Factors affecting the acceptance and usage intention toward in-feed native ads : a cross-country empirical comparison between China and the Netherlands

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Factors Affecting the Acceptance and Usage Intention

toward In-Feed Native Ads:

A cross-country empirical comparison between China and the Netherlands

Name: Wenyi Zhang Student number: 11603801 Date of submission: 22 June 2018

Institution: Amsterdam Business School, University of Amsterdam Programme code: MSc BA

Supervisor: Feray Adiguzel

Master Thesis

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Statement of originality

This document is written by Student Wenyi Zhang who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

The last few years have witnessed a rapid growth of mobile social media advertising. In-Feed Natives Ads, a new form of advertising, are ads which look like the visual design of natural social media news feeds. On the one hand, In-Feed Natives Ads have become the core of revenue growth for major platforms such as Facebook, Twitter, Instagram, WeChat, and Sina Weibo, but on the other, whether this advertising form is more effective than other advertising forms remains unsolved. In addition, the social media usage varies in different cultural and economic contexts, especially in developing and developed countries, but there is a lack of cross-country comparison research in existing mobile social media advertising literature.

In order to respond to these calls, this study conducts empirical research to investigate factors affecting the acceptance and usage intention toward In-Feed Native Ads based on developing (China) and developed (Netherlands) countries. A conceptual model based on Unified theory of acceptance and use of technology (UTAUT) is developed with performance expectancy, social influence, advertising personalization, advertising entertainment, promotional rewards, and perceived risk as key determinants, and then tested using Structural Equation Modelling (SEM) technique.

The results indicate that performance expectancy, social influence, advertising personalization, and promotional rewards have a significantly positive effect on acceptance and usage intention whereas the perceived risk is found to be negatively associated with acceptance and usage intention. For developing countries, social influence is the most important factor, followed by performance expectancy, perceived risk, and advertising personalization. For developed countries, however, advertising personalization is the most important factor, followed by perceived risk, performance expectancy, and social influence. In contrast to the hypothesis, this study does not find a significant difference in promotional rewards between two countries. Relevant conclusions, implications, and limitations and future research are also provided.

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List of Tables

Table 1. Major types of mobile social media advertising ... 6

Table 2. Previous definitions of Acceptance and Usage Intention ... 17

Table 3. Definitions of variables in the UTAUT Model ... 26

Table 4. Previous empirical studies on the UTAUT model ... 28

Table 5. The summary of hypotheses ... 41

Table 6. Demographic Variables ... 46

Table 7. Social media usage frequency and In-Feed Native Ads usage experience ... 47

Table 8. Correlations, means, and standard deviations (SD) of two countries ... 48

Table 9. Convergent validity test results ... 50

Table 10. Discriminant validity test results ... 52

Table 11. Model fit summary ... 56

Table 12. Regression weights ... 57

Table 13. Modified model fit summary ... 57

Table 14. Regression weights in the modified model ... 58

Table 15. Results of regression weights and standardized coefficients for the pooled sample (N=403) ... 59

Table 16. Multi-group analysis results ... 60

Table 17. Results of regression weights and standardized coefficients for Chinse and Dutch Sample ... 61

Table 18. The summary of hypothesis testing results ... 62

List of Figures

Figure 1. In-Feed Native Ads of Facebook ... 9

Figure 2. In-Feed Native Ads of Instagram ... 10

Figure 3. In-Feed Native Ads of Instagram Twitter ... 11

Figure 4. In-Feed Native Ads of YouTube ... 11

Figure 5. In-Feed Native Ads of LinkedIn ... 12

Figure 6. In-Feed Native Ads of Weibo ... 14

Figure 7. Components of WeChat Moment Ads ... 16

Figure 8. Model of Theory of Reasoned Action (TRA) ... 23

Figure 9. Model of Theory of Planned Behavior (TBP) ... 23

Figure 10. Technology Acceptance Model (TAM) ... 24

Figure 11. Technology Acceptance Model 2 (TAM2) ... 25

Figure 12. Unified Theory of Acceptance and Use of Technology (UTAUT) ... 26

Figure 13. Conceptual model ... 43

Figure 14. The final structural model ... 58

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1. Introduction

Over the last decade, the usage of mobile social media has experienced astonishing growth. The global social media users reached 2.56 billion in 2017, growing by 30% from 2016 (Simon, 2017). The booming of social media applications such as Facebook, Twitter, and YouTube opens a new era for businesses to promote offerings to right segmented customers (Wojdynski, 2016). Compared to traditional advertising, mobile social media ads are cost effective and less obtrusive, leading to high user acceptance (Aguirre et al., 2015). This is because advertising on social media enables marketers to interact, spread word of mouth, and connect with a potentially infinite amount of people. Also, social media advertising offers a reliable way to measure and track their reach. In the meantime, since customers are becoming increasingly sophisticated and demanding, attracting the customer attention in a competitive advertising environment requires marketers to search for more innovative and effective ways to communicate with target audiences. In-Feed Native Ads are one kind of newborn native advertising form, which are designed to fit seamlessly with their surrounding contexts and look identical to regular social media news feeds, thereby offering customers a nonintrusive experience (Interactive Advertising Bureau (IAB), 2013). Social networks like Facebook, Twitter and Instagram popularized In-Feed Native Ads as Facebook News Feed posts, Twitter promoted tweets, and Instagram Sponsored Content. Catching the attention of many brands, In-Feed Native advertising is identified as the most widely used native advertising medium by the Interactive Advertising Bureau (IAB) in 2015 (Taboola, 2017). For example, the Facebook Advertising Revenue Report in the first half of 2017 shows that mobile ads take up 87% of total advertising revenue and most of the mobile advertising revenue comes from In-Feed Ads (Forbes, 2017). Furthermore, the global native advertising market is estimated to double by 2018, reaching the annual revenue of more than $60 billion (Taboola, 2017).

Despite undeniable potentials of In-Feed Native Ads, people’s perceptions of this new advertising form are mixed. On the one hand, a study by Social Media Today suggested that native ads are four times more effective than non-native ads (Hanks, 2017). On the other hand, this advertising format also receives criticism that viewers may feel deceived because In-Feed Native Ads too hard to distinguish from non-advertising or editorial content (Berry, 2014; Dumenco, 2014; Campbell, 2015). Some researchers also

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argued that this advertising form is not actually as effective as people claim (Liles, 2009; Tutaj and Reijmersdal, 2012; Buami, 2004). For example, a survey done by Joe (2014) indicated that only 24% of respondents scroll down on native ads and more than 50% of respondents do not trust In-Feed Native Ads. Likewise, another survey by Social Media Examiner (2016) suggested that although a surprising 86% of social marketers deliver ads on Facebook, 35% of them are not sure whether the Facebook advertising is effective.

In addition, academic studies of In-Feed Native advertising are still in the initial stage. To date, there is little empirical evidence to explain what factors that affect the acceptance and usage intention toward In-Feed Native Ads. Wojdynski (2016) discussed the implications of its growth, acknowledging that this area of research required more empirical work.

Another research gap existing in current studies is that most of the current studies are confined to only one country, but the social media usage varies in different economic, cultural, and regulatory contexts, especially between developed and developing countries (Harris et al., 2005; Dai et al., 2009; Hoehle et al., 2015; Ratten, 2015; Lu et al., 2017). For example, according to World Map of Social Networks by Vincenzo Cosenza (2018), Facebook, Twitter, and Instagram are leading social networks in western countries, whereas, in China, Tencent QQ, WeChat, and Weibo are most widely used social media platforms. Compared to western netizens, Chinese netizens are more likely to be attracted by promotional campaigns on social media sites (Lu et al., 2017). In addition, the acceptance and intention to use is a type of behavior decision, which is generally shaped by cultural and economic contexts (Hoehle, 2015). As such, there is a need for cross-country comparison studies carried out, especially the comparison between developed and developing country.

To sum up, with the purpose of covering the deficiency of existing research, the main focus in this paper is to explore the factors affecting the acceptance and usage intention toward In-Feed Native Ads based on two different countries. For this purpose, China and the Netherlands are chosen, not only because this pair is economically and culturally different in many aspects, but also because both countries enjoy the great popularity of In-Feed Native Ads. For example, In-Feed Native Ads account for 14.3% of the overall online advertising market in China (iReserach, 2017), Similarly, the advertising expense on social networks is expected to amount to $396 million in 2018 in the Netherlands (Statista, 2018).

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3 The research questions are formulated as follow:

What are the factors affecting the acceptance and usage intention toward In-Feed Native Ads?

 What are the relatively important factors?

 Does the acceptance and usage intention toward In-Feed Native Ads differ in China and the

Netherlands?

This study seeks to make two academic and managerial contributions. By developing a comprehensive theoretical model, empirical results can explain what factors that induce customers to accept In-Feed Native Ads across dissimilar cultures, thereby supplementing social media advertising studies, and providing the theoretical reference for future research. Also, as more and more marketers today are embracing In-Feed Native advertising as a promotional tool, the managerial contribution of this study lies in offering guidance for marketers on how to leverage In-Feed Native Ads to attract user attention, earn active user response, and encourage more purchase behaviors.

The remainder of the paper is organized as follow. The literature review section provides a more detailed overview of In-Feed Native Ads and their background in China and the Netherlands and major acceptance theories with some previous empirical work. The subsequent section describes theoretical justification for the conceptual model. Then, following sections present the methodology of data collection as well as analysis, and the empirical results. Finally, this paper concludes with research implications, limitations, and avenues for future research.

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2 Literature Review

This paper aims to empirically examine factors that affect the acceptance and usage intention toward In-Feed Native Ads. Many adoption studies have drawn on Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Unified Theory of Acceptance and Use of Technology (UTAUT). According to the objective of this paper, the UTAUT model is selected as the conceptual model, because it presents 70% of the variance in usage intention (Venkatesh and Davis, 2000), which is credited for effectively explaining user behavior (Giwa et al., 2015). Moreover, the UTAUT model has been used by a handful of empirical studies examining the adoption and acceptance of mobile advertising (He and Lu, 2007; Choi et al.,2008; Khan and Allil, 2010). Therefore, the choice of the UTAUT model is justified on the theoretical and practical basis.

This section first defines concepts of Mobile Advertising and Mobile Social Media Advertising, In-Feed Native Ads and the acceptance and usage intention, then briefly introduce four adoption theories, and ends up with proposed variables in the conceptual model.

2.1 Key definitions

2.1.1 Mobile advertising and Mobile social media advertising

With the widespread application of mobile technology and wireless internet services, mobile advertising shows the excellent prospects for one-to-one advertising medium without time or location barriers (Leppaniemi et al., 2005).

Mobile advertising, also known as M-advertising, refers to delivering advertising messages through mobile channels, in the form of text, pictures, QR-code, APP, audio and video (Haghirian et al., 2005). Compared to traditional advertising, such as TV commercials, billboards, print or radio advertising, mobile advertising has characteristics of precision, personalization, and interactivity.

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to gain valuable knowledge of customer profiles, needs, browsing history and real-time positioning (Haghirian et al., 2005). Therefore, marketers can not only customize advertising messages for the target group but also continuously improve their offerings based customer feedback and preferences, thus enhancing the relationship with customers. Once ads are more personalized and relevant to customers, they are more receptive to advertising (Shankar et al., 2010). Besides, different from the passive communication of the classical advertising, mobile advertising realizes the timely and direct interaction with customers since each customer can directly participate in marketing campaigns and provide feedback via mobile devices.

Meanwhile, with the high penetration rate of mobile devices and the rapid spread of 4G network, the last decade has seen a host of social media applications emerging exponentially. As a study by Social Media Today indicated, netizens spend 30% of online time on social media and 60% of all social media activities happen on mobile devices (Hanks, 2017). Marketers are now increasingly combing mobile advertising and social media applications. Mobile Social Media Advertising refers to any advertising activity conducted via mobile social media platforms such as Facebook, Instagram, YouTube, LinkedIn, Google+, etc., the goal of which is to maximize brand exposure, increase website traffic, and generate more leads (De Vries et al., 2012).

Besides owning advantages of mobile advertising, mobile social media advertising enables marketers to reach a wider range of audience thanks to the dramatic impact of word-of-mouth (Kim and Ko, 2012). According to a research conducted by Oxford University, mobile social media advertising represents great effectiveness in building brand reputation, awareness, and association (Freier, 2017). Also, based on the real-time geography information collected by social applications, more accurate ads and individualized activities can be displayed to users, thus improving click-through rate (Kaplan, 2012). Since the merchants who display advertising often tend to be in the vicinity of users, users can immediately go for purchase if they are interested in the offerings shown in ads. This greatly shortens the sale period (Kaplan, 2012). The advertising revenue also becomes a major source of revenue for social media applications. For example, Facebook, the largest social network service provider, derives the vast majority of its revenue from mobile advertising. According to 2017 third-quarter results posted by Facebook, the mobile advertising revenues

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account for 88% of the total and rise significantly by 57% year-on-year in the third quarter, which is predicted to grow from $13 billion in 2015 to $60.68 by 2021. People are far more likely to “share” social media posts from their phones than on a desktop computer (Forbes, 2017).

2.1.2 Major types of mobile social media advertising

As social media applications continue to evolve, new forms of mobile social media advertising are surfacing all the time. Advertisers need to familiarize themselves with new formats of mobile social media ads. The table 1 lists major types of mobile social media advertising (Manchanda, 2006).

Table 1. Major types of mobile social media advertising

Advertising Definition Examples

Social Banner Advertising

Social banner ads, also called social display advertising, are often displayed at the bottom or the top of the screen in the static or animated form. Viewers can click on the ads to get to the website of brands or retailers.

Facebook Banners, Snapchat Geofilters, Twitter Headers, Youtube Banners, WeChat Banners Key Opinion Leaders

(KOLs) Advertising

Key Opinion Leaders (KOLs) advertising, also called influencer advertising, is a new advertising tool in the digital age. Key opinion leaders are digital content creators who have strong social influence and are professionals in their fields such as fashion, beauty, food, traveling, and exercise. These KOLs’ social accounts have many followers, and therefore can make promotional campaigns much more engaging and attract more attention than mass media (Ferguson, 2008). Key Opinion Leaders (KOLs) advertising refers to brands paying popular KOLs for promotional posts on their social accounts (Graziani, 2015).

Recommendation posts on Facebook,

Instagram, Twitter, WeChat, Weibo and other personal Blogs

Native Advertising Native advertising is a form of paid advertising integrating with the publishing platform and system. Thus, they do not interfere with the viewer experience. Native ads can be categorized by Sponsored Content, Native Hyperlinks, and In-Feed Native Ads(Wojdynski, 2016).

Instagram Sponsored Content, Twitter promoted tweets, Facebook News Feed posts, LinkedIn Sponsored Updates

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2.1.3 In-Feed Native Ads

As one of the most common forms of native advertising, In-Feed Native Ads, also called In-stream Ads, News Feed Ads or Native Social Media Posts, refer to paid ads on social networks which are mostly displayed on mobile terminals and cohesive with regular social media news feeds (a list of updated stories such as status updates, pictures, links, and upcoming events on social media home pages). These ads are often displayed in the form of text, single or multiple images (s) and video, and offer users a hyperlink to the advertiser’s website (Wojdynski, 2016).

In-Feed Native Ads have three main advantages. First and most significantly, In-Feed Native Ads are designed to naturally match the visual look and overall feel of where they serve. If users dislike those ads, they can dismiss the ads from their timeline with a simple click. Therefore, the biggest promise of In-Feed Native Ads is that ads are changed from unwelcome disturbance to natural and integral part, thereby providing users with more seamless experience as they are nonintrusive or non-disruptive (Virag, 2016). A study by Social Media Today demonstrates that native ads are four times more effective than non-native methods like banner ads (Hanks 2017). Second, the delivery of In-Feed Native Ads is precise and accurate, because advertisers can target users according to users’ profile information (gender, age, education, interest, device type), and thus match different ads for a specific audience. The third advantage is that users can also interact with these ads by Like, comment and repost, thus extending the spreading scope. Overall, compared to other digital ads, In-Feed Native Ads play an important role in getting more exposure, driving brand awareness, and promoting offers and events.

As up to 80% of social media time, today is spent on mobile devices, displaying In-Feed Native Ads to users when they scroll through posts is adopted by many businesses reach potential customers and boost sales (Sterling, 2016). According to Facebook Advertising Revenue Report in the first half of 2017, Mobile ads make up 87% of total advertising revenue and most of the mobile advertising revenue comes from In-Feed Ads (Forbes, 2017). Due to the huge impact of mobile social media advertising, this study centers on In-Feed Ads shown on mobile social media applications.

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2.1.4 Background of In-Feed Native Ads in China and the Netherlands

(1) Background of In-Feed Native Ads in the Netherlands

According to an article published on Anchovy, the leading social media platform in the Netherlands is Facebook, with nearly 11 million users as of 2018. YouTube is the second largest player among the Dutch, followed by LinkedIn, Twitter, and Instagram. Because of the massive popularity of digital social networks, a large share of Dutch companies is active on investments in social media advertising.

A report by Deloitte and IAB shows that all ad spending attributed to social media has increased with an average of over 45% in the first half of 2017, and a huge part of that is driven by mobile devices, as social networks are mainly mobile-focused (Deloitte and IAB, 2017). It is also expected that the advertising expense on social platforms will amount to $396 million in 2018 (Statista, 2018). The following part gives a brief introduction of five major social media platforms in the Netherlands, as well as In-Feed Native Ads, run on them.

a. Facebook

Facebook ads dominate social media advertising. According to a report by Social Media Examiner (2016), a surprising 86% of social markets use Facebook as a regular promotional tool. Facebook ad revenue jumps more than $39.9 billion in 2017, a new record f compared to previous years. The mobile advertising has become the most promising advertising form for the company, contributing 89% of total ad dollars for the fourth quarter of 2017. The earliest In-Feed Native Ad was displayed on Facebook in 2006. After 10 years of development, In-Feed Native Ads represent dramatically 66% of the company total revenue (Menlo, 2018).

There are six In-Feed Native advertising formats running on mobile devices (see figure 1), namely Image Ads, Video Ads, Carousel, Slideshow, Canvas, and 360 Video. Advertisers can use either images or videos to showcase product, service or promotion by Image Ads or Video Ads. They can also combine multiple images and videos within a single ad by Carousel. Slideshow enables advertisers to connect users with lightweight video ads at any Internet speed. Canvas delivers more engaging brand stories through fast loading and full-screen ad experience. 360 Video is a new advertising tool designed in a captivating way in

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which users can users drag fingers to move around within the video to get 360 degree of a scene (Facebook business, 2016).

Figure 1. In-Feed Native Ads of Facebook

b. Instagram

Instagram sets up advertising business in 2015. At present, it hits more than 800 million active users, with a total sharing of 55 million photos a day (Cunha, 2017). Similar to Facebook In-Feed Native Ads, Instagram ads appear in user feeds with image, video and carousel formats (see figure 2). As Instagram is such a visual platform that it is not suitable for text ads. The compelling advertising feature owned by Instagram is called Stories ads, which are shown as feed content on Instagram Stories. According to Instagram Internet data, as of November 2017, there are over 300 million accounts using stories feature on a daily basis (Instagram business, 2017).

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Figure 2. In-Feed Native Ads of Instagram

c. Twitter

With 330 million monthly active users, Twitter has caught the attention of many businesses (Statista, 2018). Twitter In-Feed Native Ads came into play in 2011 and soon become the most important part of Twitter advertising (Barnhart, 2017). The Twitter In-Feed Native Ads are presented as Promoted Tweet with a label of Promoted as it sounds (see figure 3). Like other In-Feed Native Ads, Promoted Tweets are ads which tend to be relevant or interesting to the user, and integrated into a user’s timeline just like regular Tweets. They can be retweeted, replied or favorited (Oetting, 2017). Besides video and image tweets, advertisers can also display promoted tweets in the form of a GIF, and thus make the most of attractive visuals without having to edit the sound. Although the number of Twitter advertisers is fairly small compared to Facebook’s (130,000 vs. 3 million), more than 67% of active users tend to purchase from a brand they follow (Karlson, 2017), showing great potentials as a rich marketing avenue.

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Figure 3. In-Feed Native Ads of Instagram Twitter

d. YouTube

As the second largest search engine (after Google) in the world, YouTube was ranked as the Top Entertainment brand of 2013 by Nielsen (Nielson, 2013). YouTube is a prime platform used by brands on an ongoing basis to extend reach, grow customer engagement, and increase the number of subscriber audience (Fontein, 2017). The YouTube In-Feed Native Ads rely solely on video, which can be either non-skippable or non-skippable (see figure 4). They appear before midway, or after the main video. Non-non-skippable ads force people to watch and can only last for 15-20 seconds while skippable ads, also called TrueView Ads, offer viewers the option to skip the ad after watching it 5 seconds (Pham, 2017).

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With two profiles opened every second and over 500 million users to date, LinkedIn, the world’s largest professional networking platform, has proven itself to be a powerhouse for marketers to promote company updates and deliver personalized ads. The LinkedIn In-Feed Native Ads, go by the name of Sponsored Content, show up in the middle of a user’ professional news feed (see figure 5). Owning the benefit of matching up ideal professionals and driving more qualified leads, Sponsored Content represents as a prime real estate for advertisers running campaigns to amplify the influence of company news, articles, slides, and videos (Mathison, 2017).

Figure 5. In-Feed Native Ads of LinkedIn

(2) Background of In-Feed Native Ads in China

In China, In-Feed Native Ads first appeared on Sina Weibo in 2013. Later Tencent Qzone, Renren.com, and other social media platform also started launching In-Feed Native Ads. According to a report by iResearch Consulting, Native Ads are developing with a fast pace in China, accounting for 14.3% of the overall online

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advertising market. It is also projected that they will maintain more than 50% growth rate in the next three years (iReserach, 2017). With the popularization of smartphones and the increasing stickiness of mobile social application users, the media platforms will continue to innovate in In-Feed Native advertising and the advertisers’ investment in it will also increase.

Owing to the massive user base, Weibo and WeChat are the two leading social media platforms in China and often utilized by businesses for advertising (Zhao, 2017). The following part gives a brief introduction of these two social applications and In-Feed Native Ads displayed on them.

a. Weibo

Weibo, similar to Twitter, is an open social media platform for sharing and gathering information, in which users can post within 140 characters. As of March 2017, Weibo had 340 million monthly active users, exceeding the number of monthly active users of Twitter (Zhao, 2017), making it easier to access and engage the mass audience.

Currently, the In-Feed advertising channel of Weibo is called Fan Connect (see figure 6). The Fan Connect ads are displayed as a post on brand accounts and often appear near or at the top of users’ news feeds with the “promoted” sign. The ads can not only be seen by existing brands’ followers but also spread to those who follow the targeted audience or are considered as advertisers’ potential followers, thus maximizing the advertising impact (Maruma, 2014).

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Figure 6. In-Feed Native Ads of Weibo

b. WeChat

WeChat is a social media application released by Tencent (one of the biggest Internet giants in China) on January 21, 2011. Starting from a platform not too different from WhatsApp, Facebook Messenger, and Line, WeChat has transformed from a simple instant communication tool into an all-in-one platform which integrates everyday essentials such as social communication, shopping, financial payment, gaming, and other life services (Gan, 2017).

According to WeChat User & Business Ecosystem Report 2017, the number of WeChat monthly active users reached 889 million in 2016 and boosted the information consumption of 174.25 billion yuan (Chinatechinsights, 2017). The data published in Bloomberg Businessweek, at present, the number of WeChat users is over 900 million, surpassing the population of the European Union (510 million) (Lauren Dickey, 2017). According to data published in the Chinese-language version of Bloomberg Businessweek, more than 900 million people use WeChat – a number which significantly surpasses the estimated 731 million total Chinese internet users. To put these numbers into perspective, the number of WeChat users out measures the population of the European Union (at 510 million)

WeChat Moments is an important feature of WeChat, acting as a platform where users can express themselves via text, pictures, or videos and share interesting articles, music, and videos. Unlike Twitter or Weibo, WeChat is a closed social communication platform with more privacy and personality, which means

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users cannot view others’ status by simply following their accounts. Most WeChat contacts are people who are close to users, such as family members, friends, and colleagues. This makes advertising on WeChat was the powerful word of mouth, as the dissemination of information in WeChat is more in-depth and interactive and thus generates more attention and motivation to share (Attract China Blog, 2015). In conclusion, WeChat posts do not have the same viral effects as Weibo messages but have the consumption rate.

It is noted that what makes WeChat distinguish from other social media is WeChat Pay. Although Facebook just launched peer-to-peer payments over Messenger APP (Rahil Bhagat, 2016), WeChat have stayed ahead for years. By liking WeChat account to a bank account, WeChat users can transfer money, pay the bills, book various tickets, ride-hailing, and even manage their investment via various means of WeChat Pay such as Quick Pay, QR Code Pay, In-App Payment (Thomas, 2017). More than 600 million WeChat Pay accounts are active in restaurants, entertainment venues, and even street food vendors (Dickey, 2017).

This revolutionary all-in-one feature show promising prospect for advertising on WeChat, as users can make transaction payments anytime anywhere. In-Feed Native Ads of WeChat are delivered on WeChat Moments. WeChat Moment Ads, launched on January 25, 2015, are similar to Facebook or Instagram ads displayed on the users’ timeline as normal Moments posts but with an additional “Promotion” tag (Chozan, 2017). WeChat users can see this type of ads when scrolling through Moments. If they do not interact with the ads, such as Like or comment in the first 6 hours, the ads will be automatically removed. However, if they engage with the ads, it will increase the likelihood of their contacts receiving the same ad (Sampi, 2017). Due to the strong sense of community of WeChat, WeChat Moment Ads have unique advantages in encouraging user engagement, bringing more qualified leads, and building brand loyalty(Tingyi 2017, Windy 2017). Components of WeChat Moment Ads are shown in Figure 7.

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Figure 7. Components of WeChat Moment Ads

1. Brand name and profile image: users can click to follow the brand. 2. Promotion tag: users can easily recognize promotional messages.

3. Promotional message and image: advertisers can post up to 6 images, the text of less than 40 Chinese characters, and full-screen or embedded videos.

4. Hyperlink: advertisers can post a Hyperlink to a promotional article or page, an App download page or am an external Html5 page.

5. Social interaction: users can interact with the ad by “Like” and comment.

2.1.5 Acceptance and Usage Intention

Acceptance and usage intention are research in acceptance and human behavior studies. Acceptance and usage intention refers to people’s evaluations about a particular object by favorability (favor or disfavor) and further assumption about performing certain behaviors (Fishbein and Ajzen, 1975; Ajzen, 1985; Davis,

1. Brand name and profile image

5. Social interaction

Users can interact with the ad by “Like” and comment.

3. Promotional message and image

Advertisers can post up to 6 images, text of less than 40 Chinese characters, and full-screen or embedded videos.

4. Hyperlink

Advertises can post a Hyperlink to a promotional article or page, an App download page or an external Html page.

2. Promotion tag

Users can easily recognize promotional messages.

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1989; Venkatesh, 2000; Venkatesh, 2003). Related to acceptance, use behavior emphasizes an individual's actual usage behaviors. Table 2 summarizes some highly recognized definitions of the acceptance and usage intention in existing studies.

Table 2. Previous definitions of Acceptance and Usage Intention

Source Definition

Peter & Tarpey (1975) An individual’s subjective possibility to perform some behavior, which can be used to predict the occurrence of the actual behavior

Fishbein & Ajzen (1975) An individual’s expectation about his own behavior under certain circumstances

Warshaw & Davis (1985) The degree to which an individual whether performs a specific future behavior or not

Peter & Olson (1996) A statement linking the individual and his actual and future behavior

From the table above, most of the definitions contain keywords such as possibility or probability, subjective judgment, future behavior etc. In this study, the acceptance and usage intention is defined as a subjective judgment about the possibility to perform the actual and future behavior.

In the context of the present study,

The acceptance and usage intention refers to the user’s favorability of In-Feed Native Ads, that is the willingness to adopt In-Feed Native Ads, such as receiving, viewing, desiring to gain more information, engaging, and generating purchase intentions.

Use behavior refers to the user’s actual usage of purchasing offerings in In-Feed Native Ads, participating in promotional events, and sharing or recommending In-Feed Native Ads to other people.

2.2 Literature review on In-Feed Native Ads

In-Feed Native Ads first appeared on Facebook, followed by Twitter, Instagram, YouTube, LinkedIn, and some Chinese social media such as Weibo and WeChat.

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of In-Feed Native Ads; 2) research on the effectiveness of In-Feed Native Ads.

2.2.1 The adoption toward In-Feed Native Ads

As In-Feed Native Ads enable users to automatically access promotional messages without active search, this triggered the discussion about privacy issues. Hoadley et al. (2010) studied 172 Facebook users’ attitudes toward News Feed feature. The results demonstrated that although a majority of respondents perceive that Facebook’s News Feed provides an easier information access, they are concerned that their privacy is violated, highlighting the importance of increasing the control of information access in order to alleviate privacy concerns. A study done by Buami (2014) indicated that respondents state the presence of ads on Facebook as nothing but an intrusion into their privacy. Results from an exploratory field study on Facebook also showed that the click-through rate of receiving personalized ads drops sharply when customers realize their personal information has been collected without permission (Aguirre et al., 2015). Therefore, despite the fact that In-Feed Native Ads, with greater personalization and less interference, have the potentials to improve the response rate, such ads could also bring discomfort to users, leading to lower response rate.

Another problem is the secrecy of In-Feed Native Ads, that is, the lack of clear advertising disclosure makes users unaware of what they are viewing is a form of paid ads (Wojdynski, 2016). Although an increasing number of advertisers are embracing native advertising, this new ad form also receives some criticism because native ads are designed to appear in-stream and blend with surrounding context, it may deceive customer into regarding these ads as non-advertising or editorial content, thereby impeding on threatening the press’s social credibility (Berry, 2014; Dumenco, 2014; Campbell, 2015). Therefore, good native ads should be less secretive and deceptive with a high transparency level of their native content (Darke, 2007; Mitra, 2008). However, more research is still needed to find out how to design native ads which are more effective and ethical (Wojdynski, 2016).

2.2.2 The effectiveness of In-Feed Native Ads

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banner ads and can draw more user attention in several ways. First, a large set of user data owned by social media platforms make it easier for brands to provide more targeted while less intrusive ads. Second, through delivering ads that resemble regular posts, In-Feed Native Ads lead to less negative reactions as users either perceive viewing the native advertising is part of social media use or fail to identify In-Feed Native Ads as an advertising form. Third, users are more responsive to In-Feed Native Ads than other online advertising formats, because they can like, repost, comment, or favorite these ads while scrolling through social news feed (Wei, 2008; Kelly, 2010; Fulgoni and Lipsman, 2014; Tucker, 2014).

With the aim to maximize the effectiveness of In-Feed Native Ads, some studies reveal how native ads are evaluated by users. A research done by Wojdynski (2016) showed that the middle-positioning ads gain more visual attention and the use of "sponsored" or "advertising" wording leads to more advertising recognition. Lee (2016) conducted a survey of 550 U.S. adult customers and found that customers show more sharing intention toward non-intrusive In-Feed Native Ads compared to manipulative In-Feed Native Ads. Additionally, native ads on social media appeal more to those respondents with stronger information-seeking motivation.

Although there are reasons to assume that In-Feed Native Ads are a boon for brands to develop nonintrusive accessibility to customers, there is no consensus on the effectiveness of these ads.

Contrary to several researchers’ claims that users are less skeptical and offensive toward native ads than ads embedded in hard news stories such as banner ads (Tutaj and Reijmersdal, 2012), Howe and Teufel (2014) argued that the native advertising has no significant effect on the user’s credibility perception. Additionally, as the information from social media cannot be seen as a very credible source by far, people are less likely to make a purchase decision when viewing ads on social media sites (Buami, 2014). A survey pertaining users’ attitudes toward sponsored content turned out that only 24% of respondents scroll down on native ads and over half of people do not trust In-Feed Native Ads. Moreover, two-thirds of people feel deceived by sponsored content (Joe, 2014). Cooper (2013) contended that poorly constructed In-Feed Native Ads may annoy users and thus have a negative impact on brands. A recent study showed a sponsorship disclosure of Facebook post cause customers’ distrusting beliefs about the post, and in turn lead to decreasing engagement in electronic word-of-mouth (Boerman et al., 2017).

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In conclusion, these studies imply that advertisers still have a long way to go to figure out what affects the user’s acceptance of In-Feed Native Ads. More empirical studies are needed to find out the factors affecting the acceptance and usage intention toward In-Feed Native Ads (Wojdynski, 2016).

2.2.3 Cross-country studies of In-Feed Native Ads

The development of In-Feed Native Ads is earlier in Western countries. The first In-Feed Native Ad appeared on Facebook in 2006 while they did not show on Chinese social media platforms until 2012 (iReserach Consulting, 2017). According to Statista, the native advertising spending in Western Europe is expected to grow from $7.09 billion in 2015 to $13.91 billion in 2018, and these ads can often be seen on leading social platforms in Western countries, such as Facebook, YouTube, LinkedIn and Instagram (Statista, 2018).

Compared to western countries, China lags behind in adopting In-Feed Native Ads, but it catches up quickly due to the largest share of mobile user worldwide (China Internet Watch, 2016). The huge amount of social media usage in China has created a platform for brands to attract and retain customers. It is reported that the market size of In-Feed Native Ads in China grew from 5.55 billion yuan in 2014 to 55.7 billion yuan in 2017, accounting for 14.3% of the overall online advertising sector, (iReserach Consulting, 2017). Now, due to policy-related reasons, In-Feed Native Ads are mostly displayed on Chinese social media applications, such as Weibo and WeChat.

In-Feed Native Ads in a form of social media advertising. Social media greatly emphasizes relationships influencing by culture (Minton et al., 2012). Furthermore, the acceptance and usage intention is a type of behavior decision, which is generally shaped by cultural and economic contexts (Hoehle, 2015). As a result, as In-Feed Native Ads have enjoyed exponential growth globally, it is necessary to add an international dimension to the study of the acceptance and usage intention toward In-Feed Native Ads, However, to the best of my knowledge, most of the studies about In-Feed Native Ads are confined to only one country, predominately in the United States. This is because the research of In-Feed Native Ads is still in an initial stage and it is difficult to conduct research across different countries (Straub et al., 2002).

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The research of In-Feed Native Ads falls into research realms of mobile and social media marketing. In reviewing the literature, I noted that there have been cross-country studies pertaining to these subjects. These are antecedents and consequences of marketing program standardization in Japan and Turkey (Özsomer and Simonin, 2004), the mobile commerce usage in the United Kingdom and Hong Kong (Harris et al., 2005), cultural values and advertising techniques in five Asian countries (Fam and Grohs, 2007), the mobile advertising effectiveness in the U.S. and Korea (Choi et al., 2008), the mobile commerce adoption in China and the United States (Dai et al., 2009), the adoption of mobile advertising in India and Syria (Khan and Allil, 2010), the customer commitment to social media ads in United States, Germany, and South Korea (Minton et al., 2012), the usage intention mobile social applications in four countries (Hoehle et al., 2015), the adoption of social networking in China and Australia (Ratten, 2015), and the mobile shopping continuance intention between China and USA (Lu et al., 2017). In conclusion, these studies show a wide range of variations in advertising effectiveness across cultures, especially between Eastern and Western cultures. This is also consistent with the statement claimed by Gregory and Much (1997).

None of the prior research explores the factors influencing the acceptance and usage intention of In-Feed Native Ads in a cross-country context, but different cultural, economic and regulatory environments may lead to different acceptance patterns. Thus, in order to better understand these issues, two countries, i.e., China and the Netherlands were chosen. The reason for the choice is that China is the largest developing country whereas the Netherlands ranked as the fourth most highly developed country in the world according to Human Development Index (TopTeny, 2018). Although the population of the Netherlands is much smaller than that of China, the GDP of the Netherlands impressively ranks the top 17 with the 65th largest population. in the world (Investopedia, 2016). As these two countries have obviously distinct cultures and different development levels of the economy, mobile technology, and social media, I believe the comparison between the two regions will shed a new light on the growing body of literature in In-Feed Native Ads.

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2.3 Literature Review on Acceptance and Usage Intention Theories 2.3.1 The Overview of Acceptance Theories

This study seeks to explore individuals’ acceptance and usage intention toward In-Feed Native Ads. Prior studies measured individuals’ intention, adoption or acceptance prevalently use such theories as Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Unified Theory of Acceptance and Use of Technology (UTAUT). A literature review of prior studies provides theoretical foundations for building a conceptual model. Toward this direction, this study examines these four related theories summarizes some relevant empirical studies.

(1) Theory of Reasoned Action (TRA)

The Theory of Reasoned Action (TRA) was proposed by Fishbein and Ajzen in 1975(see Figure 8), which is mainly used to analyze how attitudes can influence an individual’s behavior. This model argues that an individual’s behavior is determined by his behavioral intention, and this intention is influenced by both this individual’s attitudes and his subjective norms (the expectation from other people) (Fishbein and Ajzen, 1975).

However, the TRA also has some limitations. That is, in this model the relation between behavioral intention and actual behavior is not closely related, as, in some situations, behavioral intention does not necessarily lead to actual behavior. For example, people are susceptible to some factors such as money, time, and other external conditions. Thus, the TRA only applies to behaviors which can be completely controlled by the individual's will. In order to contain these behaviors, Ajzen et al. (1985) proposed the Theory of Planned Behavior (TBP).

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Figure 8. Model of Theory of Reasoned Action (TRA)

Beliefs and Evaluations

Normative beliefs and Motivation to Comply Attitudes toward Behavior Subjective Norms Behavioral Intention Actual Behavior

(2) Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB) attempts to overcome limitations of TRA. As shown in Figure 9, in addition to attitudes toward behavior and subjective norms, the TPB adds a new factor, namely perceived behavioral control, which refers to an individual's perception of the difficulty of performing the specific behavior (Ajzen, 1985).

Figure 9. Model of Theory of Planned Behavior (TBP)

Beliefs and Evaluations

Normative Beliefs and Motivation to Comply Attitudes Toward the Behavior Subjective Norms Behavioral Intention Actual Behavior Controlled Beliefs Perceived Behavioral Control

(3) Technology Acceptance Model (TAM)

a. TAM 1

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process of technologies, Davis (1989) found the TRA and TPB models are not applicable and therefore proposed the Technology Acceptance Model (TAM). In the TAM, David made two improvements: 1) used attitudes toward using (A) to predict behavior instead of subjective norms; 2) introduced two emerging variables —— perceived usefulness (PU) and perceived ease of use (PEU).

The TAM suggests that perceived usefulness (PU) is determined by perceived ease of use (PEU) and external variables, reflecting "the degree to which a person perceives that using a particular system would enhance his job performance". Perceived ease of use (PEU) is determined by external variables, reflecting "the degree to which a person perceives that using a particular system would be free from effort" (Davis, 1989). Both PU and PEU influence attitudes toward using (A), as shown in Figure 10.

Figure 10. Technology Acceptance Model (TAM)

External Variables Perceived Usefulness (PU) Perceived Ease of Use (PEU) Attitudes Toward Using (A) Behavioral Intention (BI) Actual Behavior b. TAM2

As the TAM was widely used in empirical research, Davis et al. found that perceived usefulness (PU) is the main factor affecting behavioral intention (BI). Therefore, TAM2, an intention to the Technology Acceptance Model (TAM), was proposed by Venkatesh (2000) to remedy the shortcomings of the original TAM such as the lack of practical value and limited explanatory ability Bagozzi, 2007). As can be seen in Figure 11, TAM2 aims to study factors affecting perceived usefulness (PU), including social influence process (Subjective Norm, Image), cognitive instrumental process (job relevance, output quality, and result demonstrability), and two moderating factors (experience and voluntariness).

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Figure 11. Technology Acceptance Model 2 (TAM2)

Subjective Norm Image Job Relevance Output Quality Result Demonstrability Perceived Usefulness (PU) Perceived Ease of Use (PEU) Behavioral Intention (BI) Actual Behavior Experience Voluntariness Attitudes Toward Using (A)

(4) Unified Theory of Acceptance and Use of Technology (UTAUT)

Venkatesh et al. (2003) examined and unified different variables from eight classic user acceptance models and theories, including Theory of Reasoned Action (Fishbein et al., 1975);Theory of Planned Behavior (Ajzen, 1985); Technology Acceptance Model (Davis, 1989); Model of Personal Computer Use(Thompson,1991); Motivational Model (Davis,1992); Combined Theory of Planned Behavior/Technology Acceptance Model (Taylor and Todd, 1995); Innovation Diffusion Theory (Rogers, 1995); Social Cognitive Theory (Compeau and Higgins, 1995), and finally came up with integrated model called Unified Theory of Acceptance and Use of Technology (UTAUT). Since it contains the main factors from eight models and theories regarding the acceptance of the Information System (IS) and Information Technology (IT), and can explain 70% of the variance in behavior intention (BI) and 50% in actual behavior, the UTAUT model greatly enhances the applicability of the TAM (Giwa et al., 2015).

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Figure 12. Unified Theory of Acceptance and Use of Technology (UTAUT)

Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Behavioral

Intention Use Behavior

Gender Age Experience Voluntariness

of Use

As shown in Figure 12, the UTAUT model consists of four main variables. Performance expectancy, effort expectancy, and social influence directly determine behavioral intention, and ultimately use behavior, while facilitating conditions have a direct effect on use behavior. In addition, gender, age, experience, and voluntariness of use act as moderators. Definitions of each variable are presented in Table 3.

Table 3. Definitions of variables in the UTAUT Model

Variable Definition

Performance Expectancy Performance Expectancy refers to “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al. 2003), which is similar to Perceived Usefulness (PU) in the TAM.

Effort Expectancy Effort Expectancy refers to “the degree of ease associated with the use of the system” (Venkatesh et al., 2003), which is similar to Perceived Ease of Use (PEU) in the TAM.

Social Influence Social Influence refers to “the degree to which an individual perceives that it is important others believe that he or she should use the new system” (Venkatesh et al. 2003).

Facilitating Conditions Facilitating Conditions refer to “the degree to which an individual believes that the organizational and technical infrastructure exists to support the use of the system” (Venkatesh et al. 2003).

Since its publication over a decade ago, the UTAUT model has been widely employed in a large body of research concerning a variety of technologies: Internet, websites, hospital information system, mobile technology, digital-learning environment, e-government services and social media), organizations

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(educational institutions, government agencies, and hospitals, and user groups: students and teachers, professionals, government employees, and general users (Williams, 2015).

Due to the prominence of the UTAUT model, this model can be seen as an advanced version of previous acceptance and usage intention models. Therefore, the conceptual model on in this thesis will be based on the UTAUT.

2.3.2 Previous empirical studies on the UTAUT model

Since Venkatesh et al. proposed the UTAUT model in 2003, it has been applied to diverse contexts and countries. Table 4 summarizes previous empirical work and main findings in applying the UTAUT model. Despite the continued interest in adopting the UTAUT model in digital marketing research, no known study has attempted to adopt the UTAUT model to predict what affects the acceptance and usage intention toward In-Feed Native Ads. Likewise, there is a lack of empirical research in this area.

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Table 4. Previous empirical studies on the UTAUT model

Source Context Location Research objectives Main findings

Choi and Rifon (2002)

Web Advertising the U.S. To explore antecedents and consequences of banner advertising credibility

1. The source credibility is important to improve banner advertising effectiveness.

2. When the content of banner ads is relevant to website content, it can generate greater positive brand attitudes and purchase intent.

Lin et al. (2004)

Instant Messaging Singapore To explore factors affecting the acceptance and usage of instant messaging among college students

1. The functional capability has a significant effect on performance expectancy, effort expectancy, and behavioral intention.

2. Performance expectancy does not have the significant effect on behavioral intention.

3. Attitude and peer influence have the significant effect on behavioral intention.

He and Lu (2007)

Mobile Advertising

China To explore customers’ perception and acceptance of mobile ads

1. Other people’s attitudes and behaviors influence customers’ intention and behaviors.

2. User’s permission positively influence the customer adoption of mobile ads.

Choi et al. (2008)

Mobile Advertising

the U.S. and Korea

To explore the factors affecting mobile advertising effectiveness in two countries

1. Entertainment and credibility are positively related to attitude and purchase intention in the U.S. and Korea.

2. Informativeness, perceived interactivity, and the value of mobile ads present great variations between American and Korean customers.

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(2009)

Mobile Commerce

the U.S. and China

To explore factors affecting the adoption of mobile commerce

1. Mobile commerce customers in both China and the U.S. show similar perceptions of privacy, innovativeness, value-added, usefulness, ease of use, and compatibility.

2. In China, perceived usefulness and perceived ease of use have significant effects on the adoption of mobile commerce, while in the U.S., privacy, innovativeness, perceived usefulness, perceived enjoyment, and compatibility are important antecedents of usage intention.

Khan and Allil (2010)

Mobile Advertising

India and Syria To explore the factors which induce customers to accept and adopt mobile ads

1. Customers’ attitudes toward mobile ads are thought to be the most important factor affecting the acceptance and adoption of mobile ads regardless of nationality.

2. Subjective norm significantly influences customers’ intention to adopt mobile ads in the case of India, whereas this factor does not in the case of Syria.

Salim (2012)

Social Media Egypt To explore factors affecting the acceptance of social media

1. Effort expectancy, social influence and facilitating conditions have significant effects on participants to accept Facebook.

2. There are no significant relationships between gender with performance expectancy, effort expectancy, and social influence, while the significant relationship can be found between gender and facilitating conditions. 3. Age has an only significant effect on social influence.

De Vries et al. (2012)

Social Media Marketing

Netherlands To explore factors affecting the popularity of brand posts on brand fan pages

1. The top positioning of the brand post can generate more post popularity. 2. The vivid and interactive post can increase the number of likes, which in turn increase positive comments on a brand post and the sharing of it.

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(2015)

Location-Based Advertising

the UK To explore individuals’ intention to adopt Location-Based Advertising

1. The benefit of Location-Based Advertising is regarded as a reduction in search costs.

2. A majority of respondents lack the knowledge and awareness of Location-Based Advertising applications.

3. Perceived risk has a significant effect on customers' response to Location-Based Advertising, and more relevant Location-Based Ads lead to more adoption.

Slade et al. (2015)

Remote Mobile Payments

the UK To explore the factors affecting the adoption intention of remote mobile payments

1. Performance expectancy, social influence, innovativeness, and perceived risk are proven to be significant determinants of intention to adopt remote mobile payments while effort expectancy is not a significant determinant.

2. The knowledge of remote mobile payments has a moderating effect on behavioral intention.

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2.4 Research gap and Research Question

In sum, through literature review, there are three shortcomings in extant studies. First, while In-Feed Native advertising is used by many businesses, the effectiveness of this type of ads remains unclear. Second, most of the current studies are limited to only one country, but the cross-cultural differences in social media usage suggest the need to conduct a cross-country comparison. Third, the implementation of In-Feed Native advertising is still in its infancy, and thus more reliable empirical studies are required to find out what factors that induce the user to accept these ads.

Therefore, with a purpose of enhancing users’ willingness to accept In-Feed Native Ads and ultimately improving the effectiveness of In-Feed Native advertising campaign, this paper aims to fill the research gap by exploring factors affecting the acceptance and usage intention toward In-Feed Native Ads.

From this perspective, the research questions are formulated as follow:

What are the factors affecting the acceptance and usage intention toward In-Feed Native Ads?

 What are the relatively important factors?

 Does the acceptance and usage intention toward In-Feed Native Ads differ in China and the

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3. Hypotheses Development

In order to apply the UTAUT model in In-Feed Native advertising context, simply using the original UTAUT model cannot meet the research needs. As stated by Venkatesh et al. (2003), more changes and reviews need to be considered according to research circumstances. Therefore, the extension of the UTAUT model with constructs that can reflect characteristics of In-Feed Native Ads is vital. With this regard, this study retains major variables in the UTAUT model, namely performance expectancy, social influence, acceptance and usage intention, and use behavior. Also, the UTUAT model is extended with additional advertising-related constructs to fit the research context, namely advertising precision, advertising entertainment, promotional rewards, and perceived risk.

It is noted that two variables, effort expectancy and facilitating conditions, in Venkatesh’s original UTAUT model are not included in this study because according to the results in Venkatesh’s study, facilitating condition is not a significant determinant of usage intention. This conclusion is supported by some mobile advertising studies (Lin et al., 2004; Yin et al., 2017). Also, a study done by Alkhunaizan and Love (2012) showed that facilitation facilitating conditions have no significant influence on use behavior. In terms of effort expectancy, quite a few studies have indicated that effort expectancy does not have a significant influence on usage intention (He and Lu, 2007; Dai and Palvi, 2009; Pahnila et al., 2011; Slade et al., 2015; Yin et al., 2017). As In-Feed Native Ads are ads displayed on social media platforms, people who use social media platforms are likely to be familiar with how In-Feed Native Ads work. Receiving and using In-Feed Native Ads is believed to be particularly easy and does not require a lot of assistance. Thus, this study does not include effort expectancy and facilitating conditions.

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3.1 UTAUT fundamental and extended variables 3.1.1 UTAUT fundamental variables and hypotheses

(1) The relationship between Performance Expectancy and Acceptance & Usage Intention

Performance expectancy refers to “the degree to which an individual believes that using the system will help him or her to attain gains in job performance, such as improving productivity or efficiency” Venkatesh et al. (2003). Under In-Feed Native Ads context, performance expectancy means that the user acceptance and usage intention of ads depends on whether they can gain useful information conveniently.

In prior literature, performance expectancy has been proven to play a crucial role in the technology adoption and new innovations (Iacovou et al., 1995; Tornatzky and Klein, 1982; Venkatesh et al., 2003; Nysveen et al., 2005). Quite a few of studies have shown that performance expectancy is the strongest predictor of acceptance (Davis, 1989; Thompson et al., 1991; Davis et al., 1992; Compeau and Higgins, 1995; Taylor and Todd, 1995). In mobile advertising and social media adverting studies, Bauer et al. (1968) showed that if users feel the promotional messages help them gain more information of the offering, such as the price, feature, and quality, their satisfaction with ads will increase. Jung et al. (2016) identified informativeness as a positive driver of customer acceptance and usage of Facebook advertising. Leppaniemi and Karjaluoto (2005) suggested that customers will only accept mobile advertising when they feel benefits of advertising messages such as saving money, shortening purchase time, and providing valuable information. Several researchers also presented the same view regarding the acceptance of mobile advertising (Tsang et al., 2004; Salo and Tähtinen, 2009; Khan and Allil, 2010).

Therefore, the user acceptance and usage intention of mobile advertising is positively related to perceived levels of advertising informativeness and efficiency increase.

As such, the study assumes that performance expectancy can positively influence acceptance and usage intention toward In-Feed Native Ads.

This leads to the following hypothesis:

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Native Ads.

(2) The relationship between Social Influence and Acceptance & Usage Intention

Social Influence refers to “the degree to which an individual perceives that it is important others believe that he or she should use the new system” (Venkatesh et al. 2003). In case of this study, according to Jung et al. (2016), the term social influence means that the user acceptance and usage intention of In-Feed Native Ads are influenced by people who are close to him or her, such as friends, family members, superiors, and colleagues.

Several acceptance studies have shown that social influence has a strong influence on the user acceptance and intention to use a technology (Triandis, 1971; Davis, 1989; Hartwick and Barki, 1994).

A significant reason for using social networks is to connect with others. While using social media, users’ opinions are influenced by either people they know or they do not (Jung et al., 2016). A survey by Nielsen (2012) revealed that in the era of customer-generated media, 92% of global customers trust recommendations from friends and family as they rely on word-of-mouth when making decisions. Jung et al. (2016) examined the antecedents of attitudes and behavioral intention toward social media advertising and found that social influence significantly influences user intention to accept ads. Likewise, Standing et al. (2005) explored customer perceptions of mobile advertising and found that social influence has a positive impact on customer purchasing activities. Additionally, a study by Wais and Clemons (2008) indicated that customers are more willing to receive promotional messages recommend by friends. As such, the study assumes that social influence can positively influence acceptance and usage intention toward In-Feed Native Ads.

H2: Social influence has a positive effect on acceptance and usage intention toward In-Feed Native Ads.

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(3) The relationship between Acceptance & Usage Intention and Use Behavior

In the earlier discussion, the acceptance and usage intention refers to the user’s favorability of In-Feed Native Ads, that is the willingness to adopt In-Feed Native Ads, such as receiving, viewing, desiring to gain more information, engaging, and generating purchase intentions. Use behavior refers to the user’s actual usage of purchasing offerings in In-Feed Native Ads, participating in promotional events, and sharing or recommending In-Feed Native Ads to other people.

Numerous studies have confirmed that acceptance or behavior intention has a strong positive effect on actual behavior (Fishbein and Ajzen, 1975; Ajzen, 1985; Davis, 1989; Venkatesh, 2000; Venkatesh et al., 2003). In the literature of mobile and social media advertising, acceptance is also considered as a direct antecedent of use behavior. Mehta (2000) found that the customers who show positive perceptions of print advertising are more likely to purchase offerings in ads than those who do not. This empirical finding was also supported by Jung (2006) in a study of evaluating the Facebook advertising effectiveness. In addition, the positive relationship between user acceptance and use behavior was also supported in the literature of mobile commerce (Alkhunaizan and Love, 2012), mobile services (Nysveen et al., 2005), and web advertising (Choi and Rifon, 2002).

As such, the study assumes that acceptance and usage intention can positively influence use behavior toward In-Feed Native Ads.

H3: Acceptance and usage intention have a positive effect on the use behavior toward In-Feed Native Ads.

3.1.2 UTAUT extended variables and hypotheses (1) Advertising Personalization

The development and application of Big Data enable advertisers to deliver more personalized and customized ads. Ads with greater personalization typically increase product or service relevance and

Factors affecting the acceptance and usage intention toward in-feed native ads : a cross-country empirical comparison between China and the Netherlands