weeks
above 8 weeks			3 1.25 %

Q15 –

How much amount would spend peruse?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

4.00

2.06

0.97

16

How much amount would spend peruse?

0.93

#

Answer

%

Count

1

31.25%

5

2

43.75%

7

3

12.50%

2

4

12.50%

2

Total

100%

16

0~50$

50~100$

100~200$

above 200$

Q16 – What are the main products that you would want to use social lending to purchase?

#

Answer

%

Count

1

5

2

4

3

2

4

18.18%

4

5

3

6

1

7

other

13.64%

3

Total

100%

Electronics/Devices	22.73%
Beauty products		18.18%
Virtual products	9.09%
Daily consumption
Luxury products		13.64%
Cloths	4.55%
22

Q16_7_TEXT – other

other – Text

property

Q17 –

Which loan period do you prefer?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

4.00

2.06

16

Which loan period do you prefer?

1.03

1.06

#

Answer

%

Count

1

37.50%

6

2

31.25%

5

3

18.75%

3

4

12.50%

2

Total

100%

16

1~3 months

3~6 months

6~12 months

over 12 months

Q18 –

Have you ever experience an overdue from a social lending service?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

16

Have you ever experience an overdue from a social lending service?

1.63

0.48

0.23

#

Answer

%

Count

1

Definitely yes

37.50%

6

2

Definitely not

10

Total

100%

16

62.50%

Q19 –

Have you ever encountered improper dunning behavior? (SMS or phone booming, etc,.)

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

0.50

0.25

16

Have you ever encountered improper dunning behavior? (SMS or phone booming, etc,.)

1.56

#

Answer

%

Count

1

Yes

43.75%

7

2

No

9

Total

100%

16

56.25%

Q20 –

Have you ever acting as a lender in a social lending activity?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

0.50

0.25

16

Have you ever acting as a lender in a social lending activity?

1.50

#

Answer

%

Count

1

Definitely yes

50.00%

8

2

Definitely not

50.00%

8

Total

100%

16

Q21 –

Do you ever not having your lending back?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

0.43

0.19

8

Do you ever not having your lending back?

1.25

#

Answer

%

Count

1

Yes

75.00%

6

2

No

25.00%

2

Total

100%

8

Q22 –

Will you continue support/use social lending service?

#

Field

Minimum

Maximum

Mean

Std Deviation

Variance

Count

1

1.00

2.00

1.25

0.43

0.19

16

Will you continue support/use social lending service?

#

Answer

%

Count

1

Yes

75.00%

2

No

25.00%

4

Total

100%

16

12

Q23 –

What would you like to see or improve in this area?

What would you like to see or improve in this area?

yes

YES

Q16_7_TEXT – Topics

Answer

%

Count

Total

100%

0

Cover

Content list

Abstract

Acknowledgement

Chapter 1

:

Introduction

Chapter 2

:

Literature Review

Section 2.1 Social lending

Section 2.2 Theory of Digital Capitalism

Section 2.3 Current situation of social lending in China

Chapter 3: Methodology

Section 3.1 Research Questions

Section 3.2 Data collection methods & sampling

Section 3.3 How to analyze

Chapter 4:

Data collection and analysis

Chapter 5

:

Argument and discussion

Section 5.1 A new way of e-financial business

Section 5.2 Capital monopoly by the giants

Section 5.3 The impact of Social Lending on Society

Chapter 6: Conclusion

Section 6.1 Future trends and recommendations

Section 6.2 Dissertation conclusion

Bibliography

Appendix

Chapter 1
Introduction

Chapter 2
Literature Review

Section 1.2 Social lending

Social lending is also known as peer-to-peer lending is where people borrow or lend money directly from each other boosting returns for individuals who supply capital and reduces the interest rates for those using it, but it also needs more time and time from the individuals as it also has risk. The social lending market has a perception of private loans, which is a traditional way private people borrow money without any intercession. Online social lending seems a growing phenomenon due to transfer into an internet based on the platforms for P2P lending.

The first borrowing platform known as Zopa was founded in

20

05 in Europe and since then there are numerous types of lending that have come up. There are twenty-four platforms in the world which followed the Zopa, and twelve of the platforms are based in the United States alone (Berger, and Gleisner 2009: 5). In regard to the Peer-to-Peer-Banking.com, it recorded a total of thirty-three different lending platforms globally. Prosper.com was the established as first lending platform in the U.S. in 2006 while smava.de being the first platform in German established in 2007 (Chen, and Han 1970: 12). Currently, the platforms that exist run on the national platform due to the legal necessities in various countries.

Literally, the platforms for social lending differs from one another depending on the kind and the method implemented. They are majorly alienated into non-commercial and commercial platforms. The commercial platform is strictly limited to the national markets, but the non-commercial markets majorly operate on global markets (Bachmann et al. 2011: 3). The two types of platforms have the main difference, which is the general intention of the lender and the expectations concerning the returns. On the side of the commercial platform, the lender has a chance to get a sensible interest in regard to the risk taken while the non-commercial lender may or may not get a recompense for the risks taken. In the case of non-commercial platforms, the lenders may be wanting to donate money to ventures in the underdeveloped countries in terms of economy in the world.

The social lending platform is a bilateral market that is more like the old banking scheme in line with its unique challenges. The platform activities are all targeting the lenders and borrowers. Therefore, these participants and determinants are important for the achievement of the lending process. The lenders will always try to take chances to devote money to be more lucrative at a given level of risk; the borrowers are entitled to search for foundations of liquidity with their different default risks (Ortega, Cecilia and Bell 2008: 15). The social lending website act as an intermediary of these groups as they attempt to tie the expectation of the parties. In forming the communities, borrowers or lenders engross in groups to concentrate on their interest. However, the authors didn’t explain the detail of risk clearly, behind the increasingly interest from the lenders, the risk might cost enormous price to a certain degree.The data will be collected in the following chapters to analyze the extend of the risk financially and socially.

The social lending platform is also considered a section of the financial market involving governing boundaries in different countries. The original banks acting as associates in the operational business is a necessity depending on the national regulations. Many articles as a requirement capture the involvement of bank but mainly to enable the lending process. The course of lending also comprises of authentication of the information provided by the borrower through the credit agencies (Collier, and Hampshire 2010: 197). These systems are different in the countries, making the research in this particular area not to be applicable worldwide.

In the lending process, there are platforms that directly link the borrowers to lenders while others rely on may be a bank to connect them. The social lending appears different in the manner the interest rate for the borrower is set. For instance, platforms such as prosper.com practice an active process where the borrower can set the extreme interest rate; they can pay (Freedman, and Jin 2008: 42). In case of limited time, the lenders can action again through quoting the amount they can fund and the lowest amount they are ready to accept. The lenders are allowed to place the bids after the funding of the loan through offering a lesser minimum interest rate. In situations where further bids are placed exceeding the loan to be financed, the lowest minimum interest rates bids are selected (Frerichs, and Schumann 2008: 27). On the other hand, other platforms like smava. de based in Germany compute the interest rates for the loan requested in regard to the characteristics of the borrower, mainly demographic and financial. The process of bidding terminates when the funding of the loan is complete as further bids have no consequence on the final interest rate. As the authorization could differ worldwide, it is extremely hard for the new industry to form a standard globally, the process can be horrible due to the lack of monitoring, it could be even worse in countries financial system is worse. In China, particularly, only 2.4% firms return the principle to the lenders (STCN, 2019), which caused a concern about the relationship between national financial structure and the social lending platform. In addition,

Section 1.2 Theory of Digital Capitalism and Information deficit

The social lending platform also entails the theory of digital platform that accepts for the primary hallmark of the contemporary political economy built within a systematic capacity plunging the market into a crisis (Schiller 2000: 18). It is a new configuration of the historic dynamics of production, including consumption and labor that leads to another variant. The globalized market encompassed with social lending depends on the refusal of digital capitalism to establish social restraints as the existing laws impede the transformation of digital technology (Greiner, and Wang 2009: 29). The analysis of the capitalist’s ideas as they become digital is important to challenge the process of lending. The critique issues are line with the relationship between labor and production of the commodity, as it illustrates how the analysis of Marx depends on assumptions that are not valid at all. Therefore, digital capitalism is driving the platforms used for social lending to escalate to a more advanced level that can reciprocate good funding rates to the lenders and borrowers.

There are determinants for social lending platforms as both the lender and borrower would like to get valid information. The borrower may decide to hide some of the features to receive a low-interest rate. The social lending sites require borrowers to offer financial data that are authenticated by external bureaus (Frerichs, and Schumann 2017: 25). Further, most of the platforms ask users to provide demographic data. The borrowers are granted the chance to offer social data which cannot be confirmed. These factors are considered determinants as they influence effective funding of loan and the interest rate in demand.

Social lending has expanded scientific significance over the years. The information about the transactions and markets enables us to investigate the necessities that play a role in the funding process. The review focuses on variables that influence the success of funding as well as the interest rate of the loan request. Besides, social capital can have a constructive impact to be wholly funded as it can decrease the interest rate expected to be obtained by the borrower. Therefore, fostering of social characteristics allows an opportunity to fully fund a loan in case the financial features cannot construct a successful loan request.

Among the social characteristics, in addition to being able to increase lending, there another characteristic that cannot be ignored is the labor exploitation of producers by the p2p companies.(Schiller, 2000) Companies owned numerous information through big data, disseminate information that is conducive to the growth of lending to the society, and users have too little understanding of technology and industry because of information shocks and information deficits. On the other hand, under the urging of a large amount of information, the desire for capital is difficult to control. Therefore, the capital divide is getting bigger and bigger. In the following chapter, the Case of Ant Financial will be introduced to analyzehow big firms use data to achieve digital capitalism through social lending activities.

Section 1.3 Social lending in China

In China, the business has taken over most of the crediting, therefore becoming the largest P2P industry in the world. According to research, in China, the P2P sector is worth more than 300 billion US dollars. The first online lending platform in China was launched in the year 2007, which was PPDAI Group, which went public on the New York Stock Exchange in the year 2017 (Lin, & Zheng, 2017). Due to simplicity the method provided the borrowers, there was a rapid growth of the industry in less than a decade since by the year 2016, there were more than 3,000 P2P platforms which were operational in China, and they were estimated to operate a monthly turnover of over 130 billion RMB according to research. This drastic growth was all attributed to three factors which included, the high rate of internet connection in China which had been embarked on by the government, excess of funds supply from investors as well as financial demands which emanated from small-to-medium-sized companies which were and are still not well satisfied by the existing banking sector.

Social lending is also said to be friendlier especially to the small scale businesses since most banks in China usually prefer to deal with enterprises which are owned by the state as well as large firms since according to research, of the total loans offered by the banking system, only less than 25 per cent of them is awarded to the small and the medium-sized businesses despite the fact that they are the majority in the Chinese economy accounting for more than 60 per cent of China’s economy. Despite P2P drastic growth, the banking system is still operational and more so controls the Chinese financial system especially the big four banks in China which are China Construction Bank, the Bank of China, the Agricultural Bank of China and also the Industrial and Commercial Bank of China. Another disturbing and more so advantageous factor is that more than 70 per cent of bank’s landings is allocated to commercial business hence neglecting the individuals who are the primary borrowers. This has created a window for social lending significantly.

On the other hand, the general interest rate is relatively low, which has also contributed to the development of personal financial products. China’s largest payment brand, Alipay, launched Yuebao, which encouraged Alipay users treat it as a saving service, just like its name in Chinese ‘the balance pocket’, however, what most users did not know is that it is a fund business rather than a saving business. Compared to saving services provided by traditional banks, it seemed to be inspiring and beneficial, but it is only the illusions produced by comparing different financial service. According to China Economics (2013), the scale of funds of Yuebao has exceeded 10 billion yuan (1.4 billion pounds), some fund sales payment settlement accounts in Yuebao business have not been filed with the regulatory authorities. It is the lack of supervision and information deficit contribute to the social lending services.

Chapter 3

Research Methodology

Having established the fundamental work of theoretical framework of the current situation of Social lending, which also led to the where the evidence will be collected to reflect how argument is supported by the data in the next chapters. The primary role of this chapter is explaining the research methods and methodology that are used in the research study. The main research purpose is about to evaluate the social lending technology is whether a new way of doing financial business. The chapter incorporates the research approach, the research design, sample strategy and size, the methods of analyzing the data that will be collected, reliability and validity, ethical considerations, and the limitations as a result of the methodology. All the methods are planned to be used to evaluate whether e-financial technology and products are currently suitable for the society. The purpose is not just about

Research Approach

The research study will employ quantitative research whereby the approach will emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. (Babbie & Earl, 2010) He also emphasized that quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a phenomenon. In the research study, the factors range from social to economic. As a result, for the purpose of the study, the research will apply the quantitative approach to collect statistic data and information. Social lending, as most research studies have confirmed, contains elements that are not easy to quantify. Thus, the most suitable approach for the study will be quantitative research (Peffers, 2007, pg. 67).

Research Design

This research study will employ a qualitative research in the context of it does not involve any quantitative data or numeric data. Interpretivist strategy is specifically appropriate in this study. This is because there is a link between the variables under study which were formed through the process of interpretation. The research will also apply triangulation as it provides an opportunity in approaching the objectives of the research from different points of view. The use of triangulation was particularly helpful in identifying the intersection between the variables under study.

Research Methods

For the purposes of the study, the researcher decided to employ a combination of research tools that are commonly used in social science which are interviews and questionnaires. The questionnaires will be distributed to participants of the study in helping to meet the objectives of the research as well as answering the research question. Also, as a complementary method, the researcher will use interviews on the participants in obtaining firsthand information.

Questionnaires

Questionnaires were particularly selected for this study as they were reliable and suitable method of collecting information from several respondents in a manner considered to be timely and efficient. Questionnaires are essential when the projects involved are large and have complex objectives whereby one of the major constraints is time (Marczyk, 2005, pg. 77). The study cannot be exempted from this case and as a result of using questionnaires was an effective and quick method for the research in reaching several participants in a period of several weeks. A major disadvantage for using questionnaires is that they are provided in a strict fixed format. For this reason, it eliminates the likelihood for a detailed abstract observation. Furthermore, the use of questionnaires in this study will be useful in obtaining clear results. Generally, two different data will be collected from questionnaries, the data from borrower’s and lender’s perspective. The data from borrower’s perspective include ‘how did borrowers get in touch with the social lending platform at the first time’, ‘how many users have used the platform to participate in money transactions’, and ‘where did respondent spend their capital from social lending’, the basic collection agenda from borrowers is to find out the inner relationship between respondents’ social identity and lending demand, also self-awareness during the lending process. On the other side, from lenders’ perspective, the main information needs to collect is how the platform information is presented to the public, and its way of lending. According to Lin, Li & Zheng (2017), one of the major social lending platform PPDai, it offers direct lending services between lenders and borrowers and standard loan products, whereas other platform offers shorter-term cash loan products and consumption loans, which allow customers to pay retailers in instalments.

Interviews

For the research to extensively cover the theoretical elements, the researcher has to choose a corresponding methodthrough the use of structured interviews which comprise of multiple questions. The interviews will be distributed to participants who are representative of a participant group. Interviews in research are mainly used in social sciences as they provide an opportunity for descriptive and open discussion between the interviewee and the researcher. Interviews are also described to be interactive and informal. Even though being regarded as being a limitation due to its nature of providing results that are subjective, its flexibility is its major merit as some elements such as emotions cannot be captured by the questionnaires. Apparently, the findings from an interview are ungeneralizable as a result of obtaining subjective data.

Conversely, its flexibility contributes to understanding and a detailed explanation the link between variables. Furthermore, in the event of conducting the research study, the most appropriate method would be using interviews as the primary method of data collection.

Sampling Strategy

Upon the requirements of the study, the researcher has to evaluate two participant groups. The sample population will be determined through stratified sampling due to the observing the relationship between different groups. The selection will be based on various criteria.

Methods of Data Analysis

Analysis of the results from the questionnaires and interviews will be carried out through statistical analysis. The findings of the interviews will be analyzed through manual mean as the researcher aims at detecting percentage in background, cognition, and choices and in determining tendencies and trends in the participants’ responses. The data collected from the questionnaires will be analyzed by packages used in statistics such as STATA and SPSS. The results will then be provided in charts and tables.

Ethical Considerations

Concerning the research, the researcher is likely to be faced by various issues during the undertaking of the project. The most important issue is concerned with consent of the respondents. All the respondents involved in the research study will receive prior information to the participants regarding the purpose and the objective of the project. The participants will provide their written consent to prove their voluntariness in engaging themselves in the research process. The name of the organization and the identity of the respondents will be put under secrecy which will meet requirements as provided in University’s code of ethics.

Furthermore, the policy for confidentiality and privacy by companies will be put under strict consideration as most companies have a policy which safeguards their employees in matters concerning research. As a result, the researcher will sign consent forms for privacy and confidentiality with the organizations whose workers participated in the research study. Finally, for the purpose of the research, all the collected information will be placed under strict confidentiality.

Limitations of the Study

The research study is likely to be encountered with several problems during the process of conducting the research process. The first likely challenge that is likely to be faced is the recruitment of an adequate number of respondents. The process of recruitment may be time consuming as it is a voluntary process. Therefore, gaining access and having permission from the respondents is a major challenge. Even though there are many institutes support activities like questionnaires and information collection, the transparency and authenticity of those institutes can be challenged.

The second weakness likely to be faced during the research process is the employment of interpretivism which is influenced by objectives and nature of the study. In that context, the findings and the success of the study are considered to be subjective as variables may be influenced by the judgmental and analytical skills rather than using empirical evidence.

The research study also requires the use of other methods such as observation of the participants and focus groups. This is because the elements of behavior associated with lending which will be particularly essential for obtaining data. Nonetheless, due to constraints of cost and time, the methods cannot be used in the research.

Chapter 4

Data collection and analysis

Chapter 5
Argument and discussion
Section 5.2 Capital monopoly by the giants

Information deficit in general is attributed to the scepticism or hostility among the public to ecommerce as a result of poor understanding due to the lack of information (Webster, 1995). The deficit often occurs between those with information and those that are clueless. It is the failures by potential users to embrace the prevailing technologies given their lack of information, lack of training and absence of support. The intake of ecommerce for instance in a rural setting may suffer from information deficit and thus may not be profitable compared to the urban areas. Increasing awareness is thus essential to bridge this deficit and make the new technologies adaptable and profitable. The information deficit model implies that transfer of information from experts to non-experts is essential and should be improved if ecommerce is to succeed.

In the e-financial business information deficit can be in various forms for instance the knowledge of the industry, knowledge of using big data and even knowledge on the credit system. The firm has been able to integrate enormous data resources that cover all the customers’ aspects covering their day to day lives. It has been able to acquire domains from software companies which are concerned with customers’ location, prevailing weather conditions, transport systems, entertainment and their travelling schedules. Alibaba among other network logistic firms were able to launch the smart network project in China which helped to reduce the time of doing business and making deliveries (Goes, 2014).

Alibaba, an ecommerce giant utilizes big data to analyse the shopping habits of consumers and their preference. Alibaba through it analysis releases the consumer behaviour data which are essential in highlighting the trends of product consumption throughout China for their own business. Using the data, the company is able to adjust itself to match the customers’ purchasing power. The big data are gained based on the transactions made by the consumers on the products that the company sells for a certain period of time (Chaffey, 2007). The firm provides services to individuals and small enterprises through her unique business model. Alibaba advances her services by embracing the productivity of smaller enterprises which offers an opportunity to a wider range of products and services. Using her partners and agents, Alibaba is able to focus on its core services by creating value and providing services to small enterprises. Large and diverse amounts of goods are made available for the huge customer base.

The business model, profit model and credit model adopted by Alibaba Group are driven by four major aspects: an integrative ecosystem, sensitivity to the business opportunity, support services and new transactional patterns. Some of the fundamental drivers that has made Alibaba it flourish are summarized in the figure below:

(Source: Alibaba Group, 2017)

Taobao.com was launched as an online market embracing a closed loop model for its Big Data Analytics (BDA) highlighted in the graphic below. From the model it is evident that the company was more geared towards finding information majorly for its business growth. It allows for easier and practical connection between the buyers and the sellers in reliable ways. The big data are essential in researching the functioning of the models and coming up with new and advanced ways of doing business. The customers’ information are obtained quite easily by the company’skeen on her business interest and expansion.

(Source: Qiuchen, Jinkun, Liu, and Woonkian2017).

Ant Financial Services was established in 2014 from Alipayto manage all Alibaba Group’s financial services which included Alipay and Yu’EBao. It is a company that embraces technology to enhance inclusivity for all financial services in China and the rest of the world. The platform was formed in order to expand the group’s financial services and adapt to the new regulations outlined by the government. It is dedicated to ensuring that people share equal opportunities worldwide via an ecosystem which is technologically driven. It strives to work with financial companies or institutions to meet all financial needs that the society requires. Some of the products and agents include: The Ant Micro that provide micro loan opportunities to customers while Yu’EBao focuses on individual investors and SMES as a money market funding platform. The Ant Financial platform is able to create a financial profile for all her users by analyzing their online transactions with them. They analyze the consumer preferences, their history of purchases and credit payments. These data are collected and analyze to determine the individual credit limits and that of the SMEs. The results of credit score analysis are shown in the platform ownedby Ant Micro and are opened to all Alibaba Group’s subscribers. The

ANT

’s model is highlighted in the figure below:

(Source: Ant Financial website)

The Ant Credit Pay is a virtual type of credit system that allow one to make payments. It provides credit to subscribers who can made purchase for certain products. It became commercialized through the online advertising Taobao platform which already had a lot of subscribers. The Ant Platform determines the customer’s credit behavior by analyzing their creditworthiness and provides their credit score. With the accurate financial prediction and risk assessment the company is able to determine the interest rate and customer’s loan limit. The product was attractive to loyal customers whom Alibaba had created. Ant Financial formerly Alipay is an affiliate of Alibaba which is an affiliate fintech company. Alipay ensure that online payments for purchase made on Alibaba were easy, fast and safe. Ant’s financial services can be summarized below:

(Source: Ant Financial website)

Ant now ranks among the top financials worldwide with revenues of over $150B.The performance of Ant and revenue earned from her business and affiliate activities are highlighted in the graphics below:

(Source: CBInsights)

In the first quarter of 2018 the consumer loans on the ABS sales slumped by about 74%

(Source: Bloomberg)

The credit service offered by through the Alibaba firm had no supervision. As such they are prone to abuses and exploitation in terms of repayments and high interest rates. The credit firms utilized their affiliation with Alibaba to conceal their credit business and instead embraced the online opportunities the platform offered through her wide subscribers. Customers however ought to be cautious before making transactions out of their information deficit. Yuebao on the other hand makes fortune to fund her business activities through the ecommerce in the name of providing saving services to her esteemed customers. Alibaba earns huge profit margins through the affiliation with these financial companies.

The comprehensive study on consumer behaviour and consumption trends among the Chinese population can be done using big data analysis over a given period of time. The shoppers who purchase the goods from the online platform during the period of the study are a representative of the entire population in general. From the previous studies done it is evident that majority of the Chinese consumers are preferring purchasing healthier consumables and their shopping experience is becoming smarter (Jifeng, Sulin& Han, 2012). The major areas of purchase among the Chinese buyers are the health products, cultural or traditional Chinese items, personal items and electronics. The health category market place according to Alibaba has grown significantly as more of the Chinese nationals are embracing more the healthier foods. The middle class among the population are purchasing more organic foods, sporting items and healthcare products. Technologically the Chinese appetite for new items and electronics has grown significantly. The middle class are growing found of smart devices and have embraced the internet opportunities to do business and make online purchases.

The credit model adopted by Alibaba has helped it develop its reputation and build a positive relationship with her customers. The sellers using the platform must pass through a thorough online certification process that identify them and show their credit worthiness. This helps in curbing illegal business transaction and enhance the consumer security at all times. Sells are strictly supervised and their transactions monitored for credibility purposes. The transactions done are all recorded and can be traced to the individuals who transacted. The model has helped Alibaba gain legitimacy and embrace more sellers who are reliable. The Alipay payments have additional security layer features which allow it to release payments once the buyers receive products from the sellers in perfect condition. The system embraces the online feedback system where customers and sellers can communicate freely. Customers who make purchases can provide their feedback and reviews through the online system which are insightful for other new buyers trying to use the platform (Strauss and Frost, 2012).

In conclusion, through an integrated ecosystem and information deficit theory Alibaba positions itself as an efficient ecommerce firm with simpler ways of doing business. By promoting quality transactions and enhancing customer experience it has become more attractive. The company apart from its common niche has embraced the diverse business domains which include advertising, financial services, network logistics and mobile online business services. Alibaba partnered with suitable network and logistics companies to assist the company with operations in the different segments. Customers have an opportunity to make choices from the available preferences quite easily with the smart networking platform. Users are exposed to a variety of services which range from credit facilities, information services and financial savings through the affiliate firms.

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ANT

Inclusive Finance

Green Finance

Technology

Global Platform

Deep Customer insights

Unrivalled Technology

Business Model Profit Model & Credit Model

Sensitivity to Business Opportunity

Support Services

New Transactional Patterns

Intergrated Ecosystem

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