CHAPTER 4: RESEARCH RESULTS
4.5 T HE TESTING OF OTHER HYPOTHESES
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The adjusted R square is 0.739, which means that 73.9% of the fan numbers led to total revenue.
Table 4.4-12. Linear regression results between fan numbers and total revenue of the CVS industry in Taiwan
R .863(a)
R square .746
Adjusted R2 .739
Standard error 1636984.18879
R square change .746
F change 117.232
Degree of freedom of numerator 1
Degree of freedom of denominator 40
Significance of F change .000
4.5 The testing of other hypotheses
Here we use passenger number and passenger capacity as the indicators for measuring customer growth and customer loyalty in the airline industry. In the telecommunications industry, we use total mobile subscriber, average revenue per user
(ARPU) and churn rate as the indicators for measuring
customer growth, customer loyalty, and customer satisfaction. In the banking credit-card service industry, we use“Credit Cd. in Circu.”, “CreditCard No.in For” and “Credit Card_Stop” as indicators for customer growth, customer loyalty, and customer satisfaction. Table 4.5-1 below gives an explanation of the above indicators and their meanings.
Table 4.5-1. Meanings of indicators used in the testing of other hypotheses
Industries Indicator Description Measuring
Airline Passenger Number Total number of passengers
Customer growth
Airline Passenger Capacity The number of people who can be seated in a specific space
Customer loyalty
Telecommunications Total Mobile Subscriber
The number associated with all GSM and UMTS network mobile phone users
Customer growth
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Telecommunications
ARPU
The total revenue divided by the number of subscribersCustomer loyalty
Telecommunications
Churn Rate
The number of individuals or items moving into or out of a collective over a specific period of timeCustomer satisfaction
Banking Credit Card Credit Cd. in Circu. The number of credit card issue minus stop
Customer growth
Banking Credit Card CreditCard No.in For
The number of credit card with record of consumption during six months
Customer loyalty
Banking Credit Card Credit Card_Stop The number of credit card stopped per month
Customer satisfaction
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4.5.1 The fan-page effect by customer satisfaction of the telecommunications industry in Taiwan
Churn rate is a measurement of the number of individuals or items moving out of a collective over a specific period of time. This indicator helps us understandard the shift in customers in the telecommunications industry. We analyzed the relationship between fan numbers and churn rate, and the mean value and standard error are listed in the Table 4.5-2 below.
Table 4.5-2. Analysis description of the fan-page effect by customer satisfaction of the telecommunications industry in Taiwan
Mean Standard Error Numbers
Churn rate 1.5244 .43695 66
Fan numbers 13111.1818 14715.08121 66
The Pearson correlation is -0.248, which means there is a weak relationship: the higher the fan numbers, the less churn rate in the telecommunications industry.
Table 4.5-3. Correlation coefficients between fan numbers and churn rate in the telecommunications industry in Taiwan
Churn Rate Fan Numbers
Pearson correlation Churn rate 1.000 -.248*
Fan numbers -.248* 1.000
Significance (single tail)
Churn rate . .022
Fan numbers .022 .
Numbers Churn rate 66 66
Fan numbers 66 66
*p<0.1
The adjusted R square is 0.047, which means that only 4.7% of the fan numbers led to the churn rate.
Table 4.5-4. Linear regression results between fan numbers and churn rate of the telecommunications industry in Taiwan
R .248(a)
R square .062
Adjusted R2 .047
Standard error .42654
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R square change .062
F change 4.212
Degree of freedom of numerator 1
Degree of freedom of denominator 64
Significance of F change .044
4.5.2 The fan-page effect by customer growth of the telecommunications industry in Taiwan
We also analyzed the effect on subscribers caused by fan numbers in the telecommunications industry, and the mean value and standard error are listed below.
Table 4.5-5. Analysis description of the fan-page effect by customer growth of the telecommunications industry in Taiwan
Mean Standard Error Numbers
Subscriber 7546.5000 1576.05470 66
Fan numbers 13111.1818 14715.08121 66
The Pearson correlation is 0.412 smaller than 0.5, thus there is a weak and positive relationship between the fan numbers and subscribers.
Table 4.5-6. Correlation coefficients between fan numbers and total subscribers in the telecommunications industry in Taiwan
Subscriber Fan Numbers
Pearson correlation Subscriber 1.000 .412***
Fan numbers .412*** 1.000
Significance (single tail)
Subscriber . .000
Fan numbers .000 .
Numbers Subscriber 66 66
Fan numbers 66 66
***p<0.001
The adjusted R square is 0.157, which means there that 15.7% of the fan numbers led to subscribers.
Table 4.5-7. Linear regression results between fan numbers and total subscribers of the telecommunications industry in Taiwan
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R .412(a)
R square .170
Adjusted R2 .157
Standard error 1447.20297
R square change .170
F change 4.21213.090
Degree of freedom of numerator 1
Degree of freedom of denominator 64
Significance of F change .001
4.5.3 The fan-page effect by customer loyalty of the telecommunications industry in Taiwan
Average Revenue Per User (ARPU) is a very important indicator for customer loyalty in the telecommunications industry, and is defined as the total revenue divided by the number of subscribers. Here we study the relationship between fan numbers and ARPU, and the mean value and standard error are listed below.
Table 4.5-8. Analysis description of the fan page-effect by customer loyalty of the telecommunications industry in Taiwan
Mean Standard Error Numbers
ARPU 684.1061 52.56150 66
Fan numbers 13111.1818 14715.08121 66
The Pearson correlation is -0.454, which means the relationship is negative and not very strong between fan numbers and ARPU.
Table 4.5-9. Correlation coefficients between fan numbers and ARPU in the telecommunications industry in Taiwan
ARPU Fan Numbers
Pearson correlation ARPU 1.000 -.454***
Fan numbers -.454*** 1.000
Significance (single tail)
ARPU
. .000
Fan numbers .000 .
Numbers ARPU 66 66
Fan numbers 66 66
***P<0.001
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The adjusted R square here is 0.194, which means that only 19.4% of the fan numbers led to ARPU.
Table 4.5-10. Linear regression results between fan numbers and ARPU of the telecommunications industry in Taiwan
R .454(a)
R square .206
Adjusted R2 .194
Standard error 47.19060
R square change .206
F change 16.638
Degree of freedom of numerator 1
Degree of freedom of denominator 64
Significance of F change .000
4.5.4 The fan-page effect by customer growth of the airline industry in Taiwan
The number of customer is a useful indicator in the airline industry in our study.We collected the number about internal line, made s analysis on it, and the mean value and standard error are listed below.
Table 4.5-11. Analysis description of the fan-page effect by customer growth of the airline industry in Taiwan
Mean Standard Error Numbers
Customer numbers 566771.9545 359353.59524 44
Fan numbers 9030.7727 9687.57851 44
The Pearson correlation is 0.667, which means there is a high and positive-related relationship between the customer number and fan numbers in the airline industries.
Table 4.5-12. Correlation coefficients between fan numbers and customer number in the airline industry in Taiwan
Customer
Numbers Fan Numbers
Pearson correlation Customer numbers 1.000 .667***Fan numbers .667*** 1.000
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Significance (single tail)
Customer numbers . .000
Fan numbers .000 .
Numbers Customer numbers 44 44
Fan numbers 44 44
***p<0.001
The adjusted R square is 0.432, which means that 44.6% of the fan numbers led to total revenue.
Table 4.5-13. Linear regression results between fan numbers and customer number of the airline industry in Taiwan
R .667(a)
R square .446
Adjusted R square .432
Standard error 270747.75561
R square change .446
F change 33.750
Degree of freedom of numerator 1
Degree of freedom of denominator 42
Significance of F change .000
4.5.5 The fan-page effect by customer loyalty of the airline industry in Taiwan
Passenger capacity is the number of people who can be seated in a specific space.It is an indicator to measure customer loyalty in the airline industry, and we listed the analysis results below.
Table 4.5-14. Analysis description of the fan-The page effect by customer loyalty of the airline industry in Taiwan
Mean Standard Error Numbers
Passenger capacity .778509 .0497429 44
Fan numbers 9030.7727 9687.57851 44
The result is not significant. The Pearson correlation is 0.046 smaller than 0.5, which means there is no significant relationship between fan numbers and passenger capacity.
Table 4.5-15. Correlation coefficients between fan numbers and passenger capacity in
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the airline industry in Taiwan
Passenger
Capacity Fan Numbers
Pearson correlation Passenger capacity 1.000 -.046Fan numbers -.046 1.000
Significance (single tail)
Passenger Capacity . .384
Fan numbers .384 .
Numbers Passenger capacity 44 44
Fan numbers 44 44
The adjusted R square is -0.022, which means the capacity that fan numbers led to passenger capacity is weakly related.
Table 4.5-16. Linear regression results between fan numbers and passenger capacity of the airline industry in Taiwan
R .046(a)
R square .002
Adjusted R2 -.022
Standard error .0502788
R square change .002
F change .088
Degree of freedom of numerator 1
Degree of freedom of denominator 42
Significance of F change .768
4.5.6 The fan-page effect by customer growth of the banking industry in Taiwan
The indicator “Credit Cd. in Circu” is the number of credit cards issued minus stop. It is an important indicator to measure customer growth in the banking industry.We listed the statistical results below.
Table 4.5-17. Analysis description of the fan-page effect by customer growth of the banking industry in Taiwan
Mean Standard Error Numbers
Credit Cd. in Circu 2556508.1477 1831927.52587 88
Fan numbers 22056.4432 37930.42592 88
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The Pearson correlation is 0.528, which means there is a positive and strong relationship between fan numbers and Credit Cd. in Circu.
Table 4.5-18. Correlation coefficients between fan numbers and Credit Cd. in Circu in the banking industry in Taiwan
Credit Cd. in
Circu Fan Numbers
Pearson correlation Credit Cd. in Circu 1.000 .528***
Fan numbers .528*** 1.000
Significance (single tail)
Credit Cd. in Circu . .000
Fan numbers .000 .
Numbers Credit Cd. in Circu 88 88
Fan numbers 88 88
***p<0.001
The adjusted R square is .270, which means that 27% of the fan numbers led to Transaction Credit Cd. in Circu.
Table 4.5-19. Linear regression results between fan numbers and Credit Cd. in Circu of the banking industry in Taiwan
R .528(a)
R square .279
Adjusted R2 .270
Standard error 1564957.38397
R square change .279
F change 33.215
Degree of freedom of numerator 1
Degree of freedom of denominator 86
Significance of F change .000
4.5.7 The fan-page effect by customer loyalty of the banking industry in Taiwan
This variable is the number of credit cards with a record of consumption during the past six months, which is an indicator to measure the customer loyalty in the banking industry.Table 4.5-20. Analysis description of the fan-page effect by customer loyalty of the banking industry in Taiwan
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Mean Standard Error Numbers
CreditCard No.in
For 1616161.8523 1277756.49482 88
Fan numbers 22056.4432 37930.42592 88
The Pearson correlation is 0.534, which means there is a positive relationship between fan numbers and “CreditCard No.in For.”.
Table 4.5-21. Correlation coefficients between fan numbers and CreditCard No.in For.
in the banking industry in Taiwan
CreditCard No.in
For Fan Numbers
Pearson correlation CreditCard No.in
For 1.000 .534***
Fan numbers .534*** 1.000
Significance (single tail)
CreditCard No.in
For . .000
Fan numbers .000 .
Numbers CreditCard No.in
For 88 88
Fan numbers 88 88
***p<0.001
The adjusted R square is 0.277, which means that 27.7% of the fan numbers led to CreditCard No.in For.
Table 4.5-22. Linear regression results between fan numbers and CreditCard No. of the banking industry in Taiwan
R .534(a)
R square .285
Adjusted R2 .277
Standard error 1086700.86210
R square change .285
F change 34.281
Degree of freedom of numerator 1
Degree of freedom of denominator 86
Significance of F change .000
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4.5.8 The fan-page effect by customer satisfaction of the banking industry in Taiwan
“Credit Card_Stop” is the number of credit card stopped per month. It is an important indicator to measure customer satisfaction in the banking industry. We show the statistical results below.
Table 4.5-23. Analysis description of the fan-page effect by customer satisfaction of the banking industry in Taiwan
Mean Standard Error Numbers
Credit Card_Stop 23578.4773 22761.35539 88
Fan numbers 22056.4432 37930.42592 88
The Pearson correlation is 0.407, smaller than 0.5, which means there is not a significant relationship between fan numbers and Credit Card_Stop.
Table 4.5-24. Correlation coefficients between fan numbers and Credit Card_Stop in the banking industry in Taiwan
Credit Card_Stop Fan Numbers
Pearson correlation Credit Card_Stop 1.000 .407***Fan numbers .407*** 1.000
Significance (single tail)
Credit Card_Stop
. .000
Fan numbers .000 .
Numbers Credit Card_Stop 88 88
Fan numbers 88 88
***p<0.001
The adjusted R square is 0.156, which means that only 15.6% of the fan numbers led to a Credit Card_Stop.
Table 4.5-25. Linear regression results between fan numbers and Credit Card_Stop of the banking industry in Taiwan
R .407(a)
R square .166
Adjusted R2 .156
Standard error 20910.76280
R square change .166
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F change 17.080
Degree of freedom of numerator 1
Degree of freedom of denominator 86
Significance of F change .000
We will discuss the findings and provide explanations for these analyses in the next chapter. The findings will bring new field of vision to enterprises and give some direction to the enterprises’ strategy for managing fan pages on Facebook.
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5.1 Overview of research finding
This paper aims to understand whether social networks can bring benefits to enterprises. We developed six hypotheses in our research that are listed below in table 66. In the first stage of our research, we use Spearman's rank correlation coefficient as our statistical method, and find three characteristics to help us conduct the following research. In stage two, we used linear regression as our statistic method and took 232 records from 58 global firms as our data. Our results encouraged us to continue our research in depth. In stage three, we used 11 firms in Taiwan as our research targets and tested hypothesis one. The results provided us with information to suggest to enterprises. Moreover, we collected monthly data from those 11 firms in Taiwan to analyze others hypotheses we addressed. Through step-by-step research, we gained more understanding about the benefits of enterprises using fan page.
Table 5.1-1. Finding of six hypotheses in this study
Hypotheses Contents Finding