Empirical Research on Users' Continuous Use Intention of Live

Broadcast Platform in the Context of Digital Societ

Pengyu Zhu

and Shimin Shao

School of Management Engineering, Xuzhou University of Technology Xuzhou, China

Keywords: Live Broadcast, SPSS, Use Intention, Model.

Abstract: With the rapid development of live broadcast industry, the competition between live broadcast platforms is

becoming more and more fierce. It is very important for live broadcast platform to retain users and enhance

their continuous use intention. In this paper, we construct the model of live broadcast platform users'

continuous use intention, collect data through the way of questionnaire investigation, analyze and process the

data by using SPSS software, draw conclusions and put forward relevant suggestions, which will provide

reference for enhancing the user stickiness and competitiveness of live broadcast platform.

1 INTRODUCTION

With the integration and development of the new

generation of information technology and traditional

fields, industrial and social transformation is

imminent. The digital society characterized by

digitization, networking and intelligence has become

the general trend. At the same time, with the

accelerated upgrading of people consumption and the

continuous growth of diversified and personalized

demand, live broadcast, as a new consumption mode,

has been rapidly accepted by the public. Compared

with traditional texts and pictures, live broadcast can

make people more convenient and fast to obtain

information and emotional communication.

Therefore, the live broadcast industry has achieved

vigorous development in recent years. According to

The 47th China Statistical Report on Internet

Development issued by China Internet Network

Information Center (CNNIC), by December 2020, the

number of live broadcast users in China had reached

617 million, an increase of 57.03 million over March

2020, accounting for 62.4% of the total Internet users.

(China Internet Network Information Center, 2021)

The vigorous development of the live broadcast

industry also makes it become a hot spot in the

academic and industrial circles in recent years.

Therefore, this article explores the factors that

influence the users' continuous use intention of live

broadcast platform. It can help the live broadcast

platform better understand user needs, improve

service quality, enhance user stickiness, promote

healthy and rapid development of the live broadcast

industry, and better build Internet ecosystem.

2 LITERATURE REVIEW

Live broadcast is a new trend of highly interactive

Internet. It shows the current situation of ongoing

activities to end users and meets the needs of users

through network media. This communication mode

absorbs and continues to maintain the advantages of

Internet, with intuitive expression, rich content, full

interaction and no time and space constraints.

Xiaojun Fan (2020) believed that the interactivity

of mobile live-video broadcast has a significant

positive effect on the satisfaction and the intention of

continuous use of user by improving the viewing

experience and anchor identity. (Fan, 2020) Lijia Tang

(2018) believed that different interface modes should

be developed according to the personalized experience

of different users to provide users with efficient and

accurate content. (Tang, 2018) Xiwei Wang (2020)

constructed a conceptual model of influencing factors

of webcast app users' use behavior and conducted

empirical analysis to prove that perceived

interactivity, perceived value and perceived risk have

a significant effect on users' use intention. (Wang,

2020)

Based on the rational behavior theory, Davis

(1989) proposed a technology acceptance model

Zhu, P. and Shao, S.

Empirical Research on Users’ Continuous Use Intention of Live Broadcast Platform in the Context of Digital Societ.

DOI: 10.5220/0011732700003607

In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 185-190

ISBN: 978-989-758-620-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

185

Figure 1: Framework of the model.

(TAM) when analyzing people's use of information

systems, in which there are two very important

factors: perceived ease of use and perceived

usefulness. (Davis, 1989) Bhattacherjee (2001)

proposed the expectation confirmation model of

information system continuance (ECM-ISC), Users'

perceived usefulness and satisfaction affect their

continuous use, and satisfaction is affected by users'

perceived usefulness and expectation confirmation.

(Bhattacherjee, 2001)

3 RESEARCH MODEL AND

DATA COLLECTION

3.1 Research Model and Hypothesis

Referring to TAM and ECM-ISC model, this paper

constructs the research model, as shown in Figure 1.

According to the model, the following

assumptions are proposed:

H1: Perceived ease of use has a positive effect on

perceived usefulness.

H2: Perceived ease of use has a positive effect on

satisfaction.

H3: Perceived ease of use has a positive effect on

users' continuous use intention.

H4: Perceived entertainment has a positive effect

on satisfaction.

H5: Expectation confirmation has a positive effect

on perceived usefulness.

H6: Expectation confirmation has a positive effect

on satisfaction.

H7: Perceived risk has a negative effect on

satisfaction.

H8: Perceived interactivity has a positive effect on

satisfaction.

H9: Perceived usefulness has a positive effect on

satisfaction.

H10: Perceived usefulness has a positive effect on

users' continuous use intention.

H11: Satisfaction has a positive effect on users'

continuous use intention.

3.2 Questionnaire and Data Collection

The questionnaire consists of three parts.

The first part is the introduction, which explains

the research purpose, confidentiality and content of

the questionnaire, so as to help the respondents better

understand the research background.

The second part is the basic situation of the

respondents, including gender, age, occupation,

education level and the weekly using frequencies of

live broadcast platform.

The third part is the key research content. It

includes 8 variables, including perceived usefulness,

perceived ease of use, perceived entertainment,

expectation confirmation, perceived risk, perceived

interactivity, satisfaction and continuous use

intention, with a total of 28 items.

This paper conducted a questionnaire survey

posted electronic questionnaire online. The online

questionnaire is made through the questionnaire star

platform and distributed through online channels.

Finally, 256 complete questionnaires are returned.

After excluding 11 invalid questionnaires, 245 valid

questionnaires are finally received, with a detailed

sample description as shown in Table 1.

perceived

ease of use

perceived

entertainment

expectation

confirmation

perceived risk

perceived

interactivity

perceived

usefulness

satisfaction

continuous

use intention

ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology

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Table 1: Distribution of Respondents.

Category Frequency Percentage

Gender

Male 129 52.7

Female 116 47.3

Age

Below 18 33 13.5

18-25 160 65.3

26-35 37 15.1

36-45 12 4.9

Above 45 3 1.2

Education

Specialists and below 92 37.6

Bachelor 131 53.5

Master and above 22 9

Occupation

government employees 16 6.5

employees of firms and enterprises 26 10.6

liberal professions 6 2.4

Private owners 39 15.9

students 158 64.5

Using frequencies of

live broadcasting

platform

1 time 20 8.2

2-5 times 69 28.2

6-10 times 97 39.6

11 times and above 59 24.1

Table 2: Reliability Analysis.

Research Variable Cronbach α Item

perceived ease of use 0.845 3

perceived entertainment 0.841 3

expectation confirmation 0.861 3

satisfaction 0.847 3

perceived risk 0.879 5

perceived ease of use 0.824 3

perceived interactivity 0.855 4

continuous use intention 0.898 4

Table 3 KMO and Bartlett Test.

KMO 0.881

Bartlett Test of

Sphericity

Approx. Chi-Square 3925.481

df 378

Sig. .000

4 DATA

ANALYSIS

4.1 Reliability Analysis and Validity

Analysis

This paper tests the reliability and validity by SPSS

software. In the reliability analysis, Cronbach alpha

coefficient is generally used to test the consistency of

the research variables of each measurement item. As

shown in Table 2, the Cronbach's alpha coefficient of

each variable is greater than 0.8, indicating that it has

higher internal consistency.

The validity analysis is shown in Table 3.

KMO=0.881, greater than 0.8, indicating that there is

a strong correlation between variables.

4.2 Structural Equation Analysis

In this study, AMOS 23.0 software is used for

structural equation modeling, and the relationship

between variables is verified. According to the

research hypothesis, based on the research model, the

Empirical Research on Users’ Continuous Use Intention of Live Broadcast Platform in the Context of Digital Societ

187

Figure 2: Action Path.

Table 4: Model Fitness Test.

Goodness of

Fit Index

Model Matching

Criteria

Value Result

CMIN —— 395.907 ——

DF —— 329 ——

CMIN/DF <3 1.203 Good

RMR <0.08 0.040 Good

GFI >0.8 0.898 Acceptable

AGFI >0.8 0.874 Acceptable

NFI >0.9 0.903 Good

IFI >0.9 0.982 Good

TLI >0.9 0.979 Good

CFI >0.9 0.982 Good

structural equation model is established by AMOS,

and we get the standardized path coefficient, as shown

in Figure 2.

After constructing the users' continuous use

intention model of live broadcast platform, this paper

tests and modifies the fitting relationship between the

model and the data, and finally obtains a model with

high fitting degree. The model fitting results are

shown in Table 4.

The better the fitting degree of the model, the

higher the effectiveness, applicability and persuasion

of the initial model of the influencing factors of users'

continuous use intention of live broadcast. It can be

seen from the above table that the fitness indexes of

the research model meet the research standards, and

the fitness of the model is good.

The model path coefficients and research

hypothesis test results of the structural equations are

shown in Table 5. From the model test results in Table

5, it can be seen that perceived ease of use has a

positive effect on perceived usefulness (β=0.321,

P<0.001), thus, H1 assumption is effectively

validated. Perceived ease of use has a positive effect

on satisfaction (β=0.197, P<0.05), thus, H2

assumption is effectively validated. Perceived ease of

use has a positive effect on users' continuous use

intention (β=0.191, P<0.05), thus, H3 assumption is

effectively validated. Perceived entertainment has a

positive effect on satisfaction (β=0.176, P<0.05), thus,

H4 assumption is effectively validated. Expectation

confirmation has a positive effect on perceived

usefulness (β=0.176, P<0.05), thus, H5 assumption is

effectively validated. Expectation confirmation has a

positive effect on satisfaction (β=0.164, P<0.05), thus,

H6 assumption is effectively validated. Perceived risk

has a negative effect on satisfaction (β=-0.185,

P<0.05), thus, H7 assumption is effectively validated.

Perceived interactivity has a positive effect on

satisfaction (β=0.173, P<0.05), thus, H8 assumption is

effectively validated. Perceived usefulness has a

positive effect on satisfaction (β=0.246, P<0.001),

thus, H9 assumption is effectively validated.

Perceived usefulness has a positive effect on users'

continuous use intention (β=0.23, P<0.05), thus, H10.

0.23

0.191

0.246

0.321

0.176

0.197

0.176

0.164

-0.185

0.173

0.282

perceived ease

of use

perceived

entertainment

expectation

confirmation

perceived risk

perceived

interactivity

perceived

usefulness

satisfaction

continuous

use intention

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Table 5: Model Validation Results.

Assumed Path

standardized

coefficient

Non-

standardized

coefficient

S.E. C.R. P

Test

Results

perceived

usefulness

<--- perceived ease of use 0.321 0.35 0.086 4.087 *** True

satisfaction <--- perceived ease of use 0.197 0.205 0.078 2.634 0.008 True

continuous

use intention

<--- perceived ease of use 0.191 0.208 0.091 2.275 0.023 True

satisfaction <--- perceived entertainment 0.176 0.175 0.067 2.631 0.009 True

perceived

usefulness

<--- expectation confirmation 0.176 0.159 0.068 2.355 0.019 True

satisfaction <--- expectation confirmation 0.164 0.141 0.059 2.376 0.018 True

satisfaction <--- perceived risk -0.185 -0.238 0.084 -2.828 0.005 True

satisfaction <--- perceived interactivity 0.173 0.205 0.078 2.639 0.008 True

satisfaction <--- perceived usefulness 0.246 0.235 0.059 3.988 *** True

continuous

use intention

<--- perceived usefulness 0.23 0.231 0.072 3.196 0.001 True

continuous

use intention

<--- satisfaction 0.282 0.296 0.093 3.182 0.001 True

assumption is effectively validated. Satisfaction has a

positive effect on users' continuous use intention

(β=0.282, P<0.05), thus, H11 assumption is

effectively validated.

5 CONCLUSIONS

According to the results of data analysis, the following

conclusions can be drawn:

5.1 Analysis of Factors Affecting

Continuous Use Intention

The path coefficients of perceived usefulness,

perceived ease of use and satisfaction were 0.23, 0.191

and 0.282 respectively. The path coefficient of

satisfaction is the largest, indicating that satisfaction

has the greatest effect on continuous use intention.

This means when users are satisfied with platform live

content and related services, they will continue to use

it, and their continuous use intention will naturally

increase.

5.2 Analysis of Factors Affecting

Satisfaction

The path coefficients of perceived usefulness,

perceived ease of use, perceived entertainment,

perceived interactivity, expectation confirmation and

perceived risk to satisfaction were 0.246, 0.197, 0.176,

0.173, 0.164 and -0.185 respectively. Among them,

the path coefficient of perceived usefulness and

perceived ease of use is the largest, and the path

coefficient of perceived risk is negative, but its

absolute value is large, indicating that these three

factors have a great effect on satisfaction.

5.3 Analysis of Factors Affecting

Perception Usefulness

The path coefficients of perceived ease of use and

expectation confirmation for perceived usefulness are

0.321 and 0.176 respectively. The path coefficient of

perceived ease of use is higher, indicating that

perceived ease of use has the greater effect on

perceived usefulness.

Therefore, live broadcast platform should pay

more attention to factors such as perceived usefulness,

perceived ease of use, perceived risk. Firstly, they

should timely follow up user needs, constantly update

and upgrade real-time content, so that users can obtain

the most useful and high-quality information

resources in learning, work and life, and promote the

diversification of real-time content. Secondly, the

platform layout and pages strive to be concise and

clear, simplify operation steps, abandon complex

operation functions, so that people of all ages can

accept and understand them. It will effectively

improve users' first impression and stimulate users'

continuous use intention. Finally, the platform should

Empirical Research on Users’ Continuous Use Intention of Live Broadcast Platform in the Context of Digital Societ

189

protect user privacy and data security, eliminate

unnecessary spam, build a complete information

security protection management mechanism, provide

guaranteed service measures and diversified products

to meet the needs of users.

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