The Effect of Native Advertising on User’s Behavioral Intention:

Based on the Technology Acceptance Model

Sijia Wang

and Lin Bai

School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

Keywords: Feed Advertisement, Technology Acceptance Model, User's Acceptance, Structural Equation Model.

Abstract: With the popularity of mobile applications, marketers invented a new form of internet advertisements to

promote products and services: native advertisements. This study aims to figure out what are the key factors

that affect user’s acceptance of native advertisements through empirical research methods. Therefore, a model

of user responses to native advertisements is constructed based on the technology acceptance model. With

native advertisements on Zhihu (a Chinese question-and-answer website) as the object of research, we

performed a survey on the Internet. After collecting the questionnaires of 232 users, the structural equation

method was applied to analyse the data. The results of the study showed that: user’s perceived usefulness and

perceived pleasure have a positive impact on the user’s behavioural intentions. User’s perceived disturbing

has a negative influence on user’s behavioural intentions. Among these, perceived usefulness is an important

factor that affects users' behavioural intentions. In addition, user's perceived ease of use positively affects

user's perceived usefulness. User's behavioural intention positively affects user's behaviour.

1 INTRODUCTION

Under the background of rapid development in the

Mobile Internet, people increasingly spend time using

mobile applications to acquire knowledge and

information. Marketers have to advertise on the

mobile platform. The display of online advertising is

gradually transformed into personalized delivery

based on user’s attributes and usage scenarios. Native

advertising is fast becoming a key instrument in

network marketing.

Native advertising follows the natural form and

function of the user experience in which it is placed

(Iacobucci, 2020). It can be integrated with the

characteristics of the platform, and supports the

interactive participation of users. As a new form of

advertising, native advertising is increasingly popular

with advertisers because of its accuracy and

originality, and the market of native advertising also

maintains a good vitality and development trend. In

2020, the market share of native advertising in China

increased from 28% in 2019 to 34%, surpassing e-

https://orcid.org/0000-0003-2338-4444

https://orcid.org/0000-0003-2377-4000

commerce advertising to become the advertising

form with the largest market share.

However, it has always been a difficult problem

for advertisers and enterprises to improve users'

willingness to accept advertisements and promote

users' intention of share and purchase. In addition,

most of the studies on native advertising are

theoretical studies, summarizing related concepts and

practical applications. And few empirical studies

focus on the acceptance behavior of native

advertising users. Therefore, this study chooses

native advertising on Zhihu as the research object and

applies empirical research methods to study the

influencing factors of user’s adoption behavior,

which has certain theoretical and practical

significance. Firstly, this paper explores the

influencing factors of user adoption behavior from

the perspective of user attitude. Combined with

marketing practice, it can help advertisers and

platforms find more effective ways and methods to

push information flow advertising for users. In

addition, we extended the technology acceptance

model with new factors including perceived

disturbance and trust and pleasantness. Therefore, the

Wang, S. and Bai, L.

The Effect of Native Advertising on User’s Behavioral Intention: Based on the Technology Acceptance Model.

DOI: 10.5220/0011175300003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 313-317

ISBN: 978-989-758-593-7

313

model of this study is innovative and the research can

enrich the theoretical research of native advertising.

2 MATERIALS AND METHODS

This chapter summarizes the related work on native

advertising and describes the differences between

previous studies and this study. Based on the

Technology Acceptance Model and conclusions of

previous studies, the hypotheses are proposed and the

experiments are designed.

2.1 Theories

2.1.1 Native Advertising

Native advertising is a term used to describe

advertising that takes the specific form and

appearance of editorial content from the publisher

itself (Wojdynski, 2016). From the perspective of big

data technology, native advertising is an advertising

strategy based on user data analysis. It can be

naturally mixed into the platform’s information flow

and to be more difficult for users to detect. The U.S.

Federal Trade Commission issued guidelines for

disclosing native advertisements to prevent local

advertising from confusing consumers.

With the rapid growth of native advertising in the

field of practice, there has been an increasing amount

of literature on the discussion on the characteristics of

native advertising. Most of these studies have

analyzed the characteristics of native advertising

from the perspectives of its generation mechanism,

external form, platform and context. Huang argued

that native advertising mainly has three

characteristics. First, the content of the advertisement

is similar to the content of the platform on which it is

embedded. Second, native advertising is presented in

the form of information flow along with other non-

commercial information on platforms. Third, native

advertising supports consumers to interact with it

(Huang, 2019).

Studies have shown that advertising significantly

effects users’ attitude, purchase intention and share

intention.

Haeson Park’s study suggests that consumers'

brand attitudes, preference of the advertisement have

a positive impact on user’s attitude (Park 2019).

Anocha conducted a study based on the technology

acceptance model and proposed that perceived

usefulness and perceived ease of use have a positive

impact on user acceptance of native advertisements,

while perceived risk has a negative impact on user

acceptance (Aribarg, 2020).

In this study, we consider these related studies as

well as the features of native advertisements to design

antecedents toward the behavioral intention of users

to native advertisements.

2.1.2 Technology Acceptance Model

The technical acceptance model (TAM) is one of the

most influential models to explain and predict the

behavior of the use of information system. The

original purpose of the technology acceptance model

is to explain the determinants of the widespread

application of computer technology. There are two

main factors that affect individuals' willingness to use

new technologies: perceived usefulness and

perceived ease of use. According to the technology

acceptance model, whether users use the system is

determined by behavioral intention, which is jointly

determined by users’ attitude and perceived

usefulness. Users’ attitude is determined by both

perceived usefulness and perceived ease of use.

Perceived usefulness is determined by both perceived

ease of use and external variables. External variables

include system design, user characteristics and so on.

There are two reasons for using TAM model as

the theoretical basis in this study. Firstly, TAM model

has been used in many studies to analyze the

acceptance factors of online advertising. Besides,

TAM model can be extended according to the actual

situation in the research.

2.2 Hypothesis

One of the purposes of native advertising is to provide

users with relevant product information in a novel

form, so as to make a good impression on users. In

this process, advertising content will influence users'

perception of usefulness. In addition, individual

behavior depends on the basic goal of the individual,

and perceived usefulness can be a basic goal for users

to learn about the advertisement. Hsiao built a

research model and concluded that perceived value,

perceived usefulness and perceived satisfaction affect

users' willingness to use the information system

(Hsiao 2013). Therefore, this paper puts forward the

following hypothesis:

H1: Perceived usefulness positively affects users'

behavioral intention of native advertising.

Native advertising has variable forms and can

vividly display product information. It enables users

to understand product information or use related

services as conveniently and easily as possible. When

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Davis proposed the technology acceptance model, he

proposed that perceived usefulness and perceived

ease of use have an impact on individual attitude at

the same time, and perceived ease of use also has a

positive impact on individual perceived usefulness.

Based on this, this paper puts forward the following

hypotheses:

H2: Perceived ease of use positively affects users'

behavioral intention of native advertising.

H3: Perceived ease of use positively affects

perceived usefulness.

Although native advertising reduces the

disturbance to users as much as possible by

improving the similarity with the information flow

content. It is inevitable that users will be disturbed or

even bored because of the advertisement. On the

other hand, some users will think that the mechanism

of information flow advertising based on their past

behavior and information violates their privacy.

Hence, we make hypothesis:

H4: Perceived disturbance negatively affects

users' behavioral intention of native advertising.

In terms of user trust in advertising, studies have

proved that user trust in the website will have an

impact on advertising marketing effect. Cai Peier

pointed out that if advertising is placed on the website

trusted by users, users will trust the advertising due to

their trust in the website, resulting in behavioral

intention (Cai 2016). Hence, we make hypothesis:

H5: Trust positively affects users' behavioral

intention of native advertising.

An important reason why users use mobile

applications to browse information is that they want

to get entertainment and spiritual satisfaction. A key

factor for the success of advertising marketing is

whether it can attract the attention of the users. The

novel and interesting content and form of native

advertising can please the users and promote users’

acceptance. Dai Wei constructed the value model of

Internet advertising and pointed out that perceived

pleasure can increase users' value perception (Dai,

2016). Therefore, we propose the following

hypothesis:

H6: Pleasantness positively affects users'

behavioral intention of native advertising.

According to the theory of consumer behavior, in

the process of native advertising affects users'

acceptance, users will finally make purchases or

share behaviors after generating behavior intention.

The main purpose of online advertising is to convey

product information to potential consumers, persuade

users to understand the advertised products or

services in detail, and generate the behavior of

purchase and use. Bhattacherjee pointed out that

users' intention will also be affected by various

subjective and objective factors, and whether it can

be transformed into users' continuous use behavior

needs further investigation (Bhattacherjee 2008).

Therefore, the following assumption is put forward:

H7: Users' behavior intention has a positive

impact on users' actual behavior.

On this basis of the hypothesis, this paper put

forward the research model of user responses to

native advertisements. Figure 1 shows the

components of the model.

Figure 1: Research Model.

2.3 Sample Selection and Data

Collection

This study intends to use the questionnaire survey to

study the user adoption behavior of native advertising.

Since most Internet websites have launched native

advertising, the forms and contents of native

advertising on each website are different. In order to

reduce the impact of other factors, this study selects

the native advertising of Zhihu website, a question-

and-answer website like Quora, as the research

object. And we mainly conducted questionnaires on

users who have noticed native advertisements, so that

we could gain useful feedback from experienced

users.

The questionnaire mainly includes two parts. The

first part is the background investigation, which

mainly aims to gather some sample statistical data of

participants. The sample statistical data includes the

user's gender, age, education. Other information

includes the time and frequency of using Zhihu

website and the understanding of native advertising.

The second part is the main part of the research. It

includes the measurement of user perception, user

behavior intention and actual behavior. The

measurement of all variables comes from the

previous literature and the context of this study.

The participants of this study were users who have

used Zhihu website and had purchase and share

The Effect of Native Advertising on User’s Behavioral Intention: Based on the Technology Acceptance Model

315

behavior of the products in advertising.

Consequently, 219 questionnaires were collected.

The effective rate of the questionnaire was 93.6%. In

order to improve the reliability of the structural

equation model, the number of samples should be

more than 25 times the number of variables. This

study involved 7 variables, and the amount of data

was more than 175, which met the needs. In the

effective questionnaire, men accounted for 50.2%

and women 49.8%. The ages of the participants were

19-24 and 25-30 years old. Since most of the users of

Zhihu website are young and middle-aged, the

collected questionnaire data is reliable.

2.4 Measurement

Most of the measurement scales constructs are

adopted from scales demonstrated in previous

literature. All variables in this study were carried out

by a five-point Likert-scale (1=strongly disagree,

5=strongly agree). Based on the work of D Gefen

(Gefen, 2003), we choose three items to measure

perceived usefulness. The three items are “The

advertisement helps me understand the information

about products or services; the advertisement can

improve my efficiency in using products or

downloading software; the advertisement helps me

relax.” For perceived ease of use, three items contain

“The content of the advertisement is easy to

understand; learning how to participate in the

advertisement is easy (viewing, clicking and other

operations); advertisements can link products or APP

download pages directly”. For perceived disturbance,

three items contain: “The appearance of the

advertisement disturbs my normal browsing

behavior; the advertisement distracts me; the

appearance of the advertisement bothers me” (Logan,

2013). For user’s trust, three items contain “Zhihu

website is trustworthy; Zhihu website attaches great

importance to the rights and interests of users; there

are no false advertisements on Zhiu website”. For

user’s pleasantness, three items contain: “The

advertisement is funny and interesting; the

advertisement is innovative; I think the advertisement

makes me happy”. For behavioral intention, three

items contain “After seeing the advertisement, I will

have the intention to check the advertisement

information in detail; I will have the intention to click

the link to other pages; I will be willing to download

the app or purchase relevant products”. For actual

behavior, three items contain “After seeing the

advertisement, I will check the advertisement

information in detail; I will click the link to other

pages; I will download the app or purchase relevant

products”.

3 RESULTS & DISCUSSION

The research model was tested by using SPSS23.0

and AMOS 24.0. This study checked internal

consistency reliability, convergent validity, and

discriminant validity before carrying out hypotheses

testing. The composite reliability (CR) of all

constructs was above 0.80. The reliability was

achieved. Besides, the convergent validity and

discriminant was also achieved. The resulting indices

of the model indicated a good model fit (Chi-

square/df = 2.960; RMSEA = 0.095; GFI = 0.829;

NFI = 0.890; TLI = 0.907; CFI = 0.924; GFI = 0.829;

IFI = 0.925). Table 1 shows the results of structural

model evaluation.

Table 1: Results of structural model evaluation.

Hypot

hesis

H1 H3 H4 H6 H7

Coeffi

cient

0.814 0.569 -0.160 0.363 0.938

The final validation results show that perceived

usefulness has a positive impact on users' behavioral

intentions of native advertisements (hypothesis 1 is

true). When users think that native advertisements

can enable them to obtain useful information, users

will have behavioral intentions and then actual

behaviors. Perceived ease of use positively affects

users' perceived usefulness (hypothesis 3 is true),

which is in line with the original hypothesis of the

technology acceptance model. Perceived disturbance

negatively affects users' behavioral intentions for

native advertisements (hypothesis 4 is true),

indicating that users will have negative behavioral

intentions when they feel that their APP usage

behavior is disturbed by advertisements. User's

pleasantness has a positive impact on the user's

behavioral intention to the native advertisement

(hypothesis 6 is true). Users' behavioral intention for

information flow advertising positively affects users'

actual behavior (hypothesis 7 is true), indicating that

users' behavioral intention tends to generate actual

behavior, which also conforms to the original

hypothesis of technology acceptance model. In

addition, the hypothesis that perceived ease of use

and perceived trust have an impact on users'

behavioral intentions has not been proved (hypothesis

2 and 5 are not valid). One of the reasons may be that

there are biases in the samples. Most of the surveyed

users are students in school, which may have biases

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in age and gender. Second, the surveyed users are

familiar with Zhihu platform, so their perceived ease

of use and trust of the platform are clear, which will

not affect their further behavior intentions

4 CONCLUSIONS

Native advertising has been favored by the online

advertising market since it was launched, but the

research on native advertising is still in an immature

stage. There has been little quantitative analysis of

factors that affect user’s acceptance of native

advertisements. Therefore, this study has important

theoretical and practical significance.

Based on the technology acceptance model, a new

hypothetical model is established by adding new

factors including pleasantness, trust and perceived

disturbance, which is innovative. The empirical

results show that users’ perceived usefulness and

pleasantness have a positive impact on users'

behavior towards native advertising. Users' perceived

ease of use has a positive impact on users' perceived

usefulness, and users' perceived disturbance has a

negative impact on users' behaviour.

Through empirical study results, we gained some

new findings and they are beneficial for advertisers.

Besides, we made few reasonable suggestions for

marketers and advertisers to develop native

advertisements. Firstly, in order to promote users'

behavior in native advertising, advertisers and

platforms should improve the usefulness and ease of

use of advertising, so that users can get the

information they need, find the value of advertising,

reduce the difficulty of users' operation. It’s

important to make it easier for users to get more

information about the products or download software.

Secondly, the interference caused by native

advertising needs to be weakened as far as possible.

Advertisers or marketers need to deliver personalized

advertisements more accurately and set the frequency

of advertisements within a reasonable range. Thirdly,

we can improve the user's pleasantness caused by

native advertisements by enhancing the interest of

advertisement and making the advertisement novel

and interesting. Making the display forms of

advertising more rich and diverse can improve the

attraction of advertisement to users and promote

users' intention to learn more about the product

displayed in the advertisement.

In addition, there are also some deficiencies in

this study. Firstly, there are some sample deviations

in the questionnaire survey, and there are some

limitations in analyzing the influencing factors of

user adoption behavior of native advertisements with

only one website as an example. Secondly, the

theoretical model only considers user perception. In

the future, there are some issues that need to be

investigated. For example, how do the characteristics

of advertising affect user attitudes and purchase

intention or share intention on native advertising?

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