Can Life Streaming Overcome The Viewership Of TV Life News?

Significance of the Study

The study has been conducted on a sample of the country’s population. Inclination of the people towards life streaming and social networking sites has affected the livelihood. To test these effects, this study has been conducted. The whole study has been done using SPSS. The results of the analysis are discussed as follows.

People nowadays are getting addicted towards watching life streaming other than television. This study has been conducted to see how much life streaming is affecting the day to day life. Whether life streaming can overcome the television viewership is the main matter of interest.

Life streaming is a way of filing and sharing features of an individual’s day by day social incidents online such as photos, tweets, videos and documents through a particular website such as Facebook, Twitter, Instagram and WhatsApp. According to the Urban Dictionary, the lifestream is a time-ordered stream of records, posts, comments, pictures and videos that function as a diary of an individual’s electronic existence (Urban Dictionary, 2017). Although TV life news continues to be the largely contested way to watching television, life streaming is slowly overtaking the fashion. Below is a chart showing the time shifted of live video and life streaming.

Table 1: Table showing the hours of life stream watched in a week
	Frequency Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Percent	Valid Percent	Cumulative Percent
Valid	1-3	30	18.6	18.6	18.6
3-5	51	31.7	31.7	50.3
5-7	43	26.7	26.7	77.0
7 and Above	37	23.0	23.0	100.0
Total	161	100.0	100.0

According to Nielsen’s report (2016, 2017), TV continues to be the most popular entertainment although individuals are spending more hours surfing the web and enjoying life streaming services. For this reason, a large number of homes are gradually dumping Life TV news altogether. By December 3, approximately 2.6 million households in the US use broadband only, in that they don’t pick up a broadcast signal indicating a decline in TV viewing. The viewing of traditional TV is continuing its slow decline as compared to life streaming which is on the rise hence overtaking life TV.

Life streaming and social networking services allow individuals to be in contact with their close friends, family members, celebrities and even politicians through the web by either using their handheld devices like mobile phones, smartphones or computers as compared to TV in which aspects viewed are selected for you. In this case, the social and professional boundary is becoming thinner since social life streaming offers its users with a sense of belonging and friendship while being in their respective workplaces and homes as well.

Companies that provide streaming services such as Amazon, Facebook, Twitter and Netflix have benefitted from the decline in TV viewing in recent years. According to research, it indicates that approximately forty percent of families use services like Amazon and Netflix for prime instant videos and pictures up from thirty-five percent in the year 2013. Over thirteen percent of households possess multimedia devices and smart TVs such as an Apple TV to stream such content other than watching them on TV. Relatively enough, viewing of online videos and social networking services has had an increase of about 4 to 5 hours per month yearly which approximates to 11 hours and 45 minutes of life streaming services.

Literature Review

Over the recent years, life streaming experience has led to the increase of artificial intelligence in that the websites for lifestream are easy to understand and accessible to the users. The main aim of the Semantic Web is to alter information into data for the computers and others multimedia devices to read and understand the content available. Now, web pages are made to be read by humans. According to Ray Kurzweil, media creation and the advancements in social life technology are changing to Technological Singularity. It indicates that life streaming services are slowly rising and overtaking the TV career news although, there is a connection between the two aspects in that individuals enjoy life streaming to share and engage with others on the trending issues on TV.

The continued fast accessibility of life streaming activities provided users with transparent and authenticated information that inspires and even informs the user more than TV can be because it gives individuals a variety of choices such as sports, fashion and lifestyle, politics and trending issues unlike on television programs are selected for people. According to Kevin Rose who is the founder of Digg talks about the benefits of life streaming over life TV news. He says life streaming offers people an opportunity to arrange various types of information and real life experience to the further detailed digital diary. For instance, as compared to live TV, life streaming allows individuals to view one’s lifestream right from childhood to adulthood, and people enjoy the Lifestream to share and engage with others.

Lifestreams also characterizes a broad range source of information about individuals that can be excavated and identifies more traits of a person in one go. Life streaming also allows people to follow news and trends about the budget and other physical activities in a detailed manner than on TV. According to the Activity Theory, accessing a person’s lifestream is an act of assimilation in a society in which the needs and interests of other people are perfectly observed. The rise of the personal computers and smartphones is challenging TV viewership since people prefer direct interaction with friends and family through the web directly from the handheld devices or computers than watching life TV news. In Nielsen’s Total Audience Report (2016, 2017), the monthly television watching time is gradually decreasing as compared to the number of hours people lifestream in the recent years as shown in the chart below.

Research Design and Methodology

In conclusion, individuals prefer to live the life they want the moment they want instead of waiting for something to happen on TV hence making lifestream on the rise. Currently, the number of Internet-based users outnumbers TV viewers with the increase of handheld and other multimedia devices than those of TV sets around the world. Life streaming on the Internet and other multimedia devices has outnumbered the TV viewers by a ratio of 4:1 (Wowza.com, 2017) hence answering the question, can life streaming overcome the viewership of TV life news? For instance, in the breaking news like the recent Manchester bombing, people consider the option of using their tablets or smartphones to see for themselves happening at the moment because maybe the TV set is not available at the moment. Some years back viewership in the social media was very disappointing as compared to TV viewing, but lately, lifestream is on the rising and convincingly it will overtake the life TV news.

The aim of this research is to check whether life streaming is more dominant over TV. To conduct this research, the following hypothesis can be made.

H₀₁: Age and having an account with a professional website are independent

H₀₂: Gender and having an account with a professional website are independent.

H₀₃: Age and best life streaming platforms are independent.

H₀₄: Age and average number of hours a person watches life streaming a week are independent.

H₀₅: Frequency of online streaming movies by paying is independent of age.

H₀₆: Type of content is independent of age.

H₀₇: Gender and Type of content people usually watch on TV or life streaming are independent.

H₀₈: Gender and how often people stream movies through paid online services are independent.

The above stated hypothesis have been tested in the following section using chi square goodness of fit test. The results are interpreted accordingly. Here all the variables are categorical, so the research is qualitative.

The percentage of males and females in the society are shown in the following table and represented by the following pie chart.

Table 2: What is your gender?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	71	44.1	44.1	44.1
Female	90	55.9	55.9	100.0
Total	161	100.0	100.0

Figure 1

From the table it can be seen that in the population there are 44.1% males and 55.9% females. Again, 49.1% of the sample belong to the age group 20-25 years. The other three age groups have almost the same percentage of people. This can also be seen from the pie chart in figure 2.

Table 3: What is your age?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	15-20	24	14.9	14.9	14.9
20-25	79	49.1	49.1	64.0
25-30	30	18.6	18.6	82.6
30 and Above	28	17.4	17.4	100.0
Total	161	100.0	100.0

Figure 2

The pie chart for the number of hours of lifestream people usually watches in a week and its frequency table is given below. From the table and the figure it is clear that 31.7% of the sample

Table 5: On an average, how many hours of lifestream would you usually watch in a week ?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1-3	30	18.6	18.6	18.6
3-5	51	31.7	31.7	50.3
5-7	43	26.7	26.7	77.0
7 and Above	37	23.0	23.0	100.0
Total	161	100.0	100.0

Figure 3

There are four types of contents that people watch on the television or life stream. These are Educational, Political, Science and Entertainment. From the analysis it can be said that most of the people watch entertainment programs on the television. The results are shown in the following table:

Table 6: What type of content do you usually watch on either Tv‎/life stream ?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Educational	21	13.0	13.0	13.0
Political	28	17.4	17.4	30.4
Science	31	19.3	19.3	49.7
Entertainment	81	50.3	50.3	100.0
Total	161	100.0	100.0

Figure 4

The major live streaming platforms that run in the country are Netflix, Amazon, YouTube, Hulu and AppleTV. 33% of the people cling to Netflix, followed by YouTube with a percentage of 28.6.

Table 7: What are the best life streaming platforms ?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Netflix	54	33.5	33.5	33.5
Amazon	4	2.5	2.5	36.0
YouTube	46	28.6	28.6	64.6
Hulu	2	1.2	1.2	65.8
AppleTV	21	13.0	13.0	78.9
Others	34	21.1	21.1	100.0
Total	161	100.0	100.0

Figure 5

Again, 82% of the people have a valid account with a professional website. This result is shown in table 9. From table 10, it can be seen that most of the people agree to the fact that life streaming has more content than TV.

Table 10: Life stream has more content than TV ?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Strongly Agree	54	33.5	33.5	33.5
Agree	77	47.8	47.8	81.4
Disagree	28	17.4	17.4	98.8
Strongly Disagree	2	1.2	1.2	100.0
Total	161	100.0	100.0
Table 11: Do you currently have an account with a professional webstie, or not ?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Yes i do	132	82.0	82.0	82.0
No i don’t	29	18.0	18.0	100.0
Total	161	100.0	100.0

Next, the associations between two variables are tested using the chi square goodness of fit test. At first, the association between the variables age and having an account with a professional website are tested. The hypothesis in this case can be given by

H₀₁: Age and having an account with a professional website are independent

The contingency table is given below in table 12. The goodness of fit test and the p-values are given in table 14. From table 13, it can be seen that the p-value is less than 0.05, the level of significance. The null hypothesis is rejected. Thus, having an account with a professional website is dependent of age.

Table 12: What is your age? * Do you currently have an account with a professional webstie, or not ? Crosstabulation
Count
	Do you currently have an account with a professional webstie, or not ?	Total
Yes i do	No i don’t
What is your age?	15-20	23	1	24
20-25	72	7	79
25-30	24	6	30
30 and Above	13	15	28
Total	132	29	161

Table 13: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	31.650^a	3	.000
Likelihood Ratio	27.548	3	.000
Linear-by-Linear Association	26.893	1	.000
N of Valid Cases	161
a. 1 cells (12.5%) have expected count less than 5. The minimum expected count is 4.32.

Next, the association between gender and having an account with a professional website is checked. The hypothesis is given by:

H₀₂: Gender and having an account with a professional website are independent.

The contingency table for this relation is given in table 14 and the chi square tests and p-values are given in table 15. Here, the p-value (denoted by Asymp. Sig. (2-sided)) is 0.984, which is a lot higher than the level of significance (0.05). Thus, the variables are not significant, that is, the two variables are independent of each other. The null hypothesis is accepted.

Table 14: What is your gender? * Do you currently have an account with a professional webstie, or not ? Crosstabulation
Count
	Do you currently have an account with a professional webstie, or not ?	Total
Yes i do	No i don’t
What is your gender?	Male	68	15	83
Female	64	14	78
Total	132	29	161

Table 15: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)	Exact Sig. (2-sided)	Exact Sig. (1-sided)
Pearson Chi-Square	.000^a	1	.984
Continuity Correction^b	.000	1	1.000
Likelihood Ratio	.000	1	.984
Fisher’s Exact Test				1.000	.574
Linear-by-Linear Association	.000	1	.984
N of Valid Cases	161
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 14.05.
b. Computed only for a 2×2 table

The next test of association is between age and best life streaming platforms. The hypothesis is given by:

H₀₃: Age and best life streaming platforms are independent.

Again, contingency table and the results of chi square tests are given in tables 16 and 17 respectively. The p-value is higher than 0.05, the level of significance. The two variables are not significant. The null hypothesis is accepted here. Age and life streaming platforms are independent of each other.

Table 16: What is your age? * What are the best life streaming platforms? Crosstab
Count
	What are the best life streaming platforms ?	Total
Netflix	Amazon	YouTube	Hulu	AppleTV	Others
What is your age?	15-20	8	0	6	0	3	7	24
20-25	25	3	26	1	12	12	79
25-30	8	1	10	1	3	7	30
30 and Above	4	0	12	0	2	10	28
Total	45	4	54	2	20	36	161

Table 17: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	13.225^a	15	.585
Likelihood Ratio	15.050	15	.448
Linear-by-Linear Association	1.531	1	.216
N of Valid Cases	161
a. 11 cells (45.8%) have expected count less than 5. The minimum expected count is .30.

The next two variables for testing their association are taken to be are age and how many hours a week a person usually watches life streaming. The hypothesis can be framed as:

H₀₄: Age and average number of hours a person watches life streaming a week are independent.

Tables 18 and 19 respectively show the contingency table and the chi square statistic for the two variables. Here the p value (0.003) from table 20 is found to be less than the level of significance (0.05). The two variables are significant. The null hypothesis H₀₄is rejected. Thus, age and average number of hours a person watches life streaming in a week are dependent.

Table 18: What is your age? * On an average, how many hours of lifestream would you usually watch in a week ? Crosstab
Count
	On an average, how many hours of lifestream would you usually watch in a week ?	Total
1-3	3-5	5-7	7 and Above
What is your age?	15-20	0	8	8	8	24
20-25	14	23	19	23	79
25-30	5	9	10	6	30
30 and Above	11	11	6	0	28
Total	30	51	43	37	161

Table 19: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	22.053^a	9	.009
Likelihood Ratio	31.095	9	.000
Linear-by-Linear Association	16.503	1	.000
N of Valid Cases	161
a. 1 cells (6.3%) have expected count less than 5. The minimum expected count is 4.47.

The next two variables are age and the frequency of streaming movies online by paying. The hypothesis is given as:

H₀₅: Frequency of online streaming movies by paying is independent of age.

The contingency table and the chi square test results are given in tables 20 and 21 respectively. The p-value is obtained as 0.094 which is greater than the level of significance (0.05). Thus the two variables are not significant. They are independent of each other. Thus, the hypothesis H₀₅ is accepted.

Table 20: Crosstab
Count
	How often do you stream movies thorugh paid online sevices ?	Total
Extremly Often	Slightly Often	Often	Not at all	6.00
What is your age?	15-20	10	4	9	1	0	24
20-25	17	8	34	18	2	79
25-30	2	6	17	4	1	30
30 and Above	3	4	14	7	0	28
Total	32	22	74	30	3	161

Table 21: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	18.790^a	12	.094
Likelihood Ratio	20.562	12	.057
Linear-by-Linear Association	6.001	1	.014
N of Valid Cases	161
a. 9 cells (45.0%) have expected count less than 5. The minimum expected count is .45.

Next, association between age and type of content is tested. The hypothesis is given as:

H₀₆: Type of content is independent of age.

The contingency table and the chi square test results are given in tables 22 and 23 respectively. The p-value is less than the level of significance. Thus, the two variables are significant. Age and type of content depends on each other. The hypothesis H₀₆ is rejected.

Table 22: Crosstab
Count
	What type of content do you usually watch on either Tv?/life stream ?	Total
Educational	Entertainment	Political	Science
What is your age?	15-20	3	0	3	18	24
20-25	14	8	9	48	79
25-30	2	5	11	12	30
30 and Above	2	15	8	3	28
Total	21	28	31	81	161

Table 23: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	53.742^a	9	.000
Likelihood Ratio	54.058	9	.000
Linear-by-Linear Association	11.983	1	.001
N of Valid Cases	161
a. 6 cells (37.5%) have expected count less than 5. The minimum expected count is 3.13.

The next test of association is between Gender and type of content. The hypothesis is given as:

H₀₇: Gender and Type of content people usually watch on TV or life streaming are independent.

The contingency table and the results of the chi square test are given in the following tables numbered 24 and 25 respectively. The p-value obtained from table 26 is less than the level of significance. The variables thus are not significant. The two variable type of content depends on gender. The hypothesis H₀₇is rejected.

Table 24: Crosstab
Count
	What type of content do you usually watch on either Tv?/life stream ?	Total
Educational	Entertainment	Political	Science
What is your gender?	Male	6	12	11	54	83
Female	15	16	20	27	78
Total	21	28	31	81	161

Table 25: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	15.902^a	3	.001
Likelihood Ratio	16.228	3	.001
Linear-by-Linear Association	12.251	1	.000
N of Valid Cases	161
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 10.17.

The last test of association for this study is between the variables gender and how often people stream movies through paid online services. The hypothesis is given as:

H₀₈: Gender and how often people stream movies through paid online services are independent.

The contingency table and the chi square test results are attaches in tables 26 and 27 respectively. From the results in table 28, it is can be seen that the p-value (0.168) is greater than the level of significance. The two variables under consideration are not significant to each other. Thus, they are independent of each other. The hypothesis H₀₈ is accepted.

Table 26: Crosstab
Count
	How often do you stream movies thorugh paid online sevices ?	Total
Extremly Often	Slightly Often	Often	Not at all	6.00
What is your gender?	Male	20	10	35	18	0	83
Female	12	12	39	12	3	78
Total	32	22	74	30	3	161

Table 27: Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	6.449^a	4	.168
Likelihood Ratio	7.631	4	.106
Linear-by-Linear Association	1.243	1	.265
N of Valid Cases	161
a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is 1.45.

Findings and Conclusions

From the test done above, the findings are concluded in the following table.

H₀₁	Age and having an account with a professional website are independent. The hypothesis is rejected.
H₀₂	Gender and having an account with a professional website are independent. The hypothesis is accepted.
H₀₃	Age and best life streaming platforms are independent. The hypothesis is accepted.
H₀₄	Age and average number of hours a person watches life streaming a week are independent. The hypothesis is rejected.
H₀₅	Frequency of online streaming movies by paying is independent of age. The hypothesis is accepted.
H₀₆	Type of content is independent of age. The hypothesis is rejected.
H₀₇	Gender and Type of content people usually watch on TV or life streaming are independent. The hypothesis is rejected.
H₀₈	Gender and how often people stream movies through paid online services are independent. The hypothesis is accepted.

Further analysis could be done by comparing the variables among each other except only with age and gender. This way, more associations could have been tested. The test between the variables average weekly hours of watching TV and live streaming could be compared. Then it could have been inferred whether the introduction of life streaming has affected the TV watching or not. Similarly some other variables could also be tested and inferred accordingly.